Gru's Plan: AI-Generated PRs Meet $25-Per-Review Billing

Level 1: The Lemonade Stand Problem

Imagine you get a magic pencil that does your homework instantly, so instead of one page a day you happily produce twenty pages. But your school charges five dollars to grade each page. At first the plan sounds brilliant — "I'll do SO much homework!" — until you look at the chalkboard again and realize: twenty pages times five dollars, every single day. The bald man in the cartoon is making exactly that face: he's just re-read his own plan and discovered that the part he was proudest of is the part that empties his wallet. And the company doing the grading politely replies, "Yes, that's the price. It's a very thorough grading."

Level 2: The Pieces on the Board

• PR (pull request) — a proposed code change submitted for review before merging. Historically reviewed by teammates; increasingly pre-reviewed or fully reviewed by AI tools.
• Gru's Plan — the meme format where a plan's first panels look great and the final panel reveals the flaw the planner only notices mid-presentation. The repeated panel is the joke: the flaw was on the board all along.
• Token-based billing — LLM services charge per token (roughly, word fragment) processed. A deep review that reads your whole diff plus surrounding files burns tokens fast; cost is proportional to thoroughness, not per-seat.
• GitHub Action — an automation that runs in CI on repository events. The "open source GitHub Action" mentioned is the cheaper, shallower alternative: same model family, less exhaustive context, lower bill.
• PR volume inflation — the observed pattern that AI-assisted devs ship more, smaller, faster changes. Great for iteration speed; brutal for anything priced per change.

The early-career translation: tools have cost models, not just features. The first time a junior wires an LLM into a CI loop and gets the monthly invoice is a rite of passage roughly equivalent to discovering what an unindexed query does to a database bill. Always multiply the per-unit price by the unit count after the tool changes the unit count.

Level 3: Induced Demand, Billed in Tokens

The four-panel Gru's Plan template (imgflip watermark intact, as tradition demands) lays out the business case with supervillain confidence:

1. OUR DEVS CODE WITH AI
2. OUR DEVS SEND MORE PRs
3. WE PAY $25 per PR review
4. Gru turns back to the same board, horror dawning: WE PAY $25 per PR review

Below it, the punchline gains an official seal: a verified @claudeai reply, posted three hours prior, calmly confirming the economics —

Code Review optimizes for depth and may be more expensive than other solutions, like our open source GitHub Action.

Reviews generally average $15–25, billed on token usage, and they scale bas…

The meme captures a genuinely new cost topology in software economics. For decades, code review was "free" — meaning it silently consumed senior engineers' attention, the most expensive and least measured resource in any org. AI flipped both sides of the ledger at once: generation became nearly free, so PR volume inflated (agents file ten small PRs where a human filed one), while review acquired a visible per-unit price tag. The result is a perfect feedback loop the transportation economists would recognize as induced demand: widen the code-production highway, get more code-traffic, pay tolls proportional to traffic. Gru's plan fails at exactly the panel where most AI-adoption business cases fail — the one where unit economics meet the multiplied unit count.

The token-billing detail is the quiet stinger. "$15–25, billed on token usage" means cost scales with diff size and with how much context the reviewer model ingests — so the very practices AI encourages (large generated diffs, sprawling context) are the ones that push each review toward the top of the range. And since the code under review was machine-written, you arrive at the ouroboros: one model's output is another model's input bill. The corporate dashboard version reads "AI productivity up 400%"; the finance version reads "review spend up 400%"; both are describing the same Gru panel.

There's also a meta-layer worth savoring: the vendor itself replying to the meme with measured pricing transparency inside the meme is very 2026 developer relations. The complaint and the FAQ now ship as a single artifact. Note the slightly tragic implication of "optimizes for depth" — depth pricing makes sense precisely because AI-generated PRs need deeper scrutiny, the flood and the toll booth being products of the same upstream decision.