Wintermute: Three new directions for AI x crypto that we are optimistic about

Author: Wintermute

Compiled by: Deep Tide TechFlow

Wintermute releases an industry declaration: the battle for crypto infrastructure is over, and the next battlefield is not DeFi but the machine economy. As AI agents, warehouse robots, and automated experimental systems become economic entities, the underlying assumption of traditional financial rails that "the counterpart is a person" will completely fail—while the difference in crypto rails that "the counterpart is code" will shift from a flaw to a core advantage. Three directions worth paying attention to: agent economy layer, physical AI, machine-driven discovery.

Old problems are dead, new problems are emerging

Crypto has been around for over a decade. L1 has launched, L2 followed closely, DeFi has matured, and stablecoins have become infrastructure. In every track such as exchanges, lending, perpetual contracts, and prediction markets, every category is crowded, and every obvious idea seems to have been done.

So, what else can be built in crypto?

Many builders have given up here. They are wrong—not because the answer is "nothing," but because the question itself is wrong.

For most of crypto's history, the truly interesting question was whether the rails could hold up: can they settle in seconds, can they transfer stablecoins at scale, can they run open networks under real load? These questions now have answers. The infrastructure can work now, the next interesting questions lie elsewhere.

What has truly changed is everything around the infrastructure. Models can act autonomously rather than just respond; robots learn from human videos rather than relying on handwritten code; open standards for agent payments and identities are taking shape. None of these are crypto, but each one is challenging the boundaries of the financial and trust infrastructure built for humans.

The question worth asking is no longer "what can crypto do," but "what does this world need crypto to do".

The answer is becoming increasingly clear—the machine economy.

Machines are not tools, but economic entities

When we say "machine economy," we do not mean machines as tools—like those used to send emails or write code. We mean machines as economic entities.

This shift is subtle but has significant consequences. Tools wait for instructions; entities hold context, make decisions, transact, and act autonomously in both digital and physical worlds. Current models are good enough and cheap enough to do this at scale.

Real-world scenarios:

An agent books a flight for you, negotiates the price, pays the vendor, and handles refunds—without your intervention throughout the process.

A warehouse robot takes tasks billed by the piece, charges itself, pays for computing power, and routes income to the operator.

A research system autonomously designs experiments overnight, procures reagents, and operates in a closed loop—without a graduate student present.

Our existing financial and trust infrastructure almost all assumes that the counterpart is a person or a business—a recognizable and accountable entity. This assumption fails the moment the counterpart is an autonomous entity, and our existing payment, identity, authorization, dispute, and settlement rails were never built for this scenario.

And this precisely sits at the intersection of crypto, fintech, AI, robotics, and quantum computing.

Why now

Three recent shifts that seemed unlikely a few years ago.

Models can act, not just respond

Models are no longer just answering questions; they can act autonomously, and the cost is low enough to operate unattended. The unit cost of digital work is collapsing, making tasks that were previously not worth a person's time feasible—at scales and amounts that existing systems were never designed to handle.

Open standards are maturing

Stablecoins are now the true settlement rails. Protocols like x402, MPP, and AP2 provide agents with payment methods. Faster blockchain networks and faster fiat networks are converging in the middle. Open visual-language-action models allow robots to learn from human videos and simulations rather than relying on custom programming. Standards allow builders to combine rather than rebuild—this is what drives accelerated progress in every category.

Agents can operate continuously

Unlike the tools we are accustomed to—those adapted to narrow, guided use cases—agents hold context and work unattended over long periods. This changes the economics of automation and the volume of activity any system must absorb.

Individually, these do not constitute an argument. But together, they do.

Crypto is not dead—it's changed battlefields

When most crypto founders ask "what else can be built," they overlook one thing:

The next wave of interesting companies will not be crypto vs AI or crypto vs robotics. The founders we are most optimistic about are not choosing between these technologies but stacking them.

You are no longer just building in the crypto space. You are building crypto + AI, crypto + robotics, crypto + autonomous science.

