Podbit · The a16z Show
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.
Where this was said
At 27:05 · 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.
Andrej Karpathy stating AGI is 10 years away is now considered a skeptical, long-timeline view by many in the AI community.
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.
SiteGPT attracted over 1 million visitors and $500K in total revenue without spending a cent on paid marketing. The secret: engineering as marketing — building free tools that rank on Google.
Bhanu quit his first job after just 8 months, moved back to his parents' house to cut costs, and started building. One product sold for $250K; the next hit $10K MRR in its first month.
90% of SiteGPT's Google search traffic comes not from the main product but from ~50 free tools Bhanu built. Each tool targets a low-competition keyword and funnels users back to the paid product.
50,000 monthly visitors become 200 leads, 60 trials, and roughly 15–24 new customers per month at ~$100 average revenue each. Add a $1,700–$1,800 LTV and you have a very healthy SaaS.
Start with a blank Ahrefs search, layer in keyword filters (include term, KD < 10, volume > 1,000), list candidates in Notion, design a CTA linking to your main product, then score by volume, difficulty, build effort, and product relevance. That's the whole playbook.
Marketing feels painful for most builders. Engineering as marketing flips the script: instead of writing cold emails or blog posts, you build things — and those things rank on Google forever.
Don't spend months perfecting before launch. Ship the core feature, get real users, and let their feedback dictate the product roadmap. Premature polish is a trap.
SiteGPT launched and hit $10,000 MRR within its first month. That momentum was so overwhelming that Bhanu sold his existing SaaS, Feather, for $250,000 to free up all his time.
PropGPT launched with 20 downloads a day and strong influencer marketing but hit a ceiling at $1,000–$2,000 MRR. High download numbers masked a critical flaw: almost nobody stuck around after the free trial ended.
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