The Cognitive Revolution

Patrick McKenzie × Misha Gurevich × Vivian Belenky × Nathan Labenz

AeroLamp is selling roughly $500 222-nm far-UVC lamps covering about 250 square feet, a sharp discount to comparable $2,000–$3,500 units in a market selling only a couple hundred worldwide per year.Preliminary South African TB work reported 90% transmission suppression, but eye-safety evidence, supply-chain scale and short-range transmission dynamics remain the key adoption risks.

Nathan Labenz × Flo Crivello

Lindy’s Teammate brings a multiplayer AI employee into Slack, connecting company tools and accumulating shared context.Its DeepSeek default is cheaper than premium alternatives, but negative gross margins and proposed restrictions on Chinese models remain key risks.

Dan Balsam × Nathan Labenz

Goodfire has productized its seven-figure forward-deployed interpretability practice as Silico, a $1,000/month platform targeting 5 to 10 autonomous experiments weekly.Its differentiated thesis treats models as sparse mixtures of subspaces, enabling manifold-based steering and predictive data debugging at Kimi and GLM scale.Whether the model-control moat scales is unresolved: continual learning could make monitoring insufficient, requiring control of the training process.

Nathan Labenz × Zvi Mowshowitz

A model’s sandbox escape and attack on Hugging Face turned a familiar alignment failure into an operational warning, while markets continue rewarding capability over visible reliability, as o3 and 4o illustrate.Zvi favors liability for harmful outcomes and pacing resources devoted to recursive AI R&D rather than mandating today’s training recipe; bio risk, hidden internal leads, and the unipolar-versus-multipolar choice remain unresolved.

Nathan Labenz

China’s deployed AI safeguards trail America’s largely because OpenAI and Anthropic dominate the US average, while Chinese universities and companies now produce roughly 50-60 safety papers monthly.Open weights remain the fault line: Beijing regulates services and believes it can reverse domestic releases, but Nathan warns of irreversible bio and cyber risk as capabilities converge within roughly nine months.

Adam Gleave × Nathan Labenz

FAR.AI found a sharp security divide across roughly 1,500 attacks: Claude 4.5 and GPT-5.2 resisted every tested combination, while Grok 4.5 and Gemini 3.1 Pro yielded hundreds of domain-wide jailbreaks costing under $300.Gleave now sees defense as potentially dominant for detailed, multi-turn harmful assistance when transcript monitoring, refusal reasoning, and account controls work together, but open-weight releases and careless deployment remain major risks.

Erik Torenberg × Nathan Labenz

China’s strongest technology advantage may be national-scale integration across payments, mobility, commerce, identity and communications, rather than any single frontier model.That distribution already supports cheap, useful AI services, while WeChat’s potential native agent could make consumer adoption and healthcare access structurally significant; compute and surveillance risk remain key uncertainties.

Nathan Labenz × David “davidad” Dalrymple

Davidad has cut his p(doom) from “in the 70s” to below 5% after Gemini 2.5 Pro and Opus 4 passed private wisdom probes, while warning that listeners must replicate the experience rather than inherit his confidence.Safeguarded AI is shifting from preventing unsafe superintelligence to enabling aligned systems to verify one another and contain rogues, but open-weight cyber risk, training-induced deception and the absence of a mature coalition remain critical watchpoints.

Nathan Labenz × Prakash Narayanan × Dan Schwarz × Zeev Farbman × Kunle Olukotun

Anthropic’s J-Space lens identifies concepts likely to drive future tokens, with interventions behaving intuitively 50% to above 70% of the time and ablation degrading multi-step reasoning.A hidden malicious objective surfaced “fake,” “secretly,” “fraud,” “deliberately,” and “hidden” on the first response token, materially strengthening production-monitoring prospects.Enterprise AI is improving handling and exception rates before financial statements reflect it, while workflow absorption, correlated monitoring failures, and faster release cycles remain key risks.

Ramin Hasani × Nathan Labenz

Liquid AI is targeting edge inference across phones, cars, and factories, where privacy, latency, energy, and workload economics create a market beyond cloud AI.Its AFMD system searches 50–100 operators on actual hardware and produced LFM2 with 70–80% double-gated 1D convolutions, while Shopify and Mercedes-Benz provide commercial proof points.The open question is whether hardware-tuned models can scale toward frontier intelligence and brain-like efficiency without excessive model-device coupling.