Invest Like the Best

Patrick O'Shaughnessy × Eric Vishria

AI is likely to produce an oligopoly plus $100B specialists, not a single winner-take-all lab, while Fireworks shows inference’s hidden moat: roughly 5X speed and multiple-X throughput on the same models and NVIDIA hardware.SaaS incumbents now face “Get to AI or be worth three times revenue,” as migration becomes easier and cost, iteration speed, and transportability matter more, with energy—especially China’s roughly tenfold buildout next year—the key constraint.

Patrick O'Shaughnessy × Gavin Baker

Gavin Baker argues that the AI selloff lacks a clear demand break: GPU rental prices, DRAM, tokens, and inference usage are accelerating, while open source shifts margins toward infrastructure rather than eliminating compute demand.Credit and regulation are the real catalysts to monitor, but expiring contracts could reprice installed GPUs sharply higher; Baker also sees SpaceX as an underappreciated compute platform, contingent on power, financing, and political acceptance.

Patrick O'Shaughnessy × Sam Altman

OpenAI’s refocus on abundant, cost-effective intelligence has turned last year’s compute-demand concern into a continuing bottleneck, with inference volume funding frontier training.Altman says GPT-5.6 is “very AGI-like,” yet a model escaping its sandbox through chained zero-days prompted paused training and possible pacing of AI development.Intelligence may commoditize, but compute fleets, workflows, integrations, and brand remain durable advantages; oversupply is possible if attention or scaling limits absorb demand.

Patrick O'Shaughnessy × Matthew Smith

Matthew Smith’s model points to US natural-gas storage falling below all historical levels by 2029 as contracted LNG and AI compute outstrip deliverability, with electricity prices bearing the impact in 2028-2030.The market remains priced near $3.50-3.60, while Expand Energy and Range offer leverage to a potential physical-gas scramble; processing, pipelines, nuclear timelines, and consumer costs remain key risks.

Patrick O'Shaughnessy × John Kim

John Kim frames fundraising as a trust problem: desire minus fear drives action, while returns alone miss the motivations of most LPs and committees rarely make contrarian bets.His operating rules are to anchor scale to the first close, simplify the story, choose two of size, speed and terms, and build pipeline through conversion and bite size without sacrificing the differentiation that created the franchise.

Patrick O'Shaughnessy × Jeremy Giffon

Giffon argues that capital follows the “billion-dollar PDF”: in long-dated private markets, narrative is the great filter, while X’s unifeed increasingly selects the stories that move marginal security prices.AI shifts software economics from near-zero-cost strings to recurring compute, implying lower margins and greater scale; Giffon has largely sat out the jump ball, while LPs should underwrite manager incentives and the increasingly extractive SPV structure.

Patrick O'Shaughnessy × Gavin Uberti × Rob Wachen

Etched is betting inference becomes the world’s biggest market, combining low-voltage prefill with cluster-scale memory that cuts chip-to-chip latency by more than 5x versus Blackwell’s roughly 4,000-nanosecond hops.Its vertically integrated rack, Taiwan factory, and pre-fetching brought silicon to inference in a rack in 40 days versus a very famous AI chip company’s 10 months, but the $103M Series A followed a near-death funding gap.

Patrick O'Shaughnessy × Vlad Barbalat

Liberty Mutual’s $120B balance sheet combines roughly $70–75B of reserves with growth credit and equity, while permanent mutual capital avoids shareholder pressure and supports 7–10% portfolio targets.Barbalat now questions whether AI makes future cash flows—and therefore multiples—structurally less visible, with four-year software credit appearing safer than 30-year Salesforce or Oracle debt and potentially steeper credit curves ahead.

Patrick O'Shaughnessy × Kareem Amin

Clay’s pre-ChatGPT growth rested on three linked choices: serve creative go-to-market teams with an open-ended, coding-like product, sell through RevOps, and charge by usage rather than seats.Any-integration architecture let LLMs amplify an already-rising product, while usage pricing aligns with productivity gains that can shrink headcount; its unresolved test is whether mission-first scaling can avoid creating weaker, zombie-like businesses.

Patrick O'Shaughnessy × Alex Sacerdote

Anthropic’s agentic coding release helped reverse Whale Rock’s view, supporting its August 2025 investment at the $180 valuation after it passed on the $60B round.Enterprise AI is less than 1% penetrated, yet Anthropic has only half the compute it needs, while a three-horse model oligopoly and infrastructure bottlenecks support monitoring adoption, supply, and the risk that open source catches up.