Podbit · All-In with Chamath, Jason, Sacks & Friedberg
Modern reasoning AI consumes enormous numbers of tokens internally — essentially thinking out loud before responding. That internal computation is inference, and speed directly translates to more reasoning cycles per dollar. Run Cerebras for 24 hours and you get the equivalent of weeks of AI thinking.
Where this was said
At 10:20 · chapter starts 1:50
Feldman describes a fundamental shift in human-AI interaction: early models did exactly what you told them — the classic 'computers are dumb, they do exactly what you say' formulation from a colleague two decades ago. But modern reasoning models like those in OpenAI's latest releases are beginning to infer intent: a user asks for a chart, and the model proactively offers both a line and a bar version. Calacanis illustrates this vividly with a live experiment — using an unrestricted model to build a real-time trend-scouting agent that debates with itself about where to look for emerging signals (Hacker News, Reddit, Instagram). He watches the model reason in real time, debating its own approach before collapsing into an answer. This is not ChatGPT summarizing a PDF — this is an agent doing strategic research, checking its own work, and asking the human what it missed.
Individual AI data centers under construction will draw more power than mid-sized cities, with aggregate demand set to exceed the previous 50 years of Earth's energy use.
Cerebras is sitting on $25 billion in backlog, and every hyperscaler from OpenAI to AWS faces the same problem: demand is fully booked, and they're racing to keep customers from leaving — not chasing speculative future adoption.
Cerebras has a $25 billion backlog of orders, reflecting demand that far outstrips the industry's ability to build and fill data centers.
Early AI adoption looked like everyone grabbing unlimited tokens with no strategy — exactly like someone wandering every aisle at Costco and walking out with $200 of impulse buys. Enterprises are now learning which AI models to use for which tasks, and routing intelligently between frontier and open source.
Cerebras chips can run inference 15 times faster than competing hardware, meaning a 24-hour run on Cerebras could yield weeks or months worth of AI thinking.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
Sam's top advice: when prompting Cursor, tell it to architect code for 100,000 users from day one. The AI changes its approach, building scalable frameworks instead of brittle one-user code.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
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