The biggest being that the only safe way to recycle feature flag names is to put ample time separation between the last use of the previous meaning for the flag and the first application of the new use. They did not. If they had, they would have noticed that one server was not getting redeployed properly in the time gap between the two uses.

They also did not do a full rollback. They rolled back the code but not the toggles, which ignited the fire.

These are rookie mistakes. If you want to argue they are journeyman mistakes, I won’t fight you too much, but they absolutely demonstrate a lack of mastery of the problem domain. And when millions of dollars change hands per minute you’d better not be Faking it Til You Make It.

Google generates a lot of revenue per employee not because the employees are good (though many of them are of course), but because they own the front door to the web. And the Knight Capital story has many nuances left out by that summary.

In both cases the author needed a hard hitting but terse example. But as I said, both the claims are true, so in the voice of the courtroom judge, "I'll allow it."

There's a great example of that in the linked post itself:

> Let's build a property-based testing suite. It should create Java classes at random using the entire range of available Java features. These random classes should be checked to see whether they produce valid parse trees, satisfying a variety of invariants.

Knowing what that means is worth $150/hour even if you don't type a single line of code to implement it yourself!

And to be fair, the author makes that point themselves later on:

> Agentic AI means that anything you know to code can be coded very rapidly. Read that sentence carefully. If you know just what code needs to be created to solve an issue you want, the angels will grant you that code at the cost of a prompt or two. The trouble comes in that most people don't know what code needs to be created to solve their problem, for any but the most trivial problems.

On your second point: I wouldn't recommend betting against costs continuing to fall. The cost reduction trend has been reliable over the past three years.

In 2022 the best available models was GPT-3 text-davinci-003 at $60/million input tokens.

GPT-5 today is $1.25/million input tokens - 48x cheaper for a massively more capable model.

... and we already know it can be even cheaper. Kimi K2 came out two weeks ago benchmarking close to (possibly even above) GPT-5 and can be run at an even lower cost.

I'm willing to bet there are still significantly more optimizations to be discovered, and prices will continue to drop - at least on a per-token basis.

We're beginning to find more expensive ways to use the models though. Coding Agents like Claude Code and Codex CLI can churn through tokens.