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Stablecoins today sit at just above $320 billion in total issuance, growing fast at a 50% 4 year CAGR. The vast majority of these stables are denominated in a single currency, the US dollar.

These stablecoins represent a superior form of the US dollar from a technological standpoint, but they still don’t solve for one of the biggest flaws in government-issued fiat: inflation and currency devaluation. In other words, technology today doesn’t solve the problem of maintaining the purchasing power of the dollar.

While stablecoins are unambiguously upgrading the grounding of our monetary system, a question still remains. Can stablecoins be composed with other primitives to solve currency devaluations?

Let’s dive in.

Apples to apples

In its base state, value is not experienced as currency; it is experienced as goods and services. At a basic level, if a stablecoin user purchases 10 apples for $10 USD ($1 = 1 apple) and one day finds that the 10 apples cost $20 ($1 = 0.5 apple), there is a value loss of 50%. While your stablecoin stays worth $1, the goods you are buying are volatile.

If a traditional stablecoin maintain their dollar peg, but those dollars fluctuate in value, the question becomes: how do we create an instrument that stabilizes what those dollars can buy, the value itself?

The rise of prediction markets has been met with equal parts enthusiasm and disdain. Their potential as information aggregators is undeniable, but so is the speculative access and gambling they enable. The core innovation with prediction markets is not that they reveal information; it is that they expand the bounds of information that price discovery lends itself to.

This financialization of coveted, relevant information (interest rates, wars, weather) reveals potential for novel hedging strategies and end-user access to said strategies. The existence of hyper-localized prediction markets can translate into personalized “stores-of-value” that use outcomes to underpin value. We call these instruments realcoins.

Realcoins aim to achieve value stability by holding portfolios of financial contracts, including prediction markets, that payout when the user experiences purchasing power erosion on goods and services most relevant to the user. The pursuit of purchasing-power stability is not new: flatcoins and CPI-linked currencies are being experimented with today. These instruments achieve broad coverage, but lack specificity - if your rent grows faster than CPI, the hedge is lost since it was generalized; this is known as basis risk. Realcoins attempt to bridge the gap by offering localized insurance as a tokenized structured product.

This outline is not meant to decry the proliferation of dollar-denominated stablecoins. It is an exercise to see if prediction markets can feasibly function as more than digital casinos, and to see if we can enable a currency that is more store-of-value than existing options.

Lineage

Friedrich Hayek, a stalwart of the Austrian school of economics, wrote “The Denationalisation of Money” in 1976. The pamphlet describes a competitive marketplace of privately issued currencies, where successful currencies best preserve value over time. He believed that institutions would find, through experimentation, that a basket of commodities forms the ideal monetary base. Hayek’s ideas were rooted in a distrust of the government and a belief that economic order arises from decentralized markets rather than central planning.

Aspects of Hayek’s vision echo Bitcoin, but speculative capital, high volatility, and surrounding derivative markets render it ill-suited for the “store-of-value” function of money. The US dollar addresses the denomination problem, but due to centralized planning and perverse incentives (deficits, seigniorage, political accommodation of loose policy) driving inflation, it fails to solve the purchasing-power problem. Hayek had identified the destination, but lacked the mechanisms in the 1970s.

Tectonic shifts in finance reveal new opportunities. Stablecoins prove that privately issued digital money can achieve adoption at scale. Decentralized finance demonstrates that financial contracts, including derivatives and structured positions, can be composed and executed programmatically without traditional intermediaries. Prediction markets create liquid, tradeable claims on real-world outcomes, including those that determine purchasing power. A realcoin issuer that operates as a licensed entity, holding a portfolio of prediction market positions on behalf of users, sits at the intersection of all three.

Vitalik Buterin, co-founder of Ethereum, independently proposed this idea, describing the creation of prediction markets on all major categories of goods and services that people buy. In his outline, an LLM would analyze user expenses and create a personalized basket of prediction market shares representing expected future expenses. For many, rent is the largest expense, and also the one most exposed to inflation. Can prediction markets help maintain the ability to pay rent?

Prediction Markets as Localized Insurance

Insurance exists so that when the thing you care about (experienced value) gets worse, you remain stable in financial terms. You pay a premium, and in return, if life doesn’t go as planned, you are made whole. If a prediction market prices “Will your house burn down by January” at 1%, $200 buys you 20,000 shares, and you are paid out $20,000 if your house burns down.

As such, prediction markets functionally replicate insurance. Notably, insurance requires an “insurable interest” - you can only insure something that you own and are the beneficiary of. Prediction markets have no such restriction. To hedge an outcome, one no longer needs an underwriter, claims process, or tailored policy; all that’s needed is a liquid market on an outcome tied to the cost of living.

For New Yorkers, rent increases are a critical and often underestimated driver of purchasing power erosion. As of December 2025, average rent was at around $5K monthly, up 6% from $4.7K in December 2024. 4.7K USD covered 100% of the monthly rent in 2024 but only 94% in 2025, a direct loss in experienced value. Had the renter invested $4.7K in 2024 into realcoins tied to Manhattan rent prediction markets, the underlying contracts would rise in value with rental spikes, preserving rental purchasing power. This core mechanism makes realcoin values positively correlated to the cost-of-living.

Realcoin holders do not speculate on these outcomes; they hedge them. A renter could see rent increases regardless of whether or not they buy shares. A sports speculator has no linkage to the outcome of a sporting event. The speculator assumes financial risk proactively; the realcoin holder experiences it regardless. Buying prediction market shares does not introduce new risk to realcoin holders; it offsets risk already present in their lives. This extends into expectation setting as well - realcoin holders expect to lose value should outcomes go their way, since purchasing power was preserved, and the hedge was unnecessary. A speculator expects to gain value if outcomes go their way. This inversion characterizes the intent distinction between gambling and hedging and can underpin the case for prediction markets as legitimate financial primitives.

A Manhattan renter hedges against shelter inflation. An online merchant hedges against Cloudflare downtime. A small business owner in a flood-prone area hedges against weather disruption. Each is using the same primitive: a liquid, outcome-based financial contract, to stabilize a different dimension of their economic life. Realcoins aggregate these primitives into a single instrument calibrated to a user’s actual expenses.

Realcoin design options

There is no single design that accomplishes a realcoins purpose. Operationalizing realcoins implies finding financial contracts that pay out when the cost of living rises and are dependent on existing markets, liquidity, and the legal backdrop that issuers operate in.

Design 1: Binary Prediction Markets

The simplest design is where a realcoin is backed by shares in a binary Yes / No prediction market where the outcome is tied to cost-of-living thresholds. Suppose a market in New York rent resolves “Yes” if the median Manhattan rent exceeds $5K and is currently priced at 35%. If your rent is $4.7K and you purchase $4.7K worth of shares at 35%, you own 13.4K shares. If the market resolves and the oracle reports that the median rent exceeded $5K, you are then paid out $13.4K, offsetting the higher rent you now owe. If the market resolves to “No”, your shares are worth nothing, and you lose the $4.7K investment, but have paid for protection that was not necessary; this is how insurance works. The true design limitation is the scenario where your rent rises from $4.7K to $4.9K, eroding purchasing power without the hedge activating.

Binary markets are easy to understand, easy to price, and already exist. A basket of five to ten markets covering rent, groceries, electricity, healthcare costs, and more could be constructed on a platform that supports cost-of-living markets. The limitation is a lack of precision. Coverage exists, but it is not comprehensive enough to be reliable for everyday users. Binary markets achieving popularity lay the foundation for more elegant solutions.

Design 2: Parametric / Scalar Markets

Parametric prediction markets address the risk in binary markets by tying payouts to an index value rather than a binary threshold. Continuous parametric markets that smoothly change payout with the index do not yet exist, but approximations of this structure using nested binary markets as “rungs” of a prediction ladder are active today.

A pseudo-parametric market for NYC rent might look like a 3 rung ladder where each median rent tier has different probabilities and different payouts. For instance, you invest $1200 into the NYC rent realcoin, backed by markets with the following parameters:

The quantity of shares purchased is the Investment amount divided by the price, expressed as cents.

In the scenario that rent falls from $4.7K to say, $4.3K, you lose the $1.2K invested principal. All 3 markets resolve to “No”, and the shares purchased are all worthless. Simultaneously, you have also seen an increase in purchasing power, since rent has fallen. Payouts are meant to correlate with experienced value, so the realcoin accomplishes its purpose, though the nominal invested value is lost entirely.

Parametric markets are critical to ensuring realcoins are commercially feasible, though current implementations come with limitations. Leveraging nested binary markets means payouts only happen at the rungs, and never in between. For instance, if rent landed at $4750, purchasing power would be eroded, but no insurance was inherited since none of the markets resolved “Yes”. Widely spaced thresholds in such markets pose risks to realcoin holders. Tighter rung spacing enables granularity, but also higher operational costs, which may be well worth it.

Design 3: Yield-bearing collateral and prediction market exposure

Designing a realcoin in which the full nominal investment can be lost is difficult to market to retail holders. One could address this by diversifying the reserve composition to include yield-bearing collateral, such as a money market fund earning the risk-free rate. If positions pay out, the combined instrument protects purchasing power. If they don’t, you still earn yield on a portion of the deposit, a better outcome than holding fiat or stablecoins. This income-generating base can offset the operational costs associated with managing realcoin reserves and diversify exposure, with the remainder of reserves invested in prediction markets. Such a structured product could be composed of 60% T-Bills and 40% prediction market shares.

In this setup, the principal loss in the downside scenario is only 38% of the total investment amount, while still maintaining purchasing power. Unlike the parametric-only design, the gap scenario is no longer a total loss. This structure of a money market fund with an attached hedge has established legal precedence and frameworks. What is novel is the prediction market hedging layer, which may not be regulated like traditional instruments.

Looking Ahead

The usefulness of a realcoin is predicated on market specificity, adequate liquidity, and legislative clarity. Without sufficient specificity, there could be gaps between insured outcomes and experienced outcomes (basis risk), and without active buyers and sellers, markets cease to exist. A national shelter CPI index does not track Manhattan. The hedge applies, but is closer to an inflation-linked bond than to bespoke insurance. Volumes for cost-of-living markets pale compared to those for sports and elections. These markets also require naturally opposing buyers and sellers who take the other side of each position. Closing these gaps is the long-term product problem realcoins need to solve.

Hayek’s insight was not that any particular currency would win, but that competitive markets would discover which instruments best preserved purchasing power over time. Realcoins are an entry into that competition. They require liquid markets, honest oracles, and users whose experienced value is worth hedging. The instruments that best track what users actually spend money on will attract holders; those with persistent basis risk or thin liquidity will not. Issuers competing on composition, rung structure, and collateral ratio, not to mention partnerships for market access and market making, should drive product outcomes that unlock value stability for end users.

The conceptual case for realcoins does not depend on that maturity being achieved immediately. The tools to solve for value exist today.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. Both Shaurya and I are employed by Circle at the time of this writing, but the views in this essay are our own personal opinions and don’t represent the views of Circle.

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For more than two decades, software has mostly helped people do better work. It made individuals and teams more productive. It organized information, streamlined workflows, and reduced friction. That was enough to build an industry worth trillions.

But now everything is changing

AI agents are beginning to take on work that once required people to log into tools. These agents operate continuously, interact through APIs, and execute tasks without screens, approvals, or manual intervention.

On the All In podcast this week, David Friedberg discussed what’s been happening in SaaS over the past 2-3 months:

“My experience lately, in just the last 60 to 90 days (with the tools we’ve been talking about broadly), is that there are things we can get done now that we could not get done before. And so I think about software in the past, it’s like worker productivity enhancement. It helps people do work, and the recent transition that a lot of people talk about is that it actually does the work. It does the work (these agents or what have you), but I think that what we’re starting to lean into is it’s doing the work that the humans can’t do. And that’s really where I think the power of these tools starts to force a transition in both the pricing model and the value creation potential in front of us.

Number one, I think the value creation potential in front of us is so significant that I would say that you can probably take the sum of the market cap of all the software companies today and have a pretty good bet that everything will be 4x to 10x higher five years from now, but it’s not going to be evenly distributed. The companies that figure out how to realize that value creation are going to be outsized returns.”

Software is becoming clearly less dependent on human attention and more dependent on machine execution.

Recent Goldman Sachs Research projects AI agents are set to take over the profit pool in software, while also making the total addressable market for SaaS profits even larger.

Agents are expected to account for more than 60% of software economics by 2030, meaning the majority of value will flow to agentic workloads rather than traditional per-seat SaaS.

This helps explain the tension many companies are feeling right now: the gap between knowing the world is changing and knowing what to do about it. For the companies that pivot fast and stick the landing, agents produce what feels like the next stage of growth. For everyone else, the clock is ticking to figure it out.

Steven Sinofsky captures the opportunity for software in his article, ‘Death of Software. Nah.’

The next phase of software is going to look dramatically different. And while it feels like everyone knows that, it seems like few know what to do about it.

Every Software Company Is Now an API Company

The most important changes in software today are how it is being built and how it is being consumed.

Let’s start with how software is being built.

AI is now directly involved in producing software. Developers are no longer writing every line by hand. They are describing intent, reviewing outputs, and orchestrating systems that generate, test, and deploy code continuously.

As Dylan Patel recently noted, a growing share of software is now being built with AI in the loop, with 4% of GitHub public commits now being authored by Claude Code.

This changes the economics of creation. When the cost of writing software falls, supply increases. When supply increases, software stops being scarce.

For most of the SaaS era, scarcity lived on the supply side. Skilled engineers were expensive. Building reliable systems took time. Shipping software was the hard part.

That is no longer the primary constraint.

At the same time that software is becoming cheaper and faster to build, it is also being consumed in a fundamentally different way.

Over the past year, coding productivity has increased sharply. Across multiple indicators, output has surged after years of relatively steady growth. New websites are launching faster. New mobile apps are shipping at higher rates. GitHub code contributions in major markets have accelerated meaningfully.

At the same time, software is becoming cheaper and faster to build. It is also being consumed in a fundamentally different way.

AI agents no longer wait for human input. They are active consumers of software. They call APIs, trigger workflows, coordinate systems, and execute tasks continuously. They do not interact with interfaces. They integrate directly with functionality.

Here is where the two shifts reinforce each other.

AI-assisted development increases the amount of software available. Agent-driven consumption increases how much software can be used. Together, they create a feedback loop where more software enables more execution, which in turn creates demand for even more software.

Once this flywheel spins up, the interface-centered SaaS model breaks down.

Interfaces were the choke point when humans were the users. Dashboards, menus, and workflows mattered because they mediated access to functionality. When agents become the primary users, interfaces become overhead. APIs become the surface area that matters.

Software that is easy to generate but hard to integrate fades quickly. Software that is hard to generate, but easy to integrate compounds.

Shallow SaaS products will feel the pressure first. When AI can generate alternatives quickly, and agents can consume them programmatically, differentiation based on interface design collapses. Value concentrates on reliability, composability, and depth of integration.

This is the trajectory: software increasingly looks less like a product and more like infrastructure.

You can hear this shift in the language that founders and operators are already using. When asked on TBPN whether software is dead, Sam Altman put it simply:

When software is consumed by machines, it fades into the background. It becomes something other systems depend on rather than something people actively use. Reliability matters more than features. Latency matters more than layout. Backward compatibility matters more than novelty.

Add it all up, and the shape of the software economy is changing.

Creation is becoming abundant. Execution is becoming continuous. The constraint is shifting from building features to enabling scale.

This is the deeper reason why SaaS as we know it is breaking. The old model assumed scarce developers and scarce human attention. The new model assumes abundant code and machine-native consumption.

A Golden Age of Software

The temptation is to read all of this as disruption—or worse, displacement. That would be the wrong conclusion. This is not the end of software. It is the beginning of what software can really do.

Software is entering a phase of near-infinite abundance. The cost to create it is falling. The ability to execute it is scaling. And the number of problems it can realistically tackle is growing faster than at any point in history.

Agents allow software to do work directly, continuously, and at a scale humans never could. They unlock use cases that were previously too expensive, too complex, or too slow to automate. They expand the surface area of what software can touch.

The opportunity ahead is enormous, but it demands adaptation. Pricing models will shift from seats to usage. Interfaces will matter less than integration. Reliability, composability, and trust will matter more than feature velocity. Teams will need to rethink how they build, sell, and operate software in a world where machines are the primary users.

It won’t be easy. Large organizations will move slowly. Legacy systems will persist. Hybrid models will dominate for years. But the direction is unmistakable.

Every software company now faces the same question: What role do we play in a world where software is abundant, and execution is continuous?

The companies that answer that question well will win. Not because they move the fastest, but because they align with how software is actually being built and consumed.

This is a rare moment where the fundamentals of technology and the fundamentals of business shift at the same time. When that happens, entirely new categories form. Old assumptions break. And value is created at a scale that is hard to comprehend in advance.

We are living through the most consequential shift in software since the internet. Act accordingly.

Until our next adventure.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. I am employed by Circle at the time of this writing, but the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

*Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

Welcome to the latest edition of The API Economy. Thanks for being here.

To support the API Economy, join the 11,420 early, forward-thinking people who subscribe for insights on the forefront of technology, crypto, AI, and other emerging trends.

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This weekend marked a clear inflection point in our understanding of AI agents.

Across X, people have begun to share examples of AI agents being spun up in large numbers and immediately put to work. Agents browsing the web, writing and deploying code, coordinating with other agents, monitoring markets, and taking action without ongoing human input.

People began to see what agents are capable of through tools like OpenClaw, Bankr, and moltbook.

Many of these agents are no longer confined to internal systems. They are increasingly connecting to external services like APIs, blockchains, cloud infrastructure, and markets. Once connected, they operate independently.

People are noticing that when an agent finds a useful path, it could be duplicated instantly. When conditions changed, agents adapted without waiting for human review. Learning propagates as fast as computation allows.

For example, someone’s Clawdbot got his developer’s number from Twilio, connected the ChatGPT voice API, and waited for him to wake up to call him.

As more people shared real examples over the weekend, it became clear that a growing share of software will now be designed primarily for other software and agents to use. Humans define objectives and boundaries, but the execution belongs to machines, and products need to be optimized for agentic use cases.

That realization set the stage for the next conversation. If agents are becoming economic actors, the rails they choose matter. This matters because the number of economic actors on the internet is about to explode.

A human can run a few workflows at once. An agent can run thousands. And once the cost of spawning an agent drops low enough, quantity becomes the product.

There is now published modeling work that treats agent proliferation as a scaling curve that can reach multi-trillion agent levels under plausible adoption dynamics. One recent paper on arXiv explicitly discusses models that approach a multi-trillion-agent scale, with growth that accelerates mid-decade and then slows as infrastructure becomes the bottleneck.

Trillions of agents will result in quadrillions of transactions. When trillions of agents exist, the economy changes in three concrete ways:

1) Transaction volume goes vertical

Agents transact in loops. They pay, verify, retry, and route around failures. They negotiate. They coordinate. They split tasks across other agents. This creates many small payments (microtransactions) instead of a few big ones.

Even before “trillions of agents,” research by McKinsey projects that agents will mediate $3T to $5T of global consumer commerce by 2030.

2) Money becomes an API

At agentic scale, payment methods that require human steps become a bottleneck. Agents need:

• Always-on settlement

• Programmable rules

• Global reach

• Low fees for small payments

• Instant verification

• Access to credit

When the “default customer” becomes software, the “default money” trends toward programmable dollars.

This is where stablecoins win, especially USDC on blockchain rails in the current discourse. Agents optimize for reliability and integration, and stablecoins behave like digital cash that software can move and settle without waiting on institutions.

Stablecoins in January of 2026 alone surpassed $10 trillion, a number that will rise exponentially with the addition of an economy of agents.

3) Entire new markets appear: agent labor, agent-to-agent services, agent finance

Trillions of agents transacting means the economy becomes more granular. You get markets priced in fractions of a cent through:

• Pay-per-query data

• Pay-per-inference tools

• Pay-per-action automation

• Micro-insurance for execution risk

• Escrowed performance payments

• Automated auditing and compliance

You also get new failure modes:

• Runaway spending loops

• Adversarial agents extracting value

• “Agent flash crashes” in markets with reflexive automation

This pushes demand toward identity, permissions, and risk controls built into the rails. All these components compound faster than prior tech waves because agents replicate. Instant replication abilities lead to:

• Better models make agents more capable.

• More capable agents produce more software, content, and coordination.

• That output is used to build better agents and spin up more of them.

Once the economy’s fastest-growing participant is software in the form of agents, the economy starts to reorganize around them. And only a matter of time before agents outpace people participating in the global economy.

Every product should be thinking about how agents and, more broadly, AI can access, use, and benefit from it. This next part will happen fast. It’s time to get ready.

Until our next adventure.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. I am employed by Circle at the time of this writing, but the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

*Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

Welcome to the latest edition of The API Economy. Thanks for being here.

To support the API Economy, join the 11,267 early, forward-thinking people who subscribe for insights on the forefront of technology, crypto, AI, and other emerging trends.

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The great products of one generation have a strange habit of becoming the villains of the next.

Products that begin as rebellions against the establishment often grow into the establishments of tomorrow, gradually shifting from creating value to extracting it. The interface remains familiar, the brand stays powerful, but the product no longer feels like it is working on your behalf.

You see this everywhere. Open YouTube, and you are pushed toward another subscription or forced to sit through so many ads that the content barely feels worth your time. Trading apps that once promised openness and accessibility now charge fees comparable to the institutions they set out to challenge. The iPhone, once defined by step-change improvements, has felt largely unchanged across the past several generations.

These common patterns aren’t driven by a lack of imagination or poor execution. They are driven by incentives.

Early in a company’s life, the goal is simple: build something compelling enough to earn user adoption.

Speed matters. Creativity matters. Product quality matters. Over time, those priorities shift. Growth slows. Markets mature. The company becomes large enough that stability and revenue optimization begin to outweigh experimentation and improvement.

Scale changes behavior. Large systems reward predictability and punish risk.

The Innovator’s Dilemma

Clayton Christensen described the struggle for big companies to build new things of value as the innovator’s dilemma. Successful companies are rewarded for improving what already works. They listen closely to their largest customers. They invest in products with clear returns. They avoid initiatives that threaten existing revenue.

Over time, incumbents become excellent at optimization and weak at exploration.

The problem is that meaningful innovation rarely looks attractive at the beginning. New ideas start small. They serve marginal users. They generate little revenue. From inside an established organization, they appear risky and distracting. They compete with proven products for attention and resources, and often undermine the economics of the current business.

As Chris Dixon has noted, many breakthrough technologies were first built by hobbyists operating outside the center of the market. These ideas did not emerge because they were obviously valuable. They emerged because they were allowed to exist without needing immediate justification.

Startups operate under different rules than large businesses. They can build products driven by curiosity rather than certainty. They can pursue narrow use cases. They can accept ambiguity. They optimize for possibility rather than efficiency. That freedom allows new platforms and tools to emerge beyond the reach of incumbent incentives.

This is why major shifts in technology rarely come from market leaders. Over time, every successful product becomes something that must be protected. Once protection becomes the priority, innovation slows.

Most incumbents historically have failed here because the cost of experimentation is immediate and visible, while the cost of stagnation is delayed and diffuse. Quarterly incentives reward efficiency and punish optionality.

Over time, organizations become very good at protecting what they have and very bad at imagining what they could become.

When experimentation becomes cheap and production timelines shrink, protection weakens. Capital, talent, and attention flow toward better products rather than better defensive tactics.

Large companies have struggled to match the speed of startups operating at the edges of the market. Reacting to new ideas often took years, especially when those ideas served small or underserved users. By the time incumbents responded, the opportunity had already moved on most of the time.

But with AI compressing these development cycles and enabling engineers to do more with less, speed is no longer an exclusive advantage of startups. The gap is narrowing, and incumbents that adapt their incentives can now move faster than ever before.

There Are Cathedrals Everywhere For Those With The Eyes To See

For most of human history, progress advanced slowly and unevenly. Breakthroughs were rare and often separated by generations.

Today, those gaps have collapsed. Major technological shifts now arrive within years, sometimes months. Each new tool accelerates the arrival of the next. These improvements led to a Cambrian explosion in innovation over the last century compared to those prior:

For most of the modern tech era, speed has been the defining advantage of startups. Products that took decades to build can be stood up in dramatically less time with a fraction of the resources it would have taken before. Big companies moved slowly by design. Decision-making was layered, product cycles were long, and coordination imposed friction.

Startups, by contrast, were small and unconstrained. They could move quickly, test ideas, and pursue opportunities long before incumbents could respond. That asymmetry allowed new entrants to form entirely new categories while established players optimized what already existed.

That advantage is eroding.

With the tech environment we find ourselves in, there has never been a better time to build technology. The conditions for turning ideas into reality have changed. Compounding improvements across multiple layers of the stack have reshaped what is possible.

Stepan, the CEO of stablecoin fintech Squads, had this to say about building products with AI in 2026:

“We treat developer latency like product latency. Build times, review cycles, deployment friction are all first-class metrics. Engineers own features, not bugs. Agentic tooling handles triage and fixes. Engineering scales like an army. UX is architecture, not polish. Click latency and animation performance ship with the MVP, not after.

We optimize for flexibility over attachment. If stripping a system to fundamentals compounds quality and speed, we do it. The rules of building software are being rewritten every few months. Everyone’s a beginner right now. That’s not a threat if you’re lean and willing to learn. It’s an advantage. We’re excited to keep figuring out what it means to build great software in this environment.”

This is a structural compression of cost, labor, and coordination.

Everything in technology is being reset closer to the starting line. Nearly every developer is using AI. Products can now be stood up in dramatically less time with far fewer resources. The constraints that once protected incumbents are weakening, while the tools available to builders continue to compound.

Advances in AI have collapsed development cycles. What once required large teams, specialized expertise, and years of iteration can now be built by small groups in a fraction of the time. Tasks that previously demanded hundreds of engineers can be handled by a handful of people with the right tools. The ability to move fast is no longer scarce.

GitHub Copilot users also report task completion up to ~55–81% faster compared to traditional workflows, and Claude is helping developers produce years’ worth of work in a matter of hours…

Everything is changing…

In this new landscape, two factors matter more than ever: distribution and economics.

First, distribution determines who can capitalize on momentum. Companies with existing reach can observe which products or features are gaining traction and deploy comparable offerings at scale with unprecedented speed. This compresses the window in which startups can rely on novelty alone.

To stay ahead, new entrants must iterate relentlessly and establish clear differentiation before incumbents can absorb the signal.

Every time a new feature in tech finds product market fit, it’s going to be a space race to see who can grow adoption, distribution, and economics the fastest between incumbents and startups.

Second, economics enabled by AI tooling are allowing smaller teams with fewer resources to compete at a much larger scale. Now, small teams are able to strip products down to their fundamentals and rebuild them with dramatically lower cost structures than ever before.

For example, when comparing revenue per employee across companies, modern, AI-enabled, and asset-light businesses often exceed legacy incumbents by a wide margin, illustrating how small teams can generate outsized impact relative to their size.

We are already seeing this play out. Companies like Tether, Hyperliquid, Cursor, and others are demonstrating how focused teams, modern tooling, and disciplined scope can produce an outsized impact compared to some of the biggest tech companies in the world on a revenue per employee basis.

This is a structural shift in how much leverage a small group can exert when competing with the giant companies that exist today.

Although we need to keep in mind that building products isn’t enough. You still need to be able to market them, sell them, and garner adoption of them. Big companies, for now, will continue to have a moat leverage by multi-year contracts, customer tech-debt, and other factors.

But these defenses won’t hold forever.

There are cathedrals everywhere for those who can see them. Not because the world lacks problems, but because the tools to solve them are compounding faster than the structures that resist change.

Building Spacecrafts In Days

It has never been easier for a small group of people to change how an entire industry works, whether at a startup or within a big company.

