This is interesting. Most teams I know throw Opus or Fable at every problem. It works but it’s expensive and inefficient.
Revolut: Europe's most valuable startup ($200B) just showed the world its AI strategy.
Every company will end up copying it.
At the start of this year, Revolut leaned on the frontier labs. Its 2,000 engineers used Claude Code and Codex, and some burned $1,000 to $10,000 a month each. The bills kept climbing, and the returns weren't there.
So Revolut switched its default to an open-source model called GLM. All it needs is GPUs, which it rents from Together AI.
The results, by Revolut's own count:
- The bill is about a tenth of what it paid OpenAI and Anthropic
- Engineers get unlimited tokens instead of spending caps
- Coding time is down 60% and lead time is down 70%
That's the chart below.
Revolut still uses Claude and GPT, but only as "the exception," for the tasks they do best. Everything else runs on open source.
So here's the question I keep coming back to: how many use cases can companies really justify sending to frontier models?
My answer is very few. Plenty of companies still route everything to OpenAI and Anthropic today. For how long?
I think open source ends up handling the vast majority of corporate AI work, and frontier models get a short list of the hardest jobs. That's a problem for labs whose revenue forecasts assume unlimited growth.
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