100 billion tokens in one run
Ben's Executor port run reached 100 billion tokens, illustrating how subagent-heavy loops can generate astronomically large token counts.
Where this was said
Long-Running Tasks & What Actually Changed
At 36:05 · 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.
How to Burn $65k on a Single Loop
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.
$65k on a single loop run
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.
Codex subagent limit bumped to 20+
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.
Similar snapshots
50 free tools built total
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
New tool built in under 5 minutes
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
KD filter < 10 for rankable keywords
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Minimum 1,000 monthly search volume
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
20 downloads/day at launch
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
4 months of rebuilding with no marketing
Eyal and Yali shut down all marketing and spent 4 months completely rebuilding PropGPT from scratch.
Over 40,000 downloads total
PropGPT has accumulated over 40,000 total downloads since launch.
LLM costs only $20/month
PropGPT's large language model (AI) operating costs are just $20 per month, and the cost is continually falling.
Ad monetization for games
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