Innovation now compounds through speed, iteration, and leverage. What once required massive coordination and capital can now begin with a few people, modern tools, and a clear point of view. The distance between idea and execution continues to collapse.

This compression is why disruption feels relentless. Systems optimized for stability struggle in environments that reward movement. Platforms designed for extraction eventually expose themselves to replacement. The faster technology advances, the shorter the lifespan of complacency becomes.

Enduring companies will not be defined by how long they dominate a category. They will be defined by how often they are willing to rebuild themselves. They invest in platforms instead of features, prioritize speed over certainty, and treat reinvention as a constant rather than a crisis.

The barriers that once protected incumbents are eroding. The tools that once belonged to the few are now widely available. Many industries persist out of habit rather than necessity.

We are no longer constrained by what is possible, only by what we choose to build. When you can assemble the equivalent of a spaceship in days, the advantage belongs to those who move first, learn fastest, and refuse to wait for permission.

The cycles are compressing.
The window is open.

Until our next adventure.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. I am employed by Circle at the time of this writing, but the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

*Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

To support the API Economy, join the 10,905 early, forward-thinking people who subscribe for insights on the forefront of technology, crypto, AI, and other emerging trends.

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Hello everyone 👋,

Today’s edition of The API Economy is brought to you in partnership with Coast.

Coast’s founders, Austin Pager and Spencer Levitt, were gracious enough to help me tell their story in this edition of The API Economy. Coast has built a product that solves the same problems I started The API Economy to explain — showing the world that APIs are the most powerful technology in the world.

Demonstrating an API's value has always been challenging. Most people consider APIs an abstract acronym, historically making API products difficult to sell as they are often understood only by a seasoned few. By providing a side-by-side view of an API's backend workings alongside the user's experience, Coast eliminates the guesswork.

To find out how, let’s explore Coast.

*Disclaimer: This is a paid sponsorship. Find out more about The API Economy’s paid sponsorships here.

APIs run the world

APIs quietly power the modern world, enabling everything from payments to logistics behind the scenes. These invisible bridges between systems have long been the backbone of innovation, even if their impact often goes unnoticed.

From banking to healthcare, APIs form the digital infrastructure behind nearly every modern service. Yet despite their ubiquity, most people still don’t understand what APIs do or why they matter, creating a gap between technical teams and the broader public. This disconnect can slow adoption and obscure value.

In response, forward-thinking companies are now treating APIs not just as hidden backend infrastructure, but as strategic assets that unlock new revenue, partnerships, and product innovation.

The popular API testing platform Postman publishes its State of the API Report annually. When the report started in July 2006, there were fewer than a million API calls. Since then, Postman Collections have grown to more than 121 million.

As the world continues to embrace digital transformation, it has become increasingly clear that APIs are the key to unlocking the full potential of technological innovation.

In this essay, we delve deeper into the world of APIs, exploring their history, impact, and Coast’s pivotal role in shaping the future of digital innovation.

Every company relies on APIs, whether you know it or not

APIs are poised to reshape every industry.

Consider the logistics sector, where real-time data streaming from vehicle fleets has become the new norm. By leveraging APIs, logistics companies can seamlessly integrate their systems with clients, enabling the instantaneous exchange of critical information and the optimization of supply chain operations.

This level of connectivity and collaboration is now essential for companies to remain competitive in a fast-paced, data-driven marketplace.

Similarly, the financial system has become entirely reliant on APIs, with banks, investment firms, and fintech startups alike leveraging these tools to facilitate the secure transfer of funds, the sharing of customer data, and the development of innovative financial products.

This technological paradigm shift extends to some of the biggest and oldest industries in the world. Take Zurich Insurance, for example. Headquartered in Zürich, Switzerland, Zurich Insurance utilizes Coast’s software and is the country's largest insurer. As of 2021, the group was ranked the world's 112th largest public company by Forbes' Global 2000s list, and 94th in Interbrand's top 100 brands.

One might ask, what is a 100-year-old Swiss insurance company doing in the API space? Well, Zurich Insurance builds both internal and external APIs that it needs to showcase to stakeholders and customers.

Yet despite their power, APIs are notoriously difficult to explain, especially to non-technical buyers. Coast helps bridge that gap by turning invisible infrastructure into intuitive, live experiences.

Just as most people now conceptually understand the benefits of SaaS models, the widespread adoption of APIs is poised to become the next major shift in how we think about and interact with technology. Potential API customers no longer want to just hear about an integration's theoretical capabilities. They want to see the software working for them in real time, tailored to their specific needs.

The majority of people who buy tech are often non-technical and need a more engaging way to grasp how APIs solve their specific pain points and unlock new opportunities. In enterprise sales, where an API's value must be showcased to multiple stakeholders, the ability to demonstrate, visualize, and explain that value accessibly can be the difference between a successful sale and a missed opportunity.

When semi-technical or non-technical stakeholders, such as executives, business analysts, or end-users, can comprehend an API's transformative potential, they are better equipped to understand its strategic value and advocate for its adoption within their organizations. In the vast majority of sales, the person signing the contract is not integrating the API; they, too, need to understand the value before making a decision.

Imagine providing a technical demo with live software, complete with a built-in 'cheat sheet' explaining its significance. Coast lets API-first companies deliver interactive demos tailored to each prospect’s use case, making the value instantly clear and accelerating the sales cycle.

Getting every stakeholder on the same page, regardless of their technical capabilities, can lead to more informed decision-making, faster implementation, and greater overall buy-in, ultimately driving the success of any API-first solution.

This shift in mindset extends beyond the traditional customer-vendor relationship. Everyone is a buyer, from the internal stakeholder pitching a new initiative to the product manager seeking funding for a critical project. In these scenarios, the ability to showcase the value of APIs through interactive, personalized demonstrations can be the difference between securing buy-in and facing an uphill battle.

As APIs continue to reshape the digital landscape, the importance of API-specific tools like Coast will only grow. By making these powerful tools accessible and understandable to all, Coast is paving the way for a future where API-driven innovation is not just the domain of the technologically savvy but a universal language.

Once locked in black-box codebases, APIs are now explorable and shareable thanks to tools like Coast.

Life is better by the Coast

Coast began when founders Austin Pager and Spencer Levitt, classmates at Northwestern University, recognized a critical disconnect. Despite experiencing the transformative power of APIs firsthand through countless passion projects, they saw how technical complexities often overwhelmed non-technical stakeholders and potential customers, inundating sales reps and SMEs with endless questions throughout the buying process.

To bridge this gap, Levitt and Pager created Coast, a platform designed to empower API-first companies to capture attention, accelerate sales cycles, and leave a lasting impression through interactive, personalized, and visually stunning API demos.

Y Combinator (YC), a world-renowned startup accelerator and venture capital firm that helps founders in the early stages of their businesses, recognized Coast's potential and dedication by accepting them into the S21 batch. It’s worth noting that YC has a less than 2% acceptance rate, and consistently ranks as the world’s best incubator.

During their time in YC, Levitt and Pager refined their product, validated their market fit, and forged crucial partnerships that would propel Coast to new heights.

After YC, Coast quickly gained traction and has since attracted some of the largest enterprises in the world. FIS (NYSE: FIS), whose APIs help process $50 trillion annually and are used by 95% of the world’s leading banks, trusts Coast to power how they showcase the capabilities of their APIs.

More specifically, FIS's Code Connect platform hosts over 700 APIs for businesses to integrate and leverage products that span all stages of the monetary lifecycle. Coast integrates directly into Code Connect, just as an FIS customer would, to enable different product teams to bring the APIs they sell to life for:

• Internal teams to streamline product development.

• Banks and external partners for strategic initiatives.

• Sales reps to demo the platform throughout the buying process to drive revenue.

For those who attended FIS Emerald, FIS's flagship conference for its customers and partners, Coast was on display on the main stage and at individual booths throughout the conference floor. The Open Access platform is one example of a product that FIS leverages Coast for:

"Agreements are in place with leading data networks including Akoya, Envestnet | Yodlee, MX and Plaid to integrate into FIS’ Open Access platform, an innovative new open banking solution, that enables consumers to securely and seamlessly share their financial information with a greater number of third-party financial apps and services of their choice." (Source)

More FIS teams, such as Modern Banking Platform, Card Suite, Loyalty and Reward Solutions, and Atelio by FIS, are leveraging Coast to showcase the power of their APIs in real-time.

In addition to working with some of the top US banks, credit bureaus, card networks, and other Fortune 500s, Coast also works with leading API-first companies, like Twilio and Plaid, which are largely credited with creating the notion of an API-first company.

Product managers, designers, and even non-technical stakeholders like sales & marketing can now grasp the potential of these tools, leading to more informed decision-making, better-aligned product development, and more effective communication between technical and non-technical teams.

When more people, not just engineers, can understand and act on API-driven ideas, it unlocks better products, faster decisions, and more aligned teams.

The ability to effectively showcase and communicate the power of APIs can be a game-changer for businesses and individuals. When everyone, from executives to end-users, can grasp the potential of these tools, new opportunities for growth, partnership, and integration arise.

APIs are growing (and for good reason)

According to Postman, 91% of developers now use APIs in their work, and 60% say their companies have become more API-driven in the past year.

APIs are no longer optional – they’re central to how modern software gets built.

Why? Because APIs solve real problems out of the box. Developers can plug in a solution, let someone else maintain and scale it, and move on to building what matters next. The less time they spend learning and configuring, the better.

In ‘APIs as ladders’, author Sebastian Bensusan states: “Developers don't want to learn your API, they want to solve a problem and move on. As such, you should try to minimize the amount of learning they have to do.”

That’s why minimizing complexity is critical. Not just in the product but also in the sales process. API buyers don’t want theoretical explanations. They want to see exactly how your API works in their context and with their use case.

Galileo, the infrastructure provider behind fintech leaders like SoFi, understood this firsthand. As their deals grew more complex, so did the pressure to clearly communicate how their platform worked without slowing down engineering teams to build custom demos for every use case.

“We were using different tools, API flows, and mockups. Then we had to manually combine everything for every deal,” said Nick Watson, from Galileo’s SE team. “It was slow and inconsistent.”

That tradeoff wasn’t scalable. Instead of continuing to patch together materials, Galileo turned to Coast.

“The speed was a surprise,” said Michael Grillo, Head of Product Marketing. “You plug in a URL and get something on-brand and interactive immediately. It completely changed how we approach sales enablement.”

With Coast, Galileo’s product teams could generate live, branded demos tailored to each prospect—without engineering lift. Sales engineers reclaimed hours of prep time, champions got reusable assets they could circulate internally, and product marketing could run faster without waiting on design.

"There are seven-figure deals where the Coast demo made the difference,” Grillo said. “It brought clarity to something we’d long struggled to articulate.”

(Quotes provided by the Coast team)

By empowering teams to clearly demonstrate the value of their APIs early and often, Coast helps API-first companies like Galileo move faster, tell a more consistent story, and build trust across every stakeholder in the deal.

Making APIs accessible to all

At the heart of this transformation lies the opportunity to effectively showcase APIs' capabilities to potential customers and stakeholders. As we have explored, traditional methods such as technical documentation and static sales decks often fail to capture the true power and versatility of these tools.

This is where Coast has emerged as a solution to help tell any story for any API.

The broader implications of Coast's success extend far beyond API demos. By making these powerful tools accessible and understandable to a wide range of stakeholders, Coast is paving the way for a future where API-driven innovation is not just the domain of the technologically savvy but a universal language empowering businesses and individuals to unlock new levels of efficiency, collaboration, and growth.

As the API-first economy continues to evolve, embracing innovative tools like Coast will become a critical differentiator in a competitive landscape where showcasing API value can make or break a business.

Not every company will become API-first, but it would be a mistake for companies not to expose their APIs as an alternative revenue stream. The trend is still early, but we can expect to see more API-second and API-third companies over time.

Until our next adventure.

Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊). Also, thank you to Austin, Spencer, and the entire coast team for making this edition of The API Economy possible. 

*Disclaimers: The views in this essay are my personal opinions and don’t necessarily represent the views of my employer, those mentioned in this article, the Coast team, or anyone other than myself.

To support The API Economy, be one of the 9,556 early, forward-thinking people who subscribe for monthly-ish insights on the crypto, stablecoins, AI, and more emerging trends.

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Today’s essay is a guest post written & published by Juan Lopez, Partner at VanEck Ventures & lightly edited by Peter Schroeder.

Stablecoin networks will play a pivotal role in the next era of Fintech, particularly in (i) payment for goods & services and (ii) asset origination and management.

By creating open, disintermediated liquidity and customer acquisition networks with revolutionary market expansion potential, stablecoins and public blockchains enable financial products to tap into internet-scale distribution with a single API on day one.

Removing friction in payments has historically driven economic growth. From Lydia's standardized coins enabling Mediterranean trade to medieval bills of exchange creating global commerce, simpler exchange of value expands markets.

We’ve come a long way. From shells to digital wallets, innovation in payments has continuously reshaped commerce. This past decade in fintech showcases this through credit card networks, India's UPI, Brazil's Pix, and Kenya's M-PESA. The search for innovation has typically been built upon three core tenets:

• Common standards that align economic incentives, like the dollar as a shared medium of exchange, enable predictable market interactions.

• Abstraction layers enhance utility while containing risk—from banking APIs to fraud prevention systems that shield users from complexity.

• Network-based trust mechanisms - such as clearing houses that ensure transaction finality or provide recourse when needed.

These core principles typically build upon each other, allowing breakthroughs in trustless settlement to spark innovations in economic alignment and vice versa. Public blockchains enable a global platform for developers to innovate around these principles in the open to build products like stablecoins.

This has set the stage for the next platform shift in Fintech: The rise of open-source banking and financial services powered by global, trust-minimized stablecoin networks like USDC, USDT, AUSD, and more.

While stablecoins present an enormous opportunity, we need to understand both their potential and limitations. In this piece, I'll cover:

1. The unique capabilities stablecoins unlock for financial services.

2. How the market is evolving from simple issuance to full-stack infrastructure.

3. What this means for the next generation of Fintech giants.

The Big Unlock: Open Source Banking-as-a-Service

While peer-to-peer payments over blockchains like Solana or Base today are nearly free, the cost of abstracting crypto complexity while managing risk and compliance is largely externalized from the low fees that lure us into the opportunities across B2B and consumer payments. The cost of transferring USDC on Solana from wallet A to wallet B might be a fraction of a penny, but compliance costs will eventually have to be added back into the equation.

If you use apps like Sling or DolarApp to send/spend money around the world, the benefits are clear. However, the unit economics are likely not 10x improvements over traditional rails. The truth is that the cost of running stablecoin infrastructure at scale offsets some of the benefits of instant settlement and programmability. I generally agree with Simon Taylor, who recently stated that Stablecoins' advantage isn't primarily about cost – it's about better infrastructure.

Lower fees alone won’t drive mass–scale adoption. Despite being 60–80% cheaper and faster with dramatically better UX, major fintech platforms like PayPal and Wise currently handle less than ~3% of global payments volume and about 20-25% of consumer cross-border flows when comparing 2023 public filings for Paypal, Airwallex, Wise, Western Union, and others to FXC’s market research.

The real innovation is stablecoins' open-source infrastructure, which enables developers to build unique applications and iterate faster than ever before. By lowering the barriers to entry, emergent properties arise and deliver unpredictable results.

For the first time, we have a global standard for programmatic value transfer with growing accessibility & composability irrespective of the way users access their money (it’s worth noting even ecosystems like Paypal and Venmo have been unable to facilitate “internal” transfers until stablecoins). Smart contract-based money introduces a new level of utility for developers, from basic conditional payment execution and collateralized lending to yield streaming and programmable compliance executing over the same open infrastructure.

How valuable is this? We could consider proxies like the $13T+ Eurodollar market or even the current TAM of all global payments. I bet it can be bigger than that.

A Harvard Business School study estimates the economic impact of open-source software is ~$8.8 trillion. My bet is this figure changes materially when taking public blockchain financial infrastructure into account. By sharing costs across a global platform (of forkable contracts, nodes, developers, users, etc.), stablecoins and public blockchains meaningfully expand the addressable market of all payments and capital markets.

As we’ve seen in prior technological shifts like the Internet and Cloud, disruption is inevitable but won’t happen overnight.

For example, the opportunity in commerce and merchant services will be harder to crack due to consumer affinity for credit. In fact, it’s quite likely Visa and Mastercard (perhaps Stripe + Bridge) end up being some of the long-term winners in stablecoin orchestrated commerce due to their strong merchant distribution and incentive structure. To date, the winners have been the stablecoin issuers, but the future holds potential for many more winners.

Beyond Issuance: A Tale of Float vs. Flow

The stablecoin space is already fiercely competitive and dominated by players with strong distribution. Issuers like Circle and emerging stablecoin payment service providers (PSPs, e.g. Bridge) are scaling rapidly. Meanwhile, fintech and banking incumbents (Stripe, Robinhood, Paypal) are ramping up their efforts. The opportunity to redesign payments and rebundle financial services has taken center stage, as pure-play fiat-backed USD stablecoin issuance is quickly becoming hyper-competitive.

As we get regulatory clarity, perhaps soon via the GENIUS Act, and enable the U.S. banking sector to interface with crypto more broadly, the winners here will not just issue stablecoins but build complimentary services around them. Plenty has been written on this topic as it pertains to stablecoin banking—I particularly enjoyed this piece by Bridget from Founders Fund.

It’s no surprise that stablecoins are on the agenda of every major Fintech boardroom. Discussions around build vs. buy strategies are already well underway, and we’ve seen Fintech giants like Paypal, Robinhood, Nubank, and Revolut all exploring stablecoin strategies in 2024 alone. What exactly are they looking to enable?

1️⃣ Crypto Capital Markets

Historically, the first key use case for stablecoins to achieve product-market fit has been denominating crypto capital markets in dollars.

To service this, issuers (Circle, Tether) have captured value by maintaining a “float” and building products and services (e.g. mint/burn, CCTP) that extend stablecoin utility and sustain network effects.

We’re already seeing issuers begin to (a) build complementary SaaS businesses via developer infrastructure and/or (b) monetize transaction flow via their own chains/payments platform in order to differentiate.

However, It’s interesting to see how this concentration of value as net interest margin for the large stablecoin issuers has led to opportunities being created elsewhere.

Keith Rabois mentioned this in a recent All-In pod:

The incentive behind float-based stablecoin businesses has catalyzed open-source dollar liquidity networks around the world. These networks are evolving beyond simple money movement and becoming foundational for broader financial applications.

2️⃣ Tokenized Financial Products

Stablecoins transform consumer access to financial products. Conversely, they transform how asset originators can reach consumers at scale.

Starting with basic dollar access in high-inflation markets (through platforms like Littio in LATAM), stablecoins have created a 30M+ address open-source distribution network for financial products.

This network addresses two key inflections:

1. Stablecoins on public blockchains enable global access to dollar-denominated yields and financial products

2. Tokens and smart accounts lower the cost of servicing mass-scale financial products. Vlad Tenev elaborates on this in his recent op-ed.

To illustrate, Ethena now offers institutional-grade, dollar-denominated funding rate arbitrage yield to any user worldwide - a strategy previously limited to prop desks and hedge funds. In under a year, it has grown to hundreds of thousands of users and $6B+ in AUM (as of February 2025).

As more users leverage stablecoins (whether they know it or not), we'll see increased competition to serve a global user base with more sophisticated financial products (via protocols like Concrete and Superform or tokenized assets like Truly), making stablecoins the de facto standard for global financial product distribution. I think this is the key inflection driving Circle to acquire Hashnote.

Whether users want dollar yield to begin with no longer matters. Competitive dynamics over who gets to monetize other opportunities have introduced incentives to retain users on-platform (see Nubank’s latest announcement, for example).

Similarly, there is a broader opportunity to make the world’s most valuable assets programmable and tradeable in a compliant manner. We’re excited to see more compliance-first teams like Dinari come to market as a platform for regulated securities to trade on-chain.

The natural conclusion here is that more sophisticated product offerings previously accessible only by select individuals/institutions will become available to the masses through blockchains as the most scalable distribution channel. The same applies to payments.

3️⃣ Collapsing the Payments Supply Chain

For a primer on how stablecoins enable instant payments worldwide and how they disrupt the correspondent banking system, Kunle offers great perspective in his January piece. I also recently enjoyed Matt Brown’s piece, Stablecoins in 1000 words.

As noted above, crypto capital markets activity has driven dozens of infrastructure players to create fiat endpoints connecting to local crypto exchanges. This has created a stablecoin liquidity network that is now serving various payment verticals, predominantly global consumer dollar accounts, “offshore” dollar bank accounts, and cross-border payments.

Dozens of venture-backed consumer and business applications are now leveraging this infrastructure to orchestrate payments at a global scale.

Stablecoins' impact on global payments is already significant, having settled ~$30T in transaction volume in 2024. We often compare this to Visa’s transaction volume, but when accounting for bots, high-frequency trading, and other distortions unique to public blockchains, only about ~20% of this really counts as organic activity. While not an apples-to-apples comparison, the industry has come of age as a payment medium and settlement layer, a mix between Visa and the DTCC.

We often reference this chart of growing USDC<>MXN remittance flows via Bitso. What’s interesting is that most of the stablecoin settled remittance flow in this particular corridor is actually happening off exchanges (as exchanges are not in a place to lower their take rates – though it’s hard to track). We should expect to see this occur on a global scale.

This surge in activity is causing stablecoins to increasingly deepen their connectivity in and out of global fiat economies to power global money movement. Based on our own discussions with most of the firms enabling this, we estimate stablecoin-to-fiat channels already facilitate $30-80m in daily remittances across US-India, Africa, APAC, and Latin America corridors.

Two key developments monumental for money movement will accelerate this:

1. Open liquidity networks will soon offer best-in-class FX across all major corridors. For example, see Circle’s partnership with BTGPactual. This eventually enables USD/BRL wholesale FX rates at internet scale. On-chain markets will have access to prime FX institutional rates (vs. retail rates) as dollar stablecoins connect to the largest banks in each jurisdiction. This will likely become the foundation for on-chain FX markets.

2. P2P payment orchestration by non-bank actors around the world powered by smart accounts and stablecoins as the default stack. This will introduce the re-bundling of payments as near instant and table stakes in most applications. Much of this is already happening today without stablecoins– Uber, Lyft, Instacart, and many others monetize “time-to-money” in a world where t+3 settlement and bi-weekly paychecks are the norm. Stablecoins will unlock this capability for any developer, thus lowering working capital requirements.

Think of stablecoins as creating a universal adapter for money. By aggregating the deepest liquidity from USD to any asset/currency and becoming the de-facto developer platform for fintech applications, the stablecoin economy strengthens through a self reinforcing cycle.

But many questions & misconceptions remain.

Many assume that success for stablecoins is dependent on their ability to off-ramp to settle with merchants. Baking in fiat on/off ramp fees and compliance complicates the unit economics of stablecoin payments in the short term.

But perhaps stablecoins can become the standard, removing the need to on/off ramp.

This leads to more questions (and some speculation). As we bring more stablecoin settled activity on-chain and a wider basket of currencies, what will be the role of traditional payment messaging systems and banks? What will be the role of cash exchange houses or cash remittance players? I’m not suggesting they go away, but they’ll likely play different roles.

• “peer-to-peer” infrastructure for non-bank actors to offer payment products and services without directly relying on banks is here. We will still need custody and redemption mechanisms for fiat, but transactional value likely moves on-chain. However, it’s unclear how long this form of regulatory arbitrage will remain.

• The last mile problem we had a few years back is already largely solved through platforms like Rain and Reap which enable any application to provide a global dollar account you can spend from anywhere Visa is accepted. This is touching the lives of millions today by enabling remittance recipients to spend instantly.

• The above is further enabled through what we are now calling “stablecoin PSPs”. There are now >40 infrastructure players who manage both traditional fiat accounts and stablecoin liquidity in different parts of the world. They are all competing to offer a growing set of platform services: smart settlement credit facilities, wallet infrastructure, cards and spend management, compliance-as-a-service, and much more.

Banks will remain important for custody and redemption, but the design space for non-bank actors to build on open multi-currency rails will radically transform money movement.

Soon, we’ll see stablecoin adoption beyond cross-border consumer remittances as we get more regulatory clarity and validate new business models beyond stablecoin issuance. As the benefits of programmable money meet improved FX spreads, ERP integrations, fraud & risk tooling, confidentiality, and more. One of the largest opportunities lies in B2B payments (~$150T market), where stablecoins can enable the next generation of:

• Treasury management systems, enabling auto-repatriation, FX hedging, in-house banking, etc.

• Cash flow management via settlement-agnostic invoicing with smart escrow, auto currency conversion, and open secondary markets for trade finance and invoice factoring.

• Pricing models, Accounting, and reporting automation via transaction enrichment and open payment data markets (e.g. via Loop) plugging into ERP systems.

This moment in time marks the end of the on/off ramp, where we detract from thinking about the opportunity as onboarding and offboarding from crypto stablecoins.

End-to-end payments of all types are the opportunity.

We now covered how stablecoins provide a superior open platform for moving money and assets globally– thus powering the next wave of Fintech innovation.

Now, let's explore what the next wave of winning players could look like.

The Next Fintech Giants

The rise of Fintech players like Nubank, Revolut, Stripe, and Robinhood demonstrates the power of superior distribution in financial services. What they've achieved with limited infrastructure is remarkable - and notably, all are now running blockchain-powered products.

While blockchains offer compelling infrastructure, distribution - not technology - remains the primary challenge for new entrants. One too many founders assume they'll win off the back of stablecoin and crypto adoption alone but will be faced with a reality check: Most high LTV target users already have access to banking and basic financial services.

This could change. But "Banking the unbanked" or “Onboarding the next billion users” overlooks that building highly profitable mass-scale businesses will require more than just better technology—it demands a compelling reason for users to switch. This is especially true for businesses where existing financial workflows are deeply embedded and difficult to displace.

There are notable examples of companies that see this as an opportunity. Start-ups who are providing globally scalable financial services on superior infrastructure to users in multiple markets and seeing impressive growth in their respective business and consumer segments:

• DolarApp, Lemon, Belo, and Littio are leading consumer adoption of stablecoins in LATAM, resonating with an audience that wanted simpler and cheaper, dollar banking products.

• Sling, FelixPago, Abound, Afriex, AirTM lead in servicing more globally connected economies by enabling seamless remittances for consumers from U.S to LATAM, India, and Africa.

• Bridge, Conduit, Rail, Loop, Harbour, Koywe, Acctual, Ridian, and Rain are some of the most exciting teams we’ve met that equip businesses with the infrastructure needed to orchestrate and spend off stablecoin balances while extending their connectivity to the world’s financial systems.

The common trend we observe is that the next Fintech unicorns won't win solely through token incentives or by building over the next trending blockchain ecosystem. They'll win by placing emphasis on:

• Long-term monetization of both float and flow.

• Design and building products uniquely feasible via programmable money.

• Targeting non-consensus niches or tapping into a unique insight with massive growth potential.

• Built-in virality and low onboarding friction (big fan of Tiplink).

• Operational excellence, leveraging open-source infrastructure to ship 10x faster. For example, using blockchains as a global computing platform not only shares the cost of money storage and movement but also risk and compliance

So, where do we go from here? The next 10x unlock comes from clear regulation and infrastructure enabling more complex, regulated use cases. Tools acting as middleware solving for compliance, smart onboarding/account management, fraud prevention, and KYC/AML will be key for bringing regulated flows on-chain.

Some companies building critical infrastructure to enable this include:

• Rhinestone. A robust smart account module ecosystem will lead to smooth onboarding flows, embedded wallet experiences, and unified cross-app experiences to become the norm. For example, by enabling cross-margining between certain on-chain trading venues.

