Avi Felman × Alexander Good × Jonah Van Bourg

Alex Good’s ad-buying strategy linked acquisition economics to market attention, from long EA/short Booking to Tesla clicks driven by “Elon Musk.”His “goldfish theory” favors recurring attention over forecasting, but fragmentation and trust can break the trade; he is bearish Palantir at 100x sales.He expects AI layoffs within 3 months, while politics and 6% annualized productivity could reshape the opportunity set.

Kevin Roose × Casey Newton × Dr. Dana Suskind

Washington’s reversal of Anthropic’s Fable 5 restrictions and reported limits on GPT 5.6 shift frontier AI from default release to politically granted access, creating availability risk for businesses built on critical model APIs.Chinese open models may gain from being downloadable and controllable, while parents face a parallel product-screening challenge: Suskind’s DETECT framework favors AI that enhances human connection and rejects companions until safety is established.

Avi Felman × Ari Paul × Jonah Van Bourg

Ari Paul warns frontier AI labs could face a Los Alamos style lockdown or full US government control within three years, a risk he says markets are not pricing.He also argues AI is where crypto was in 2021-22, with 95% of companies still likely to fail and data-center overbuilding potentially echoing late-90s fiber.The monitor is whether nationalization, stolen IP, private alpha, rotating bottlenecks, and locked-in distribution reshape which AI economics reach shareholders.

Kevin Roose × Casey Newton × Sebastian Mallaby

Google’s AI Mode replaces ten blue links with synthesized answers as it defends a search business that generated $54 billion last quarter, putting the link economy and publishers’ traffic at risk.TollBit found AI interfaces delivered 91% less click-through traffic than conventional search, while Chegg’s collapse shows the exposure; crypto’s expanding ties to ordinary savings and vibe coding’s maintenance debt add further risks to monitor.

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

AI has become Davos’ dominant economic story, with roughly $50 trillion in annual human labor, only a few hundred billion dollars spent on infrastructure, and opportunity spanning chips, power, AI factories, and applications.The US retains better frontier models and chips, but China’s power buildout, deployment speed, and 83% versus 39% AI optimism could outweigh benchmarks, while energy timelines and agent rails remain deployment risks.

Kevin Roose × Casey Newton × PJ Vogt

AI’s investable signal is agency: systems that pursue goals and act in the world may require regulation beyond media-style infrastructure.Potential gains include faster scientific iteration, but alignment, job automation, and the speed of government oversight remain unresolved risks.

Data centers became 2025’s defining technology as AI infrastructure reached economy-scale spending, tying chips, electricity, cooling, rare earths, and environmental politics into one concentrated build-out.Claude Code and Granola show clearer monetization where AI removes concrete work, while DeepSeek, Sora, TrumpCoin, and age-verification laws leave competitive, governance, and regulatory risks to monitor.

David Sacks × Marc Andreessen × Ben Horowitz × Erik Torenberg

Sacks and Andreessen argue that US AI leadership requires permissionless innovation, infrastructure, energy, and exports, while fragmented state rules and model licensing could make incumbency the moat.Their near-term thesis rests on specialized models, broad consumer adoption, open-source freedom, and grid flexibility, but China’s lead in open source and the unresolved power bottleneck remain material risks.

Bill Gurley × Brad Gerstner

Circular AI financing, including Microsoft–OpenAI credits and Nvidia’s CoreWeave capacity backstop, can turn investment support into reported revenue while obscuring demand weakness.Mag 5 CapEx reached 66% of operating cash flow in 2025, while OpenAI’s deals imply roughly $150B of 2030 CapEx and at least $150B revenue, intensifying hyperscaler concentration.The key monitoring points are neoclouds and startup chips further out the risk curve, state-level AI regulation, and whether incumbents allow stablecoin rails to scale.

Lex Fridman × Pavel Durov

Telegram’s operating model combines 100% founder ownership, a roughly 40-person core team, and automation across almost 100,000 servers, creating exceptional leverage but extreme key-person concentration.It became profitable for the first time in 2024 through Premium subscriptions, contextual ads and ecosystem commissions, without behavioral advertising or sharing private messages.France’s investigation—roughly 15 alleged user-generated-content crimes attached personally to Durov—alongside encryption proposals and renewed bans keeps sovereign intervention the central risk.