Patrick O'Shaughnessy

Patrick O'Shaughnessy × Noah Shinn

Invite-only Instinct is nearing $1 billion in annual transaction volume, growing 10–11% day over day with $0 spent on marketing.Shinn envisions Apple Pay-style merchant-funded distribution; 40% share a personal credit card within three weeks, and trusted users show about 80% retention.With compute procurement taking months, 5–8% daily compounding could imply 100 million users, while mis-sizing capacity can cost 3–4×.

Patrick O'Shaughnessy × Gabe Stengel

Rogo’s model-era progression—from o1 Pro’s reliable search to Opus 4.5 handling junior finance work—supports a two-year race to redesign investment firms, not wait for better models.Its wedge is private-market dealmaking, where data rooms, DDQs, CRM and LP reporting still lack infrastructure, while high-value ideas can justify extreme token spend.The watchpoints are compaction, standardization, and whether incumbents move aggressively enough.

Patrick O'Shaughnessy × Neil Movva

Sola Research is repositioning inference around long-running background agents, forecasting workloads shift from 50/50 background and real-time by year-end to 90/10 long term as cheaper tokens unlock unbounded demand.By trading latency for throughput, using overlooked chips and 1MW sites with 95% uptime, Movva targets radically lower costs, while KV-cache waste, idle GPUs, HBM supply, and the durability of frontier labs’ three-to-six-month premium remain key watchpoints.

Patrick O'Shaughnessy × Ben Thompson

AI’s near-term bottleneck may be capital rather than compute or power, as funding shifts from free cash flow and debt toward Google equity and NVIDIA’s $500 billion vehicle.Thompson’s Berkshire analogy makes Search a possible funding engine for AI’s “basically all white-collar work” TAM, while payback periods, hyperscaler chips, and an air-gap risk remain watchpoints as 2028-29 capacity arrives.

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 × 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 × 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 × Dara Khosrowshahi

Uber is making AV supply its moat through 30+ partnerships, financing, depots, charging, and insurance; network vehicles are 30% or more busier, improving fleet ROI.The upside is a potential trillion-dollar marketplace, but AI budget overruns, Chinese manufacturing advantages, social backlash, and execution in Uber One and hotels remain risks.