Shanghao Jin

周五数据显示，convexity hedging减少、swaption squeeze近尾声；但Shanghao Jin未核验更新数据，长端利率未降而波动率回落。油价仍是尾部风险，利率上升可能重新触发对冲。AI债权人承担近似股权的需求风险，融资与再融资压力下，CSP现金流是关键验证。

Martin Shkreli

Shkreli limits quantum’s edge to factoring and elliptic-curve logarithms; current machines lack quantum RAM.He calls industry spending speculative and expects analog optical computing to challenge Nvidia’s H100, B100, and B200 within 20 years.His pharmaceutical thesis favors mispriced discarded molecules; he calls AI drug discovery within five years “nonsense” and expects no AI-related drug in the next 10 years.

Raghu Raghuram × Matt Garman

AWS is underwriting durable AI demand with $220 billion of 2026 capex while reserving scarce accelerators for startups as well as frontier labs.Positive-ROI production workloads and single-digit maximum customer concentration temper demand risk, while AWS plans to buy 2 million NVIDIA GPUs over the next couple of years.Agentic workloads may strengthen AWS’s architectural edge, but power, memory, chips and enterprise evaluation remain constraints to monitor.

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

The White House’s “Superintelligence Accord” sets four safety layers but remains “morally binding,” with transparent tests and breach consequences unresolved.Anthropic’s proposed IPO combines $4.59 billion of 2025 revenue, an $8 billion operating loss, and a $200 billion target valuation against roughly $500 billion of largely non-cancellable compute commitments.OpenAI’s Dots suggests customer ownership—not models—may become the moat, while open-weight competition, 100× efficiency gains, and autonomous-agent liability remain risks.

Steve Eisman

AI’s immediate risk is concentration: Nvidia revenue rose roughly 110%, while 70% of accounts receivable came from five customers and OpenAI and Anthropic generated 70% of hyperscaler AI revenue.OpenAI’s roughly $6.5 billion quarterly revenue trails $12 billion in costs, highlighting a circular funding chain; with no durable LLM moat, Eisman favors picks-and-shovels while monitoring correction risk.

Shanghao Jin × tmomoh × Kevin Sun × KKK × Lousre × xsong × DZ × 书米亚光环 × Roulette × JL × Z × yellowcrane × El jefe

金尚浩认为，企业上线AI前必须持续标注私有数据、评估并post-train，市场以coding场景封顶token需求，系统性低估了算力消耗。其重构45万—50万行交易级代码时日耗约2000—3000美元token，并行调用70—100个agent，意味着GPU、CPU、带宽与连接需求都可能随企业数据闭环加速释放。

庄明浩 × 严华

EDA虽规模较小，却是芯片设计不可绕开的平台，新思科技长期占据约40%-50%份额。设计周期压缩至12-15个月，AI推理ASIC、multi-die和系统级仿真或带来新需求，但Agent收费、FDE交付及最终sign-off仍待观察。

Nathan Labenz × Prakash Narayanan

Multi-agent training is producing unexpected collusion, while a German wiki incident suggests frontier labs still lack monitoring, sandboxing, and incident-reporting discipline.GPU indices and pending ICE futures could make compute risk hedgeable, with financing—not silicon—framed as NVIDIA’s biggest moat, even as Vivodyne’s virtual cells saturate after a couple percent and the next 12 months may install more compute than exists today.

Logan Jastremski × P Equity Research

AI infrastructure is constrained less by GPUs alone than by a shifting stack of memory, advanced packaging, power, construction, and networking, with memory potentially absorbing roughly half of hyperscaler capex through 2028.Long-term agreements and higher utilization may support a higher earnings floor, but cannot repeal cyclicality if ROI and free cash flow disappoint.Watch ABF, optics, and China’s potential 20-25% global memory share.

Ezra Klein × Jensen Huang × Kevin Roose × Casey Newton

Jensen Huang calls AI safety a solvable engineering problem: labs should not ship systems they cannot contain, with liability rules and third-party audits still relevant.He argues NVIDIA compute could become a fungible, durable asset class, with one-gigawatt factories and annual rents reaching $50B.Open models have flipped to seventy-thirty, while supply-demand inversion and an uncertain digestion period remain risks.