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15:43ENHow AI Data Centers Work: Power, Cooling and Community TradeoffsMatthew Berman · Forward FutureMatthew Berman explains the physical infrastructure behind AI prompts, from accelerator racks and backup power to water use and local planning.
13:53ENTiny Recursive Models and the Limits of Puzzle Benchmark ComparisonsMatthew Berman · Forward FutureMatthew Berman examines a small recursive network that reports strong ARC and Sudoku results, and what its experimental setup means.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.Presenta: Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few PointsPresenta: Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward LoopPresenta: Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.
26:17EN21 Practical AI Uses for Design, Work and ResearchMatthew Berman · Forward FutureMatthew Berman demonstrates ways to use image tools, ChatGPT and NotebookLM for visual concepts, research, everyday work and study.
2:20:35ENChatGPT Is a Thousand Days Old. Emad Mostaque Says We Have About a Thousand Left.Presenta: Nathan LabenzThe Stability AI founder’s new economics argues that when intelligence is abundant, scarcity-based money and GDP stop making sense.
0:58ENMicrosoft’s Nebius GPU Infrastructure AgreementMatthew Berman · Forward FutureIn a sponsored video, Matthew Berman discusses a 2025 infrastructure agreement and Nebius’s AI services.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Presenta: Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.
15:29ENBuilding a Multi-Agent Research Team with Strands AgentsMatthew Berman · Forward FutureMatthew Berman builds from a calculator agent to a multi-agent business-report demo, with examples of tools, model selection and orchestration.
1:02:05EN$100 Million Researchers and a $200 Billion xAI: An AI Roundtable Does the MathPresenta: Jacob EffronDatology’s Ari Morcos and Radical Ventures’ Rob Toews argue over whether models are plateauing, whether RL-environment startups will last, and why Google is the lab they would pick.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14Presenta: Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
14:47ENAI News in September 2025: OpenAI’s Programming Test, Meta’s REFRAG and New Agent ToolsMatthew Berman · Forward FutureMatthew Berman surveys competition results, efficient retrieval, agent payments, 3D worlds and model launches, alongside updates on Waymo and AI video. The episode distinguishes announcements, research claims and rumors from established outcomes.
42:20ENA Name Invented at 2:30 a.m. Put Gemini on Top of the App StorePresenta: Jacob EffronGoogle's Nicole Brichtova and Oliver Wang on Nano Banana, why image models got smart when language models did, and why a prompt still is not a finished product.
8:47ENWhy Identical LLM Prompts Can Produce Different Answers at Temperature ZeroMatthew Berman · Forward FutureMatthew Berman explains Thinking Machines Lab’s work on batch-invariant inference and what its reproducibility experiment actually showed.
1:32:47ENA transformer co-author wants a $160 million model no company ownsPresenta: Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
1:44:35ENJoe Hudson: $500 Billion Went Into AI, and Nobody Defined 'Good for Humanity'Presenta: Nathan LabenzSam Altman says the OpenAI coach's superpower is emotional clarity — and that it will be one of the most critical skills in a post-AGI world.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't HoldPresenta: Jacob EffronThe FlashAttention author and Together AI chief scientist on why hardware portability is a myth, how Claude Code made him 1.5x faster, and why workloads will split the chip market.
3:14:49ENNothing This Summer Jumped the Curve: Why Zvi Lengthened His AGI TimelinesPresenta: Nathan LabenzOn his tenth visit to The Cognitive Revolution, Zvi Mowshowitz argues that GPT-5 and IMO gold stayed on trend, while chip policy, energy, and reinforcement learning nudged his p(doom) slightly up.
1:51:39ENA native ad engine for AI—and the questions it cannot dodgePresenta: Nathan LabenzRyan Hudson, who sold Honey to PayPal for $4 billion, joins Nathan Labenz to explain ZeroClick’s bet on paid inference-time ads, and the societal incentives he has not yet had to confront.
1:55:02ENA hundred experiments a day, and the valley of death in materialsPresenta: Nathan LabenzRadical AI’s co-founders tell Nathan Labenz how a closed loop of frontier models and a self-driving lab is meant to collapse a forty-year materials cycle into less than a decade.
9:45ENMatthew Berman Reviews the First Reactions to Gemini's Nano Banana Image ModelMatthew Berman · Forward FutureEarly creators combine Google's image editor with 3D and video tools, while exposing errors, limitations, and safety questions in the results.
17:51ENAI News in August 2025: Claude for Chrome, New Models and AI InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a browser-agent preview, model releases, reported Apple discussions and research on inference and weather.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsPresenta: Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
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