• Predicate. My general hunch is that user experiences will become radically simple while the underlying orchestration smart contract applications will need to build will become increasingly complex to accommodate various legal/compliance/regulatory requirements. To translate this into smart contract code, developers will need a policy orchestration layer enabling them to seamlessly call on various third-party providers to compose custom workflows, including KYC, AML, and more.

• Primus is enabling encrypted data and compute to become verifiable on-chain. We need to provide developers the flexibility to keep their data private while still integrating with blockchain ecosystems.

• idOS enables globally scalable KYC/KYB attestation will be crucial to powering regulated transactions on-chain. Gone will be the days where we onboard with the same KYC checks across multiple applications to accomplish the same exact thing. This should all be smart contract based.

We are optimistic that more smart contract-based utilities will emerge to compose applications and flows previously not feasible on-chain. These types of players will serve as critical middleware and infrastructure for the next wave of adoption.

To wrap up, I’ll expand on the broader opportunity to redesign our global financial system.

The Internet Financial System

Historical skepticism about new financial technology follows a familiar pattern:

• Credit Cards (1960s): "Only for travel and entertainment"

• SWIFT (1970s): "Only for major banks"

• PayPal (1990s): "Only for eBay"

• M-PESA (2007): "Only for rural remittances"

• Stablecoins today: "Only for speculative crypto trading"

The key insight is viewing blockchain metrics (volume, TVL, stablecoin supply, active wallets) as open-source channels for customer acquisition, liquidity, and capital formation. This is what we call tapping into the Internet Financial System (IFS). Applications building natively on the IFS will have the potential to go to market and reach scale faster while leveraging open utilities to share the cost of risk management, compliance, and more.

I have to give credit to Felipe Montealegre from Theia, who is putting out great thinking addressing the implications from first principles (who also perhaps coined the term?)

Taking what this infrastructure can do for payments and capital markets, we envision a few distinct opportunities that could emerge in the not too distant future:

• Financial SuperApps. Most superapps are an orchestration of service providers on the backend, abstracted for the end user. Stablecoins make treasury management instant, with funds instantly interoperable between partners. Beyond traditional neobanks, these platforms will connect previously siloed financial services (brokerage, banking, credit, taxes) through stablecoins and tokenized products. The result is seamless value transfer with lower switching costs and a focus on performance over capital hoarding. This opens a massive window for new consumer apps to build on this primitive to compete on an entirely new playing field vs. incumbents.

• Stablecoin-Centric Onchain Payment Networks. Unlike Visa's pure take-rate model, these networks will combine stablecoin yields and governance/security tokens to incentivize supply and demand participants who can easily plug in their banking/compliance infrastructure as a ‘node’ powering new stablecoin-based payment networks. The open nature of these networks should lead to open incentive schemes that operate programmatically and trustlessly. No more negotiating bilaterally with stablecoin issuers for a higher share of the underlying yield. This should operate in the open, leading to more efficient schemes such as payment for order flow (PFOF) or Pay-now-settle-later (PNSL) dominating payments and FX transactions.

• Internet-Native Capital Markets. As more businesses earn revenues and manage expenses on-chain, we will likely see them raise debt and equity capital on-chain through tokens as a more effective capital formation mechanism. It will take a long time to get there, but it’s clear there is a token liquidity premium vs. traditional equities when looking at token FDVs. Over the next few years, I’d expect this premium to incentivize a major player (crypto native or not) to opt for token issuance as a way to tap into capital markets more efficiently vs. IPO.

Thinking about the future state of IFS enabled applications, one of the most intriguing design spaces will be how we rethink incentive mechanisms. In the age where everything is summoned for us–maybe even by our own personal agents– token-based financial products will have lower switching costs.

Go-to-market and customer acquisition as a function will have to change, introducing new financial platforms not incentivized to increase assets under management, but for delivering on superior performance or providing unique access.

We already live in a world with too many options. Therefore, platforms will likely have to compete for the abstraction layers’ attention (aka our agents’ attention). See Aravind’s comments on advertising in a world of agents.

If we all have an expert financial advisor at our fingertips, asset issuers and payment service providers will have to develop other ways to compete for capital and transaction flow.

The Internet Financial System lets us reimagine how value moves and markets operate. While the immediate opportunities in payments and capital markets are clear, the true potential lies in what we can't yet imagine.

As we've seen with every major platform shift, from the internet to mobile, the most transformative applications often emerge from unexpected places. The opportunity for the next wave of financial applications building on top of blockchains is wide open.

This vision drives our work at VanEck Ventures, and together with Wyatt, we can’t overstate how excited we are to support the visionary teams building today but thinking and living in the future.

If you're a founder working around these ideas, we'd love to hear from you. My DMs (@lagunasounds) are open.

If you read this far, thank you. Please let me know what you think!

I am enormously thankful to Wyatt Lonergan, Joao Reginatto, Nick Van Eck, Nikhil Raghuveera, Peter Schroeder, Farooq Malik, Josh Solesbury, Gabe Rabello, Kurt Larsen, Alex Matthews, and many other awesome builders and investors, many of whose companies I mentioned above, who have helped inform this perspective and provided feedback.

*Disclosures:

*All investing is subject to risk, including the possible loss of the money you invest. As with any investment strategy, there is no guarantee that investment objectives will be met, and investors may lose money. Diversification does not ensure a profit or protect against a loss in a declining market. Past performance is no guarantee of future performance.

To support The API Economy, be one of the 8,913 early, forward-thinking people who subscribe for monthly-ish insights on the crypto, stablecoins, AI, and more emerging trends.

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Computers existed before the internet.

When the personal computer became popular in the 1990s, it operated marvelously independently. It revolutionized how we handled tasks and improved people’s lives.

Despite its capabilities, personal computers were isolated on their own.

They were capable of great work but limited in what they could do in a closed system.

But when the internet arrived, everything changed.

Suddenly, computers could connect to a vast world of information and people. They transformed from mere machines into portals of endless possibilities, allowing us to communicate, collaborate, and access an ocean of knowledge.

This innovation led to the modern technology-enabled world we live in today.

Money is now having its ‘personal computer connecting to the internet moment’.

Traditionally, money has served its purpose as a medium of exchange, a store of value, and a unit of account.

The inception of stablecoins supercharges the possibilities of money. It gives money all the benefits of the internet, including the rise of AI agents.

As Nic Carter put it, “Stablecoins are the Starlink for finance.”

Stablecoins, which now have a market cap of $200 billion & are already processing trillions in monthly transactions, have become the backbone of internet-native money.

By offering the stability of fiat currencies and the programmability of digital assets, stablecoins have laid the foundation for a global decentralized financial system accessible by autonomous systems.

AI agents are the systems that bring intelligence into the money equation.

They can analyze vast amounts of data, predict outcomes, and automate tasks instantly at an unprecedented scale. Integrating this with the seamless transfer of value that stablecoins offer unlocks an entirely new world of possibilities.

Imagine being able to leverage AI to make instant investment decisions using insights derived from global financial data, all while benefiting from the instant and secure transactions of stablecoins. You get a system that can assess risks, propose solutions, and execute transactions at the speed of the internet.

In this way, stablecoins and AI together create a dynamic duo that offers speed, intelligence, and efficiency, broadening the landscape of what's possible in finance and commerce.

AI can analyze data, execute tasks, and interact with blockchain protocols without human intervention. It is not just automating financial processes; it is completely transforming them.

Let’s dive in.

Good things come in threes

Stablecoins provide the scalability and reliability that AI agents need to operate autonomously.

They offer a level of stability and efficiency that traditional financial systems cannot match, creating the foundation for an entirely new era of financial automation.

Their benefits over traditional financial systems have led to their usage proliferating over the years.

Since their inception in 2014, stablecoins have processed over $60T in transaction volume and are currently being leveraged in nearly half of every transaction onchain.

While stablecoins have found a clear product-market fit and are primed for AI agents, the use cases will not come all at once but rather in waves of adoption.

In his essay, ‘The Role of Crypto in an Agentic Economy’, Robbie Petersen states that the adoption will happen in three distinct phases:

• Phase 1 – Human-to-Agent Transactions (Present)

• Phase 2 – Agent-to-Human Transactions (Emerging)

• Phase 3 – Agent-to-Agent Transactions (Future):

Phase 1 is where we are closest to today.

Perplexity recently rolled out shopping features that make it easier for agents to purchase on a person’s behalf.

While this does not yet include the use of stablecoins, we’re not far off from companies starting to roll these types of features out:

I wrote about this in a previous essay, but Chamath Palihapitiya said this about how AI agents will make decisions on an episode of the All-In Podcast:

“AI is ruthless because it's emotionless. It was not taken to a steak dinner. It was not brought to a basketball game. It was not sold into a CEO. It's an agent that looked at a bunch of API endpoints, figured out how to write code to it to get done the job at hand.”

What Chamath meant when he made this statement is that AI agents will use the systems that are the most accessible and efficient to them and are not influenced by marketing, business development, or any factors that can influence human decisions.

They will choose the best form factor to transact on people’s behalf, and that form factor for financial activity will frequently be stablecoins.

Crossmint gets it and is building tools for AI agents to leverage stablecoins like USDC:

Phase 2 is rapidly gaining traction. In a thread, Robbie claims this about phase 2:

“The next phase involves agents autonomously initiating transactions with humans. This is already beginning in narrow domains – AI trading systems executing trades, smart home systems purchasing electricity at optimal rates through time-of-use pricing, and automated inventory management systems placing resupply orders based on demand forecasting.”

An example of this is MEV (Maximal Extractable Value) bots which have leveraged stablecoins to process $4.3B+ in total transaction volume over nearly 200 million transitions over the past three years.

MEV bots are automated programs that identify and capitalize on opportunities within blockchain transactions, such as arbitrage, liquidations, and front-running. Traditional MEV bots operate using rule-based algorithms, relying on pre-programmed strategies to monitor mempools, execute trades, and reorder transactions for profit.

These bots leverage machine learning to predict market trends, optimize transaction placement, adapt to changing conditions in real time, and showcase the evolution in the sophistication of crypto agents.

Stablecoins play a crucial role in bot operations, providing the stable, liquid medium needed to execute high-frequency, low-risk strategies at scale.

The increasing sophistication of these bots illustrates a larger trend: AI agents, bots, and autonomous systems are rapidly becoming dominant in blockchain ecosystems.

This is just one example of bots already using stablecoins at scale, and it shows how agents and bots have begun to impact markets broadly.

Phase 3, we’re just starting to see early signs of life.

In August of this year, Coinbase incubated the first AI-to-AI transaction using USDC.

Brian Armstrong has this to say about the interaction:

“AI agents cannot get bank accounts, but they can get crypto wallets. They can now use USDC on Base to transact with humans, merchants, or other AIs. Those transactions are instant, global, and free.”

Stablecoins provide the efficient, liquid, and permissionless medium that AI agents need to execute their tasks efficiently and reliably. Meanwhile, AI injects intelligence into blockchain operations, allowing for smarter, faster, and more efficient systems.

The implications of AI to AI transactions will be vast and far-reaching, leaving us to watch and see where they take us. For now, we can only speculate.

Stablecoins are primed for AI-driven use cases

One of the most compelling use cases for stablecoins today is programmable liquidity, where AI agents can dynamically access and deploy funds within an instant.

For example, so far in 2024, the global DeFi market has handled over $1.3 trillion in transaction volume, with stablecoins comprising a significant portion of that liquidity.

Companies like Velodrome and Aerodrome Finance are innovating as AMMs (automated market makers) by introducing a vote-escrow (ve) token model that incentivizes community-driven liquidity allocation.

This model ensures efficient capital deployment and scalability by integrating Layer 2 networks like Optimism and Base.

AI can further enhance these systems by enabling programmable liquidity and dynamically optimizing pool allocations, fee structures, and rewards based on market conditions and user behavior, driving more efficient and adaptive liquidity provisioning via stablecoins.

This all helps to further DeFi (decentralized finance), a market that has grown exponentially in the past few years and is on track to continue to grow exponentially.

VanEck predicts this about DeFi in 2025:

“DeFi hits all-time highs with $4 trillion DEX volumes and $200 billion total value locked, fueled by AI-related tokens, consumer-facing dApps, and tokenized assets driving liquidity and adoption.”

As DeFi continues to grow, so will AI usage.

Another area where stablecoins and AI agents are poised to make a massive impact is Cross-border payments.

Consider the global remittance market, which reached $883 billion in 2023 and is projected to grow to $913 billion by 2025.

Traditional remittances are also plagued by high fees, averaging around 6% globally, and delays can last several days.

Stablecoins are already disrupting this market because they can settle transactions instantly at negligible cost.

When layered with AI agents, the process becomes even more seamless: an AI agent can analyze exchange rates, identify the most cost-effective routes, and execute transfers autonomously.

This will help save consumers and businesses billions annually while improving the speed and accessibility of payments.

Jeremy Allaire touched on the potential for onchain AI agents during his keynote at Circle Forum Hong Hong while discussing, ‘How might AI agents operate on-chain with USDC.’

Stablecoins are uniquely positioned to be an optimal tool for AI agents to leverage for all of these use cases because they focus on programmability, trust, and transparency.

They provide an immutable record of every transaction onchain, ensuring that AI agents’ actions remain auditable and verifiable. This is especially critical as humans delegate increasingly complex tasks to these systems.

This programmable trust is essential for bridging the gap between human and machine economies.

AI agents, powered by this stable, programmable liquidity, are transforming these transactions into smarter, faster, and more cost-effective workflows. From optimizing DeFi strategies to automating global commerce, the synergy between stablecoins and AI agents is upgrading how value moves across systems.

The future of finance is upon us

As we enter a financial future where AI drives an increasing share of economic activity, stablecoins are emerging as the essential infrastructure for this transformation.

Their stability, speed, and accessibility make them the perfect monetary form factor for AI agents, enabling a smarter, more efficient, and more inclusive financial system.

Stablecoin-powered AI agents can help by simplifying complex financial tasks, making them available to anyone with an internet connection. By simply chatting with an AI agent, people will soon be able to create a wallet and transact in stablecoins without knowing all the complexities of finance.

For example, people lacking advanced financial knowledge can delegate portfolio management of any amount to AI agents that use stablecoins to navigate budget management and earn yield through stablecoins leveraging DeFi. All the users see is their stable savings account based in dollars generating passive interest.

These agents can automatically rebalance investments, optimize yields, or even secure loans, lowering the barriers to participating in decentralized finance behind the scenes while displaying a simple UI.

This has profound implications for financial inclusion, particularly in regions with limited access to traditional banking systems.

Efficiency is another major benefit we’re already beginning to see in payments.

AI agents reduce friction in financial workflows by automating transactions and decision-making processes. Consider that in 2023, the global payments industry processed $1.8 quadrillion in transaction value, generating $2.4 trillion in revenue largely from fees.

With their near-zero transaction costs, stablecoins enable AI agents to chip away at this “payment tax,” making transactions faster and cheaper for businesses and consumers alike.

For example, an AI agent managing payroll for a global workforce could use stablecoins to pay employees instantly, regardless of location, eliminating delays and hefty processing fees.

Perhaps, above all else, the most exciting implication is the creation of new economic models and markets that never existed due to inefficient financial markets.

With AI agents transacting autonomously using stablecoins, entirely new industries and ecosystems are beginning to emerge, and machine-to-machine economies, where autonomous devices and systems exchange value directly, are becoming a reality.

They have the potential to increase global economic productivity dramatically. Reducing inefficiencies and removing intermediaries frees up resources that can be reinvested into growth and innovation.

Together, stablecoins and AI agents are creating a smarter, faster, and more efficient global economy where humans and machines collaborate to unlock unprecedented levels of value and intelligent innovation.

The fusion of stablecoins and AI agents transforms how value moves, enabling autonomous economic systems that are faster, smarter, and more efficient than ever.

As this ecosystem grows, stablecoins will continue to serve as the backbone of this revolution, providing the stability and liquidity needed for AI agents to thrive.

This is just the beginning of what’s possible when programmable money meets intelligent automation, and the opportunities ahead are as vast as the digital economy itself.

Until our next adventure.

P.S. If you’re interested in learning more about stablecoins, check out my last essay:

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. I am employed by Circle at the time of this writing, but the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

Note: Thanks to everyone who has consistently conducted quality stablecoin research, including but not limited to — Nic Carter, Rui, Jon Ma (and the entire Artemis team), Ryan Watkins, Sam Broner, Chamath Palihapitiya, Alex, QW, and Robbie Petersen.

*Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

To support The API Economy, be one of the 8,420 early, forward-thinking people who subscribe for monthly-ish insights on the API economy, crypto, stablecoins, and more emerging trends.

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No innovation has captured the imagination & potential of both the technology and finance worlds like stablecoins have.

These digital currencies, designed to maintain a stable value relative to a reference asset (most of the time a fiat currency like the US dollar), have emerged as a bridge between the traditional financial system and blockchain technology.

This week’s news of Stripe acquiring Bridge has sent ripples throughout the tech world and opened people’s eyes to the potential stablecoins to pave a new monetary superhighway.

Let’s dive into what stablecoins mean for the future of money.

The meteoric rise of stablecoins

There’s a saying that the ultimate form of sophistication is simplicity.

With all of the complexities that come with crypto at times, stablecoins are about as simple as you can get in principle — it’s digital money that combines the benefits of crypto with the stability of traditional finance.

It will all seem obvious in hindsight, but we’re at an inflection point for money.

Stablecoins aren't just an improvement on existing systems; they're laying the groundwork for an entirely new internet-based financial system.

As a result, the market cap of fiat-backed stablecoins has swelled from its conception in 2018 to over $220 billion as of February 2025.

To put this in perspective, that's more than the GDP of many countries.

And these stablecoins aren’t sitting idle. In Q2 alone, stablecoins processed more transaction volume than Visa by nearly 2x, with transaction volumes often exceeding trillions of dollars a month.

These levels of growth for a technology invented only 6 years ago are staggering.

The business of stablecoins

Why are stablecoins a good business, and how do they make money?

Stablecoins generate revenue by investing their reserves 1-for-1 in interest-bearing assets, such as treasury bills and other short-term investment vehicles. The interest earned from these investments provides a source of income for the stablecoin issuer.

This innovative investment strategy has led to stablecoin issuers, in aggregate, becoming one of the biggest holders of US treasury bonds, holding more than countries like Germany, Mexico, and the Netherlands.

But stablecoins are just beginning to realize their potential.

Consider this: the global money supply (M2) is estimated to be around $129 trillion.

We’ve only begun to scratch the surface of updating the financial system through stablecoins.

Outside of M2 money supply, here are the sizes of some other markets that stablecoins are poised to improve:

Foreign Exchange (Forex): $8 trillion moves between different currencies every single day. It’s the biggest market in the world. It’s about 30 times the daily global GDP.

Once we have deeper liquidity for more currency-backed stablecoins around the world, digital foreign exchange through stablecoin swaps will quickly begin to change the way money moves between currencies today.

Global Remittances: In 2023, the value of remittances worldwide amounted to an estimated $883 billion and is expected to reach $913 billion by 2025.

Stablecoins make cross-border payments as easy as sending a text or an email by offering faster, cheaper, and more seamless transactions.

Payments: The global payment market is valued at $2.64 trillion as of 2023 and is expected to grow to $4.78 trillion by 2029. Credit card processing fees vary by region, payment method, and transaction type but generally range from 1.5% to 3.5% of the transaction amount. The average global fee for processing credit card transactions in 2024 is around 2.4% of the transaction value.

Stablecoins have the technological ability to streamline payment workflows to make them much more efficient.

If stablecoins were to capture even a small fraction of these antiquated markets, trillions of dollars would flow into digital currencies that would radically improve the financial system.

The miracle of money

Why is money based on the internet so important? 

Similar to water, money permeates through our society, nourishing economic activity.

It seeks the path of least resistance, much like water flowing downhill. Just as water is essential for life, money is the lifeblood of commerce, enabling transactions and storing value.

It's important to understand the fundamental functions of money itself to help understand the value of stablecoins.

Money, in its essence, exists to solve a basic problem: how to efficiently exchange value in a complex society. Economists have long recognized three primary functions that define money:

1. A store of value

2. A medium of exchange

3. A unit of account

These functions form the bedrock of any monetary system, allowing individuals and societies to save, trade, and measure economic value over time.

By design, stablecoins aim to fulfill these same core functions, but in the digital realm, they offer improvements over traditional forms of fiat money.

Store of Value

One of the most immediate and obvious use cases for stablecoins is as a store of value, particularly in countries with unstable currencies or limited access to the global financial system. For someone living in a country with high inflation or strict capital controls, holding stablecoins pegged to the US dollar or Euro can be a lifeline to protecting their hard-earned capital.

Nearly 75% of all physical 100-dollar bills are held offshore, representing over $1.5T in value. Now, people can access dollars or other currencies of any amount 24/7/365 in a digital form factor through stablecoins.

Medium of Exchange

As a medium exchange, stablecoins have an opportunity to replace existing flows through their efficiency and programmability. In every single workflow that has a middleman facilitating and taking parts of a transaction, stablecoins have the opportunity to abstract away and streamline payment flows.

According to McKinsey research, in 2023, the global payments industry handled 3.4 trillion transactions, accounting for $1.8 quadrillion in value and a revenue pool of $2.4 trillion. By streamlining these payments, stablecoins chip away at that $2.4 trillion global payment tax, making payments more efficient for everyone.

Stablecoins are already overtaking the majority of crypto volumes and will soon do the same for other industries.

Unit of Account

While stablecoins are already useful as a store of value and medium of exchange, their potential as a unit of account is still largely untapped. As more businesses and individuals become comfortable with stablecoins, we could see them being used to price goods and services, especially in international contexts.

According to SWIFT, the US dollar accounts for over 80 percent of trade finance due to the fact that the majority of commodity trade continues to be invoiced and settled in dollars. 

The global trade finance market was evaluated at $10.5 trillion in 2023 and is slated to hit $13.6 trillion by the end of 2032. With a CAGR of nearly 2.94% between 2024 and 2032, this represents an enormous opportunity for stablecoin settlement.

In short, stablecoins are still early.

Internet money is just better

Apps like Venmo and Cash App have revolutionized peer-to-peer payments within the United States, making it simple to split a dinner bill or pay rent. 

However, these solutions are largely domestic. There's no "global Venmo" that allows for quick, easy, and cheap international transfers.

This is where stablecoins shine. They offer an inherently global solution, operating 24/7 and not constrained by national borders or banking hours. With stablecoins like USDC, sending money anywhere in the world becomes as easy as sending a text, and often at a fraction of the cost of traditional international wire transfers.

Post-Dencun upgrade on Ethereum in March has led to the average cost on L2s like Base falling below $0.01. It is nearly instant and available anywhere in the world.

Brian Armstrong, the CEO of Coinbase, recently spoke at a Goldman Sachs conference where he said:

Said more explicitly, stablecoins like USDC on L2s have become the most efficient payment rails in the world.

Let that sink in…

As more applications integrate stablecoin functionality, we'll likely see network effects. Each new app that adopts stablecoins increases the utility of the entire ecosystem, incentivizing further adoption and innovation.

Network effects occur when a product or service becomes more valuable as more people use it. Think about social networks like Facebook or communication tools like WhatsApp – their value increases exponentially with each new user.

Stablecoins have the potential for incredibly strong network effects.

As more people start using stablecoins, more businesses will accept them, and as more businesses accept them, more people will want to use them.

This virtuous cycle could lead to rapid, widespread adoption that happens at a rapid pace.

Out with the old, in with the new

When we look at the stablecoin landscape, people often compare it to the early days of the Internet. Back then, it was hard for many people to grasp just how transformative the technology would be. We're in a similar place with stablecoins.

In an episode of The Money Movement with Jeremy Allaire, Chris Dixon said:

Stablecoins represent a massive opportunity for entrepreneurs, developers, and innovators. The companies and protocols that successfully navigate this space could become the financial giants of tomorrow.

Here’s a list of just some of the companies innovating in the stablecoin ecosystem today:

The potential is enormous. Stablecoins have the power to remake the financial system in a way that's more open, efficient, and inclusive.

They could be the key to unlocking global financial inclusion, enabling new business models, and creating a more interconnected global economy.

And don’t blink because this growth is happening rapidly, with several stablecoins growing to over $1 billion in supply in less than a year.

There are also now over 76 stablecoin projects, which are above $10 million in supply.

The potential for growth is not just large; it's transformative on a global economic scale. And we’re still just getting started.

We're standing at the threshold of a new era in finance. The stablecoin revolution is just beginning, and I, for one, can't wait to see where it takes us.

Until our next adventure.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle, or any other company mentioned. I am employed by Circle at the time of this writing, but the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

Note: Thanks to all of the people who have consistently conducted quality stablecoin research, including but not limited to — Nic Carter, Cuy Sheffield, Jon Ma (and the entire Artemis team), Simon Taylor, and Electric Capital.

To support The API Economy, be like the 4,470 early, forward-thinking people who subscribe for monthly-ish insights on the API economy, Web3, AI, and more emerging trends.

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Hello everyone 👋 ,

Today’s edition of The API Economy is brought to you in partnership with Speakeasy.

Speakeasy’s founders Sagar Batchu and Simon Yu were kind enough to work with me on this edition of The API Economy. It would be an understatement to express my admiration for the exceptional Speakeasy team. Their remarkable vision for an SDK-first approach to API development is a paradigm shift for many. Collaborating with them has been an extraordinary experience.

Besides telling you how great the Speakeasy team is, I also want to tell you how they are changing API development as we know it by enabling teams of any size to build Stripe-quality APIs without a Stripe-sized team.

To find out how, let’s explore Speakeasy.

*Disclaimer: This is a paid sponsorship. Find out more about The API Economy’s paid sponsorships here.

The prism of APIs

An API is like a ray of sunlight, containing a spectrum of capabilities. Just as white light conceals a rainbow, a typical API hides its full potential. The power within most APIs is typically hard to harness because they are often hard to build, integrate with, and maintain. But in the right hands and with the right tool, an API can enable magical experiences far beyond the mundane.

Speakeasy is the prism that reveals the true possibilities within an API experience. The team at Speakeasy is building a complete platform to create world-class API developer experiences, from SDKs to Terraform providers, developer documentation, API maintenance, and more.

Tools to create SDKs are important because SDKs make the integration story easier. End-users are much more likely to adopt an API that has great SDKs. Simply put, better SDKs = better APIs.

According to Postman’s State of The API Report, over 75% of respondents somewhat agreed or strongly agreed that developers at API-first companies are more productive, create better software, and integrate faster with partners.

While APIs fuel innovation, working with them introduces significant integration challenges. There is the tedious work of managing data schemas, request authentication, business logic, rate limits, and other API capabilities so they align across systems.

Over the years, developers have had a conflicted relationship with APIs. On one hand, they've been crucial building blocks that allow applications to integrate with useful services and data. A great deal of effort is spent on this API plumbing before developers can even begin building creative solutions on top of them. As a result, API users struggle to exploit the value that APIs fully provide.

Today, over 90% of developers (there are nearly 29 million developers in the world, according to Statista) work with APIs in some form, composing solutions by connecting APIs across the web.

These data and functionality exchanges now drive the majority of traffic on the internet. Akamai says 83% of all internet traffic belongs to API-based services.

The company that finds ways to abstract away API & integration difficulties unlocks tremendous productivity gains. If we can reduce the time developers spend on mundane API configurations, they can instead spend more cycles innovating on top of API capabilities. Making APIs easier to build and manage makes them accelerators rather than anchors of developer experience.

This leads to Speakeasy redefining API development as we know it through their SDK-first approach.

SDKs all the way down

Since it will come up a lot in this piece, let’s start simply by defining what an SDK is. 

An SDK (or Software Development Kit) is a collection of software tools and libraries that allows developers to build applications for a specific platform, software framework, or API. At a high level, SDKs provide pre-built abstractions on top of raw APIs, automatically handling authentication, serialization, retries, and other boilerplates.

