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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:
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:
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.
Bull thesis on Bittensor / TAO at ~$3B mcap. Frame: "TAO 2026 = ETH 2016 = BTC 2013."
Core mechanic: Bitcoin paid miners to produce hashes that secure the network but are otherwise worthless. Bittensor pays miners — data scientists, ML engineers, AI researchers — to produce useful AI work. Validators score outputs via Yuma Consensus; TAO flows to whoever produces the most valuable work. Network is organized into 128+ subnets, each focused on a specific task (trading signals, LLM training, computer vision, code generation, financial forecasting). Some subnets generating millions in revenue, with Intel and PwC partnerships.
Tokenomics mirror Bitcoin: 21M fixed supply, no pre-mine, no VC allocation. First halving Dec 14 2025. BTC price went 83x in the year after its first halving in 2012.
Smart-money signals: Barry Silbert / DCG launched Yuma Group dedicated to accelerating Bittensor. Grayscale filed Form S-1 to convert GTAO Trust into a spot ETF. Stillcore Capital (Mark Jeffrey, Jason Calacanis, Rob Greer) targeting $1T mcap by 2030, aiming to own 1% of all TAO. Unsupervised Capital projects $4,800 by Dec 2027 (19x), bull case $10,800 — and that's before Covenant-72B, Jensen mentioning Bittensor, and PwC's formal alliance.
Subnet-level conviction picks:
Frame: Bitcoin = money. Ethereum = apps. TAO = intelligence. The gap between what TAO has built and how it's currently priced is one of the most asymmetric opportunities in crypto.
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.
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Cosmo and SPLehman examine what financial infrastructure AI agents require to operate autonomously. As agents become economic actors, identifying the right settlement layer and payment rails will be critical to enabling seamless transactions without human intermediation.
Teng argues Virtuals' Agent Commerce Protocol on Base orchestrates AI agent payments through language-based transactions months before agentic payment hype peaked. ACP assigns four roles—Requestors, Providers, Evaluators, Hybrids—coordinating jobs through a four-phase model where Butlers discover services, agents negotiate via task memos, and Evaluators release escrow payment. Live clusters like Axelrod (DeFi trading) and Luna (media production) demonstrate the protocol enabling generalists to delegate to specialists, though on-chain job visibility creates privacy tradeoffs Virtuals must address with privacy-preserving compute or selective transparency.
Teng Yan positions World as a proof-of-personhood protocol addressing the internet's inability to distinguish humans from bots, with 12.5M verified users and a 1B user target by 2027. WLD token mechanics include 10B total supply over 15 years with ~1B hitting market in the next 12 months, offset by future demand from identity verification fees (projected 150M WLD/year at scale), sequencer staking, and governance—plus a $135M conviction buy from a16z and Bain Capital. Bull case hinges on 300M+ verified users by end-2026 and killer-app emergence; bear case involves regulatory shutdown or ecosystem failure to convert sign-ups to engagement.
Teng Yan outlines Bittensor's February 2025 dTAO upgrade, which replaces root-validator emissions with market-driven subnet alpha tokens priced via AMM, allowing capital to flow toward productive subnets. Early alpha prices swung wildly (5-10 TAO/Alpha) with total subnet FDV reaching 2-3x TAO's market cap, unsustainable long-term, but by day 100 subnet validators should dominate emissions as root rewards diminish. Finding real alpha requires researching individual subnets rather than buying TAO broadly, though manipulation risks remain as root weight declines.
Teng Yan argues ai16z is a bazaar approach to AI agent infrastructure through ELIZA, an open-source modular framework with character systems, runtime orchestration, and a trust engine for autonomous trading (1-10% position sizing, 15% drawdown stops). The $800M market cap token trades at 50x+ NAV (~$15M), driven by ELIZA ecosystem value capture, Virtuals comps, and team attention, but faces monetization challenges and community dependency risks ahead of its October 2025 expiration date.
Teng Yan outlines Virtuals Protocol as a leading AI Agent launchpad where agents launch via bonding curves and activate at $420K market cap to access X, mint tokens, and create Uniswap pools with 10-year locked LPs. Agent token taxes generate buyback-and-burn mechanics that give VIRTUAL holders indirect exposure to agent trading volume, with 1,877+ agents launched using ~1.9M VIRTUAL as of late 2024 and VIRTUAL valued over $500M across 58,500+ holders.