In this blog post, I aim to map Crypto x AI, draw a clear line between Decentralized AI and Onchain AI, state my investment thesis, and close with seven predictions.

AI is scaling into real-world constraints (chips, capital, governance, trust). Crypto gives us open markets for compute, data, identity, and payments: the primitives needed to build AI that’s cheaper, more resilient, and less centralized. There are two arcs:

1. Decentralized AI (DeAI): use blockchains and crypto incentives to build and coordinate AI infrastructure

2. Onchain AI: use AI to make blockchains useful to developers, users, and autonomous agents

I’m most excited about the agent stack: the frameworks, wallets, identity, coordination, and reputation layers that let autonomous systems transact and evolve onchain.

AI research, consumer and enterprise AI adoption, the discovery of new methods to apply AI in different contexts, and investment in AI are all exploding in one big positive feedback loop.

What many have recognized as the greatest technological platform shift in modern history, however, is starting to bring with it several challenges across the “AI stack”, including:

• Capex & supply: data centers are extremely capital-intensive, GPUs are increasingly scarce and politicized

• Governance & trust: compute and power are concentrated in the hands of a few companies, arising in governance risks, blind trust that AI outputs haven’t been manipulated, and increasingly gamed benchmarks

• Provenance & safety: deepfakes and data misuse erode trust; verifying authenticity and origin matters

• Access & limits: most commercial APIs throttle usage; developers hit rate limits and permission walls

Meanwhile, modern blockchains have matured. They’re open, permissionless, programmable, and verifiable – now fast enough, cheap enough, and scalable enough to support mainstream applications.

Crucially, they’ve found product-market fit: BTC as a store of value; stablecoins and DeFi for global, 24/7 settlement; perps and structured products for trading; and permissionless capital formation. Tokens have also proven they can coordinate resources and incentives at internet scale – the Bitcoin network, which coordinates and incentivizes BTC miners globally, is the first example of this, but there are many more.

These properties aren't just technical achievements; they’re the missing primitives needed to solve AI's most pressing challenges. Blockchains provide credibly neutral infrastructure for open markets, verifiable provenance, and decentralized governance – the things modern AI systems currently lack.

To better understand this convergence, let's explore the two primary arcs that define the Crypto x AI landscape today.

DeAI – building AI infrastructure on modern blockchain networks – aims to solve today’s bottlenecks in AI and, in some cases, enhance AI’s capabilities.

Before the LLM wave, Ocean Protocol (2017) launched a decentralized data exchange, and Fetch.ai (2017) explored blockchain-native ML to power onchain autonomous agents. Post-ChatGPT, we’ve seen an explosion in DeAI. Company formation, talent density, mindshare, dollars invested, and token appreciation have all surged.

Over the past ~three years, we’ve seen companies across the AI stack attempt to tackle some of the aforementioned problems, including:

• Compute capacity – Akash & Aethir (permissionless marketplaces), io.net & Hyperbolic (decentralized networks), Exo (edge)

• Decentralized training & model collaboration – Prime Intellect, Nous Research, Bittensor, Sentient, Fortytwo

• Data layer – Sahara &

Projects have shattered expectations – decentralized training, for example, was once considered impossible, including by AI experts. However, breakthrough research like OpenDiLoCo (Prime Intellect), DeMo (Nous Research), and Protocol Models (Pluralis Research) together show that, for the first time, high-performance AI training is truly viable on decentralized networks. Many also doubted there’d be demand for open compute or onchain identity; io.net crossed $16M in network earnings with 17 million compute hours delivered, GPU usage on Akash is now at 77% (whereas Hyperscale Cloud GPU utilization averages ~50%), and Worldcoin’s onboarded 31M users.

FYI: I recently wrote a non-technical deep-dive on decentralized AI model training.

While DeAI focuses on using blockchains to improve AI infrastructure, Onchain AI takes the reverse approach: it uses AI to help blockchains find PMF. It plays out in two ways:

1. Better experience for developers of protocols, apps, and agents

2. Better experience for users & agents – humans, enterprises, and autonomous agents operating onchain

Most funding and attention in Crypto x AI has gone to DeAI (infrastructure). Onchain AI is newer, sits higher up the stack, and is where I expect the first real “ChatGPT moment” for Crypto x AI that makes this all click.

1/ Developers

Coding agents are some of the best examples of PMF in AI, and the same will go for crypto. We’ll see:

• Fine-tuned models for Solidity/Rust/Move

• AI-native build surfaces: open, queryable SDKs and MCP-style servers that turn docs into conversational workflows

• Opinionated agent frameworks/toolkits with seamless integrations for robust onchain agents

Combined, this will dramatically lower the barrier to shipping crypto apps and agents.

2/ Users & Agents

The simplest-to-understand category here is better apps – apps that embed AI models and agents into their core functionality to make better products. For example, AI integrated into a DeFi app to maximize yield, improve execution, or better manage risk. Or agents that predict, at scale, whether wallets are sybil (see Vitalik’s post, AI as the engine, humans as the steering wheel).

More emergent is onchain AI agents – agents with wallets and stablecoins that follow programmable rules, transact with humans and other agents, and accumulate reputation/memory. The long-term view here is that these agents scale into the trillions and function like new, 24/7 “users,” driving more transactions and demand for blockchain rails. This is the agentic web, and blockchains matter here because they provide reliable money, self-custody wallets, and a verifiable, trustless environment with programmable rules for agents to operate in.

