Tarun Chitra

Tarun Chitra sees crypto’s frontier narrowing from world-changing infrastructure to “TradFi plus,” but not necessarily its addressable market. If stablecoins rise from roughly $300 billion to $1 trillion, he expects lending and trading to grow more than 3x through financial reflexivity. Logan Jastremski’s sharper formulation is that crypto can remain narrowly about finance while on-chain volume still grows 1,000-fold.

The investable crypto exposure is increasingly order flow and execution, not blanket ownership of protocol tokens. Chitra argues DeFi value capture migrated from the “thick protocol” toward wallets such as Phantom and toward MEV and execution, leaving fee switches “stuck in the middle” without durable control of flow. Hyperliquid offers a comparatively legible volume-times-basis-points model; Solana and ETH have much less defined token-accrual stories.

AI agents could change market microstructure by replacing part of passive investing rather than magically beating professional traders. Instead of buying a uranium ETF, a user might state a thesis and let an agent construct and continuously rebalance a personalized basket, dispersing trades across a 24/7 market. Chitra estimates that if agent-directed retail flow reached 20-30% of volume, it could materially weaken today’s opening-and-closing concentration and the predictable arbitrage surrounding ETFs.

Chitra’s strongest crypto-AI thesis is sovereign computation and cryptography, not decentralized training for its own sake. He concedes, “I was wrong” that decentralized learning could not work, but still sees InfiniBand and network optimization as structural advantages for centralized data centers. The higher-value opening may be selective FHE, TEEs, ZK proofs, and GPU integrity attestations—privacy as a targeted feature for expensive operations, not a mass-market product people willingly pay double to use.

Open-source AI is unbundling into the same functional layers as DeFi: interfaces resemble wallets, routers resemble DEX aggregators, models resemble protocols, and inference providers resemble liquidity providers. Beneath an apparently simple interface lies an order-flow market deciding “who processes your token, who picks you a GPU, who guarantees the price.” If DeFi’s history rhymes, value could concentrate at the interface and execution edges rather than automatically accruing to the model itself.

Compute is becoming a financial commodity with spot tokens, dated futures, GPU capacity curves, and routing economics. OpenRouter reportedly takes about 5%, while inference providers compete on price, speed, latency, uptime, and hardware; some discount standard pricing by 30-40%, while others charge more for faster Cerebras-based output. Chitra expects nonlinear premiums for networked 4x, 8x, and 16x GPU clusters and sees on-chain markets as a natural venue once hardware can prove what it computed.

Third-party wrappers retain strategic value even if models become capable of generating tools and learning inside enormous contexts. Enterprises do not want to hand their workflows and proprietary context to two model companies, which is why Ramp, Cursor, Databricks, and Palantir are all building routers. Chitra’s bet is that active learning will not eliminate this market because its compute demands are “excessive,” while wrappers can preserve context, sovereignty, and model interchangeability.

🔗 Original source & video: Why AI is Unbundling Faster Than You Think with Tarun Chitra | Ep 164