421–424 of 424 posts
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> Revenue: $3.7 billion > Cost of Revenue: $2.65 billion That's how standard accounting rules for public companies would measure it.
> OpenAI Lost $38.5 Billion In 2025 > 2025 — OpenAI Had $13.07 Billion In Revenue, $34 Billion In Costs and Expenses, and $20.92 Billion In Losses, with a net loss attributable to the company of $38.53 Billion
GPT-5.6 Luna xhigh/max from Codex is my daily driver now. Good economics, good performance. It sits in the most attractive quadrant on the Intelligence Index.
any particular reason not using terra or sol, given the generous limit quotas and frequent codex resets?
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> How so? Without looking at the specific changes made, it is impossible to know whether this represents a real capability improvement. For example, maybe the 20% gain was due to a very obvious/easy to catch inefficiency while the 1% improvement was due to something subtle.
I'm not convinced. Both are improvements on something implemented by highly trained/credentialed/successful teams. Something "missed" by either team is impressive either way.
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At some point I imagine you’d add a software layer on top that holds more current training and can be called as needed trading off for slower responses. There’s already work out there splitting models across networks. You could have the base on silicon, some stuff in memory on the machine, and another frontier tool in the cloud.
There are already studies proving that a "stupid" model with a good harness + tool calling will outperform a "smart" model. Things like this give me hope for a system that can be fully local and private, but also with the ability to be almost infinitely extendable with tools.