One Engineer, $400 in Tokens, 7 Hours. Result: $500K/Year Saved.

Reco.ai published a case study that might be the clearest cost-benefit story I've seen for AI-assisted development. A single engineer used AI to rewrite JSONata (a JSON expression language written in JavaScript) as a pure Go library called "gnata." Seven hours of work. Roughly $400 in API tokens. The result: a 1,000x speedup on common expressions that cascaded into $500K per year in cloud cost savings across their data pipeline processing billions of events.

Simon Willison highlighted this as "vibe porting", and I think that's the right frame. This isn't greenfield AI code generation where you ask the model to build something from scratch (and hope it's correct). This is taking proven logic in one language and using AI to faithfully translate it to another, guided by an existing test suite. JSONata's comprehensive tests were the key enabler. The AI didn't need to understand the problem domain. It needed to produce Go code that passed the same tests the JavaScript version already passed.

The Hacker News thread (164 points, 147 comments) is worth reading for the practitioner debate. The skeptics raise a valid point: how do you verify the output beyond the test suite? Edge cases that the tests don't cover could lurk in the Go version for months. Reco addressed this by running a week-long shadow deployment with both versions in parallel, comparing outputs on production traffic. That's the pattern. You don't trust the AI output. You verify it against reality.

This story connects to the CVE story (Story #2) in an important way. The reason this worked so well is that JSONata had comprehensive tests. The reason AI-generated code has an 87% vulnerability rate is that most projects don't. The test suite wasn't just a nice-to-have. It was the entire reason the AI could produce trustworthy output. Without it, you're vibe coding. With it, you're doing verified translation.

For builders: if you have a performance-critical component written in Python or JavaScript with good test coverage, the "vibe porting" pattern to Go or Rust is immediately replicable. The ROI math is straightforward: measure your current compute costs, estimate the speedup from a compiled language, check if your test coverage is strong enough to validate the translation. If you're spending $40K+/month on compute for something that could run 100x faster in Go, $400 in tokens is the best investment you'll make this quarter.