The fintech firm deployed the model across 56 Nvidia B200 processors to deliver a blended rate of 30 cents per million tokens. Prior third-party solutions like Claude Code and Codex were relegated to an exception list for unique, specialized scenarios. Because engineers no longer need to ration access, they apply machine assistance across all assignments rather than reserving it for complex edge cases. This widespread adoption has significantly accelerated the overall development cycle across the engineering organization.
Key points
Self-hosted open-weight models have become the default daily coding solution for roughly…: Self-hosted open-weight models have become the default daily coding solution for roughly 2,000 internal engineers.
Removing token limits encourages broader utilization for routine work rather than saving…: Removing token limits encourages broader utilization for routine work rather than saving capacity for difficult tasks.
External frontier offerings like Codex and Claude Code are now restricted strictly to an…: External frontier offerings like Codex and Claude Code are now restricted strictly to an exception list.
Sources checked by our analysis pipeline: 1
BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list.
Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%.
Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything.
Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO.
h/t @OliverMolander
Would you move your team off frontier APIs if the bill dropped 90%?
The joint initiative brings together a domestic power producer, an investment firm, and a cloud provider to expand regional technological infrastructure. This collaborative effort addresses the rising computational demands of modern machine learning workloads by securing dedicated large-scale energy capacity.
Legal scrutiny is mounting over tech companies utilizing media publications to train and power automated generative tools. An adviser to the European Union's highest court has warned that such practices could run afoul of regional intellectual property protections. This development signals significant regulatory hurdles ahead for technology firms deploying news material within their artificial intelligence ecosystems.
European Union legal scrutiny is mounting over tech companies utilizing published journalism without proper authorization. An opinion from a court adviser highlights rising regulatory friction surrounding automated systems and intellectual property protections. The position signals potential legal liability under current European Union copyright frameworks for developers deploying publisher content.