Ben's single run to port the Executor project to Rust and Svelte ran to 100 billion tokens and cost $65,000. The real driver wasn't the orchestrating reasoning model — it was the dozens of subagents it spun up underneath, which is the only way to blow through API usage this fast.
Where this was said
At 35:30 · chapter starts 35:00
This chapter traces the evolution of how both hosts actually use these models. Fable was the catalyst: it was the first model that made genuinely long, complex runs feel worth attempting. When Fable was taken away, they found 5.6 could carry those same expanded workflows further than any previous OpenAI model. Theo explains the economics: a $200 Codex subscription, per Semianalysis measurements, delivers up to $14,000 worth of compute per period — and with two usage resets that restore a full week, this can reach $20,000 per month [1] — Theo "According to Semianalysis measurements, a $200/month Codex subscription can yield as much as $14,000 worth of token usage in a single perio…" 34:00 . He's careful to note that the outrageous dollar figures on their dashboards ($131,700 and $93,000 respectively) represent deliberate stress-testing experiments, not practical work. His real workflow tasks — auditing open PRs, rebasing, coordinating merges — ran on a single 5.6 thread spawning targeted subagents, and were both cheaper and more valuable.
Ben's Executor port run reached 100 billion tokens, illustrating how subagent-heavy loops can generate astronomically large token counts.
Ben's single longest run — porting the Executor project to Rust and Svelte — cost $65,000 worth of API tokens via xHI reasoning model orchestrating massive subagent swarms.
Theo increased Codex's subagent limit from the default of ~3 to over 20 to enable more aggressive parallel work, which also pushed macOS to its limits.
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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