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GPT-5 is a smaller, more economical model — not a frontier capability push. Using it to claim scaling laws are dead is completely wrong. Baker's message: don't conflate efficiency optimization with a capability ceiling.
Where this was said
At 22:35 · chapter starts 20:30
Baker identifies a pivotal inflection point in AI economics: the emergence of reasoning models. Before this, a frontier model without unique data and internet-scale distribution was — in his memorable phrase — the fastest depreciating asset in history. The problem was that the consumer internet flywheel (good product → large users → improved algorithm → better product) wasn't available to AI models. Reasoning changed that because reinforcement learning during post-training means a large, active user base now directly improves the model [1] — Gavin Baker "Pre-reasoning, a frontier AI model without unique data and internet-scale distribution was the fastest depreciating asset in history. Reaso…" 20:30 . The flywheel isn't fully spinning yet, Baker admits — but you can squint and see it beginning. This insight fundamentally changes the competitive dynamics for Anthropic, xAI, and OpenAI, giving large user bases a compounding strategic value they previously lacked. Baker also fires a short, pointed salvo at GPT-5 scaling law skeptics: GPT-5 is a smaller, economical model, not a frontier capability push — citing it as evidence scaling laws are dead is simply wrong.
Pre-reasoning, a frontier AI model without unique data and internet-scale distribution was the fastest depreciating asset in history. Reasoning changed that: RL post-training means a large user base now unlocks the same data flywheel that powered Google and Facebook — and Baker says you can already squint and see it starting to spin.
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.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
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.
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