Podbit · The a16z Show
The humanoid vs. non-humanoid robot debate is settled. Humanoids can learn from YouTube videos and human demonstrations, and the verification problem — did you put the glass in the dishwasher correctly? — is trivially solved. The robotics race is Tesla versus China, same as electric cars.
Where this was said
At 29:56 · chapter starts 26:20
David George presses Baker on the harder question beyond obvious use cases like customer support: how does AI actually displace human services at scale, and what business models emerge? Baker's framework is simple — humans are paid for outcomes, AI will increasingly be paid for outcomes too. Customer service is the easy first example: the data is textual, LLMs are strong at text, and the reward signal (happy customer, first call resolution) is easily verifiable with RL. The harder, more interesting question is consumer business models [1] — Gavin Baker "Google never built a marketplace because advertisers systematically overpay, and that inefficiency was Google's profit. AI agents will sque…" 27:05 . Baker's vision: personalized AI agents that know your preferences and actively negotiate on your behalf — booking hotels by soliciting competing offers rather than passively directing you to a website. This eliminates the systematic inefficiency that made Google's advertising model so lucrative: advertisers have always paid more than they should because the customer conversion happened post-click. AI closes that loop, compressing margins but creating more genuine value. He closes with Elon Musk's tweet that 'work will become optional' — a scenario Baker says is not wildly implausible given the technology's trajectory.
Google never built a marketplace because advertisers systematically overpay, and that inefficiency was Google's profit. AI agents will squeeze that out, shifting to outcome-based models and affiliate fees. Baker's vision: a personal AI that negotiates hotel prices on your behalf, paid only on results.
Andrej Karpathy stating AGI is 10 years away is now considered a skeptical, long-timeline view by many in the AI community.
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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