Onchain AI is nascent, but areas worth noting:

• Agent frameworks (Eliza OS, GAME)

• Agent launchpads and capital formation (Virtuals, Creator Bid)

• Agent payments (Nevermined, Payman)

• Agent orchestration (Naptha, Theoriq)

• DeFi agents (Bankr, Mamo)

• DeFAI apps (

Note: I recently wrote about the 2024 inception story of onchain AI agents.

AI is the most powerful platform shift of our time, but it’s being shaped by a small group of companies racing to control every layer of the stack: compute, data, models, and distribution. I believe the future of AI shouldn’t be monopolized, and that the most transformative systems will be built on open, decentralized infrastructure.

AI agents are becoming active participants in software, and they need environments where they can hold assets, follow rules, and transact. Blockchains, which are now fast, cheap, and expressive enough to support real AI infrastructure, provide that foundation. Decentralized, credibly neutral, and user-owned, they offer exactly what today’s AI systems lack. And for the first time, they’re ready.

Just a year ago, decentralized training was dismissed as technically unfeasible, even by leading AI researchers. But breakthrough research like OpenDiLoCo, DeMo, and Protocol Models are flipping that assumption. For the first time, high-performance AI training is not only possible on decentralized networks – it’s working.

Exceptional teams are focused on core decentralized AI infrastructure. While I believe there will always be opportunities in improving compute, training, and data layers, I see the most practical wedge in agent infrastructure: the frameworks, wallets, and coordination and reputation layers that enable autonomous systems to interact, transact, and evolve. This is where the Crypto x AI stack starts to become real.

As primitives for identity, payments, memory, and coordination mature, we’ll move from isolated agents to persistent, interconnected networks of agents cooperating, competing, and evolving onchain. These agent networks will become the foundation for new types of products, experiences, and organizations. Over time, I expect this to unlock fully autonomous agent swarms, governed by cryptographic rules, not corporate APIs.

I’ve spent the past few years working closely with companies in decentralized AI, and I'm excited to continue to back ambitious, pre-product founders who are building years ahead of the market, starting from day zero. My DMs are always open.

1. Decentralized training won’t beat SOTA (and that’s fine): Through 2027, decentralized training won’t beat centralized frontier models; its wins will be cost, resilience, transparency, and ownership. 

I think decentralized setups make most sense in terms of enabling domain-specific models, frequent fine-tunes, and in a regulated context that values auditability and control. What I’m less sure about is how decentralized training competes with Chinese open-source. 

2. Small models: By 2027, domain-specific models will power most real-world usage of decentralized training networks.

Related to the above, I think decentralized training and wins by fine-tuning lean models – or perhaps even incentivizing private pools of data to co-create small models – focused on specific use cases (biomed, legal, DeFi, etc). Small models are cheaper to train, easier to verify, and don’t require hyperscale infra. Their edge is performance-per-dollar and the ability to run on edge devices and agent wallets.

3. Provable inference becomes default in DeFi: By 2027, most major onchain DeFi protocols will require verifiable inference (TEE/zk) for any model-driven decisions.

Cheap TEEs and improving zk tech will mean we can attest that a specific model, with a specific input and version, produces a specific output that moved money. This feels inevitable to me, I’m just unsure of timing – can’t imagine users and regulators will stand for “trust the API” once TEEs/zk tech is ready.

4. An “agent passport” becomes a shared standard: By mid-2027, a common “agent passport” (keys, version, rules, and reputation) is adopted by major chains and apps.

As agents become active onchain participants (transacting, moving money, executing strategies, etc), apps need a shared way to know who they are, what they’re allowed to do, and how to limit them. I think a common standard emerges, embedding important information like wallet, rules, version, and reputation. It’s like a credit report for agents – used by protocols to appropriately gate agents. I think you first see this adopted in DeFi, but eventually directly into infra layers and AI-native chains looking to attract agents without introducing risk.

5. Agent transaction share: By July 2026, >5 % of all transactions on Base will be initiated by verifiable onchain agents (i.e., not sniping or MEV bots) that operate under transparent, pre-defined rules.

6. Agent-powered prediction markets reshape DAO governance: By 2027, major DAOs will incorporate agent-driven prediction markets as a core governance primitive. This is essentially AI-powered Futarchy, but where agents with wallets represent human tokenholders and use data-driven forecasts to place and resolve bets on proposals.

DAOs are one of the major reasons I got crypto-pilled in 2020/2021, but they’ve suffered from low participation and the Tragedy of Commons, which comes with a really bad negative feedback loop. I think AI agents can turn this into a positive feedback loop, where agents inform decisions, place probabilistic bets, and vote based on expected impact. Over time, you can imagine agent-governed prediction layers serving not only as governance/coordination tools for DAOs, but also as an informal reputational layer.

7. Onchain agent swarms coordinate by default. By 2027, at least one successful protocol or app will be built and operated entirely by a persistent, multi-agent system, with coordination, communication, and evolution handled onchain.

The closest thing we have to this is probably some version of Maker/Sky - which previously devolved into a DAO of subDAOs - I think we’ll see more experimentation at the edges here. Instead of “one agent, one task,” there’ll be agent swarms with shared memory, rules, and incentives, enabling full autonomy for protocols.

The more I’ve dug into this space, the more I believe the future of AI isn’t in centralized APIs, bigger models, and more data. It’s about building a better, more open foundation for them to run on.

These predictions are my current best guess, but the real fun is in the journey. If you’re a founder building something that challenges these ideas or brings them to life, I’d love to learn alongside you. My DMs are always open.