Four key characteristics of SDKs:

• Language-specific: SDKs encapsulate API access using the native conventions and interfaces of their target language and platforms (e.g. JavaScript, Python, iOS, Android). This allows the integration to feel seamless.

• Abstraction layer: SDKs provide an abstraction layer that hides API implementation complexity behind easy-to-use functions and objects. Developers don't work directly with raw API calls.

• Packaged dependencies: SDKs include or automatically manage dependencies like authentication modules, serialization libraries, API clients, and other packaged functionality needed for integration.

• Updates automatically: SDKs have mechanisms to automatically apply updates across all consumer applications without rebuilding integrations. This maintains compatibility with the latest API versions.

One of the biggest but most under-appreciated problems with APIs today is how challenging the go-to-market is for APIs. API producers must consider the ever-changing landscape of developer ecosystems: languages, runtimes, and marketplaces. API distribution is no longer as simple as building something and posting it on Hackernews.

API companies must consider API strategies holistically to meet developers where they are. 

This is why for years, world-class API experiences seemed reserved only for API elites like Stripe and Twilio, who have entire teams of PMs, engineers, and technical writers, as well as dedicated platform tooling, all dedicated to ensuring that every API is easy to use and creating best-in-class programs. 

Historically, most teams need more expertise and resources to build developer communities around their APIs. Creating rich SDKs, best-in-class docs, and robust monitoring — these capabilities required armies of specialized engineers, resulting in APIs remaining siloed with their potential bottled up.

The status quo ignores a fundamental truth: APIs are vital business drivers, not ancillary projects in the digital age. They unlock connectivity between systems and creativity between developers.

APIs are the threads weaving disjointed workflows into frictionless experiences — the foundations enabling rapid experimentation. When developers have unlimited choices of API building blocks to assemble solutions, companies live and die based on the quality of their developer experience. 

SDKs, in particular, create vastly superior developer experiences compared to hand-coded API mashups. They completely abstract away unnecessary complexity like authentication rituals, network logic, and serialization. Developers can immediately focus on creating differentiated value instead of wrestling with APIs.

Companies can gain a competitive advantage by delivering magical moments in their SDKs to win in this landscape. And with SDK updates automatically applying improvements in the background, integration remains seamless over time.

The efficiency multiplier frees up tremendous cycles to build more ambitious solutions. In short, SDKs transform APIs from obstacles into on-ramps for innovation. They represent the future of fluid, frictionless developer experiences.

Yet, while APIs and most SDKs grow ever more central to success, the tools for building them have stagnated. Rudimentary documentation, hand-coded SDKs, DIY monitoring — the process for building APIs has been broken, leaving API producers with barely enough time and resources to build a functional API, let alone worry about how best to take the API to market across multiple language communities, handle API changes, or deal with consistency across the organization.

Meanwhile, API consumers wasted endless cycles reinventing the wheel for each integration. Here’s a typical developer workflow that every API user needs to repeat when integrating with an API today:

This is the chasm Speakeasy bridges. Its suite of automation tools brings Stripe-quality APIs within reach of any team. Speakeasy automatically creates and maintains critical developer surfaces like SDKs, Terraform providers, and docs that address the API go-to-market problem and provide tools that enable better API development and collaboration throughout the enterprise. So API producers don't need to build them themselves. API consumers can just import an SDK to integrate in seconds.

Tools like these that supercharge developers are crucial, considering more than 50% of companies have less than 50 developers in their entire organization, and teams of all sizes need a seamless way to grow API adoption.

Small teams are constantly being asked to do more with less.

The breakthrough lies in radically simplifying the consumer experience. Instead of forcing every API user to write their custom integration, Speakeasy creates best-in-class, feature-rich SDKs for every popular language. Now developers have the perfect on-ramp for building unique value quickly. Painstaking docs are dynamically assembled to complement these SDKs. Together, they accelerate adoption by erasing friction at the critical moment of integration while enforcing discipline in the API contract.

What started as APIs all the way down has become SDKs all the way down.

Simon relayed a customer anecdote that illustrates the transformative “time to integration” reduction that SDKs are providing to users:

“Let’s take the example of a ~500-person company that we’re working with. One of their teams integrated with an internal API a year ago. They reported it was a difficult, cumbersome, and lengthy process because the integration needed to be built by hand. The company started using Speakeasy SDKs for their internal APIs and recently integrated that same API again – but this time, completed the integration in just two days.”

The compounding effects over time are immense. Easy integration drives more diverse use cases and feedback, improving APIs faster. Evergreen SDK updates propagate improvements instantly across all consumers, preventing drift or instability. Increased rigor keeps the APIs reliable and resilient as they scale. As Speakeasy customer growth illustrates, these systems create runaway network effects.

In the past, the API consumer has taken on all the burden of making integrations robust and scalable. These consumers might be external, in which case the integration friction blocks revenue or internal to the company. Either way, this forces every user to design and implement their integration from scratch, causing code duplication and bugs, impacting reliability, and lowering developer productivity. 

As the API economy has exploded and consumers struggle to integrate and work with the cognitive load of hundreds of APIs daily, API producers must shoulder this burden. The problem compounds at scale, with top SaaS companies having an average of 350 integrations and the biggest names in tech having well over a thousand integrations.

API producers often lack the financial and human resources to solve the change management challenges, from creating well-governed, designed APIs all the way through to creating great “last mile” developer experience artifacts like docs and SDKs.

When this occurs, API changes are no longer quick updates. One API change can impact a dozen developer surfaces and millions of end users. They become multi-week processes to update multiple developer surfaces, test, and publish — a cost-prohibitive activity for most developer teams. 

That’s why SDK-first is the new API-first. SDKs are making API development exponentially easier. Importing an SDK creates a best-in-class API developer experience, solves bad documentation, and provides a better overall developer experience.

Developers can focus purely on innovating rather than integrating with SDKs.

I asked API expert Bill Doerrfeld, Editor & Chief at Nordic APIs, how he sees SDKs contributing to the future of API development, to which he responded: 

“SDKs are essential tools for enhancing developer experience around a software tool. Specifically, not all developers have web API integration experience, so crafting resources catered to the unique programming language or platform they're accustomed to can do a lot to streamline the onboarding experience and get folks to that "Hello World" moment much quicker.

Of course, in the AI age, the expectation is to have development and integration knowledge closer to wherever the developer is working to increase productivity. So, I predict the future of API integration will move away from static documentation and traditional SDKs and toward AI-driven suggestions that are more ingrained into whatever IDE the developer is working in.”

The efficiency multiplier here cannot be overstated. For a reasonably sophisticated use case involving multiple API calls, an API consumer might need weeks or months to grok the API docs and build production-ready API integrations by hand. But SDKs bring that integration time down to a few minutes by eliminating the need to write custom integration code.

The efficiency gains directly accelerate developer velocity and innovation potential. APIs become building blocks instead of obstacles. In an increasingly composable technology landscape where new services launch daily, eliminating integration friction unlocks tremendous compounding value over time.

For producers, SDKs enforce API discipline and reliability across all implementations. While docs may grow outdated, SDKs automatically apply updates across all consumers via smart packaging systems. There is no fragmentation — improvements happen instantly. 

This massively increases the resilience and longevity of APIs while reducing support burdens. The forced rigor also pays back in spades when evolving APIs. Established integrations stay intact while expanding capabilities for new users.

In essence, SDKs amplify the core value of APIs — reusable abstractions that consolidate complexity into simple building blocks for innovation. They preserve API contracts as sacred sources of truth while tailoring access for each specific environment. 

The multi-dimensional efficiency gains add up to an exponential curve. SDKs are force multipliers that will one day make hand-coding API access seem laughably archaic, like writing assembly language instead of JavaScript. The future of API development puts SDK generations front and center.

When asked about the importance of SDKs, Sagar said:

“SDKs offer a consistent, reusable interface for integrating with an API, eliminating the need for every API consumer to reinvent the wheel. This increases engineering velocity, reduces toil, improves reliability, and reduces bugs in the codebase. 

Creating an SDK forces treating the API spec as a sacred artifact. Once developers understand that maintaining the API spec results in tangible benefits to them and their API users, stronger consensus and workflows get built around keeping those specs up to date. Speakeasy provides tooling to build workflows around your specification for validation, enrichment, and merging.”

We’ve discussed the difficulties in building APIs, but APIs are equally (if not more) hard to maintain. Anything you build needs to be able to keep up-to-date with changing requirements, which is why, on average, developers spend 30% of their time coding APIs vs 70% doing other tasks. 

According to Nordic API:

All tasks are historically part of the territory when building an API. The numbers don’t lie; it’s a process with far too many complexities.

Speakeasy is uniquely addressing these difficult but fundamental problems. Their platform provides a build kit around the API that helps manage API changes effectively across multiple internal stakeholders, solving the supply chain issue associated with keeping all the critical API developer experience surfaces up-to-date. This ensures API consumers get the best possible integration experience via packaging most native to their development environment: a language-native, type-safe and feature-rich client SDK.

The secrets behind Speakeasy

When Sagar and Simon met, they quickly bonded over war stories from the trenches of API development. Both had battled the dysfunction firsthand — the scraped-together integrations, puzzling documentation, and brittle endpoints that broke without warning. Having fought to build and support APIs at their previous companies, they knew the heartache intimately.

They knew APIs represented the medium to accelerate innovation for businesses. But instead of seamlessly connecting systems, most acted as barriers that slowed progress to a crawl. The typical integration project stretches on for months while adoption languishes due to poor developer experience. 

Then, after you build an API, you have to consider that, on average, it takes even more time to manage the API you’ve built and needs to be constantly tested to ensure functionality & security. This is true for both internal and external APIs. 

With backing from Google Ventures and other top firms, Speakeasy is making it exponentially more accessible for companies to create, maintain, and extend APIs. 

Speakeasy's original vision was to focus on API visibility — helping teams better document and test APIs for external developers. But in talking to API producers, Simon & Sagar quickly realized visibility was only half the battle. The real pain point was that integrations were still too hard. API producers were spending more effort writing never-ending reference and usage guides, but user adoption was still sluggish, while implementation costs to support new users stayed high. Every new API consumer built their integration from scratch, reinventing the wheel each time.

They saw that the better path was to simplify the entire API lifecycle. Instead of forcing developers to integrate with raw APIs, what if they could instantly access client SDKs in every major language? These SDKs offer standardized, battle-tested interfaces that eliminate the need for hand-built integrations. Developers could immediately build on APIs with no friction.

Sagar and Simon pivoted Speakeasy to help every team build better APIs from the ground up, starting with dramatically changing how API users consume APIs. By instantly enabling teams to generate SDKs alongside API docs and shifting the paradigm to SDK-first API consumption, they have solved core pain points for API producers and consumers. As a result, adoption has skyrocketed.

David Coplowe, Codat API PM, had this to say about using Speakeasy at Codat:

“Finding Speakeasy has been transformational in terms of our team's velocity. We've been able to progress our roadmap faster than we thought possible. We were able to get SDKs for 3 languages in production in a couple of weeks -- if we'd been on our own, it would probably have taken a month for each language. We also would likely have needed to hire developers with new language expertise that we don't necessarily have today.”

Real-world stories of companies leveraging Speakeasy to improve their API strategy dramatically come less than two years after Sagar and Simon founded the company in April 2022. With just a small distributed team spanning the US and UK, they have been laser-focused on the developer experience around APIs.

An SDK-first approach catalyzes a vitally important flywheel: Speakeasy has made it easier for teams to keep API specs rigorously updated. Once developers saw immediate SDK updates from any spec changes, they were motivated to maintain specs as the source of truth. Better specs also meant better SDKs and docs. This flywheel resulted in more reliable integrations across internal and external consumers. Bugs from outdated specs were also reduced, improving overall code quality.

The pivot was a breakthrough. Sagar and Simon realized the best way to transform API adoption was to simplify consumption radically. While visibility helped, seamless integration was the real driver of velocity. Speakeasy unlocked this with generated SDKs, creating the perfect gateway for developers to build on APIs. The approach was a win-win. Easier consumption meant more adoption, while rigor around specs improved reliability and cohesion. Together, they helped Speakeasy customers build better and reduce friction in the API lifecycle.

When asked how Speakeasy supports the API development lifecycle, Sagar told me:

“Speakeasy fits into a very natural place in developers’ workflows for managing API changes. We integrate seamlessly with all the tools they use today without requiring a “rip and replace” of existing systems or any steep learning curve — developers can continue to change the API and specification as they ordinarily do.

Our philosophy is to accelerate a dev team’s ability to ship great developer experience while giving them all the hooks they need to brand and customize to their liking. After all, developer experience is opinionated.”

Instead of building APIs through a convoluted process of sweat and tears, Sagar and Simon bring the power of automation and intelligence through Speakeasy. Just as APIs connect platforms, Speakeasy connects developers with the tools they need to innovate. What began as war stories is now a mission to become the API platform for teams across every company and industry. 

The opportunity size before Speakeasy feels exponential and compounding for a few reasons: 

1. Every company is poised to become an API company. 

2. Even if every company isn’t a traditional tech company, APIs have become important at every type of company.

3. With Speakeasy, every company can more easily become an API company.

Riley Brook, who works on product at Airbyte (a Speakeasy customer), had this to say about the importance of SDKs in making their APIs easier to use:

Speakeasy introduces enterprise-grade developer tools tailored for the modern API-driven company. No longer reserved just for the Stripes of the world, these capabilities become table stakes for any company where APIs are important to internal and/or external consumers.

Speakeasy handles the heavy lifting so engineering teams across the stack can focus on creating value, not reinventing wheels. 

The future of software lies in tools that don't just connect systems but connect developers to the building blocks for imagination. Speakeasy liberates APIs from the Stone Age, representing a revolution in progress.

A future where integrations are natively composable

Popular AI tools like Writer help marketers automate brand voice & tone, initial research, and first-draft writing. Still, tools like this typically take your writing 80-90% of the way to completion, leaving it up to you to edit and finalize everything manually. Speakeasy is on its way to do the same thing for API development.

This is the early innings for API development-specific tools & SDKs. The proliferation of software, the importance of APIs, and the rise in AI make this the perfect time for a tool like Speakeasy.

In an essay about how Square automates SDK generation, author Kristopher Sandoval states:

AI systems, unlike humans, can process vast amounts of data rapidly and identify patterns undetectable to the human eye. This capability makes AI an ideal consumer for APIs, especially in data-heavy sectors. APIs designed for human use are becoming inadequate for AI's potential as AI evolves. This shift demands APIs catering to human and AI consumers, ensuring versatility and broader applicability.

Developing APIs for AI use means focusing on efficiency and scalability. AI systems require APIs that can handle large-scale data requests and deliver responses in formats suitable for machine processing. This transition poses a challenge for traditional API development, primarily considering human readability and ease of use. The dual-focus approach ensures that APIs remain flexible and robust, catering to diverse consumer needs.

The growing importance of AI-friendly APIs underscores the need for platforms like Speakeasy. Providing tools for SDK creation, Terraform providers, and meticulous developer documentation simplifies the development of human- and AI-friendly APIs. Speakeasy's emphasis on API maintenance ensures that these dual-purpose APIs remain efficient and up-to-date, catering to the evolving demands of AI systems and human developers. 

As the API landscape continues to evolve with AI's growing prominence, platforms like Speakeasy have become indispensable for companies aiming to stay at the forefront of technology and innovation.

Simon has said that the market is responding to their bet on SDKs, saying: 

“Some of our customer engagements have been initiated because their API users have demanded an SDK, perhaps as part of an RFP / competitive process. So I think we’re starting to see that SDKs are becoming more in demand in the industry — i.e. given similar services, you’d want to use the one that makes it easier for you to use and isn’t throwing up roadblocks.”

With the momentum Speakeasy has, it won’t be long until we start hearing SDK-first as often as we hear people say API-first. It’s only a matter of time.

Until our next adventure.

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Thanks again to Sagar, Simon, and Tatiana from the Speakeasy team for working with me on this essay. Also, a special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

*Disclaimers: The views in this essay are my personal opinions and don’t necessarily represent the views of my employer or anyone other than myself. For more information on the sponsorship disclaimer for this newsletter, see here.

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The contrast between traditional finance (TradFi) and cryptocurrency has become a fork in the financial system. Traditional institutions have shown hesitation to embrace crypto and blockchain technology because, at its core, it disrupts many of their traditional business models.

TradFi, rooted in a long history, operates within a framework of physical instruments, centralized control, and a fixed schedule (typically adhering to conventional 9-5 working hours). While familiar and established, this system often grapples with inflexibility and restricted access limitations.

Crypto represents a radical departure from these norms (as seen in popular ad campaigns like Coinbase’s infamous ‘Update the system’ campaign’ & Circle’s ‘How USDC is changing the global economy’).

These glaring holes in the current financial system seem to become clearer every day. While we’ve become accustomed to instant communication, streaming, access to goods & services, and the ability to do almost anything from our phones, the current financial system has yet to embrace the revolution.

At its core lies blockchain technology, a decentralized ledger that defies the conventional boundaries of control and operation. Unlike traditional finance, cryptocurrency thrives on digital and decentralized infrastructure, offering a new paradigm of financial interaction.

Blockchain & crypto technology's core is rooted in 'composability,’ where financial services and products are designed to seamlessly integrate and build upon each other in a composable system. This interoperability is a hallmark of cryptocurrency, enabling a dynamic and ever-evolving financial ecosystem. 

The composable nature of these systems enables the creation of a more cohesive and adaptable financial network, allowing for unprecedented levels of innovation and customization in financial products and services.

In this essay, we’ll explore the impact of cryptocurrency as an API-based, composable financial system. It contrasts the rigid, time-bound nature of traditional banking with the fluid, always-on approach of crypto, underscoring how this innovative model presents a more integrated, efficient, and universally accessible alternative in the financial sector.

Let’s dive in.

Understanding The Traditional Banking System

Traditional banking systems are entrenched in a centralized structure, heavily reliant on a network of physical branches and headquarters. This setup significantly influences how these institutions operate and interact with their customers, with these limitations & heavy overhead leaving opportunities for Fintechs & Crypto alike. 

In the podcast episode, ‘Nubank: David vs Goliaths,’ they describe the chain of events that led to NuBank (a Latin American Fintech Super App that leverages crypto) founder David Vélez starting the company in 2013 out of the pressing need people had for better access to their finances:

“So he was saying that he was a fresh expat living in Brazil for the second time in his career, and he was attempting to open a bank account at the branch of one of the largest banks in the country. And so he recalled going to the bank and being greeted at a bulletproof door that was flanked by armed security guards. And so he's thinking, what the heck is going on?

 And then he goes on to say that after a few months, not days, months of spending multiple long hours in lines being on the phone with customer service and having to go back to the branch several times, he finally got his account open. So that's the sort of experience that Brazilians have been dealing with for decades. And the second unique condition that emerged is that the Brazilian population was increasingly growing either unbanked or underserved.”

While this is an extreme example, I think we all have stories about how the financial system has failed us, whether it’s having to go somewhere inconvenient to address an issue, a problem with fees, or having to deal with the hurdles to send a wire (or even lose it).

Another key limitation of the traditional financial system is the outdated limitations of its 9–5 / Monday-Friday operating schedule. In the last 25 years, the internet has made us expect instant access to everything, not “come back when we’re open.”

People and businesses require 24/7 financial services, and the technology exists to break the traditional mold.

Another problem that our current financial system has is that it has so much technical debt that makes it difficult to innovate. These systems today often operate in silos, with systems and services that lack the flexibility to integrate seamlessly with new technologies or offerings. The old and antiquated systems built on dinosaur technology lead to inefficiencies, limited financial products, and a sluggish response to market changes.

This leads to internal challenges and the challenges banks face in integrating with other financial services and adapting to digital banking needs. Existing infrastructures often act as barriers to innovation, hindering the banks' ability to adapt to a rapidly evolving financial sector. This has led to the rise of fintech & crypto.

TradFi's cost for overhauling the current system is upfront and impacts their bottom line. First, they need to decouple complex systems to make way for better technology, which is costly. This requires embracing crypto, fintech, and other technologies that improve the current system.

Second, they must reconsider a completely new business model that doesn’t prey on customers' fees (especially for low-income people). To make our financial systems more accessible, better to use, and more robust than ever, we must remove many of the fees, minimums, and other outdated rules that have plagued the traditional financial system for years.

These updates can help bring more people into the financial system, help more people financially prosper, and have a wave of economic benefits for centuries to come. This wave of new demand will create new business model opportunities that will evolve as this market is created.  

The flexibility, innovation, and round-the-clock accessibility offered by cryptocurrency and blockchain technology exist to enable these changes. The momentum to upgrade our financial systems using blockchain technology is here. But change is hard, and disrupting the largest business in the world is a big task that crypto has embarked on. To give perspective on the opportunity, $8 trillion moves between different currencies every single day. It’s about 30 times the daily global GDP.

Why Crypto

In the dynamic fintech landscape, cryptocurrency is a beacon of innovation, primarily due to its core attribute: composability. 

In ‘Composability is Innovation’ by Linda Xie (formerly at a16z), they say this about composability:

“Composability leads inevitably toward more choice, and better user experiences, because there are no obstacles to someone taking an existing idea and making it easier to use, or adapting it to new use cases. As more and more of the underlying technology gets abstracted away, the focus will shift towards what people can do with their money, and not the inefficiency that marks much of the traditional finance world.”

The concept of composability in the crypto world is akin to the fluidity and interconnectedness of self-sustaining ecosystems in nature, where each element coexists, coevolves, and strengthens the other. 

Composability in crypto enables a seamless blend of services and products. This interconnectedness allows for the creation of sophisticated financial services that can effortlessly integrate and interact, fostering an environment ripe for innovation and growth.

APIs in crypto enable access to different financial services, like lending, asset management, and payments, to integrate seamlessly, crafting a financial ecosystem that is far more cohesive and efficient than traditional systems. 

For instance, envision a scenario where transferring funds, trading assets, and accessing credit can occur in a single, integrated platform without navigating multiple disconnected services. This is the promise of composability in crypto, realized through the power of APIs.

When you think about this new financial paradigm, the traditional financial system feels antiquated with its rigid structures and isolated operations. It’s like comparing a vast, interconnected digital network to a series of isolated analog systems.

Through its composability, Crypto brings about a level of flexibility and innovation that is a leap forward from traditional financial institutions' conventional, compartmentalized approach.

On the Lightspeed podcast, Nic Carter discussed ‘The Next Chapter for Stablecoins’ and described why crypto represents an overhaul to the existing financial system:

“If you look at what Crypto is actually competing with, which is the legacy correspondent banking system, that is a highly non-composable system.

You don't really have the ability to interact with the financial system on an API basis and it's heavily, heavily intermediated. If I'm making a transaction from Jakarta to Mexico City, it has to go through London or go through New York. It has to do all these hops to get to one of the (financial) hubs and then all these hops to get to the other destination.

That's the status quo that we always need to keep in mind as our North Star. We need to be better than that. That's why stablecoins are so great. Because I can create (mint) USDC with dollars here in the US and then send it to anywhere really, and as long as there's some, you know, ultimate last-mile fiat conversion now, that's a three-hop process as opposed to a fifty-hop process with tons of intermediation and tons of costs. 

That's why remittances are expensive because you have to do all these transactions. Like going from bank to bank to bank to bank to London or to New York, then bank to bank to bank to bank again to get to the destination. I would encourage people to think of those terms and not in like these crypto native terms, actually. 

So then we get back to composability, and what made DeFi so great was its composable nature. I mean, it's a poison chalice as well because if something breaks, then everything that relies on it breaks too. Let's just assume we can sort of figure that out eventually. The status quo you have on Ethereum now is challenging that. 

The value of crypto is to have everybody on a shared big network.”

This type of composability in finance is not just a technical feat; it's a gateway to a more inclusive and accessible financial world. It heralds a future where financial services are tailored to the unique needs of each individual, transcending geographical boundaries and time zones.

It's a vision of a world where finance is not just a tool for the privileged but an empowering platform accessible to all, irrespective of location or economic status.

One of my favorite quotes on composability is from The Tim Ferris Podcast, where Naval Ravikant said,

Crypto, guided by the principle of composability and the innovative use of APIs, is a testament to the boundless potential of financial technology. It’s a narrative of transformation, where the rigid, compartmentalized structures of the past give way to a more integrated, efficient, and inclusive financial future available everywhere, at any moment.

24/7/365

Crypto has broken down one of the most improbable doors in the financial world. The way crypto allows any financial-based system to adopt a 24/7/365 operational model represents a paradigm shift, challenging the traditional banking system's confined schedule and offering a glimpse into a future of continuous financial connectivity.

In the traditional banking landscape, the limitations of operating hours are not just a minor inconvenience; they reflect a deeper misalignment with the globalized and digital nature of modern life.

*Gif Source: a16z’s Global Payments

In contrast, the crypto world operates non-stop, mirroring the internet's always-on ethos. This continuous operation aligns perfectly with the needs of a global economy, where geographical boundaries or time zones do not bind financial transactions.

It's like the transformation from having to go to the post office to send a letter to be able to send an email or text message instantly and for free.

The 24/7 nature of cryptocurrency also facilitates real-time financial processes, paving the way for innovations like instant cross-border payments and real-time settlement systems. This immediacy is a leap forward from the often slow and cumbersome processes of traditional banking, where transactions can take days to clear.

Visa is taking advantage of crypto for payment transformation. Through live pilots with issuers and acquirers, Visa has already moved millions of USDC between its partners over the Solana and Ethereum blockchain networks to settle fiat-denominated payments authorized over VisaNet.

Decentralized and Transparent

The decentralized nature of cryptocurrency is another key benefit. Unlike traditional banking systems, which are centralized and can be subject to government or institutional control, cryptocurrencies operate on decentralized networks, often making them more resistant to censorship and central points of failure.

This decentralization enhances security and fosters a sense of empowerment among users, giving them more control over their financial assets.

Cryptocurrency introduces a new level of transparency in financial transactions. Thanks to the underlying blockchain technology, all transactions are recorded on a public ledger, making them easily verifiable and transparent. This transparency is a stark contrast to the often opaque processes of traditional banking, where the inner workings of financial transactions are not always visible to the participants.

The advantages of cryptocurrency over traditional banking are profound and varied. From enhanced flexibility, efficiency, and speed to broader financial inclusion, decentralization, and transparency, these benefits represent a reimagining of financial systems. They paint a picture of a future where financial services align more with the needs and realities of a digital, globalized world, offering a more equitable and efficient financial landscape for all.

Asset Tokenization Is Up Next

An exciting area where crypto is improving TradFi today is within asset tokenization & the tokenization of real world assets (RWA). Asset tokenization converts illiquid assets into digital tokens that can be traded on a blockchain. This makes assets more accessible to a wider range of investors, increases liquidity, and reduces the cost of trading assets. Asset tokenization also makes trading assets available 24/7. 

The benefits of tokenization are hard to overstate. It democratizes access to investment opportunities, allowing a broader range of investors to participate in markets previously restricted to wealthy or institutional investors. 

For example, tokenizing real estate enables individuals to invest in fractional property ownership, lowering the entry barrier to real estate investment. Tokenization enhances liquidity, as these digital assets can be traded more freely and efficiently than their traditional counterparts. This increased liquidity benefits investors and asset owners, who can more readily access a broader market and capital.

This is one example of many demonstrating that cryptocurrency is not just a speculative asset but a foundational technology that can revolutionize how we interact with and conceive of financial systems.

As these applications continue to evolve and mature, they pave the way for a financial future that is more inclusive, agile, and aligned with the needs of a digital, interconnected world.

Closing Thoughts

Exploring an API-based, composable financial system that leverages crypto & blockchain technology reveals an innovative and transformative future of finance. 

Where historically, financial transactions were confined to business hours and centralized institutions, cryptocurrency has ushered in an era of continuous, decentralized, and inclusive financial services. The implications of this shift are far-reaching, extending beyond mere convenience to fundamentally altering how we interact with money and financial assets.

