So, I find it hard to agree with opinions claiming that these budget cuts are bad news for AI labs. The reason why budget caps are necessary is that coding agents have become undeniably
AI
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The evolution of AI costs: from $20 to $1000 per employee
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Last year, it was laughable for a company to spend $1,000/employee/month on AI – the $20 plans were enough for everything anyone did with it This year, setting a cap of $1,000/month is seen as the financially prudent thing to do, and the one
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Fully solving coding shows it wasn’t the hard part
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Once we *fully* solve coding, we’ll realize that coding wasn’t the hard part.
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Terminal command executing codex with GPT-5.5-cyber
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codex -C ~/projects/openclaw -m gpt-5.5-cyber time pic.twitter.com/6ANgzM1JKJ
— Peter Steinberger 🦞 (@steipete) 12 juin 2026codex -C ~/projects/openclaw -m gpt-5.5-cyber time
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Claude model unpredictability compared to a box of chocolates
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Claude is like a box of chocolates. You never know which model are you going to get.
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Model improvements in diffs: structure, less repetition, file splitting.
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you look at the diffs and see that the model improved structure, reduced repetition, broke apart large files etc.
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AI as leverage: the importance of knowing how to apply it
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🙂 AI is leverage, just have to know how to apply it!
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Appshots in Codex: most useful Mac software for cmd+cmd prompts
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appshots in codex is the most useful piece of software on my Mac most of my prompts these days are:
– cmd + cmd investigate this
– cmd + cmd open a PR to fix this
– cmd + cmd run the eval on these set of prompts and discussion
– cmd + cmd set a heartbeat to keep following up -
Reverse Jevons Paradox: Fable Could Reduce Spending
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I wonder if Fable might cause a sort of 'inverted Jevons paradox', where companies would realize how much tokenmaxxing would cost them with Fable, to the point that they would reduce their spending beyond what they would have done if only Opus were