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57:31ENThe New Ruler for Models Is How Long They Can WorkModeration: Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a DecadeModeration: Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
51:19ENSal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the ProductModeration: Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.
51:08ENThe Transformer Co-Author Who Says Scaling Hit a WallModeration: Jacob EffronCohere CEO Aidan Gomez tells Redpoint that a $100 million model is still an amnesiac genius, and that enterprise AI money is concentrating in support, research, and the back office.
43:51ENThe $177 Billion GPU Bet Only Works If AI Becomes a $1.5 Trillion MarketModeration: Jacob EffronFour Redpoint partners walk their limited partners through where AI value will accrue—and why revenue is now the easiest metric to misread.
1:01:53ENOpen-source models are 5% of enterprise use — and GPT wrappers wonModeration: Jacob EffronLatent Space's Swyx and Alessio join Redpoint to map what actually has product-market fit in AI, and why the application layer is the fight that matters.
1:10:23ENA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still UntappedModeration: Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a YearModeration: Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
44:34ENThe Cheaper Intelligence Gets, the More of It We'll BuyModeration: Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.
57:11ENA Million Tokens Cannot Write a Good JokeModeration: Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.
1:24:57ENA 50,000-GPU cap and a 1,700-chip loopholeModeration: Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.
1:07:32ENo1 Is a New Generation — and the Day AGI Arrives Will Feel BoringModeration: Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small expertsModeration: Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
48:18ENNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of HeadroomModeration: Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companiesModeration: Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
56:10ENDeepL Beat Google at Translation by Refusing to Be a General ModelModeration: Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning PointModeration: Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
35:24ENScott Belsky: Models Are Commoditizing. Controls, Data, and Taste Are the MoatModeration: Jacob EffronAdobe’s chief product officer tells Unsupervised Learning why Project Neo leaking into Firefly, a three-second Premiere extend, and Superhuman-priced software all point to the same shift.
52:41ENA Broken Belt Buckle Turned Gucci's Call Center Into a Sales FloorModeration: Jacob EffronSalesforce AI CEO Clara Shih on Service GPT, Einstein Copilot, and why trust—not cost—is the real bar to enterprise scale.
1:06:39ENOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need YearsModeration: Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.
1:23:56ENA Three-Month Window: The AI 2027 Authors on Superhuman Coders, Faked Alignment and Who Ends Up in ControlModeration: Jacob EffronDaniel Kokotajlo and Thomas Larsen walk through their AI 2027 scenario, where they disagree on timelines and alignment, and what they think governments and the public should do.
1:05:22ENBuild First, Optimize Later: Harrison Chase on LangChain, Evals, and the State-Machine Future of AgentsModeration: Jacob EffronHarrison Chase explains how LangChain grew from an orchestration layer into LangSmith and LangGraph, why evaluation datasets force product clarity, and why reliable agents look more like state machines than autonomous loops.
54:44ENLegal Tech's Step-Function Moment: Why Legora Bet on the Last 20%Moderation: Jacob EffronLegora CEO Max explains the $550M Series D, AI-native legal work, foundation-model competition, and the culture required to keep moving fast.
1:01:47ENInside Notion AI: Prototypes That Annoy Everyone, Evals Built In-House, and Models Rented from PartnersModeration: Jacob EffronNotion AI engineer Linus Lee explains how a dozen-person team went from a hotel-room hackathon to Writer, Autofill and workspace Q&A.
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