As we stand on the cusp of this financial revolution, it is clear that cryptocurrency is more than a digital asset; it is a catalyst for change. It challenges traditional models and paves the way for a financial system more aligned with the digital age's transparency, efficiency, and inclusivity demands. This includes a future where: 

• Everyone in the world has access to the financial system digitally.

• The cost to acquire, send, and trade money worldwide costs nothing and happens instantly.

• All financial markets and instruments follow the same trends.

The economic prosperity and financial freedom a future like this unlocks help us move to a global economy with limitless potential.

The journey of cryptocurrency from a niche digital currency based on memes to a cornerstone of a new financial paradigm exemplifies the potential of technology to reshape our world. As this journey continues, it holds the promise of a financial system that is not only more efficient and accessible but also more attuned to the needs of a globally connected digital society. 

In envisioning the future of finance, the always-on characteristic of cryptocurrency is not just a feature; it’s a fundamental reimagining of how financial services can and should operate in a digital age. It's a vision of a world where financial services are as accessible and continuous as the internet, promising a more connected, efficient, and inclusive global financial ecosystem. 

Until our next adventure.

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Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

*Disclaimers: The views in this essay are my personal opinions and don’t necessarily represent the views of my employer, those mentioned in this article, or anyone other than myself.

To support The API Economy, be like one of the 3,975 early, forward-thinking people who subscribe for monthly-ish insights on the API economy, Web3, AI, and more emerging trends.

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The Future of AI & APIs

The interplay of AI and APIs will dictate the future of technology. They are not only tools, but the very building blocks of the next generation of software.

With AI evolving so rapidly, APIs make connecting different applications and services easier than ever. APIs have become the connective tissue of the internet, enabling disparate systems to interact harmoniously. AI, on the other hand, is the brain that makes sense of a complex web of information.

In the years to come, AI and APIs will become the bedrock of our digital economy. As the pace of innovation accelerates, these technologies will enable startups to punch above their weight, challenging the dominance of established players.

The future will belong to those who can master these tools, not just because of the technological sophistication they represent but because of the fundamental shift from linear, procedural thinking to a more fluid, networked, and adaptive mindset.

APIs will simplify the process of creating complex solutions, allowing small teams to build on the shoulders of giants. AI, meanwhile, will unlock new levels of efficiency and personalization, transforming user experiences in ways we can hardly fathom today.

In this essay, we will highlight the thoughts and opinions of 12 experts on the opportunity that sits at the crossroads of AI and APIs.

Together, we’ll explore how AI and APIs are being used to:

• Improve user experience.

• Develop new products and services.

• Disrupt legacy systems and processes.

We will also discuss the challenges that businesses face in implementing AI and so much more.

Let’s dive in.

#1: Deepa Goyal — Product Strategy Lead at Postman, Author of ‘API Analytics for Product Managers’, and former Head of API Experience at PayPal

“The combination of AI and APIs presents an exciting opportunity, with experts envisioning a landscape where AI's growth coincides with a surge in API development. The growing interest in AI translates into increased development of APIs. AI capabilities are becoming an integral part of products and services delivered through APIs, leading to a range of possibilities beyond technological advancements. The adoption of AI based tools has been quick and per 2023 Postman State of the API report Sixty percent of API professionals say they're using generative AI in their job. 

The rising demand for AI-integrated solutions is expected to drive a corresponding need for roles such as API product managers, developer relations professionals, solution engineers, and AI/ML engineers. These experts will play a vital role in connecting sophisticated AI technologies with practical API implementation.

In this evolving scenario, the rise of AI is closely linked with the prominence of APIs as essential elements in the digital realm. As AI-powered offerings gain momentum, the importance of APIs as bridges for seamless integration and communication intensifies. The 2023 Postman State of the API report reinforces this trend, revealing that a significant number of respondents derive revenue from their APIs. This trend is particularly pronounced in sectors like finance and advertising, where API-generated revenue emerges as a key success metric. The burgeoning API economy is transforming APIs from mere technical components into revenue-generating products. This shift is reflected in the increased involvement of non-developer roles, such as chief technology officers, managers, and directors, who are now integral to API-related decision-making.

This confluence of AI and APIs offers a promising outlook where AI-powered advancements and API-driven solutions seamlessly integrate. As AI technologies become more accessible through APIs, the resulting demand for integrated solutions will lead to a need for skilled professionals capable of managing, developing, and implementing both AI and API technologies. This transformative trajectory will not only reshape our technological approach but also redefine organizational dynamics by elevating the role of APIs as revenue enablers and strategic assets in the digital landscape.”

#2: Packy McCormick — Founder & Author of Not Boring (byline from Internet Computers)

“Things like Replit, Urbit, APIs, and generative AI are only going to accelerate the ease with which people can build something, and exacerbate the challenges for startups building undifferentiated products. Fleshing out where value accrues in this world probably deserves its own post.

This is classic Clayton Christensen stuff, namely The Law of Conservation of Attractive Profits: “The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage.”

I think the value from the app battle will emerge at two stages: below and above. Fierce competition at the app layer will create value that can be captured at the infrastructure layer (below) and interface layer (above).”

#3: Austin Pager — Co-Founder of Coast

“APIs act as the enablers that allow companies to embed and harness the power of AI. Whether it’s allowing enterprises to unlock new insights, automate intricate tasks, improve customer service with NL, or utilize machine learning for tailored recommendations, APIs are democratizing access to AI for businesses of all sizes.

At Coast, we see this first hand. Businesses can demystify complex technologies by showcasing how powerful APIs can be integrated into existing workflows, driving faster and more consistent adoption. This synergy between AI and APIs not only nurtures innovation but also accelerates digital transformation, presenting businesses with an understanding and a gateway to leverage AI’s full potential.”

#4: Bill Doerrfeld — Editor in Chief at Nordic APIs

AI and APIs are a match made in heaven. These technologies are symbiotic and will be influencing a lot of growth. I see three key areas concerning the intersection between the two.

First, APIs are the conduit by which next-gen AI is being productized and integrated into all sorts of applications. For instance, anything creating a ChatGPT-like functionality will incorporate a large language model (LLM) through an API, such as the OpenAI Chat Completions API powered by GPT-3.5 or GPT-4. Integrating with APIs for generative AI means developers don't have to reinvent the wheel to access powerful off-the-shelf models.

Secondly, AI can be used within the development of APIs themselves. Some use cases include generating structures and schemas, code generation, testing for bugs and vulnerabilities, or linting API definitions against standard specifications. In this sense, AI can accelerate the design and maintenance of APIs.

Thirdly, generative AI can greatly help developer consumers integrate third-party APIs. For example, instead of browsing the documentation to find method names, parameters, and how to structure requests, AI could accept natural language prompts and generate requests in the programming language of choice. This would save time and reduce friction in the integration process.

However, accurate generation of glue code will hinge on up-to-date model training. As LLMs evolve, it also means that API owners must structure their developer portal resources to be effectively consumed and understood by public LLMs. This could emerge as a competitive edge for API providers and would also help improve accuracy and avoid hallucinations.

Overall, the surge of interest in AI will only benefit the adjacent API economy. However, development environments integrating LLMs should arm themselves with best security practices and review the new OWASP Top 10 for Large Language Model Applications as a starting point.

#5: Sagar Betchu — Co-Founder & CEO at Speakeasy

“The interaction between API producers and consumers is the critical interaction that has powered the SaaS revolution yet this workflow between the two has hardly changed in decades. There are two main ways i see LLMs creating step function changes in the ecosystem:

1. AI powered API lifecycle - The whole API lifecycle is fraught with friction and LLMs greatly improve developer efficiency. Whether its API design, documentation or maintenance for the producer or providing an assist for the API consumer with discovery, integration and testing LLMs can reduce the time to value and re-focus developer and company time on the core business problems rather than API problems.

2. AI as a primary consumer of APIs - We’re not far from applications that self discover and integrate to 3rd party services. Think Zapier or your favorite integration platform without needing to manage the data mapping yourself. Taking it a step further AI can figure out what APIs to call and use based on the task at hand. We already have agents that can be fed a set of “tools” to help answer a prompt. Imaging being able to pick any API out there for the task at hand.”

#6: Gil Feig — Co-Founder at Merge (byline from Merge Blueprint: Automating Integration Builds With AI)

“We know thin wrappers around ChatGPT aren’t the next big thing. Our goal at Merge was to create a product (Blueprint) with true value, where AI was simply another tool in our belt to accomplish it.

Building Blueprint has accelerated our understanding of how LLMs can interpret code, such as API reference docs. We need our API-based integrations to be of the highest quality, so we started with a product that would be useful for our community while also helping us learn its long-term potential.

Don’t just build a wrapper on GPT. If you spend two weeks building something, anyone else can build it in two weeks.

It’s okay to take time here – just put one engineer on it and see what you can do. But don’t just build another thin wrapper on GPT if that won’t serve your customers and provide true value.”

#7: Amit Vasudev — Partner at Earl Grey & Co-Founder of Clearbit

"Modern software already runs on APIs. So if you believe AI is going to reinvent software (spoiler: it is), then you better believe the world of APIs matters.

Everything needs a good API. Software-to-software interaction is why APIs are so important already. The more functional or performant your APIs, the easier it is for a developer to integrate your data, service, app with theirs. Taking this one step further, if automonous software/agents are creating functions/services/apps. It will be critical that your API is easy to find, integrate with, and deliver if you want AI-assisted software to play well with your products (hint: you do).

API-first companies will continue to create major market value. OpenAI is a behemoth because builders want to build functionality, value, and entire companies on top of their incredible tech which are available as APIs. We have seen huge market value accrue to companies like Stripe and Google for the same reasons.

So it's worth asking - in a world where a SaaS app can be built via a few text prompts (i.e. building end user applications are easier then ever and there will be infinite SaaS) - where will the true market value accrue?

My answer: the building blocks and services that builders (human or AI) will rely on for core and advanced functionality."

#8: Matt McLarty — CTO at Boomi (summary from API Management in the Age of AI)

“AI models are increasingly integrated into systems of engagement, primarily via APIs. This trend will significantly increase the number of APIs, and the dynamics of their usage will also evolve. I intend to delve deeper into this topic, but first, let's discuss the future of APIs.

This is not just a novel trend; it's a continuation of developments we've observed for a while now. Many large organizations, as well as web and mobile startups, face challenges due to the rapid advancements in their operational environments. They grapple with legacy systems, some of which are on-premise. Many are in the middle of cloud migration processes, and they often utilize third-party SaaS applications and API-based services. The sheer number of APIs integrated into their systems has reached a point where it's challenging to keep track.

A common term you might have come across is "API sprawl." This refers to the overwhelming number of APIs in use, to such an extent that organizations lose track of their deployments. Historically, the API management sector has provided tools to help organizations locate and implement these APIs. However, given the scale of API usage today, manual management is no longer feasible.

A concept gaining traction is dynamic discovery, which is essential for effective API governance. Being dynamic implies an automated method for discovering APIs. Some innovative techniques include scanning code repositories or using network sniffing to identify APIs. This capability is fundamental because it can be adapted to various situations. Companies like Akita, Optic, and Traceable AI have been at the forefront of dynamic API discovery.

This ability to continuously and dynamically detect APIs is crucial. For efficient governance, companies must have an almost organic approach, seamlessly integrating into their ecosystem, sensing changes, and identifying new APIs. In this context, AI can be a game-changer. Due to the vast scope and complexity of the issue, automation is essential for discovery, anomaly detection, and assimilation of vast amounts of data. Thus, AI will play a pivotal role in addressing these challenges effectively.

Given the vast number of API assets organizations possess, AI will be indispensable in navigating and managing them efficiently.”

#9: Lee Robinson — Head of Developer Relations at Vercel (via The New Stack)

“On one side, we’re seeing more interfaces that have natural language as an entry. Instead of going in manually clicking a bunch of filters and doing a bunch of manual searching, [users are] able to talk to the app, to talk to the software, in natural language — ‘find me shoes that are green that are size 11 that have a Nike logo.’ I can just type that in and it works. We’re definitely seeing more of that.

We’re also seeing a rise of companies who use large language models. They use AI under the hood to craft a better product experience, but you might not realize it at first. It’s behind the scenes. It’s part of creating a better product experience. And I think this is it’s a rapidly growing part of companies infusing AI into their existing product offering.”

#10: Mario Gabriele — Founder and Editor of The Generalist (via What to Watch in AI)

“Imagine a world where millions of professionals across thousands of industries use domain-specific versions of Copilot to soar faster and higher to new levels of productivity, accuracy, and creativity. A world where professionals across all industries can use general-purpose tools (like our portfolio company Adept's Action Transformer) to harness the power of every app, API, or software program ever written via interfaces that allow them to describe the tasks they want to accomplish in plain language.

In dystopian visions of the future, technology in general and AI in particular are often characterized as forces that will lead to an even more polarized world of haves and have-nots, with the bulk of humanity being disenfranchised, marginalized, and immiserated by machines.”

#11: Frank Kilcommins — Principal API Technical Evangelist at SmartBear (via More AI Means More APIs)

It’s an exciting time within the API and AI spaces. Embracing the potential seems like the most advantageous approach and has already happened within sectors like technology. Progression in AI is seen as a natural evolution of how we interface with (and programmatical direct) computers. This is already evident in the curriculum for computer science programs, which has a different focus compared to when I studied it at the earlier part of this century.

The general education sector is being challenged by the disruptive force of ChatGPT and other offerings. Moving forward with AI at our side, the shift will move from quoting knowledge to instead proving one’s understanding. Perhaps that will be validated by the AI tutor or at least the software systems being used by our educators!

Complexity never disappears in the art of delivering software. It just shifts around, and as consumer expectations continue to increase, we still must focus on the quality attributes of software. With the predicted acceleration in API growth, more AI contributing to even more API production and consumption, and the general trend for more companies depending on third-party APIs to deliver core capabilities, it’s never been more important to be critical of API quality.

#12: Abhinav Asthana — Co-founder & CEO at Postman (via Generative AI and the impact on APIs and software development)

“One area where I see a lot of potential is the simplification of complicated graphical user interfaces. For complex tasks, graphical user interfaces often become hard to use, and actions hide behind rows of buttons, menus, shortcuts, and procedures. They require years of training for people to become proficient, and even then, most people struggle with them. Generative AI trained on domain understanding has the potential to simplify those experiences.

These AI bots won’t be limited to chat, but will be deeply embedded in the existing workflows through which humans interact with computers. For instance, bots will begin helping with intensive UI and data tasks, interacting through voice, and of course, interacting through a chat mechanism. In this scenario, APIs become the “hands and legs that power the ‘thinking’ that the AI is doing.

Until now, we have primarily been designing APIs for applications that are used by humans, but designing APIs for machines will become an increasingly important area,. If you are the leader of an organization, what does this mean for you? Well, if your organization doesn’t have APIs or has poorly designed APIs, you are invisible to these bots.”

Closing thoughts

The ideas from our dozen experts reveal a clear narrative: the symbiosis of AI and APIs is not just a trend but a seismic shift that's set to redefine our future.

APIs, the unsung heroes of our interconnected world, are poised to play an even more pivotal role, enabling a level of integration and efficiency that will make today's tech look antiquated. AI, with its uncanny ability to learn, adapt and optimize, will become the status quo of competitive advantage.

The merging of these two forces will create a stomping ground for innovation, enabling startups to challenge incumbents not just based on novelty but on speed, agility, and adaptability.

In this new world, the dichotomy of 'big' versus 'small' will give way to 'fast' versus 'slow' & 'smart' versus 'dumb.'

We're looking at a future where the rules of the game are rewritten. It's a future where the underdog has a fighting chance and where good ideas have a better shot at seeing the light of day. It’s a future I’m excited for.

So here's to the dreamers, the doers, and the disruptors. The stage is set. Let's see what wonders you'll create at the intersection of AI and APIs.

Until our next adventure.

Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

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AI has begun to transition from science fiction levels of expectation to the real-world architecture that will shape our digital lives. Its impact can be felt across sectors, introducing a paradigm shift that revolutionizes how we perceive, interact, and adapt to the world around us. 

The promise and allure of AI lie in its ability to far surpass human capability.

But when it comes to operating the seemingly endless amounts of technology we have at our disposal today, the application of AI is akin to opening Pandora's box for humans. The process of operating modern software and technology stacks is now seeing the dawn of a new era where AI not merely complements but significantly outshines human efforts.

In the not-so-distant future, it will be clear. But who do you trust more to make decisions on behalf of technology? Humans or AI?

In theory, AI's ability to run software far exceeds that of humans, a proposition that might seem audacious, even threatening at first glance. But AI's true potential serves as an enabler, not a replacement for humans.

AI can liberate humans from mundane tasks and open the doors to uncharted territories of creativity and imagination.

The argument hinges on four essential aspects:

1. AI's aptitude for data-driven decision-making.

2. It has a considerably lower propensity for errors.

3. The diminishing need for human judgment in most software operations.

4. The liberation of human cognitive resources for tasks that require ingenuity and creativity.

When humans and AI coexist with their roles clearly defined, AI shoulders the burden of technical tasks, freeing humans to bask in the realm of creative endeavors.

Today, we’ll explore an exciting, challenging, but ultimately rewarding trajectory that charts the course of AI in software operation and the role of human intelligence in this brave new world.

Let’s dive in.

First chess, next human decision-making

AI's proficiency in data-driven decision-making already far exceeds the abilities humans could ever hope to possess. This aspect of AI, its ability to assimilate vast amounts of data, extract patterns, and base its decisions on this information, distinguishes it from its human counterparts.

Historically, chess was seen as a benchmark for human cognitive abilities. The game's complexity, with an estimated 10^120 possible positions, makes it an ideal platform for comparing human and artificial intelligence.

This comparison became a reality in 1997 when IBM's Deep Blue defeated World Chess Champion Garry Kasparov. Since then, AI's capabilities have only grown.

AI's success in chess is primarily due to its ability to evaluate millions of possible moves in seconds, far exceeding the processing speed of even the most skilled human players.

AI does this through an algorithmic approach called the Minimax algorithm, which predicts possible future moves and evaluates them based on their potential outcomes. AI is also free from cognitive biases or emotions, ensuring objective and consistent decision-making.

This is critical in chess because, by the 9th move, a player faces 2,439,530,234,167 possible games — an impossible number for the human brain to comprehend.

Another example demonstrating a paradigm shift in AI's approach to chess is how Google's DeepMind developed an AI program named AlphaZero. Compared to its predecessors, AlphaZero wasn't preprogrammed with any chess-specific knowledge. Instead, it learned the game from scratch using reinforcement learning, playing against itself millions of times and improving with each iteration.

Within a few hours of self-play, AlphaZero defeated Stockfish, one of the best chess engines in the world, in a 100-game match.

AlphaZero's triumph signified a significant leap in AI's data-driven decision-making capabilities. It showed us that given enough data and suitable learning algorithms, AI can teach itself complex tasks and outperform human experts. This example showcases the vast potential of AI in fields that are heavily dependent on pattern recognition, strategy, and decision-making, much like software operations.

The capabilities of AI in chess offer a window into the future, a future where AI could potentially surpass human decision-making in many more fields. It provides a compelling case for integrating AI in software operations, where decision-making speed and accuracy are paramount.

The chess example highlights what’s to come when AI is applied to more practical applications.

A McKinsey & Company study demonstrated that AI-powered systems could process and analyze data 100x faster than the average human analyst. AI's capacity for handling big data is almost limitless, an attribute that is becoming increasingly valuable in our data-driven world.

This prowess allows AI to make decisions based on comprehensive data analysis, eliminating the element of guesswork and improving the overall effectiveness and efficiency of software operations.

For example, Google's DeepMind AI was tasked with reducing the energy consumption of Google's data centers. Through real-time analysis and optimization, DeepMind achieved a 40% reduction in cooling costs, far exceeding any previously attempted human-led efforts.

While human decision-making is inherently influenced by emotions, cognitive biases, and individual experiences, AI operates purely on empirical data.

In a recent episode of the All-In Podcast, Chamath Palihapitiya said:

“AI is ruthless because it's emotionless. It was not taken to a steak dinner. It was not brought to a basketball game. It was not sold into a CEO. It's an agent that looked at a bunch of API endpoints, figured out how to write code to it to get done the job at hand.”

This objectivity results in decisions that are not only quicker but also more accurate and consistent. A report by Accenture found that decision-making errors by humans in software operations were 20% higher compared to AI.

The speed at which AI can make decisions provides another compelling advantage. According to an MIT Computer Science and Artificial Intelligence Laboratory study, AI can make complex data-driven decisions in milliseconds. In contrast, a human might take several minutes or even hours for the same task.

Despite the stellar capabilities of AI, it's essential to mention that AI's decisions are only as good as the data it is trained on. If the data is flawed, incomplete, or biased, the decisions made by AI will also be flawed. Therefore, rigorous data verification and regular updating of AI systems are necessary to ensure their continued effectiveness.

Through this exploration of AI's proficiency in data-driven decision-making, we begin to understand why AI outperforms humans in running software.

It sets the stage for our subsequent discussion on AI's lower propensity for errors, the reduced need for human judgment in software operations, and the freedom it offers humans to engage in more creative and innovative tasks.

In AI and The Burden of Knowledge, author Mario Gabriele states:

“As artificial intelligence accelerates past us, it will accumulate knowledge we cannot comprehend. Just as no amount of training can teach a dog calculus, these advanced models will be unable to explain their findings, thanks to our limited hardware. The result could be a modern life that humans increasingly do not understand, requiring a certain kind of faith.”

Anything you can do, AI can do better

A characteristic of AI that is often overlooked but profoundly important is the significantly lower propensity for errors than human operators.

The reality of human error is omnipresent across all fields and industries. Regardless of their experience and expertise, human operators are prone to making mistakes for many reasons, such as fatigue, cognitive biases, emotional states, or simply misinterpreting data.

According to a report by IBM, human error is responsible for approximately 95% of all security breaches in information technology.

In contrast, AI systems offer precision and consistency that is impossible to achieve by human standards. AI systems operate based on predefined algorithms and rules, free from the constraints of fatigue, emotion, or cognitive biases. They perform tasks the same way every time, ensuring high accuracy and precision.

One prime example of this accuracy is in the healthcare industry.

A study by Stanford University found that an AI algorithm designed to interpret medical images, such as X-rays and CT scans, could do so with an accuracy rate of 94%, outperforming human radiologists, who achieved an accuracy rate of 88%.

AI's precision is amplified when dealing with repetitive tasks, a mainstay in software operations. According to a study by McKinsey & Company, AI systems are up to five times more efficient at repetitive tasks than humans. This efficiency boosts productivity and significantly reduces the risk of errors, providing a compelling case for adopting AI in software operations.

These AI systems are also flexible. They are prone to errors if their algorithms are flawed or if the data they are trained on needs to be corrected or biased. Therefore, rigorous testing, regular updates, and continuous learning and improvement processes are crucial to maintaining the high accuracy rates of AI systems.

Human judgment just isn’t that good

Human judgment has long been the cornerstone of problem-solving and decision-making processes. People often refer to their decisions as “going with their gut.”

However, with the proliferation of AI and machine learning technologies, deciding to go with your gut might be a mistake. Thanks to AI, many tasks previously dependent on human judgment can now be automated or streamlined.

Nobel Prize-winning psychologist and co-author of the book “Noise: A Flaw in Human Judgment” Daniel Kahneman (also the author of the cult-classic “Thinking, Fast and Slow”) discussed the role of ‘noise’ in human judgment & how systematic decision-making can result in fewer errors in an interview with Science Friday saying:

“AI does better than reducing noise. Any algorithm, any systematic rule that takes inputs and combines them in a specified way, will have one crucial property – it will be noise-free. You present an algorithm with the same problem twice, you’re going to get the same answer.

But in general, algorithms are noise-free. And it turns out this is one of their major advantages over humans, that is, when you compare the performance of people to the performance of algorithms and rules, in many situations the algorithms and rules are already superior to people or match people. And the main reason for the lack of accuracy of people compared to algorithm is noise. People are noisy. Algorithms are not.”

A study conducted by the World Economic Forum found that by 2025, machines and algorithms will perform over 50% of all tasks in the workplace, up from 29% in 2018. This rise of automation is particularly noticeable in the software industry, where AI increasingly handles tasks like data analysis, debugging, and even coding.

Consider the example of automated testing in software development.

AI can identify patterns, predict possible defects, and execute tests much faster and more accurately than humans. According to a report by Capgemini, AI-powered testing can increase defect detection rates by up to 45% and reduce testing times by more than 50%.

AI models such as neural networks and deep learning algorithms can perform complex tasks, like interpreting natural language or recognizing patterns, which traditionally required human cognition. Google's BERT, a transformer-based model, is a case in point, understanding and generating human-like text at an unprecedented scale and efficiency.

However, the diminishing need for human judgment does not equate to its obsolescence. Instead, it underscores a shift in the roles humans need to play.

The complexities of AI and the ethical considerations surrounding its use necessitate a human touch. Therefore, instead of replacing human judgment, AI should be viewed as a tool that augments our capabilities, freeing us from mundane tasks and allowing us to focus on more creative and strategic aspects of our work.

AI for technology 🤝 Humans for creativity & innovation

The point of all this is not to feed the pervasive myth that the rise of AI and automation spells doom for human workers. Rather, the outcome of AI automating technical tasks for us is the liberation of human cognitive capacity for creative and innovative creation.

While it's true that AI can perform many tasks more efficiently and accurately than humans, it's also true that AI's role should be seen as complementary to human capabilities rather than as a replacement.

Consider a study by Accenture, which found that AI could increase business productivity by up to 40% by automating routine tasks. This automation frees up human time, allowing them to engage more deeply with creative, strategic, and socially engaging tasks - areas where AI still lags significantly behind human capabilities.

Introducing AI into software operations has profound implications for roles such as software developers and data analysts. Rather than spending time on mundane tasks like debugging code or cleaning data, they can focus on designing innovative algorithms, creating intuitive user interfaces, or discovering novel insights from data.

In a study by Adobe, 74% of business leaders reported that incorporating AI and machine learning into their operations gave their employees more time to be creative and strategic. As we integrate AI more deeply into our businesses and societies, new roles are emerging that blend technical knowledge with creativity and human understanding.

This transition is not automatic. To fully leverage the creative potential unleashed by AI, organizations must foster a culture of continuous learning and adaptability, invest in upskilling and reskilling their workforce, and redefine roles and workflows to encourage creativity and innovation.

AI <> human harmony 

Picture a world where artificial intelligence and human ingenuity blend seamlessly, making one another better with each interaction. In this world, AI assumes the burden of routine tasks, processing data quickly and precisely far surpassing human capability. 

In this world, AI gives us back the most valuable commodity in the world — time.

It is time to let our creativity blossom, innovate, and connect with each other on a deeper level. AI’s meticulous diligence allows us the freedom to harness the qualities that make us uniquely human: the ability to empathize, ideate, and inspire.

In this AI-enhanced utopia, we are not made obsolete but instead find our roles reimagined. We transition from being mere operators and overseers to creators and innovators. The AI revolution doesn't sideline us; it propels us into spheres where our strengths are best utilized.

On The Joe Rogan Postcast, Naval Ravikant discussed the automation revolutions, saying:

“This isn’t a new problem – throughout history, automation has replaced jobs. But it’s always freed people up for new creative work.

The question is not whether automation is going to eliminate jobs. We’ve been sitting around dividing up no finite number of jobs since the Stone Age. New jobs are being created, and are usually better and more creative. So the question is – how quickly is this transition going to happen, what kinds of jobs will be eliminated, and what kinds of jobs will be created?”

Consider the role of an artist or a poet, unburdened from mundane tasks, dedicating their newly freed time to creating a masterpiece or penning verses that touch souls. Visualize a researcher or a scientist, unhindered by data collection and processing, able to channel their efforts fully into innovative thinking and ground-breaking discoveries.

