Key Takeaways
- Vitalik Buterin says the “race to AGI” narrative is a misleading frame that encourages undifferentiated acceleration and winner-takes-all thinking.
- He lays out four near-term goals.
- The “better markets” piece lands as U.S. regulators rethink event-contract oversight and courts clash with prediction markets over sports contracts.
Ethereum co-founder Vitalik Buterin is pushing back on Silicon Valley’s favorite storyline: that the future is a race to build artificial general intelligence (AGI), and that “winning” is mostly about getting there first.
In a Feb. 9 post on X, Buterin said the “work on AGI” framing “contains an error.”
He argued it treats AGI as an undifferentiated status project, not a design space with real choices about power and safety.
Buterin wrote after Solana co-founder Anatoly Yakovenko suggested it would be net-positive for the world if Buterin “worked on AGI instead.”
Instead, Buterin argues for choosing a direction for AI development, one that protects human agency and reduces catastrophic risk, while integrating crypto and AI perspectives rather than treating them as separate philosophies.
The Roadmap: Four Buckets, Not One Finish Line
Buterin points readers back to his 2024 essay on crypto + AI intersections.
The Ethereum founder then updates his emphasis toward practical, shorter-term infrastructure—work he says is more aligned with “defensive acceleration” than an abstract AGI sprint.
He summarizes the agenda as a 2×2 matrix (Infrastructure → Impact, Survive → Thrive) with four clusters:
1) Private, Trust-Minimized AI Interaction
This is about reducing the default tradeoff most users face today: powerful AI services in exchange for identity trails and blind trust in servers.
Buterin lists local model tooling, privacy-preserving payment patterns (including the idea of unlinkable payments for API calls), and client-side verification of assurances like proofs or trusted execution environments (TEE) attestations.
The broader tech world is already sliding toward “local-first” AI.
Apple, for example, has published details on on-device and server foundation models built into Apple Intelligence.
The giant explicitly positions privacy as a design goal.
2) Ethereum as an Economic Layer for Agents
Here Ethereum’s role isn’t “run the model on-chain.”
It’s to provide rails for agents to coordinate economically.
This includes paying for services, posting deposits, hiring other bots, and escalating disputes when something breaks.
Buterin points to ERC-8004 (“Trustless Agents”), a proposed standard that aims to let participants discover and interact with agents “across organizational boundaries without pre-existing trust,” enabling open-ended agent economies.
3) Local LLMs for ‘Don’t Trust; Verify’
Buterin revives an old crypto dream—“don’t trust; verify”—and admits why it failed: humans won’t verify everything.
His fix is to have local models do the verifying: proposing and checking transactions, auditing contracts, interpreting formal verification artifacts, and reducing reliance on third-party UIs as trust chokepoints.
4) AI-Amplified Markets and Governance
Finally, Buterin argues that decision markets, prediction markets, quadratic voting, and other governance mechanisms have been limited by human attention, not by theory.
He claims LLMs can scale judgment, letting societies revisit coordination tools that previously collapsed under cognitive load.
That argument arrives during a live regulatory reset in the U.S.
On Feb. 4, the Commodity Futures Trading Commission (CFTC) withdrew its 2024 proposed rulemaking on “event contracts.”
It also withdrew a related staff advisory on sports event contracts, stating it does not intend to finalize them.
At the state level, a Massachusetts judge ordered prediction-market operator Kalshi to stop offering sports-event contracts within 30 days.
Kalshi must obtain a gaming license to continue offering these contracts in the state.
Rails, Not Rulers
Buterin isn’t claiming Ethereum “solves AI.”
He’s arguing that as AI becomes more agentic, the critical battle is over constraints.
These constraints include privacy, verifiability, user-side control, and decentralized coordination.
The “race” story—fast, centralized, status-driven—picks a direction by default. His alternative is trying to choose one on purpose.
PhD, researcher and writer exploring AI, blockchain, and the philosophy of tech, with a focus on DeScAI, governance, and trust.
