A friend of mine added some pretty extensive iOS UI tests to a keystone feature hit by millions every month. They'd been kicking the can down the road for years, trying to fit it in their roadmap, and with Claude running overnight they were able to bang out the whole suite in a week.
I'm not sure how it would show up in quarterly results.
I see these kinds of stories here a lot, and I'm curious whether they reflect a steady stream of need for AI coding, or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward.
Is it like the stereotypical dad who rents a power washer, powerwashes every exposed surface on his property, and then doesn't need to do any powerwashing for a few years; his neighbor who gets an Instant Pot and uses it for every meal for a month, then sees it gathering dust when the family gets tired of pressure-cooked stews; or like their neighbor who gets a microwave oven and uses it multiple times a day for decades?
I guess only time will tell.
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> I'm not sure how it would show up in quarterly results.
Over here our CTO replaced Intercom with an internal equivalent that costs less than $20 / month to run, haiku and sonnet support agent costs included. In less than a few weeks, in his spare time.
In my limited experience with using agents to create tests it tends to code the tests to the existing code instead of ensuring the correctness from a spec. Great for regression testing but still limited in effectiveness for catching existing issues.
It wouldn't, at least not directly. That's why it wasn't done pre-AI.