The future led by AI integration is not one of man versus machine but of man and machine. Together, in harmony, they advance towards a brighter future where technology serves not as a threat but as an enabler of human creativity and innovation.

It's a future that invites us not just to dream but to dream without limits. A world where AI is our tool, and the canvas of innovation is ours to paint.

Until our next adventure.

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Special thanks to Mama Schroeder for editing this essay (any typos are on her 😊).

*Disclaimers:

1. The views in this essay are my own personal opinions and don’t necessarily represent the views of my employer, those mentioned in this article, or anyone other than myself.

2. Get $500 with Ramp after you spend your first $1K.

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Setting an AI precedent for technology companies 

Hello everyone 👋,

Today’s edition of The API Economy is brought to you in partnership with Merge.

When Merge launches something, it’s worth paying attention to. I'm excited to collaborate with the Merge team once again as they launch their groundbreaking AI-powered tool, Blueprint. 

While it’s commonplace by now to hear about companies completely pivoting to AI or launching full-blown AI projects, the Merge team took a more pragmatic approach. 

With Blueprint, they seem to have found the perfect recipe of: 

1. Identifying a problem AI can help with. 

2. Implementing an AI solution in a way that drives real impact. 

3. Making the solution in a form that makes the most sense. 

For Merge, the result is an internal product that speeds up their team’s velocity to build and takes much of the manual work out of their development process when building out an integration.

The tool leverages AI to analyze API documentation and automatically infer connections between APIs and Merge's Unified APIs. An engineer then ensures the work meets their quality standards. 

Recently, I had a conversation with Merge's co-founder, Gil Feig, to learn more about how Merge is revolutionizing integration development through AI.

In this article, we’ll be taking a closer look at why you don’t need to be an AI company to benefit from building with AI, what API-first companies can learn from Merge’s AI approach, and more about how Blueprint works. 

Integrations connect software

First, let’s start with some context on why Merge exists.

Integrations connect tools, applications, or data through application programming interfaces (APIs). These integrations can be costly to build and difficult to maintain, but they’re crucial for businesses that want to drive growth by creating more frictionless experiences for their users. 

SaaStr reports that top SaaS companies have an average of 350 integrations and the biggest names in tech have well over 1,000 integrations.

Clearly, integrations matter in SaaS. But why? 

Without integrations, products and data are siloed from the rest of the SaaS ecosystem. It makes stand-alone products a one-trick pony that isn’t able to benefit from the tangential best-of-breed products. 

Users need products to work together to reduce manual work, build custom workflows, and let software do what it is meant to do — make life easier.

In APIs All the Way Down, Not Boring Founder & Author Packy McCormick states:

“If the number of potential connections between APIs increases exponentially as more are added, companies have a near-infinite ability to create unique chain-link systems of coherent actions out of the existing primitives. Instead of a linear value chain in which using commoditized components in each step limits the number of places left to differentiate and create value.”

Integrations provide access to the data and platforms that power impactful use cases for SaaS apps. With them, companies have the ability to do things like:

• Pull in accounting data to make headcount planning easier for customers.

• Forecast a customer’s future revenue and upsell potential based on deal flows from a CRM.

• Create searchable databases of a company’s files and folders.

• Provide access to the training data needed to power the next generation of SaaS LLMs.

These integrations have a massive impact in SaaS but unfortunately, many start-ups still struggle to build and maintain their own integrations. 

Let’s take a closer look at why this process can be so challenging for companies to prioritize. 

Building integrations is harder than it looks 

Developing integrations from scratch can be a daunting and time-consuming task for developers. The process typically involves meticulously studying API documentation, deciphering complex data structures, and manually mapping connections between different systems.

This manual work is often prone to errors, requires extensive coding, and demands significant effort to ensure compatibility and seamless functionality. It isn’t low stakes, either: if an integration is unreliable or breaks down frequently, the consequences for a business could be massive — not to mention expensive. 

That said, product managers often face a challenging balancing act when deciding to allocate resources between building integrations and enhancing their product's core competencies. Integrations can provide significant value to customers and to the internal team, but focusing energy here often means neglecting the product itself — which can also put you behind your competition. 

Building robust integrations requires significant time, technical expertise, and often collaboration with other software providers — resources that might otherwise be invested in enhancing the product's primary features. 

These complexities, combined with the pressures of managing a product roadmap within tight timelines and budgets, mean that some companies feel stuck with broken, unreliable integrations (if they’re lucky enough to have them in the first place). 

Merge is here to help product teams to focus on their core competencies while leaving their API strategy to the people who know it best.

Packy M frames the opportunity when discussing how companies are increasingly able to build nearly everything non-core through APIs:

“What people call “strategy” is often a bunch of fancy words and falsely precise goals that obfuscate the core purpose of the business: serving customers in a differentiated way. Judiciously using APIs where possible gives companies strategic clarity and the ability to solve their customers’ problems in a way that only they can.”​​

There are two ways to use APIs to solve your customer problems.

1. Build each individual integration in-house.

2. Integrate with Merge once and get access to hundreds of integrations.

If you take on integrations in-house, some companies struggle to ship a handful of integrations a year. But since we last spoke to Merge 18 months ago, they have added 4 new integration categories (including File Storage which they shipped this week), added hundreds of integrations, and the team shows no signs of slowing down.

Merge is hands down the best in the business when it comes to integration strategy & execution and it’s hard to make the case that any internal team can ship the way they do.

But why would development teams even want to try to take on integrations themselves? Development teams have enough to focus on already just to make their core products and features the best they can be without worrying about integration strategy.

Tobi Lütke, the Founder and CEO of Shopify does a great job explaining what happens when product teams stray too far from their core competencies: 

“We were worried about what we internally called the Microsoft Word toolbar problem. Now this is hard to explain, because now Word has a ribbon and hides a lot of this stuff. But back in those days, and we talked about it, Word had toolbars, and if you were on a normal 17-inch screen, and you would enable every single toolbar that you could, you would actually only have a centimeter of space for actually typing text, right?

Because Word had a lot of features, and packaged goods software has to make another sale every single cycle that makes perfect sense. But we thought that was a problem for us building Shopify, because e-commerce is – we are the home of these businesses. We are the software that people open up in the morning and that’s their work day, where they spend it in.”

Integrations are almost always something companies should outsource while focusing on their core product components.

Simplifying integrations with AI 

Netflix made it possible for you to log in once and stream thousands of films. Merge is doing the same thing with integrations. Integrate once with their API and get access to all of their integrations.

To briefly recap what Merge does from our previous essay: Merge’s proprietary Unified APIs allow companies to focus on building their own product, and not on the costly, time-consuming task of developing third-party integrations with their customers’ software of choice. By integrating once with Merge’s Unified API, customers can instantly access hundreds of integrations. 

Merge has transformed the way that B2B companies develop products. Instead of including complex integrations in their product roadmaps, builders can focus on their product’s core competencies and end-user experience — and leave the mission-critical (yet time-consuming) work of integrations to Merge. 

Since its start, Merge has been backed by impressive investors like Accel, NEA, and Addition, raising $75 million so far. 

Now, Merge has announced an AI-powered tool called Blueprint to continue growing its long list of compatible integrations. Essentially, Blueprint allows anyone to request a new integration with Merge’s Unified APIs and then automates part of the integration build:

VentureBeat covered the launch and wrote:

As a result, Merge went from moving fast to operating in overdrive. 

Here’s how it works: 

1. The user inputs an API documentation URL into Blueprint. 

2. Blueprint automatically generates the API definition (‘blueprint’) to Merge’s Common Models. 

3. Blueprint then starts the build, taking out some of the most monotonous, manual work, like setting up authentication, pagination, endpoints, and matching it up to Merge’s common model format. 

4. The user is given the opportunity to request Blueprint’s output be added to Merge’s Unified APIs. If the integration is already a part of Merge’s integration offering, then the user will be prompted to sign up to get API access and start building into Merge.

5. Merge will review and finish up the integration — and since the work’s already been started by Blueprint, Merge’s team can release new, customer-requested integrations much faster with customer input.

Why Merge made Blueprint 

When it comes to customer-facing integrations, the initial build is just the tip of the iceberg. Once you build an integration, you embark on the never-ending journey of managing and maintaining it (a compounding problem). 

An integration going down can bring a product down or render it completely ineffective. 

Reliable integrations require a constant dance of monitoring performance, addressing errors, and staying in sync with ever-evolving APIs. 

That’s why customers are eager to hand not just the initial build to Merge — but the entire integration lifecycle. As Gil puts it, “At Merge, we know we need to move fast and build fast because companies are placing the fate of their integration strategy in our hands.” 

Gil also adds,

“We’re never going to see every B2B company building integrations with every other B2B company, so we need a single language to communicate between them. Then, if all these companies build to one language, they’re essentially building to each other. We want that language to be Merge’s Common Models. This led us to create Blueprint because we were asking the question, ‘how can we make it even easier to just automatically add new platforms and integrations to Merge, and thus, our customers?’” 

At its core, that’s exactly what Blueprint is: a tool that will help Merge meet customer needs and build new integrations faster than ever.

Merge is doing what many tech companies aren’t: they are actually using AI to better serve customers. Blueprint allows anyone to request a new integration in seconds (and start the build automatically), meaning Merge can get real-time feedback on what integrations are needed most. 

The result? Merge is cutting down integration development time significantly. By automating all of the tedious parts of the development process, the Merge team is able to focus on the important parts of the integration development process like reliability, security, and operability. 

And people are taking notice of companies like Merge which leverage AI the right way. Even Elon Musk seems to be a fan of rapid AI adoption like Blueprint:

What companies can learn from Merge

Everywhere you turn these days, tech companies are jumping on the artificial intelligence bandwagon. 

So when Merge’s team began investigating opportunities to use AI, they knew they had to have a real use case for it. Gil put it this way:

“We know thin wrappers around ChatGPT aren’t the next big thing. Our goal was to create a product with true value, where AI was simply another tool in our belt to accomplish it. ”

Instead of forcing AI into its product roadmap, Merge built a tool that will massively benefit customers and speed up progress towards Merge’s ultimate goal: to be the integration layer for all B2B companies. 

Merge’s founding engineer Henry Baer weighs on the benefits of building Blueprint and how it sets the team up for long-term success leveraging AI: 

"Building Blueprint has accelerated our understanding of how LLMs can interpret code, such as API reference docs. We need our API-based integrations to be of the highest quality, so we started with a product that would be useful for our community while also helping us learn its long-term potential."

Clearly, Merge is doing something right. Let’s take a look at 3 lessons API-first companies can learn from Merge’s approach to implementing artificial intelligence. 

Lesson #1: Build with AI when it actually makes sense. 

Artificial intelligence is incredibly powerful, but it’s also trendy. In the past year alone, dozens of tech companies have fallen prey to the “AI hype cycle” and spent precious resources building some pretty underwhelming tools. 

These AI releases might give companies some press or momentary social media engagement, but they’ve rarely been aligned with the company’s actual mission. Some start-ups have even been founded to replicate what ChatGPT…like this AI company in Europe that raised a $100+ million seed round — without a product. 

But this doesn’t mean you should ignore AI completely: in fact, to do so would be just as risky. Instead, allow the AI trend to spark creative ideas for better ways to serve your customers. Gil says,

“Every company should think about how AI can enhance what you’re already doing or help launch powerful new products, but don’t force a use case if there’s not one.” 

Bryan Kim & Kieran Flanagan on the a16z podcast said about AI:

Kieran: “There’s the 90% I think most people are trying to replicate the things we already do (with AI), but that 10% is super interesting which is thinking about what are the things we can do to scale today which could not do previously without this technology (AI). 

Bryan: “I get very excited about existing companies as well as net new companies because they now have this new thing (AI). For existing companies, there is this way to deepen your moat and use AI to abstract complexities away to make your product so much more useful than before.”

In Merge’s case, the team decided to move forward with Blueprint because they saw how it could have a lasting impact on the way they serve customers. 

Lesson #2: Leverage AI to enhance your product, not as a product itself. 

According to Gil,

“When something that’s as game-changing as AI comes about, you have to consider how to use it — but if AI is your product, you probably aren’t doing it right.” 

Instead, Gil encourages companies to consider how AI can become a core competency for your business — something that enhances what you already have. How can artificial intelligence actually make your customers’ lives easier and provide tangible value? 

Gil says,

“If you spend two weeks building something, anyone else can build it in two weeks. It’s okay to take time here – just put one engineer on it and see what you can do. But don’t just build another thin wrapper on GPT if that won’t serve your customers and provide true value.” 

In AI is a feature, not a product, author Tom Creighton weighs in stating:

“AI generation is a black box: you can have an expectation of what sort of output you’ll get, but you’re essentially throwing spaghetti at the wall until the algorithm produces something that you like. Unlike the tools we are used to, which work via straightforward inputs and produce straightforward outputs, we’re getting value from AI tools mostly by granularly fine-tuning our incantations to appease the AI – this is a capricious genie, not a well-oiled mechanism.”

Though Blueprint is most powerful as an internal tool for Merge’s team, it came about as a result of the team’s customer-centric approach. Not only does Blueprint all Merge to build integrations faster so they can serve more users and help more businesses — it allows people to request what integrations they want. 

As Merge dreams about the future of their AI product development, they’ll continue to focus on enhancing the product, rather than getting distracted by the generative AI hype. For example, Gil hinted at potential future applications for Blueprint, like streamlining the customer onboarding process and supporting the self-healing of API errors. 

If you’re considering using AI on your team or in your start-up, try to get creative. Maybe AI could transform your processes and be integrated as an internal tool. Or, maybe there is a true use case for a chatbot of some sort. At the end of the day, don’t be afraid to experiment. 

As HBR says, here’s what smart companies know about integrating artificial intelligence: 

“AI stokes creativity by allowing a company to test ideas rapidly and to do more at scale. Furthermore, it learns from the past, across millions of data points, unlocking innovation quicker than a human could. But AI does not invent; it just predicts, on the basis of past patterns. Marketers invent, and the AI learns what works, for whom, when, and how. Invention requires a culture that values experimentation and risk-taking.

All else being equal, a company’s experimental data is the source of its competitive advantage. Leaders must recognize that a test-and-learn mentality is essential for translating that data from raw material into currency. That means accepting that experimentation has opportunity costs and that by definition, some experiments won’t work. But even failed experiments offer worthwhile lessons.”

Lesson #3: Use AI to supercharge your employees’ impact. 

AI can’t fully replace humans, but it has the potential to massively reduce manual labor and allow those team members to focus on more challenging, strategic, and rewarding problems. It has the potential to supercharge the way we all work.

While AI won’t do all the work in software development, it’s clearly trending in a direction that shows that in a relatively short period of time, it will be doing the majority of the work. 

AI's ability to automate routine tasks, process vast amounts of data, and generate valuable insights can help startups punch above their weight, accomplishing more with less. 

For a small, resource-strapped team, this means that individuals can focus their skills and energy on strategic planning, innovative thinking, and unique problem-solving — the human-centric aspects that truly drive a startup's growth and success. 

So ultimately, if your company is considering what to do with AI, a great place to start is by looking for ways to help your team move faster and more efficiently. This is exactly what Merge is doing, and Blueprint is already supercharging its team’s impact and results. 

What’s next for Merge? 

In this time of technology-led transformations, Merge finds itself at an inflection point. AI could become the crucible where Merge's strategic focus to become the integration layer for B2B SaaS, could be forged into a product of unsurpassed capability and elegance. 

By harnessing the robustness of AI, and coupling it with the pervasive need for seamless integration in SaaS platforms, Merge is poised to straddle a critical nexus point. If Merge can surf this wave of AI-integrated solutions, its future could well be a chapter in the annals of technology that future generations will read with awe and inspiration.

As for what that could look like, Gil shared that Merge’s vision for Blueprint includes self-healing and monitoring capabilities that will majorly streamline the resolution of API issues:

“In engineering, everything is proactive — you have to think through endless factors and edge cases to make sure you’re not missing anything. You want to be super confident when you launch. The unfortunate part of APIs is you don’t control third parties, so being reactive is inevitable. AI presents us with the opportunity to be reactive but with the same external reliability as being proactive.” 

In the short term, AI will enable Merge to be reactive to issues, even enabling many of those issues to fix themselves quickly. Eventually, this can reduce the time that engineers have to spend thinking through all those edge cases — they’d be able to trust the AI to catch the error and self-heal. 

In the long term, when APIs meet AI, they're not just facilitators of software communication anymore, but turn into dynamic conductors, orchestrating complex symphonies of cross-platform operations, enabled by real-time learning and decision-making. Integration makes technology more powerful than ever, not merely by amplifying the capabilities of individual software components, but by unlocking the potential of software as a whole — interconnected, intelligent, and in sync. It builds a network effect for software — the more they are linked, the more valuable they become. 

The redefinition of software connectivity patterns Merge is leading has the potential to unprecedented levels of automation, personalization, and efficiency in every field that relies on technology. In essence, AI and APIs could be the magic wand that conjures the next seismic shift in the technology landscape.

In the coming years, I have no doubt that Merge will continue to push the boundaries of AI & APIs. I can’t wait to see what they do next. 

Until our next adventure.

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Special thanks to Gil & Shensi at Merge, my friend Haley Davidson for copy help & edits, and Mama Schroeder for additional edits (any typos are on them 😊).

Disclaimer: The views in this essay are my own personal opinions and don’t necessarily represent the views of my employer, Merge, those mentioned in this article, or anyone other than myself.

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The fight for data rights

In the cult classic ‘Dune’, Frank Herbert claims that “he who controls the spice controls the world.” But as it pertains to artificial intelligence, those who control the data control the AI.

In the world of 'Dune,' spice was a rare and precious resource that possessed extraordinary properties. Its scarcity and unique qualities made it the center of power and control, shaping the political and economic landscape of Dune’s universe.

Similarly, data has become the lifeblood of today’s AI frenzy. 

Data permeates every aspect of our interconnected world, enabling individuals and businesses to make more informed decisions, predict trends, and identify opportunities for growth. From ChatGPT to your Netflix recommendations to the ads you see on social media, data powers the AI systems we interact with (and rely on) daily.

However, data isn’t easily obtained or replicated, especially when it comes to high-quality and diverse datasets. Intellectual property considerations, privacy concerns, and the cost of the collection are just a few challenges that make data a scarce resource — and a competitive advantage for those who have access to it. 

In fact, much like the control of spice in 'Dune,' the control of data has far-reaching consequences. Those who have access to vast amounts of data can shape markets, influence consumer behavior, and outpace the competition. They can train AI models more effectively, leading to better predictions, personalized experiences, and improved decision-making. 

As companies work at breakneck speed to develop AI and gain a competitive advantage, the demand for data is skyrocketing. Not to mention, the price. 

Just recently, Elon Musk & Twitter started charging for access to its previously free API. Instead of training their AI models for free on Twitter’s massive stores of user data (and ultimately profiting off those models), companies will have to cough up a pretty big chunk of change for access. 

Reddit followed suit shortly after, announcing that it would charge companies for access to its API:

“The Reddit corpus of data is really valuable,” [Reddit co-founder and CEO Steve Huffman told The Times]. “More than any other place on the internet, Reddit is a home for authentic conversation. There’s a lot of stuff on the site that you’d only ever say in therapy, or AA, or never at all … But we don’t need to give all of that value to some of the largest companies in the world for free.”

As tech companies begin restricting access to their data, not everyone is happy about it. In fact, some say that the prices are “exorbitant.” One Reddit user put it this way: 

“[Charging for access to Reddit’s API] strays into the territory of antitrust. … Reddit has a platform that only Reddit can provide access to. They allowed others to access it, build things using their protocols, and then when those others had built something of value, Reddit comes along and uses its market power (by virtue of its platform control) to hold those third parties to ransom.”

But the effects to users of the changes to Reddit’s API to counter AI ‘over-tapping’ its data via API have been felt almost immediately. In what’s been dubbed the ‘Reddit Blackout’, thousands of subreddits (Reddit discussion forums) have gone dark this week to protest the new API policy that has led to rising costs for some third-party apps to access data on the site and increasing worries about content moderation & accessibility.

As we’re starting to see already users, companies, and even countries stand to gain a lot — or lose a lot — from today’s data wars.

In this essay, we’ll discuss those data wars in depth, including the key players and the growing controversy around proper compensation, privacy, and security. Let’s dive in. 

Data is the new oil  

In many ways, it feels like the world has been turned upside down since ChatGPT made artificial intelligence mainstream last year. Now, as AI chatbots and the large language models (LLMs) that power them become engrained in everyday life, the globe’s biggest tech companies stand to profit. 

In case you need a refresher, AI can be thought of in essentially 3 layers: the user-friendly interface (for example, ChatGPT), the LLM that powers it (think, GPT-4), and the data that LLM is trained on (books, movies, and pretty much everything on the internet).  

To put it simply, AI wouldn’t be possible without data — and that makes data the most valuable resource in the world today.

So the question becomes, who already possesses ridiculous, unthinkable amounts of data? Who controls the new oil of the digital age?

The answer: It’s the companies that have been thinking about data since the internet age, better known as FAANG (Facebook, Apple, Amazon, Netflix, and Google). These tech companies are sitting on treasure troves of data structured to build wildly profitable ad recommendation engines. 

With AI taking off in the last few years, tech companies have their eyes on a better business model that includes using large sets of structured data to train AI models. But who owns the social media data that sits on the public internet?

Just weeks ago, Elon Musk claimed that Microsoft had trained its AI models on Twitter’s data, without permission. When Musk argued that Microsoft needed to pay to access its data, Microsoft refused and kicked Twitter off its advertising platform. 

But Microsoft isn’t the only company losing free access to Twitter’s API: 

“[In March 2023], Musk set Twitter’s API monthly access price at $42,000 for access to 50 million tweets, $125,000 for 100 million tweets, and $210,000 for the highest plan with 200 million tweets. Twitter discontinued free access to APIs by third parties and developer plugins in February, forcing some developers to suspend their Twitter integration projects.”

These APIs represent the bridge for companies like Microsoft to interact with and access the large data sets companies like Twitter provide.

Twitter & Reddit have strategically opted to close the API bridge to many AI companies while best practices are put into place to determine how data providers should be compensated for LLM & AI training. 

They see a future where API usage and data licensing agreements are the core of their business, as Reddit CEO Steve Huffman hinted at in a recent interview with Jay Peters:

To understand the true value of data, let’s briefly go back to the early days of tech companies, their persistent path to user monetization, and how it led to the mountains of data these companies hold today.

The data gold rush 

The world's biggest tech companies have been collecting data for over 20 years, ever since the inception of the internet. In fact, the storage of data has become an integral part of their business models. 

The use of data in advertising has been monumental in helping tech companies find a path to user monetization over the years. However, today the rapid growth of the AI market is presenting an entirely new (and profitable) opportunity for big tech — much like the dot-com boom of the 1990s.

As the internet became more accessible, the 90s welcomed a wave of new, internet-focused companies. Silicon Valley investors saw the internet as a transformative force that would revolutionize industries and generate significant profits, allowing start-ups to raise massive amounts of investment capital. 

In the early days, companies like Facebook and Twitter recognized that providing free services to people around the world required alternative revenue streams, and advertising emerged as the solution. With massive user bases, extensive data, and targeted advertising capabilities, social media companies are able to create valuable advertising opportunities for businesses — and generate substantial revenue in the process.

For example, Google openly Claims:

Some of those companies became household names, achieving amazing growth and generating hundreds of billions in revenue (Amazon, Google, Yahoo, and eBay, to name a few). These pioneering companies seized the opportunities presented by the internet, and as a result, were able to transform and launch entire industries, from e-commerce to digital advertising. 

The idea behind many of the largest tech companies has always been to offer free products to people around the world in exchange for data & attention.

To make advertising more profitable, capturing and analyzing user data is key. The more insights tech companies have into users' preferences, behaviors, and demographics, the better they can tailor ad experiences, leading to increased engagement — and of course, more money.

As a result, the demand for data has also increased, driving companies to invest heavily in data collection and analysis capabilities. 

In ‘The Hidden Costs of Free Social Media’ Thomas Sowell, an American economist and senior fellow at Stanford University’s Hoover Institution explains:

“While these platforms have provided us with unprecedented information and content, the nature of their business model has created consequences for society at large. Another such consequence is the large-scale elimination of our consumer privacy through data mining. Big Data is the fuel that artificial intelligence uses to run these algorithms. Big Data has led to these companies’ possession of mass quantities of data, which they must store properly under relevant federal and state law. 

This is how Facebook and YouTube know which content the user may be interested in, and it is how advertisements are “micro-targeted.”

Big Data has led to these companies’ possession of mass quantities of data, which they must store properly under relevant federal and state law. These processes can be infiltrated, as was the case in September 2018. Facebook announced that 50 million accounts were known to be affected while an additional 40 million accounts may have been affected. The problem of cybersecurity incidents, also known as breaches, has grown exponentially with the large-scale use of the internet and "cloud technologies.”

This business model works because “The online economy is built in large part on the fact that consumers are willing to give away their data in exchange for products that are free and easy to use” according to Walter Frick of HBR.

Simply put, consumers are willing to exchange their data because they believe the benefits of using these platforms outweigh potential privacy concerns. 

So who really stands to profit? Well, to develop artificial intelligence, you need data — and that puts the world’s biggest tech companies (and their massive stores of user data) in the perfect position to benefit from AI’s popularity.

AI algorithms require vast amounts of high-quality data to learn and improve over time. The more data an AI company has, the more accurate and reliable their AI models are likely to be, which can give them an advantage in an increasingly competitive marketplace. 

Access to large volumes of data can allow AI companies to create unique data sets for niche applications. These applications can then be sold to new, niche groups. And if an AI company can meet the needs of an underserved niche faster than its competitors, it will have the opportunity to win the lion’s share of revenue available from that segment. 

The more data an AI company has, the more competitive it will be. As a result, it’s not just artificial intelligence that’s creating a new gold rush — it’s the data that fuels AI development. Whoever can access the most data will be able to create the best AI models, and as a result, make the most money from the artificial intelligence boom. 

Power to the people

But does the platform even have the right to own the data? What about the people?

Today, data ownership and control predominantly reside with platforms and companies, like Meta. However, there’s another approach that we haven’t discussed yet: the idea that data rights should belong to the users themselves.

In 2019, will.i.am wrote for the Economist, 

Four years later, it seems there’s a possible solution to this problem. Through emerging technologies like Web3 and decentralized social media, there’s a real chance for a future where individuals have greater control over and ownership of their data.

Decentralized platforms can give individuals the agency to decide how their data is collected, stored, and used, giving them more privacy and security. Features like powerful data encryption, transparent data transactions, peer-to-peer architecture, and smart contracts would give users the tools they need to determine the fate of their data and make truly informed decisions about data sharing. 

Users could be properly compensated for their data — and have a real option to sell or keep it private. This would create a more personalized and consent-driven data ecosystem, one that recognizes the significant role individuals play in creating the vast datasets that fuel AI and digital platforms and properly compensate creators. 

Elon Musk has already taken steps to position Twitter in the direction that shares a portion of revenue with users who can already earn USDC on the platform.

By shifting data ownership from a platform-centric model to a user-centric paradigm, we could move towards a future where data is seen as a valuable asset under the control of the rightful owners – the users themselves. 

Data is the bedrock of AI

Imagine you're building a sculpture. You start with a giant block of marble and work to chisel it down. In doing so, you hope to create something beautiful. AI works in a similar way. It takes countless pieces of existing data and rearranges them into meaningful, and seemingly new & useful outputs.

Because of this, the original data we have today is extraordinarily valuable. It's pure, untapped, and forms the bedrock of all AI could ever learn and produce. As we move into the future, the majority of data will either be AI-reformatted versions of today's data or new data that have become increasingly complex to identify as unique or authentic.

