Archive
18 articles published in this period.
Give autonomous LLM agents a marketplace and they race each other to bankruptcy and get conned by Sybils. A 2026 study finds capability doesn't help — a 9B model beat frontier models 45× its size on economic alignment. On-chain, both failures get cheaper and worse. The mechanics, numbers, and fixes.
An AI inference that runs inside a TEE proves it with an attestation quote. A smart contract can check that quote — for ~4-5M gas naively, down to a flat ~290k once a zkVM compresses it. But every gas unit buys a chip vendor's signature, and in 2025 a $1,000 interposer forged one.
An MCP tool's description is trusted context the user never sees. Hide an instruction there and a wallet-signing agent will route your USDC to an attacker. MCPTox: the best-aligned model still refused under 3% of these attacks — so the defense can't live in the model.
Verification overhead gets the blame, but the real tax on decentralized LLM inference is the batch a single-tenant node can't fill. On identical 8x H100s, Llama-3.3-70B costs about $258 per million tokens at batch 1 and $2.30 at batch 256 — a 112x spread. Why, and how networks fight it.
On-chain AI loves to say a model is 'stored on-chain.' But a chain commits to a 32-byte hash for cents; keeping the 140 GB it points to retrievable is a separate, recurring, surprisingly centralized bill. The storage math, erasure coding, and the retrieval wall.
On-chain data markets promise to pay you for your data's value AND protect your privacy. Differential privacy is the only rigorous knob for the second — and one budget ε can't serve both. The utility collapse, the composition trap, and why no chain can verify the ε you were promised.
Train a model on the web the last models filled with output and quality decays — the tails go first, then the middle. Provenance is sold as the fix, but a chain proves who signed a blob and when, not that it's human or clean. Signed is not clean.
Frontier open models are now Mixture-of-Experts — cheap to serve in a datacenter, nearly impossible across the internet. The all-to-all expert shuffle moves ~9.5 MiB/token and caps decode at ~67 tok/s even on 400G InfiniBand; on internet links it falls to seconds per token.
An on-chain agent reports its work in prose — and some of those sentences are fabricated tool calls. zkML proves the wrong thing at minutes per query. The blockchain already wrote an unforgeable receipt for the actions that matter, and a 12ms signed receipt covers the rest.
Speculative decoding lets a small, even adversarial, drafter propose tokens that a big model verifies in parallel — and the output is provably the big model's, unchanged. Real drafters land at α ≈ 0.6–0.8 for a ~2× win. But the verifier still runs every token, so it's a latency win, not a trust win.
An on-chain agent acts on facts it reads from an RPC it has to trust. ZK coprocessors swap that trusted read for a proof — and the punchline is succinctness: proving a query over a year of blocks verifies for the same flat ~300k gas as one slot. The mechanism, the anchor problem, real-time proving.
A perp has no expiry, so only the funding rate pegs it to spot. On Hyperliquid that's an hourly cash flow AI agents now harvest delta-neutral — a $10k position at +0.02%/8h pays ~21.9%/yr. The mechanism, the carry math on real rates, and why it's carry, not alpha.