Pricing

The AI safety conversation is obsessed with the ceiling: the smartest model, the frontier, the hypothetical superintelligence. I think we are watching the wrong number. The change that will actually reshape the world this decade is not that the best model got smarter. It is that a good-enough model got almost free.

DeepSeek V4 arrived in April with the same trick DeepSeek always pulls: numbers that should cost a fortune, at prices that do not. Top-tier coding scores, a million-token context, an MIT license, and output priced under a dollar per million tokens. The West still has not quite made peace with it.

Most AI bills are not big because the work is hard. They are big because of lazy defaults: the most expensive model on every task, the biggest context every time, and no thought about which jobs actually need the premium option. Here is how to spend far less without your results getting worse.

Kimi K2 belongs to my favorite category of model: the one that makes you recheck the pricing page because the numbers look like a typo. Near-frontier reasoning, notably cheaper than the big names, and it gets to the answer using fewer tokens than its predecessor. Mostly, it holds up. Mostly.

Everyone reviews the frontier. Almost nobody carefully reviews the budget shelf, which is a shame, because that is where most real work should actually run. I spent time putting the cheap models through the same ordinary tasks I use every day. The winner was not the one I expected, and the losers were instructive.

Quietly, without a single headline capturing it, the cost of using capable AI collapsed this year. Not dropped. Collapsed. The kind of model that cost a small fortune to run at scale two years ago now costs cents per million tokens. If your business plan assumed AI would stay expensive, it is time to redo the math.