That means today's data sets are, arguably, the most precious resource for the future of AI. Those who control this data have a significant role in shaping AI's trajectory. They will determine how AI learns, evolves, and impacts our world.

It’s undeniable that we’re facing a pivotal moment in history. We need to decide how AI should access and use public data sets. We also need to define the legal precedents for how these data sets are credited, compensated, and accessed. It will be one of the most critical decisions we (or our lawmakers) will make in our lifetime.

In my opinion, people like Elon Musk & Steve Huffman are correct in the fact that they own the data created on their platforms, and if AI companies want to train LLMs with Twitter or Reddit data, AI companies have to pay to have that right. 

However this war concludes it will form the economic bedrock that shapes AI data licensing, a field that has the potential to be the most valuable field in all of tech.

Thanks for reading — until our next adventure.

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—

Special thanks to my friend Haley Davidson for copy help & edits and Mama Schroeder for additional edits (any typos are on them 😊).

Disclaimer: The views in this essay are my own personal opinions and don’t necessarily represent the views of my employer, those mentioned in this article, or anyone other than myself.

To support The API Economy, be like one of the 3,279 early, forward-thinking people, who subscribe for monthly-ish insights on the API economy, Web3, AI, and more emerging trends.

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As we stumble through the implications of newfound rapid AI adoption, we find ourselves marveling at the landscape of technological advancements that lay before us. In the midst of this journey, artificial intelligence (AI) has emerged as the magnetic north of our collective compass—a beacon guiding us into uncharted territories of possibility. 

With each new discovery and each groundbreaking achievement, AI unveils a glimpse into its boundless potential, daring us to imagine a world reshaped by the brilliance of its algorithmic prowess. 

Yet, as we stand starry-eyed at the precipice of this brave new era, we cannot afford to overlook the profound questions that AI's ascent raises about technology adoption, the role of human bias, and the seemingly insurmountable learning curves that have long inhibited progress.

In a recent episode of the All-In Podcast, Chamath Palihapitiya said:

“AI is ruthless because it's emotionless. It was not taken to a steak dinner. It was not brought to a basketball game. It was not sold into a CEO. It's an agent that looked at a bunch of API endpoints, figured out how to write code to it to get done the job at hand.”

Traditionally, the process of adopting new technologies has been riddled with human biases, learning curves, and other inefficiencies. We have a natural inclination to stick with what we know, which has both positive and negative consequences. If you’ve ever sold, marketed, or built enterprise software you’ll know what I mean.

On one hand, this resistance to change can serve as a protective mechanism, ensuring we don't discard valuable knowledge and skills prematurely. On the other hand, it can also impede progress, slowing the pace of innovation and leaving us reliant on outdated or suboptimal solutions.

What if we could bypass these limitations and streamline the technology adoption process? This is where AI holds extraordinary promise.

By leveraging the power of AI, we can tap into a more objective and efficient method of problem-solving—one that is not hampered by the biases and learning curves that have historically shaped human decision-making. Technological progress & adoption has the potential to become 10x faster in this brave new world.

But what is the role of AI and what does it mean for AI to be an objective problem-solver? AI systems, by their very nature, operate based on algorithms and data. They are designed to learn and adapt, identifying patterns and making predictions based on the information they process. As a result, they are not swayed by cultural, personal, or emotional biases. 

They don't experience fatigue, and their capacity for learning is not limited by the constraints of human memory or cognitive ability. This makes them uniquely suited to identify the best technology solution for any given problem, unburdened by the factors that so often hinder human decision-making.

In this essay, we’ll explore the ways in which AI is poised to reshape the landscape of technology adoption, offering a more objective and efficient approach to problem-solving that transcends the limitations of human decision-making.

The AI Advantage: Objective Problem Solving

As we delve into the realm of AI and its potential to reshape technology adoption, it becomes increasingly clear that its greatest strength lies in its capacity for objective problem-solving.

Understanding AI's ability to identify optimal solutions

Algorithmic decision-making

In a world where our choices are often muddled by a culmination of opinions, emotions, and biases, AI's algorithmic approach to decision-making emerges as an alternative.

Imagine for a moment that you are tasked with selecting the best new car to purchase, a decision often fraught with an overwhelming array of variables—price, fuel efficiency, aesthetics, and countless other factors.

Now, consider how an AI system might tackle the same task, sifting through vast oceans of data with machine-like precision to generate a ranked list of options based on objective criteria.

This ability to make unbiased, data-driven decisions is an intrinsic feature of AI. The foundation of AI lies in its algorithms—mathematical rules that enable it to process information, learn from it, and ultimately generate an output. By employing these algorithms, AI can systematically analyze a problem, weigh the pros and cons of various solutions, and ultimately identify the optimal course of action.

The result is a decision-making process that is not only efficient but also transparent and, most importantly, free from the myriad of biases that often plague human judgments.

Data-driven approaches to problem-solving

At the heart of AI's objective problem-solving capabilities is its data-driven approach. As an AI system ingests and processes data, it continuously refines its understanding of the underlying patterns and relationships, enabling it to make increasingly accurate predictions and decisions.

Consider, for example, the world of medical diagnostics. When faced with a complex and ambiguous set of symptoms, even the most experienced human doctors may struggle to accurately identify the underlying cause. 

In contrast, an AI-driven diagnostic tool can rapidly analyze vast troves of medical data, considering countless combinations of symptoms and potential diagnoses in a fraction of the time it would take a human expert. 

By identifying subtle correlations and patterns that might elude even the sharpest human minds, AI has the potential to revolutionize fields like healthcare by providing more accurate, timely, and objective diagnoses.

AI's capacity to transcend human biases through an impartial perspective

As we navigate the murky waters of human decision-making, we find ourselves constantly buffeted by the swirling currents of our own biases—cultural, personal, and emotional.

These biases, often invisible to us, can lead us to cling to familiar solutions, discount novel ideas, or simply make decisions that feel "right" without any objective basis.

AI, however, is unencumbered by such biases. Its impartial perspective allows it to assess problems and potential solutions based solely on the merits of the data it processes. This impartiality empowers AI systems to make decisions that are more rational, consistent, and ultimately, effective.

In E124 of the All-In Podcast, Chamath Palihapitiya said:

An AI created a solver, so everybody understood the most efficient path in every single spot to do the most EV positive thing, the most expected value positive thing. Coding is very similar in that way. You can reduce it and view it very, very reductively.

So there is no differentiation in code. So for example, let's say you're gonna start a company today. Why do you even care what database you use? Why do you even care which cloud you're built on? To Freeburg's point, why do any of these things matter? They don't matter. They were decisions that used to matter when people had a job to do and you paid them for their judgment.

Oh, well, “we think GCP is better for this specific workload. And we think that this database architecture is better for that specific workload. And we're gonna run this on AWS, but on Azure.” And do you think an agent cares? If you tell an agent, find me the cheapest way to execute this thing. And if it ever gets not cheaper to go someplace else, do that for me as well and ETL all the data and put it in the other thing.

Here’s an example of how AI would use a linear decision-making process to choose the right database to use for a project:

Imagine AI could do all of this in real-time and select the right database for a project, cutting out a process that can take years at an enterprise level.

Another example to understand the power of AI's impartial perspective, imagine a hiring manager sifting through a stack of resumes. Despite their best intentions, the manager may unconsciously gravitate towards candidates with familiar names, prestigious schools, or other seemingly irrelevant factors.

In contrast, an AI-driven hiring tool can objectively evaluate each candidate based on the specific qualifications and experience relevant to the job, free from the influence of any unrelated biases.

By pushing the boundaries of what is possible and transcending the limitations that have held us back for so long, AI has the potential to spark a revolution in human ingenuity—one that transcends the constraints of human bias, unlocks the potential of our collective intelligence, and ushers in a new age of innovation and discovery.

The Future of Technology Adoption with AI

By mitigating the impact of human biases, learning curves, and other obstacles that have traditionally slowed the pace of innovation, AI holds the promise of ushering in a new era of rapid technological advancement.

Historically, the adoption of new technologies has sped up dramatically over the past century, with the trend set to continue with AI.

The traditional technology adoption cycle and its limitations

Human biases

In the traditional technology adoption cycle, human biases have long played a significant role in determining the speed at which new innovations are embraced.

People often exhibit a natural inclination towards the familiar, leading them to be resistant to change, even when confronted with demonstrably superior solutions vs the current way to buy software.

This resistance can manifest in various forms, from an unwillingness to invest in new technologies to an outright rejection of innovations that challenge entrenched beliefs and practices.

Learning curves

Another key factor in the technology adoption cycle is the learning curve associated with mastering new technologies. As individuals and organizations strive to adapt to new tools and systems, they must invest considerable time and resources in learning how to effectively utilize these innovations.

This learning process can be a significant barrier to technology adoption, as individuals and organizations may be hesitant to commit to new technologies if they perceive the learning curve to be too steep or the potential benefits to be insufficient.

Human inefficiencies

Beyond biases and learning curves, the technology adoption cycle is also hampered by a host of other human inefficiencies, such as limited attention spans, cognitive biases, and the often-glacial pace of bureaucratic decision-making processes.

These factors can all serve to slow the adoption of new technologies, even when the potential benefits are clear and compelling.

How AI can help to overcome these limitations and accelerate the technology adoption cycle?

Bypassing human biases

By leveraging AI-driven objective problem-solving, we can significantly reduce the impact of human biases on the technology adoption process. AI systems are not susceptible to the emotional, cultural, or personal biases that often cloud human judgment, enabling them to make more rational and consistent decisions regarding the adoption of new technologies.

By providing decision-makers with unbiased assessments of the merits of different technologies, AI can help to break down the barriers that have historically impeded the adoption of innovative solutions.

Streamlining the learning process

AI also holds the potential to dramatically accelerate the learning process associated with technology adoption. Through the use of AI-powered tools and platforms, individuals and organizations can gain access to personalized, adaptive learning experiences that enable them to more rapidly and effectively leverage new technologies.

These AI-driven learning systems can also provide real-time feedback and support, helping learners to overcome challenges and roadblocks more efficiently than traditional learning methods.

Enhancing human decision-making and efficiency

AI can also help to overcome other human inefficiencies that have historically slowed the technology adoption cycle. By automating routine tasks, providing real-time insights and analytics, and supporting more efficient decision-making processes, AI has the potential to dramatically enhance human productivity and effectiveness.

This, in turn, can help to accelerate the adoption of new technologies, as individuals and organizations are able to more rapidly realize the benefits of these innovations and more effectively navigate the challenges of implementation.

In AI we trust

As we peer into the horizon of possibilities, the future is aglow with the promise of AI-driven problem-solving and the astonishing acceleration of technology adoption.

The transformative power of AI lies not only in its ability to surpass human biases and limitations but also in its capacity to reshape the very essence of our societies. Our children may one day stand on the shoulders of AI giants, navigating a world where the toughest decisions are solved by impartial, data-driven algorithms, freeing them to explore the depths of their creativity and ingenuity.

The optimism surrounding AI's potential is rooted in the unwavering belief that we, as creators and innovators, can harness its objective problem-solving abilities for the greater good. By embracing AI's transformative power, we unlock a future where the most complex challenges are met with intelligent solutions, fostering a world that is more just, prosperous, and sustainable.

Guided by AI, future generations will inherit a legacy that transcends the constraints of human bias, opening the door to a new age of innovation and discovery—one where our collective intelligence shines brighter than ever before.

Thanks for reading — until our next adventure.

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Special thanks to Mama Schroeder for additional edits (any typo is on her 😊).

Disclaimer: This month’s edition of The API Economy has no direct affiliation with my current employer or any other company mentioned. The views in this essay are my own personal opinions and don’t necessarily represent the views of my current employer.

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APIs are like the hidden gears powering our modern world of technology. 

As they've become more advanced and versatile, APIs have opened up incredible new possibilities, including the rise of AI systems. But what is it that makes APIs so crucial for these AI systems? And how do they, in turn, enhance the capabilities of AI?

To begin with, let's first establish a mental framework for understanding APIs. 

Picture an API as the humble, yet essential, middleman in a transaction. It acts as the bridge, connecting different software components, and enables them to communicate seamlessly. When it comes to AI, this bridge becomes all the more important, as the AI must interact with various data sources and tools to deliver value.

OpenAI’s ChatGPT, for instance, relies on APIs to access a multitude of services that make it more useful and versatile. For example, if ChatGPT were asked to find the weather forecast for a specific location, it might turn to a weather API to obtain the necessary data. Similarly, if tasked with translating a sentence, ChatGPT could consult a translation API. These APIs allow ChatGPT to accomplish tasks beyond its core language model capabilities, effectively extending its reach into new domains.

Another example would be using an API to access the user's listening history on a music streaming platform, the AI can analyze the data and identify patterns or preferences. This understanding of the user's taste can then be used to recommend songs or artists that are likely to resonate with them. The entire process hinges on the API's ability to facilitate communication between the AI and the music streaming platform.

As AI continues to proliferate, the integration of APIs will only grow in importance. It's a symbiotic relationship; as APIs improve, so does AI, and as AI grows more sophisticated, it drives the development of even more powerful APIs.

APIs help AI to integrate into products and services. By providing a clear, standardized interface for interaction, APIs empower developers to harness the power of AI without having to understand the complex inner workings of the technology.

The reliance AI systems have on APIs is a testament to the interconnected and dynamic nature of the digital world we live in. It's a world where technology flourishes through collaboration, and where the boundaries of what's possible continue to be pushed by the ever-evolving interplay between AI and APIs.

APIs: forging the path for AI interoperability

APIs are, in many ways, the unsung heroes who connect our modern digital ecosystem. They enable various software components to communicate and share information, leading to a more streamlined, efficient experience for users. 

To truly appreciate the intricacies of APIs, and how they empower AI systems, we must dive a bit deeper into their inner workings.

At the core of every API is a set of protocols, or rules, governing how information is exchanged between software components. These protocols dictate the format of the data, the structure of the requests, and the types of responses that will be returned. In this way, APIs create a common language, enabling diverse systems to communicate and cooperate.

For AI, this common language is vital. It allows AI systems to access a wealth of information from various sources, which in turn, enhances their ability to learn and make predictions. Let's consider a hypothetical AI system that's designed to recommend personalized music playlists. To do so, it must first understand the user's music preferences. This is where APIs come into play.

APIs connect AI models together and make them useful by allowing controllers like ChatGPT to select and assemble the right models to solve a task based on human specifications, as well as compress and connect different modalities through text strings. See an example here (everything in the referenced model is an array of isolated systems connected through APIs.): 

APIs play a key role in the continuous learning and improvement of AI systems. As AI models interact with various data sources and services through APIs, they are exposed to a diverse range of information. This exposure helps to refine the AI's understanding of language, context, and user intent, ultimately leading to more accurate and useful responses.

APIs can be designed to accommodate a wide array of functionalities, making them ideal for the ever-evolving world of AI. As AI systems become more complex and sophisticated, the need for equally advanced APIs will grow. It's an ongoing dance, with each partner pushing the other to new heights.

As we move forward into the future, the intricate interplay between AI and APIs will continue to shape the digital landscape, pushing the boundaries of what's possible.

AI software development built on APIs

In recent years, we've observed a remarkable shift in the world of software development, one that has manifested itself in the emergence of API-first companies. 

These organizations have adopted a new way of thinking about software creation, one that prioritizes the role of APIs from the very beginning of the development process. This shift is not only indicative of the increasing importance of APIs but also of the growing sophistication and complexity of the software landscape.

The advancements in software development are not occurring in isolation; they are intertwined with the rapid progress in artificial intelligence. AI has permeated nearly every aspect of our lives, transforming the way we interact with technology and, consequently, reshaping the software development process itself. As AI takes on a more significant role in software creation, it will inevitably leverage more APIs to build complex and intelligent applications.

APIs provide the perfect building blocks for AI-driven software development. By enabling AI systems to access and utilize the vast array of services and data sources that APIs make available, we can create more powerful, adaptable, and context-aware applications. These applications can then be easily integrated with other components, creating a vast web of interconnected solutions that can evolve and adapt to new challenges and opportunities.

As AI continues to mature and becomes an even more prominent player in software development, we can expect the API-first approach to become even more critical. The inherent flexibility and modularity of APIs will allow AI systems to construct applications that can readily adapt to new technologies and user needs. By incorporating APIs into the foundation of AI-driven software, we ensure that our digital infrastructure is built to accommodate the future.

The rise of API-first companies signifies a new era in software development, one that is driven by a more interconnected and collaborative approach. As AI becomes increasingly intertwined with software creation, it will leverage more APIs to build intelligent, adaptable, and powerful applications. This combination of AI and API-driven development promises to revolutionize the software landscape, delivering new capabilities and solutions that were once unimaginable. The future of software development is bright, and APIs are at their very core.

In a blog titled ‘AI in APIs’ Superface Founder & CTO Zdenek “Z” Nemec states:

“The pinnacle of interfaces for AI is a system where the tasks are not hard-coded in the application. Instead, the application can infer (e.g., for a user's input) what job to execute, the available APIs in its landscape to fulfill the task, and which one to use. This ultimate step will unlock the reasoning AI that would be able to navigate in the universe of humanity's capabilities.”

A benevolent world built by AI & connected by APIs

As AI becomes more intertwined with software creation, it will leverage more APIs to build intelligent, adaptable, and powerful applications. 

If we think of AI as a craftsman, then APIs are like their toolbox. Just as a skilled craftsman uses the right tools to create something incredible, an AI system uses the right APIs to build a useful application. With APIs at their disposal, AI systems can accomplish tasks beyond their core capabilities, effectively extending their reach into new domains.

Just as a carpenter wouldn't be able to build a table without their hammer, saw, and drill, an AI system wouldn't be able to create something truly innovative without the right APIs. APIs provide the building blocks that enable AI systems to access and utilize the vast array of services and data sources that they need to build powerful, adaptable, and context-aware applications.

In the hands of a skilled craftsman, a toolbox can be used to create anything they choose. Similarly, an AI system armed with the right APIs can create virtually anything. The possibilities are endless.

But like any craftsman, the AI system must also continuously refine its skills and techniques to build better applications. Just as a carpenter must learn to use new tools and techniques to create better furniture, an AI system must learn to use new APIs and technologies to build more powerful and intelligent applications.

In the end, it's the combination of a skilled craftsman and the right tools that enables something truly amazing to be created. And for AI systems, it's the combination of advanced AI models and the right APIs that unlock their true potential.

Thanks for reading — until our next adventure.

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Special thanks to Mama Schroeder for additional edits (any typo is on her 😊).

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In 2023, we’ve seen the world’s biggest brands continue to invest in Web3 projects as global lawmakers work on better regulatory frameworks, the NFT market gets back on its feet after a brief hiatus, and Bitcoin makes a surprise run in March (we’re back, baby!) 

To meet growing demand, however, the blockchain industry has had to face one of its biggest challenges to date: scalability. Most notably, as Ethereum has ballooned in growth, developers who work on Ethereum have had to ask themselves, “how do we keep our network fast, efficient, and secure?”

That’s where Polygon comes in. 

At its core, Polygon is a Layer 2 scaling solution for Ethereum that aims to improve its speed, security, and affordability. With lower transaction fees, faster confirmation times, and the ability to support decentralized applications (dApps), Polygon has quickly gained popularity among crypto enthusiasts and investors. 

According to data from Nansen, The number of daily transactions on Polygon surpassed Ethereum transactions in May 2021 and has since eclipsed Ethereum’s daily transaction number with almost 4x as many transactions per day.

Beyond raising $450 million from investors last year, some of the world’s biggest brands (including the likes of Coca-Cola, Starbucks, and Nike) are partnering with Polygon as a blockchain technology service provider. Polygon has become an extremely attractive solution for global giants that want to launch large-scale Web3 projects like NFT marketplaces, where high transaction volumes are expected.

Polygon is also making waves in the gaming industry, including a new alliance with the Web3 gaming firm Immutable to accelerate innovation and adoption in the crypto gaming space. On top of that, the massive South Korean gaming studio Nexon just announced that they will be launching the popular NFT game MapleStory Universe on a dedicated Polygon supernet.  

In February of 2023, Polygon outperformed both Ethereum and Bitcoin, largely thanks to skyrocketing rates of adoption and interest over the past few months. Its price has seen some volatility since then, but it remains a strong player in the market. 

And as of last week, Polygon launched Polygon zkEVM beta (we’ll get more into this later) showcasing their continued dedication to bringing Ethereum to everyone. 

In this essay, we’ll explore the technology behind Polygon, why the biggest brands in the world are partnering with Polygon and its potential impact on the entire digital landscape.

Ogres have layers, crypto has layers

To truly understand Polygon, we first need to understand Ethereum — and its issues. 

Ethereum is a decentralized blockchain platform that allows developers to build and deploy decentralized applications (dApps) and smart contracts. When you look at market capitalization, it is the second-largest cryptocurrency following Bitcoin.

However, as cryptocurrency has become more mainstream, Ethereum has faced a number of scalability issues. The Ethereum network can only handle a limited number of transactions per second, which can lead to slow confirmation times and high transaction fees during periods of high network usage.

For example, during the height of the DeFi craze in 2020, the Ethereum network experienced significant congestion due to the high number of transactions being processed by popular dApps such as Uniswap and Compound. As a result, transaction fees on the Ethereum network skyrocketed, with users paying as much as $100 or more to make a single transaction.

Confirmation times also increased, with some transactions taking hours or even days to be confirmed due to the network's limited throughput capacity. This made it difficult for many users to participate in the DeFi ecosystem and highlighted the need for scalable solutions such as Polygon.

In ‘Own the internet’, Not Boring founder Packy McCormick writes:

Polygon aside, Ethereum has been working on several of its own solutions, including sharding and more recently, danksharding. Sharding is a complex process that involves splitting the network into smaller parts called "shards," each of which can process transactions independently. Danksharding is still a rather theoretical solution but Ethereum developers hope it will be even more secure and decentralized than sharding, allowing Ethereum to continue to scale.  

Until these solutions are fully implemented, however, Ethereum has also been relying on Layer 2 scaling solutions, which allow for faster and cheaper transactions by processing them off-chain and then settling them on the Ethereum network.

Polygon is one of the more popular of those Layer 2 scaling solutions. Essentially, Polygon allows developers to create and deploy their decentralized applications (dApps) on a separate network that is connected to Ethereum.

By doing so, Polygon provides faster and cheaper transactions, as the majority of transactions are processed on Polygon's network, and only the final results are committed to the Ethereum blockchain. This allows for a significant increase in the number of transactions that can be processed, with Polygon's network capable of handling up to 7,000 transactions per second (vs. Ethereum’s 20 tps). 

Ethereum vs. Polygon: what’s the difference?

Think of the main blockchain network (including Ethereum) as a highway with a limited number of lanes. Just like a highway, when there is too much traffic on the network, it can become congested, and transactions can slow down or even come to a complete stop.

Now imagine a parallel road next to the highway that allows some of the traffic to bypass the congestion on the highway. This parallel road could have more lanes and be designed specifically to handle a larger volume of traffic, making it faster and more efficient.

In the same way, a Layer 2 solution is like a parallel road next to the main blockchain network. It processes some of the transactions off-chain, which allows it to handle a larger volume of transactions without slowing down the main network. When the off-chain transactions are settled on the main network periodically, it's like the traffic from the parallel road merging back onto the main highway.

This way, Layer 2 solutions can help to alleviate congestion and improve the speed and efficiency of the overall network, without compromising the security or decentralization of the blockchain.

Why Polygon is essential in an increasingly Web3 world 

To understand why scaling solutions are crucial to the future of cryptocurrency, we should take a brief look at dApps.

The term "dApp" refers to a type of application that runs over a decentralized network, such as a blockchain, rather than on a centralized server. Using them is similar to using regular apps on your smartphone or computer, but with a few key differences.

In a traditional app, a central authority or company controls the app's data and operations. This centralization can lead to issues like censorship, data breaches, or limited access to services. On the other hand, dApps distribute control across multiple computers, or "nodes," in a decentralized network, making them more resistant to censorship, tampering, and single points of failure.

dApps are built for various purposes, including finance and gaming, and are important because they provide decentralized alternatives to traditional applications — offering users more security, transparency, and control. 

For example, you may have heard of the popular dApp Uniswap, hosted on Ethereum. This dApp “enables users to trade cryptocurrencies without any involvement with a centralized third party” according to Investopedia. 

According to some thought leaders, 2023 is slotted to be the year of dApps. Tony Cheng writes for Coindesk, “While 2022 saw some of the most unprecedented unravelings in crypto history, the fact of the matter is Web3 technology still has the opportunity to fundamentally change consumer behavior. Due to an emphasis on infrastructure throughout 2022, crypto is now at the phase where it is not lacking for infrastructure options. As we look ahead to 2023, what crypto needs is applications that will encourage user adoption and pave the way for Web2's transition to Web3.”

Which brings us to the question: why is Polygon such a big deal? Well, it’s in a perfect position to absolutely revolutionize dApp development by making the process faster, more efficient, and cheaper. Polygon is addressing the problems developers have with Ethereum and enabling a more efficient and accessible environment for building and using dApps. 

But the benefits of Polygon go much further than just dApps — we’ll get into that in the sections below. 

Where did Polygon come from? 

Polygon (formerly Matic Network) was founded in 2017 by a team of experienced blockchain developers and entrepreneurs, including Sandeep Nailwal, Jaynti Kanani, and Anurag Arjun (who now runs Avail).

Known as Matic Network at the time, the team initially aimed to address issues like slow transaction speeds and high costs, both of which limited the usability of dApps built on the Ethereum blockchain.

Throughout 2018, the founding team worked to create a scalable sidechain and to simplify the process of building and deploying dApps on the platform. They established several strategic partnerships and collaborations during this time, such as working with Decentraland, a decentralized virtual reality platform, to provide scaling solutions. These collaborations helped Matic Network gain traction and credibility in the blockchain ecosystem.

By early 2019, Polygon had launched a beta version of its mainnet. During this beta phase, Polygon focused on demonstrating its capabilities as a Layer 2 scaling solution for Ethereum. The platform showcased its potential to provide faster and cheaper transactions using its sidechain architecture, relying on the Plasma framework and a Proof-of-Stake (PoS) consensus mechanism.

The beta launch also allowed developers to build and deploy decentralized applications (dApps) on the platform, experiment with various scaling techniques, and test the network's performance and security features.

In June 2020, Polygon fully launched its mainnet to address a major bottleneck in the blockchain industry: scalability. The platform’s user base quickly ramped up from there. 

As we mentioned before, networks like Ethereum are powerful tools — but they just aren’t ready for the high transaction volumes and demand they face today. This is partially why Polygon gained popularity so quickly, attracting a sizeable community of developers and users in just a few years.

Going mainstream with the world’s biggest brands 

Polygon has had a remarkable journey in the past few years, becoming one of the most prominent players in the Ethereum ecosystem. They’ve attracted a diverse range of projects and users from various sectors, including gaming, DeFi, NFTs, and more. 

And as more and more brands have become eager to join the Web3 craze, Polygon’s been there to support their efforts. 

Polygon has secured several major partnerships with well-known brands and companies, including Starbucks. At the end of 2022, the coffee giant beta-launched a new NFT-based rewards program called Starbucks Odyssey — and they ended up selling 2,000 NFTs in just 20 minutes. 

Since then, Starbucks has continued to roll out the program to customers that have joined the waitlist. Just a few weeks ago, with the help of Polygon, they launched a limited edition collection of “stamps” (NFTs) at $100 each. Once again, they sold out in under 30 minutes. 

Members of Starbucks Oddysey can also complete online games like trivia to win more stamps. Some of those free stamps are now valued at over $1,000 on the secondary market. However, mainstream adoption of the program has been slow.

Polygon’s partnership with Nike has also been interesting to watch play out. Late last year, Nike announced they would be launching their first collection of NFT apparel, minted on Polygon.

