Podbit · All-In with Chamath, Jason, Sacks & Friedberg
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.
Where this was said
At 4:58 · 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.
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.
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.
Audience-building isn't a shortcut — it's a 3-year content grind before the product even exists. The speaker reveals that his monetisation success was entirely downstream of years spent tweeting daily and creating content, not talent or luck.
Building a monetisable audience on Twitter costs just 5 minutes a day — but it has to happen every day for years. The time barrier is low; the consistency barrier is where most people fail.
Sam built Algrow, a SaaS for finding viral content formats, with zero coding experience using ChatGPT and Cursor. Six months later: 10,000 users, $14K/month in revenue.
Sam's first MVP threw an application error on its very first user — and he shipped it anyway. The core idea worked, and that was enough to validate the product and keep users coming back.
Sam joined Discord voice chats, muted himself, and silently screen-shared his product. Users in the chat started tagging him asking what the tool was. No pitch needed — curiosity did the selling.
Most founders post links in Discord and immediately get banned for self-promotion. Sam's approach was the opposite: build rapport, help people with the tool, let word of mouth do the work.
Find where your ICP lives. Listen before building. Validate with DMs and Loom recordings. Build in public with users inside your own Discord server. Turn early adopters into advocates with free access.
Instead of fearing the self-promo ban in large Discord servers, create your own private server for your product. You funnel in ideal customers and build a relationship that email can't replicate.
Algrow helps creators find and replicate viral video formats, starting at $25/month. It analyzes subscriber counts, average views, and trending formats — and can even generate the videos with AI.
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