Ep. 035 - Tech DD’s, Performance Projections, Supply Chain, Investment Thesis (Consulting)
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.
Crusoe CEO: Why Everyone Gets GPU Depreciation & AI Energy Costs Wrong
Harry StebbingsChase Lochmiller
Crusoe says AI infrastructure will follow cheap, available power, targeting roughly 200 MW in Abilene within one year versus 2.5 years elsewhere.Vertical integration cut medium-voltage delivery to 28 weeks from a 100-week quote, while five-year GPU contracts and managed services diversify cash flow.Rising Hoppers usage prices challenge simple depreciation assumptions, but labor, community disruption, and uncertain IPO timing remain risks.
The Future of Frontier Model Architectures with Walter Goodwin, Founder & CEO of Fractile
Fractile is betting frontier inference will be constrained by memory bandwidth and cost, not compute, shifting from SRAM to a DRAM architecture expected to be fully operational in the second half of next year.Goodwin claims 25 times more bandwidth per chip than an HBM-based design, potentially making sparse MoE models economical, while its 150-person team targets a three-to-six-month lead; foundry cycles, ramps, and three-to-five-year amortization remain constraints.
The $10T AI Buildout Has a Photonics Problem
Molly O'SheaHerwig Van HoveYannick De Koninck
AI’s next bottleneck is the interconnect fabric: models no longer fit on one GPU, while agentic calls make latency critical, putting photonics alongside compute as core infrastructure.Optical bandwidth addresses copper and power constraints, but lasers, tools, substrates, throughput, and yield remain scarce as NVIDIA’s demand shock runs ahead of supply; Themaa targets 2027 production and 2028 full ramp.
当AI开始设计芯片---聊聊EDA与AI时代的芯片设计
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。
Eclipse's Lior Susan on $12.5B AUM and the Bet on Physical Industries
Eclipse is targeting the physical economy—about 85% of global GDP, or $100 trillion—where deglobalization, supply-chain vulnerability, government support, and customer demand are reopening neglected opportunities.With about $12.5 billion in AUM, roughly 90 portfolio companies, and 30 it helped build, its operators-with-capital model links CapEx, manufacturing, policy, and systems execution, while its free-cash-flow focus and non-formulaic incubation leave execution, timing, and repeatability as key variables.
Meta's Dina Powell McCormick: The Case for Data Centers, Backlash, AI Job Boom & Meta’s Future
Meta’s multibillion-dollar Richland Parish project lifted sales-tax growth from 5–10% to a 260% peak and certified employees’ checks from $10,000 to $50,000.Louisiana shifts generation, grid resilience, upgrades and storm costs to Meta; the Academy produced 250 graduates and 90% retention from 40,000 applicants, while Google and BlackRock support scaling amid backlash and Cotton and Warner’s adversary theory.
E251|推理芯片之战:聊聊Groq、Cerebras与OpenAI三大路径与Bill Dally的设计哲学
推理竞争正从峰值算力转向每百万Token成本,decode反复读取全模型,使带宽成为核心瓶颈,SRAM的带宽经济性因此显现。Groq依靠编译期调度,Cerebras依靠晶圆级集成,但MoE路由、通信、良率、供应链与动态增长的KV cache仍是规模化验证的关键风险。
Jensen Huang: The Doomer Hoax, Superintelligence is Here, and The Future of AI (ft. President Trump)
Jensen Huang cites failed forecasts on radiology, AI-generated code and entry-level jobs to challenge doomsday narratives, while arguing that AI incidents so far call for root-cause engineering and independent evaluation rather than sweeping regulation.With 80% of AI-native companies receiving $400 billion in recent venture funding using open models, NVIDIA is building infrastructure bottlenecks as Jensen predicts China will reach advanced lithography by 2030, leaving commercialization, lab controls and data-center execution to monitor.
52. 智力的价格:AI 研究员凭什么比 NBA 球星贵?- 高岱恒|Dinq
AI人才正在被代表作而非履历重新定价:硅谷总包过亿美元者或仅几十人,Meta可将年总包50万—100万美元的人抬至近5000万。多模态算法边际价值下行、OPD与基础设施上行且技能半衰期或仅18个月;DeepSeek十年vesting和禁写博客暴露留才机制的风险,Dink的PLG与企业招聘定位仍待验证。