Traditional financial rails are built around human accountability: verifiable identities, disputable intentions, and people who can be held accountable when things go wrong. Crypto rails are built around different things: auditable code, on-chain records that anyone can read, and rules enforced by the network.

When the counterpart is autonomous, this difference is no longer a flaw—it becomes key. As the volume of machine-driven activity grows, crypto-built rails are better suited to this need than those designed for humans: open, programmable, permissionless, second-level settlement, and identity without intermediaries.

The opportunity for crypto builders is not to compete with the crypto builders of the last cycle but to become the underlying foundation for the next wave of AI, robotics, and physical autonomy.

And the biggest platforms are already sprinting. Coinbase, Robinhood, and Binance have each launched agent trading infrastructure in the past few months: agents operate wallets, execute autonomously—Robinhood even built a new chain specifically for this. This is no longer a niche crypto conversation; it is happening on one of the largest retail user platforms globally.

Today's failure modes

The bet above is that permissionless, programmable rails are better suited for autonomous entities than those built for humans. This bet has not yet been proven at scale, and two failure modes have already illustrated why more work is needed:

Security: Agent wallets have become attack surfaces

In May 2026, an attacker used a Morse code-style prompt injection to make Grok output a transfer instruction, which the automated trading agent subsequently executed on-chain—transferring about $150,000 to $200,000, most of which was later recovered (SlowMist).

Accountability: Who bears the consequences of AI system failures

Even when AI, human reviewers, and governance votes have signed off, it remains unresolved who bears responsibility when a system involving AI fails. In February 2026, a bug in an AI-assisted smart contract code on Moonwell led to a $1.78 million bad debt incident—no link in the review chain captured it (rekt.news).

Three directions Wintermute is optimistic about

Currently, most activities are concentrated at the component level: foundational models, robotic hardware, stablecoins, exchanges. These markets are crowded and well-funded; the opportunity is not there.

The opportunity lies in what connects them—the rails for trading, coordinating, and trusting machines that do not yet exist. Three directions stand out:

Agent economy layer

The difficult part is not whether agents can pay, but: who holds authority when agents make mistakes? Who bears the fraud risk? How does all this reach merchants without requiring them to rebuild their checkout processes?

The form of agent commerce is still being written: authorization layers, agent identities, neutral routing between rails, markets for agents to purchase their own computing power/data/access. Here, better teams charge for authorization and risk reduction rather than a share of payment value—making the business viable before the true scale of agents arrives.

Physical AI

Robots are gaining capabilities far faster than they are gaining economic scale. A model can now generalize across tasks and different robotic bodies, and non-engineers can simply tell robots what to do to redirect them. But robots still cannot pay for their own computing power, charging, or maintenance, nor can they be compensated for the work they do.

What is lacking is not hands, but wallets. We are more focused on structured scenarios—warehousing, logistics, and retail back-end—where the economics are already viable, and real deployments exist, rather than household humanoid robots.

Machine-driven discovery

Laboratory orchestration, automated experimental design, software that closes the loop between hypotheses and results. Founders building autonomous layers for science have already been selling to materials and drug discovery labs. Quantum is the wildcard next to this direction: simulation and sensing could leapfrog what can be discovered, and post-quantum security is already a real need at the settlement layer. Hard to insure, unclear winners—but there is something here.

R[3]sidency × Construct accelerator

The infrastructure needed for the machine economy does not yet exist. This is where the work lies, and it is also where Wintermute is looking.

They want to support founders who see new problems emerging at the intersection of financial rails, autonomy, and trust—founders who can deliver products on existing rails while maintaining adaptability as standards evolve.

This is the purpose of R[3]sidency × Construct: 8 teams, each $300,000, 12 weeks in London, 30+ mentors, London and New York Demo Day. Operated in collaboration with top partners: Fabric Ventures, Solana, and Coinbase.

If you are building for a world where machines and humans trade and operate in parallel— they want to support you.