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.

Peter Diamandis × Salim Ismail × Dave Blundin × Alexander Wissner-Gross × Emad Mostaque

Memory and reserved compute—not model quality alone—have become AI’s binding bottleneck, with an NVL72 order moving from $3.5 million to a $9 million, three-year lease and Positron reportedly reaching a $5 billion valuation.Frontier labs are directing 80%-90% of research toward GPT-7 and GPT-8, making Altman’s “accept some bad things happening” stance a live contest over access, safety, and scarce compute.

Jeff Yan

Hyperliquid is positioning composable primitives such as Portfolio Margin as neutral public infrastructure for institutions, not a replacement venue.Real-world-asset open interest rose from $300 million in January to $3.9 billion, while HIP-3 could extend it into regulated markets with opt-in controls.Options are the clearest unmet opportunity via HIP-4, but timing is uncertain and some primitives may never take off.

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.

Patrick O'Shaughnessy × Andrew Huberman

Sleep is the near-term neurotechnology wedge because users already accept physiological measurement, while an eye mask reportedly measures REM, induces sleep-promoting eye movements, and amplifies REM, with related technology expected in roughly seven to 12 months.Huberman predicts Meta, OpenAI, Anthropic, and Elon’s Neuralink effort will converge on neural read/write biotech, but missing models of memory and thought, plus a possible 2027-28 downturn in optimization content, remain key risks.

Peter Diamandis × Astro Teller

X says the cost of producing a graduate has fallen roughly threefold over 16 years, while its funnel yields only two graduates from up to 200 code-named ideas annually.Protected governance and early kill discipline make that portfolio economically plausible, but AI cannot replace human-led acceptance and distribution, while clean water still must reach roughly one cent per liter and industrialization remains 95% of the work.

Jordan Nanos × Abhilash Jain

SemiAnalysis Consulting is turning bespoke AI-infrastructure diligence into scalable products, including a token-supply simulator linking chip deployment, workload allocation, throughput, and token pricing.NeoCloud underwriting centers on contractual survivability: 99.9% availability expectations, sub-90% performance, and mismatched 15–20-year leases versus five-year GPU contracts can threaten financing and trigger remedies, while capacity timelines determine whether clients build or lease.

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.