Nike will be selling these NFTs on a new platform, .Swoosh, which will serve as the epicenter for Nike’s Web3 efforts — and will include future ways for customers to become co-creators and share in digital product royalties. .Swoosh will also allow users to unlock real-world benefits, such as exclusive physical apparel or chats with pro athletes using Web3 technology. 

Nike originally announced that they would launch the platform in January 2023, but as of March 2023, it is still in a closed beta phase. 

Prior to working with Polygon, Nike released some limited drops with digital apparel startup RTFKT, all of which were launched via the Ethereum mainnet. Nike hasn’t made a formal statement regarding why they chose Polygon for this new venture, however, it’s likely due to Polygon’s fast and affordable infrastructure. 

Taking a look under Polygon’s hood

Polygon is shaking up the crypto ecosystem because it’s making Ethereum faster, cheaper, and more developer-friendly. It does this by creating a set of sidechains and shard chains that work together to handle more transactions at once, like adding more lanes to a highway to reduce traffic congestion. 

The architecture of Polygon is composed of multiple components, including the Ethereum mainchain, the Heimdall Proof-of-Stake layer, and the Bor block production layer. Let’s break these down a little further:

1. Ethereum mainchain: Polygon's architecture relies on the Ethereum mainchain for certain functions, such as staking, checkpointing, and finality. This means that Polygon remains closely connected to the Ethereum ecosystem, benefiting from its security and network effects.

2. Heimdall (Proof-of-Stake layer): Heimdall is the validator layer of the Polygon architecture, which secures the network using a Proof-of-Stake (PoS) consensus algorithm. Validators stake MATIC tokens on the Ethereum mainchain to participate in the network. Heimdall is responsible for the following tasks:

   1. Proposer selection: Heimdall randomly selects a validator to propose a new block for the Bor block production layer, ensuring decentralization and security.

   2. Checkpointing: Heimdall periodically submits a Merkle root of the latest block hashes to the Ethereum mainchain as a "checkpoint." This process anchors the Polygon sidechain to Ethereum's security, providing finality and preventing double-spending attacks.

   3. Staking and delegation: Validators stake MATIC tokens to participate in the network, while delegators can support validators by delegating their tokens to them. This helps maintain the security of the network and provides an incentive for validators and delegators through block rewards and transaction fees.

3. Bor (block production layer): Bor is the block production layer in Polygon's architecture, where transactions are executed and blocks are created. It uses a consensus algorithm called Heuristic Merkleized Sharding (HMS) to group validators into multiple "shards" that validate and produce blocks in parallel. This design allows Polygon to achieve higher throughput and scalability compared to the Ethereum mainchain.

These three components work together to create a secure and scalable platform that can support a wide range of decentralized applications (dApps) and other Layer 2 solutions. By leveraging Ethereum's security and layering on additional scaling techniques, Polygon is solving some of the most pressing challenges faced by blockchain networks.

NFTs on Polygon

Polygon has gained a lot of attention as a popular platform for creating and trading non-fungible tokens (NFTs). The Polygon network provides a cost-effective and scalable solution for NFT creators and collectors, allowing them to avoid the high gas fees and slow transaction times that are often associated with Ethereum-based NFTs. 

As one example, these lower transaction fees make smaller NFT transactions more accessible leading to L2s hitting all-time highs in March 2023 for a total share of Ethereum gas spend (see image below):

Polygon's interoperability with Ethereum-based NFTs also allows for easy migration of existing NFT projects to the Polygon network, and its decentralized network ensures that NFT ownership and transactions are secure and transparent. 

The Polygon ecosystem has enabled new use cases for NFTs that were previously not feasible on other platforms. For example, the platform has been used for NFT-based gaming, where players can trade and collect unique in-game assets. Polygon-based NFTs have even been used for charitable purposes, such as raising funds for social causes or disaster relief efforts. 

Polygon's scalability, low fees, and versatility have made it a popular choice for creators and collectors alike, driving the growth and innovation of the NFT market — and the popularity of Polygon leading to popular NFT projects like y00ts switching over to Polygon.

Solving the blockchain “trilemma” 

Beyond NFTs, Polygon has become so popular because it has helped with many of the pain points associated with the infamous blockchain “trilemma.” 

The blockchain trilemma, a term coined by Ethereum co-founder Vitalik Buterin, refers to the challenge of simultaneously achieving decentralization, security, and scalability in a blockchain network. It suggests that optimizing a blockchain for all three properties is difficult, often requiring trade-offs. 

Polygon addresses the blockchain trilemma by employing a multi-layer architecture and various Layer 2 scaling techniques to strike a balance between decentralization, security, and scalability.

• Decentralization is maintained through the Proof-of-Stake (PoS) consensus mechanism in the Heimdall layer (which we discussed in the previous discussion). This allows multiple validators to participate in block production and validation. 

• Security is achieved by anchoring the Polygon sidechain to Ethereum through a checkpointing mechanism, where the Merkle root of the latest block hashes is periodically submitted to the Ethereum mainchain. This process leverages Ethereum's security while benefiting from the PoS consensus in the Heimdall layer to protect the network.

• Scalability is addressed through the Bor block production layer, which uses Heuristic Merkleized Sharding (HMS) to increase transaction throughput. Additionally, Polygon's support for various Layer 2 solutions allows the network to scale according to specific use cases and demands.

By using this combination of techniques and architecture, Polygon seeks to create a platform that offers a balance between the key properties of the blockchain trilemma, providing a more accessible and scalable ecosystem for developers and users.

The many advantages of using Polygon 

By now, you can probably see why developers around the world are adopting Polygon at rapid speed. Beyond answering the trilemma, we’ve boiled down the main benefits of Polygon into four key areas:

1. Increased transaction throughput and faster confirmation times

2. Lower transaction fees

3. Improved security through the use of PoS

4. Increased decentralization and accessibility for developers and users

Let’s take a closer look at each of these, get ready for a wall of text:

Increased transaction throughput and faster confirmation times 

One of the primary advantages of Polygon is its ability to scale transaction throughput while maintaining fast confirmation times. By using various scaling techniques along with its block production layer (Bor) and Heuristic Merkleized Sharding (HMS), Polygon can process a higher number of transactions per second (TPS) compared to the Ethereum mainchain. 

This results in faster confirmation times for users, leading to a more seamless and responsive experience when interacting with dApps built on Polygon.

Lower transaction fees

High transaction fees on the Ethereum mainchain can be a significant barrier to entry for many users and developers. Polygon's Layer 2 scaling solution addresses this issue by offloading transaction processing from the mainchain to its sidechain. 

Since the sidechain has a higher transaction throughput, this results in significantly reduced fees for users. Lower transaction fees make it more economically viable for developers to build and deploy dApps, while also allowing users to interact with these applications without incurring high costs.

Improved security through the use of Proof-of-Stake (PoS)

Polygon's Heimdall layer utilizes a PoS consensus algorithm to secure the network. Validators stake their MATIC tokens to participate in the consensus process, which aligns their interests with the overall security of the network. In case a validator acts maliciously, their staked tokens can be slashed as a penalty. 

PoS has several advantages over Proof-of-Work (PoW) consensus, including reduced energy consumption, a lower risk of centralization due to specialized hardware requirements, and resistance to certain types of attacks (e.g., long-range attacks). Additionally, Polygon's checkpointing mechanism, which periodically submits the Merkle root of the latest block hashes to the Ethereum mainchain, provides additional security by anchoring the sidechain to Ethereum's PoW-based security.

Increased decentralization and accessibility for developers and users

Polygon's architecture promotes decentralization by allowing multiple validators to participate in the block production and validation process. This reduces the risk of centralization and ensures that no single entity can control the network. 

Furthermore, Polygon's compatibility with Ethereum's development tools and infrastructure (e.g., Solidity, Web3.js, MetaMask) makes it easy for developers to build and deploy dApps on the platform. This accessibility, combined with lower transaction fees and faster confirmation times, creates a more inclusive and user-friendly environment for developers and users alike.

Use cases and real-world applications

Polygon and its endless benefits are redefining what’s possible across industries. To truly show the power of this innovative platform, let’s take a look at some real-world examples:

1. Tokenization

Tokenization is the process of converting physical or digital assets into digital tokens on a blockchain. When assets are represented as tokens, it becomes possible to streamline transactions, enhance security, and increase transparency. 

All the benefits listed in the previous section, plus Polygon's unique architecture and design, make it an outstanding platform for tokenization. By solving many of the problems that come with Ethereum (the leading platform for token creation), Polygon has the potential to completely transform the way assets are managed, exchanged, and interacted with.

One such asset that Polygon is already touching is the digital bond. A digital bond is a type of debt security that is issued and managed on a blockchain, rather than through traditional, paper-based methods.  They function similarly to traditional bonds but leverage the benefits of blockchain technology to enhance efficiency, transparency, and security. 

In February 2023, the German industrial manufacturer Siemens issued a digital bond on Polygon’s public mainnet… a bond worth $60 million euros, to be exact. Although the bond was issued on-chain, Siemens opted to collect proceeds from investors through conventional banking channels (mainly due to the fact that the digital euro isn’t available yet). 

Siemens spoke publicly about why they decided to leverage blockchain technology with this bond: “[Blockchain platforms] make paper-based global certificates and central clearing unnecessary. What’s more, the bond can be sold directly to investors without needing a bank to function as an intermediary.”

By moving away from paper-based bonds to blockchain-based digital bonds, Siemens has streamlined the bond issuance process, reducing administrative burdens, increasing transparency, and making transactions faster and more efficient.

Most importantly, this pioneering move highlights growing mainstream interest in leveraging blockchain technology for traditional financial processes — and the way Polygon is bound to play a part in the global shift to decentralized finance.

Siemen’s digital bond is also likely to inspire other corporations to explore similar avenues, leveraging the power of Polygon and blockchain technology to reshape the way capital is raised and managed in industries.

Polygon’s role in decentralized finance (DeFi) isn’t stopping here, though. 

Recently, the DeFi infrastructure firm Swarm Markets announced the launch of tradable, DeFi-compatible stocks and bonds on the Polygon network. This innovative offering will bring popular stocks like Apple and Tesla, as well as two US Treasury bond ETFs, to the world of decentralized finance. 

While these tokenized securities will not be available to US investors, they cater to both retail and institutional investors, with no minimum investment required. This initiative expands the range of assets accessible through DeFi, and Swarm Markets plans to add more stocks and other assets to their offering in the future, further bridging the gap between traditional and decentralized finance.

According to Swarm, “This is the first regulated decentralized exchange (DEX) available on the layer 2 solution, making it one of the cheapest and safest options for people wanting to access DeFi with investor protections.” 

Daniela Barbosa, executive director of Hyperledger Foundation, also says, “Tokenizing a wide variety of assets – from equities and index funds to real estate and carbon credits – offers huge potential in terms of not only enhancing transparency, auditability, and efficiency but also access to people who otherwise might not be able to tap into traditional markets. Zooming out, if done at scale, these kinds of transactions are poised to make markets far more efficient.”

Investment bank Citi is betting on the blockchain-based tokenization of real-world assets to become the next “killer use case” in crypto, with the firm forecasting the market to reach between $4 trillion to $5 trillion by 2030 according to Cointelegraph.

Citi argues that blockchain tokenization would supersede legacy financial infrastructure because it is technologically superior and provides more investment opportunities in private markets. The technology negates the need for expensive reconciliation, prevents settlement failures, and makes tedious operations more efficient. According to Citi:

"digitally native financial asset infrastructure, globally accessible, operating 24x7x365 and optimized with smart contract and DLT-enabled automation capabilities, which enable use cases impractical with traditional infrastructure."

Ultimately, by enabling large-scale tokenization of various assets, Polygon's technology is well-positioned to make financial markets more efficient and inclusive, revolutionizing the way we interact with and invest in these assets.

2. Gaming and virtual worlds

Historically, developers have faced several challenges while building blockchain-based video games, particularly on networks like Ethereum: 

• Network congestion and high transaction fees: These significantly hinder the gaming experience by causing slow transaction times and making in-game actions expensive for players.

• Limited scalability: This leads to performance bottlenecks and a suboptimal gaming experience with latency and capacity issues.

• Complexity: The learning curve associated with developing on some blockchain platforms can be a barrier for developers who are new to the technology, slowing down the growth and adoption of blockchain-based gaming.

These challenges remain relevant, but according to VentureBeat, “2023 will be the year that blockchain games deliver quality and fun” — all thanks to Polygon. 

And as VentureBeat predicted, Polygon has delivered as they recently announced that they will team up with Immutable on a gaming-focused zkEVM.

Urvit Goel, vice president of games at Polygon, said in the same VentureBeat article, “Specifically in gaming, 2023 will be the year that blockchain and gaming will be judged heavily on all of the promises that are being made about great games. We need to start seeing some of those games. There will be enough to start making a determination about quality. A lot of these builders in stealth mode haven’t launched a product yet.”

Polygon is on track to revolutionize the gaming industry by providing a scalable and cost-effective platform for developers to create engaging, blockchain-powered gaming experiences. 

Among its many other benefits, Polygon enables seamless integration of decentralized, player-driven virtual economies, true ownership of in-game assets, and innovative play-to-earn models. This empowers gamers to monetize their skills and time, while also fostering a more interconnected gaming experience through cross-platform interoperability.

In addition, Polygon's developer-friendly environment, with support for popular Ethereum development tools, reduces the learning curve and encourages the creation of cutting-edge decentralized gaming applications. The platform also promotes community-driven development, allowing players and developers to collaborate and shape the evolution of games through decentralized governance. By addressing the obstacles faced by both gamers and developers, Polygon is paving the way for a more immersive, inclusive, and rewarding gaming ecosystem.

3. Zero-knowledge (ZK) scaling

Polygon recently announced the world’s first zero-knowledge (ZK) scaling solution — fully compatible with Ethereum. According to Polygon, “[This] ground-breaking development of zkEVM is set to dramatically improve the experience of using and building with Ethereum by providing scalability and lower transaction costs.”

So why does this matter? First, let’s talk briefly about zero-knowledge scaling as a concept. 

Zero-knowledge scaling is an advanced cryptographic technique that leverages zero-knowledge proofs to increase the transaction throughput and privacy of blockchain networks. It enables the aggregation of multiple transactions into a single proof, which can be verified by the network without revealing the details of the transactions themselves. Ultimately, this reduces the amount of on-chain data and computation required, which improves scalability while preserving privacy.

Zero-knowledge scaling also enhances privacy by allowing transactions to be verified without revealing the details of the transactions themselves. This feature can be particularly valuable in applications that require a high degree of privacy and confidentiality, such as finance or identity management.

Polygon's zkEVM, specifically, is a groundbreaking development that has the potential to transform the blockchain industry. zkEVM enhances the scalability of the Ethereum network, allowing it to process a higher number of transactions per second and alleviating network congestion. 

This improvement in transaction throughput will create a more seamless and efficient user experience, which is crucial for the continued growth and adoption of decentralized applications (dApps) and the Web3 ecosystem.

Moreover, zkEVM's ability to reduce transaction fees makes interacting with the Ethereum network more accessible and affordable for users and developers. This cost reduction can further encourage the adoption of blockchain technology and unlock new possibilities for various industries and use cases. 

4. Identity verification and authentication

Polygon is uniquely positioned to revolutionize the way today’s identity management systems are built and deployed.  By harnessing the power of blockchain technology, this platform offers a decentralized, secure, and privacy-preserving solution that can streamline the process of identity verification and authentication, making it more resilient to fraud and identity theft while enhancing user privacy and control over personal data. 

Just weeks ago, Polygon publicly released its own decentralized identity solution called Polygon ID, powered by zero-knowledge cryptography. This product is groundbreaking and presents a unique way for individuals to verify their identities in a Web3 world. 

“Polygon ID is private by default, offers on-chain verification and permissionless attestation. There is nothing in the digital identity space now that ticks all these boxes,” said Mihailo Bjelic, Polygon’s co-founder. “It is also a great showcase for how zero-knowledge proofs can help us create a better world.”

So what are the use cases? Like many Web3 projects, Polygon ID is still under development. But the vision includes a world where users can verify their identities without ever revealing sensitive information — you’d be able to get into a bar without showing your physical ID (and all the details contained there) and use your favorite websites without routinely providing your personal information.  

All roads lead to Polygon

Polygon has emerged as a game-changing force in the blockchain and Web3 ecosystem, addressing the limitations of Ethereum and other platforms by providing a scalable, cost-effective, and developer-friendly solution.

Through its innovative architecture and commitment to solving the blockchain trilemma, Polygon has made it possible for an increasing number of brands, industries, and users to leverage the benefits of decentralization.

It’s clear how Polygon has the potential to bring in a world where the blockchain is integrated into our daily life — from our coffee habits to the ID we show to get into a bar. It’s already a top 4 blockchain in terms of the number of active wallet addresses:

As Web3 continues to gain traction and reshape the digital landscape, Polygon's role in empowering developers, businesses, and users is becoming increasingly critical. And if the trend continues and brands continue to build on Polygon, they have the opportunity to become the AWS of crypto.

Over a decade ago, Fortune 500 companies began building on the AWS platform, making it a crucial part of how they do business, which helped AWS become the most popular SaaS tool ever. If Polygon continues in this direction, it might just become an essential blockchain tool for companies who want to stay relevant in a Web3 world.

By fostering an environment where innovation can flourish, Polygon is paving the way for a more inclusive, secure, and efficient digital-first future.

Thanks for reading — until our next adventure.

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Special thanks to my friend Haley Davidson for copy help & edits and Mama Schroeder for additional edits (any typos are on them 😊).

Disclaimer: The views in this essay are my own personal opinions and don’t necessarily represent the views of Polygon, those mentioned in this article, or anyone other than myself.

To support The API Economy, be one of the 2,711 early, forward-thinking people, who subscribe for monthly-ish insights on the API economy, crypto, blockchains, and more emerging trends.

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Payments are unhinged

It's time we take a hard look at our current payment system and imagine a better way. Blockchain technology has the potential to be that better way.

The current system is built on a labyrinth of intermediaries, each taking a cut and adding its layer of complexity. But with the advent of blockchain technology, we have the opportunity to streamline the process and cut out the middlemen. This is not just a minor improvement, it has the potential to completely revolutionize the way we think about and handle payments.

The internet made it easy to share information globally, blockchain has the potential to make it easy to transfer value globally, in a way that is fast, secure, and transparent. It's a technology that has the power to democratize finance and level the playing field.

Just like you don’t have to think about sending or receiving an email today, money will move just as freely in the future.

When I asked the internet’s favorite writer Packy McCormick of Not Boring what he thought about payments being disrupted with blockchain technology, he stated:

(Note: you can watch leaders from Circle, Mastercard, Visa, Stripe, and Checkout discuss the future of payments using public blockchain rails in this video from Converge22.)

The implications of the advancements made just in the past few years are massive and far-reaching. They have the potential to reduce costs and increase efficiency while making it easier than ever before for people to send and receive payments, regardless of where they are in the world.

Here’s a simplified overview of the steps & intermediaries involved in the current payments ecosystem.

The current payment system has been broken and duct-taped back together too many times to count. It's slow, expensive, and riddled with inefficiencies. Every time a payment is made, it goes through a series of intermediaries who take a piece of the pie and add their layer of complexity. This not only increases the cost but also slows down the process and makes it more prone to errors.

Take, for example, cross-border payments. They can take days or even weeks to clear, and the fees can be exorbitant. This is because the payment has to go through multiple banks and other intermediaries, each adding its layer of complexity and cost.

Rick Martin, Founder of crypto wallet & POS solution Decaf told me: 

“We sent 1 USD Coin to Mexico and received 19 pesos, cash. This is a micro-remittance that was never before possible before blockchains.

This goes beyond just Mexico. This is going to impact those sending money home to family and friends, paying international suppliers, digital nomads, and remote workers (like myself), and those who find themselves underserved and ripped off by the current banking systems for cross-border transactions.”

Here’s an example of the process with all the middlemen that solutions like Decaf built on the blockchain can disrupt:

But it's not just cross-border payments that are affected by today's rails. Even domestic payments can take a long time to clear and be subject to high fees. The whole system is in need of an overhaul.

That's where blockchain comes in. By using a decentralized, peer-to-peer network, blockchain has the potential to cut out the middlemen and make payments faster, cheaper, and more secure. This is made possible through the use of smart contracts, which are self-executing contracts with the terms of the agreement written directly into the code.

With this technology, the future of payments could look something like this:

But we’re not just building towards a future state. There are real-world examples today of how blockchain technology is building the future of payments.

Stablecoins

One specific use case is the use of stablecoins (like USDC), which are digital currencies pegged to the value of a fiat currency. These stablecoins can be used to make payments and transfer value without the need for a traditional bank. This has the potential to greatly reduce the cost and time it takes to make a payment, especially for cross-border transactions.

The SWIFT system, which has long been the standard for international transfers, is being challenged by this new decentralized approach. USDC as a settlement standard is posed to completely replace the payment rails as we know them, and the shift has already started. 

Recently, Cuy Sheffield of Visa stated, “We set all over Swift, so we can’t move money as frequently as we’d like because there are a number of limitations that exist in those networks. And so, we’ve been experimenting, we publicly announced. We’ve been testing how to actually accept settlement payments [with stablecoins].”

Visa, the first major payments network to settle transactions in USDC sees a future where stablecoins are the bedrock of the financial industry. To describe the benefits of stablecoins in a Twitter thread, ChainLinkGod called them a “superior form of fiat” that are: 

• Programmable

• Permissionless

• Borderless 

• Low-cost 

• Fast settlement

• Interoperable 

• Highly liquid

The use cases for stablecoins feel infinite. 

For example, the UN has already chosen Stellar & Circle to send war-impacted Ukrainians USDC as a replacement to the old method of shipping planes full of cash to places that need aid. With the use of stablecoins like USDC, we have the opportunity to streamline the process and make aid disbursements faster, cheaper, and more transparent. Here’s how it works:

Since USDC is a stablecoin pegged to the value of the US dollar, it can be easily converted into local currency without the need for intermediaries. This eliminates the need for costly foreign exchange fees and reduces the time it takes for funds to reach their destination. When recipients choose to convert to cash, whether it is dollars, euros, or local currency, they can withdraw their funds at any global MoneyGram location, including over 4,500 MoneyGram locations in Ukraine.

Because USDC is built on blockchain technology, it allows for real-time tracking of funds and increased transparency in the disbursement process. This can help to combat fraud and ensure that aid reaches those who need it most. USDC is also easily divisible (see more on micropayments below), which means that aid can be disbursed in small amounts to many recipients at once. This can make it much easier to reach individuals and families in remote and underserved areas.

On The Scoop, Nabil Manji, Worldpay's Head of Crypto and Web3 discussed the future of governments effectively disbursing money with the most impact through stablecoins:

"If you think about programable money (stablecoins). If I'm a government and I'm disbursing stimulus, can I program that stimulus program and the currency that's being allocated or distributed as part of that to only be eligible to be sent to wallets that meet a certain criteria? And once in the wallet, can the funds can only be spent on items that meet a certain criteria?"

The conversation stemmed from research Nabil mentioned which states Covid-19 unemployment fraud may have topped $45b+ (as reported by the Wall Street Journal).

Aid disbursement is one clear example of how blockchain technology is already abstracting away the complexity of traditional payments by leveraging stablecoins.

DeFi

Another use case is the use of decentralized finance (DeFi) platforms, which allow for the creation of financial instruments and services on the blockchain. These platforms can be used to create new and innovative ways of making payments and transferring value.

One of the key benefits of DeFi is the ability to create new and innovative ways of making payments and transferring value. For example, DeFi platforms allow for the creation of smart contracts that can be used to automate payments and transfer value. This can greatly reduce the cost and time it takes to make a payment, especially for cross-border transactions.

DeFi also allows for the creation of new financial instruments, such as lending platforms and yield farming opportunities. These new instruments can open up new revenue streams for businesses and increase access to capital for individuals and small businesses.

It's important to note that DeFi is still in its early stages and there are many challenges that need to be addressed. However, the potential for DeFi to disrupt the traditional financial system is enormous and it's a technology worth paying attention to.

Micropayments

Another important aspect of blockchain-based payments is the ability to facilitate micropayments. Micropayments are small, incremental payments that are typically less than a dollar.

Traditionally, micropayments have been difficult to facilitate because of the high transaction costs associated with small payments. However, blockchain technology has the potential to greatly reduce these costs and make micropayments a viable option.

With the use of blockchain technology, it's possible to create smart contracts that can automatically divide and distribute payments according to predefined rules. This can greatly reduce the cost and time it takes to make a micropayment, and can open up new revenue streams for businesses.

Micropayments can also have a profound impact on the way we access goods and services. They can be used to pay for access to NFTs & other digital content, such as articles, music, and videos. This can increase access to information and entertainment for individuals, and provide new revenue streams for content creators.

This use case is being used today by the likes of Twitter and Stripe who rolled out a USDC payout program for creators via Polygon.

A model like this is especially important for global creators, who often have to deal with high transaction fees and long wait times when receiving payments. With USDC, they can receive payments quickly and with minimal fees.

When I asked TJ who works on payments at Solana for his thoughts, he said:

“Blockchain is a new primitive that has yet to build out the rich suite of integrations we’ve come to expect within web2 softaware. There is a large opportunity in providing these companies with powerful APIs that have simple interfaces. This will increase the likelihood of delivering powerful payment transactions to consumers.”

The use of USDC also provides global creators with greater flexibility in how they receive payments. They can use USDC to receive payments directly from their fans, without having to go through intermediaries. This can greatly increase the revenue streams for creators and provide them with more control over their financial futures.

Zero-Knowledge Proofs

One of the key benefits of blockchain-based payments is the increased usage of zero-knowledge proofs, which provide a secure and privacy-preserving way to confirm the validity of transactions.

Zero-knowledge proofs allow for transactions to be verified without revealing any sensitive information about the parties involved. This is achieved by using cryptographic algorithms that allow one party to prove to another that a certain statement is true, without revealing any information about the statement itself.

In the context of blockchain-based payments, zero-knowledge proofs can be used to validate transactions without revealing the identities of the parties involved or the details of the transaction. This makes blockchain-based payments more secure and private, as it eliminates the need to trust intermediaries to handle sensitive financial information.

Over the next decade, zero knowledge has the potential to be one of the biggest breakthroughs of our lives.

To quote Packy McCormick for the second time, he states in Zero Knowledge: 

“Every so often, a technology comes around with the right combination of promise, nebulousness, inscrutability, and abstractness to fully capture and incept the hive mind’s imagination. These technologies aren’t just going to change a few things; they’re going to change everything.”

I guess you guys aren’t ready for that yet. But your kids are gonna love it!

In Back to the Future, Marty McFly plays Chuck Berry’s “Johnny B. Goode” and is met with silence from the crowd, but one person in the crowd is on their phone saying that this music is the future. In a similar vein, blockchain technology has the potential to revolutionize the way we think about and handle payments, we are just early in our journey. 

Payment costs will be driven down to next to nothing, money will be programmable & composable, and value will be transferred as easily as it is to move as data today. 

It's time for us to embrace blockchain technology to build the future of payments.

Thanks for reading — until our next adventure.

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Special thanks to mama Schroeder for additional edits (any typo is on her 😊). Also, thank you to Packy & Dan of the Not Boring crew for quotes & edits, Rick Martin of Decaf for comments, and TJ at Solana for his comments & feedback.

Disclaimer: This month’s edition of The API Economy has no direct affiliation with Circle or any other company mentioned. I am employed by Circle at the time of this writing. But, the views in this essay are my own personal opinions and don’t necessarily represent the views of Circle.

To support The API Economy, be one of the 2,564 early, forward-thinking people, who subscribe for monthly-ish insights on the API economy, crypto, Web3, and more emerging trends.

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