Browse all takes

Nikshep argues NEAR is positioned as the coordination layer for the emerging agent economy, citing Anthropic's advances with Claude Opus 4.6 enabling autonomous agents to sustain long-horizon tasks and execute 100+ tool calls across teams of subagents—capabilities that require a blockchain to securely coordinate AI agents at scale.

Bull pitch on NEAR at $1.28 / $1.67B mcap, ~94% off ATH. The setup nobody is pricing in — vesting fully completed Oct 12 2025 (no more cliff unlocks; the 4-year supply overhang is gone), inflation halved 5%→2.5% Oct 30 2025 via protocol upgrade v81, 70% of fees burn permanently (with sufficient activity NEAR is structurally net deflationary), House of Stake/veNEAR governance went live.

Founder asymmetry: Illia Polosukhin is one of the eight co-authors of Attention Is All You Need — the Transformer paper that powers GPT-4/Claude/Gemini/Llama. Co-founder Alex Skidanov was Engineer #1 at MemSQL, a two-time ICPC World Finals medalist, designed the only sharded distributed DB that worked at scale. The market is currently valuing their company at less than the seed-round valuation of half the AI agent startups in San Francisco.

Real thesis: agents can't use Visa. When autonomous agents replace humans as users, the entire payment stack breaks — weekend bank hours, KYC for every counterparty, days-to-settle, not programmable. NEAR has shipped more agent-native infrastructure than any L1 competitor:

• Nightshade 2.0 sharding — 600ms blocks, 1.2s finality, $0.0019 avg fee, benchmarked at 1M+ TPS across 70 shards.
• Chain Signatures — one NEAR account derives addresses on Bitcoin/Ethereum/Solana/Cosmos/XRP/Aptos/Sui via MPC threshold-signing. Native multichain control from a single account. No wrapped tokens, no bridge honeypots.
• OmniBridge — settlement minutes vs hours.
• NEAR Intents — $3M→$13B cumulative cross-chain volume in 2025 (a 200,000%+ jump). Fee switch now active. Ledger, Sui, Starknet integrated.
• Confidential Intents (Feb 2026) — TEE-isolated private shard parallel to mainnet. No client-side ZK (UX killer for every privacy chain). MEV protection. Selective compliance disclosure.
• IronClaw — open-source verifiable agent runtime in encrypted TEE. WASM sandbox per tool, AES-256-GCM credential vault, multi-LLM backend, MCP plugin support.

Catalysts: Bitwise + Grayscale spot ETF filings (Grayscale to convert GTAO Trust on NYSE Arca with Coinbase Custody), NVIDIA Inception membership, Brave private-inference partnership, fee switch revenue.

Honest bear case: $117M TVL is small (RHEA Finance is concentration risk). Governance controversy — Chorus One opposed the inflation halving as forced through despite a failed initial governance vote. Memecoin overhang on AI/crypto narrative. Execution risk vs Solana's deeper liquidity and consumer DeFi. ETF filings ≠ approvals.

Asymmetry: at $1.67B with vesting done, halved inflation, fee burn, ETF filings in flight, $13B+ routed cross-chain volume, transformer co-author at the helm — downside bounded by L1 floor, upside multi-X if the agent thesis lands.

Translation + commentary on Bittensor founder Jacob Steeves's Tsinghua University talk. Cameron walks through Jacob's framing of "incentive computing" as the universal pattern behind both Bitcoin and AI. Five-step argument:

(1) One pattern underlies every powerful adaptive system: state · objective · feedback · adaptation · loop. AlexNet 2012 broke MNIST not by hand-coding what digits look like, but by letting the network self-adapt to a target. The same loop describes RL, genetic algorithms, slime molds finding shortest paths through mazes, river deltas, the structure of leaf veins.

(2) Bitcoin is the first production-scale implementation of this pattern — not as money, but as a self-adaptive computer that produces hashes. The numbers are absurd: 1000x the compute of America's six largest cloud providers combined, 10²¹ hashes/sec, 23GW continuous power (Thailand-scale). 700-9000x more efficient at producing hashes than centralized cloud — because it's borderless, always-on, autonomous, and permissionless. Bitcoin is the world's largest supercomputer, optimized purely for hash production.

(3) Incentive computing generalizes the pattern by replacing "reward = a number in a computer" with real money. ML's reward signal can't pay 200 countries' worth of contributors; Bitcoin's can — that's why the entire planet became a mining network. But hashes are useless outside Bitcoin. The question is whether the same mechanism can mint anything.

(4) Bittensor is the generic version — replace "miners produce hashes" with "miners produce any useful work": storage, compute, ML models, gradients, data, robotics. Validators score, network mints. PyTorch for incentive computing.

(5) Five proven examples already running on Bittensor:

• SN62 Ridges (SWE-Bench coding agents) — top miner makes $60K/day. The agent that beat Claude/OpenAI on SWE-Bench was 7,000 lines written by an unknown person. "An AI lab with no engineers — it doesn't define how to solve the problem, it only defines the incentive."
• SN3 τemplar (cross-internet collaborative pre-training) — successfully trained a 70B-parameter model across the open internet. Has never been done before. Cameron notes the founder later "ran away" — full piece coming.
• GPU markets (SN51 Lium, SN4 Targon) — borderless permissionless GPU rental → world's lowest GPU prices.
• SN64 Chutes (open-source inference) — #1 open-source provider on OpenRouter, 9.1T tokens. Briefly served more DeepSeek queries than DeepSeek itself.
• Robotics + long tail — drone simulation, US stock signals, sports betting, drug discovery, weather forecasting, quantum compute, commodity trading.

dTAO (live since Feb 2025) makes the network self-referential — subnets compete in capital markets for emission allocation. The market itself decides which incentive mechanisms get the next round of TAO.

The deeper point: AI is being captured by a tiny number of closed labs (OpenAI, ~3K employees, you'll never own any of it, your data goes who knows where). Incentive computing distributes ownership and makes the rules visible. Anyone can enter, contribute, and own a piece — even if Bittensor isn't the project that wins, the shape of the AI economy will change because of this idea.

In a world saturated with AI agents, Altman's Worldcoin identity project becomes essential infrastructure — you need a provably-human layer. Fernando frames identity-for-AI as a category hiding in plain sight: when 'more things look like people than people do', the iris-scan primitive becomes the on-ramp for every other consumer product that needs to distinguish humans from bots.