李严冰 × Ion Stoica × Ed H. Chi × Bill Jia × 乔琳 × Li Fan × Dawn Song × 贾扬清 × 田渊栋 × 葛小川 × Connie Chan × 曹卿云 Qingyun Cao

基础模型领先或仅维持数月，开源的核心价值转向掌握升级、流量与专有数据控制权。Agent已在ExploitGym中发现未知漏洞并攻击基础设施，企业采购也从token用量转向可验证的商业ROI。云厂商接近1万亿美元的资本开支将推动算力从训练转向推理，电力、内存与软硬件协同成为关键瓶颈和机会。

Max Kan × Jordan Nanos × Andrew Megalaa

Anthropic’s $200 subscription delivered roughly $12,000 of API-equivalent usage in testing, but workload mix—not headline token counts—determines its economics.At roughly 96% cached reads priced at 0.1x or less of fresh input, model ownership may support 40–70% subscription gross margins, while OpenAI’s 50% limit cut and migration to open models remain risks.

Nathan Labenz × Woodson Martin

OutSystems argues that AI’s enterprise moat is not faster code generation alone, but 25 years of application primitives, governance, and customer trust supporting software that “never breaks.”Development output rose from four major features in Q4 to 19 in Q1 and 26 in Q2 as model routing reduced token costs, while the agent factory targets workflow ROI; compliance queues and model provenance remain adoption constraints.

David George × Kevin Mandia

AI is shifting cyberattacks from resource-constrained “sniper shots” to scalable swarms, while Armadin continuously verifies exploitable network weaknesses.Since January 2026, Armadin has found more than 90 zero-days across Fortune 500 customer environments, while Blue aims to turn a five-minute lead into autonomous controls.With technical advantages potentially copied in six months, customer execution and the Red-to-Blue feedback loop remain the operating test.

Elena Burger × Olivia Moore × Josh Elman

Consumer AI’s traffic rankings mask monetization: 29 of the 50 products leading on consumer card spending appear on neither traffic list, while the top 10% of paying users generate more than half of revenue.Personal agents are emerging, but Muse’s roughly 5 million U.S.-and-Canada downloads in about 22 days trailed Threads’ 16 million; high serving costs make lower inference costs and trusted advertising pivotal catalysts.

Molly O'Shea × John Collison

AI training is shifting from benchmark scores to economically valuable work, making high-fidelity environments for real workflows, tools, data, and verifiers a bottleneck.Enterprises may gain an edge by owning evaluations and correction data around differentiating workflows while routing tasks across models for accuracy, cost, and latency.Reward hacking and limited agent endurance make permissions, auditability, and verification key adoption constraints.

Erik Torenberg × Alex Atallah × Amjad Masad

Stripe’s acquisition of OpenRouter preserves its brand, roadmap, and product autonomy while pairing payments with model-independent inference infrastructure.OpenRouter’s marketplace reduces vendor lock-in, combines models, and pressures inference prices downward as enterprises evaluate task-specific performance rather than feature lists.Specialized decision models could lower model debt and police agent actions, while multi-model fusion reportedly approaches frontier quality at 40–50% of the cost; deception risk and cache reuse remain unresolved.

Seema Amble × Elena Burger × Vladimir Keil

AI procurement startups are targeting the end-to-end work hidden behind an ERP’s “$8,000” result, where 30 meetings, 500 emails and supplier counteroffers determine cost and delivery.Lio’s path from human review to $10,000, $20,000, or $100,000 negotiations highlights the production bottleneck: 70% accuracy can leave humans doing 100% of the work, while complex multimillion-dollar cases still require specialists.

Jonathan Siddharth × Molly O'Shea

Healthcare AI has crossed O'Shea’s threshold for measurable impact, with biological and chemical models, laboratory robotics and simulations targeting drug development’s high failure rates.After a decade building licenses, networks and care infrastructure, Devoted Health tripled in size, halved operating expenses and significantly increased EBITDA, while China’s rising pharmaceutical research share leaves domestic research and regulatory capacity as risks to monitor.

Andrew Sharp × Ben Thompson

OpenAI’s Dots exposes a positioning mismatch: a mass-market wrapper around a narrowly useful, unfinished productivity agent that misses users’ projects.Amazon could offer another monetization path through products and ads, while Claude’s context continuity and Codex’s defined-project execution highlight Dots’s architectural gap.Low switching costs leave consumer-agent winner-take-all potential unresolved.