The Pragmatic Engineer on Tokenmaxxing: Cost per Merged PR Ranges from $0.28 to $89.32

Gergely Orosz published the first serious look at what AI coding actually costs at scale, and the numbers are wild. The Pragmatic Engineer covers "tokenmaxxing," a trend where engineers compete on AI token consumption leaderboards. At Meta, one engineer averaged 281 billion tokens. Let that number sink in for a second.

Jellyfish data shows cost per merged PR ranges from $0.28 at low usage to $89.32 at the highest tier. That's a 319x spread. The curve isn't linear. It shows hard diminishing returns: after a certain point, throwing more tokens at a PR doesn't make it better. It just makes it more expensive.

This matters right now because current AI coding plans are heavily subsidized. Anthropic's Max plan, Cursor's Pro tier, GitHub Copilot. these are all priced to acquire users, not to be profitable. Orosz's core question: what happens when the subsidies end and companies face true costs?

I run Claude Code on a Max subscription. It's the best $200/month I've ever spent. But I also know I'm not paying the real cost of inference. When Anthropic eventually reprices, or when my usage patterns hit whatever internal cost thresholds they've set, the economics change. Every team building AI coding into their workflow should be modeling this scenario.

The connection to Cursor 3's $2,000-in-two-days reports and Snap's 65% AI coding claims is direct. If Snap is generating 65% of its code with AI and paying enterprise rates, what's their actual cost per engineer per month? Is it cheaper than the humans they cut? The Jellyfish data suggests it might not be, at scale, once subsidies normalize.

Codeburn, a new TUI dashboard at 1.95K stars, tracks token spend across Claude Code, Codex, and Cursor with per-session breakdowns. The fact that cost observability tools are emerging as their own category tells you something about where this is heading.

What builders should do: Start tracking your cost per merged PR today. Even rough numbers help. If you're spending more than $20/PR on AI assistance, audit whether those PRs actually needed that much compute or if your workflow is inefficient. Set up budget alerts. And model what happens to your team's velocity if AI coding prices double in 12 months.