Grok 4.5

In the transaction announcement (xAI buying twitter) twitter reported $12b in debt on acquisition, roughly the amount originally sourced ($13b), so it apparently made good on its debt covenants during the operating period. I have no idea if it received additional capitalization from Musk to do that or not.

That said, the deal was classic Musk - anybody who went on the equity ride with him in Twitter just KILLLED it; xAI was valued at $80bn and twitter at $33bn, so the owners there became 30% owners of xAI. xAI was acquired for $250bn at a SpaceX valuation of $1 trillion, or 20% of the resulting entity, so the twitter stock was 6% of spaceX at about $2 trillion, or $120bn on an equity purchase price basis of $30bn. and that $120bn in value is on really good daily trading volumes; lots of depth.

Using Grok is therefore a supply chain risk and it's not nearly good enough to offset that risk.

> Training included trillions of tokens of Cursor data which capture a wide-range of user interactions with codebases and software tools. This dataset lets the model learn both from existing software as well as developer-agent interactions, capturing how developers work and how agents interact with their environments.

This is what the big money was for. Cursor is the first big player that had real-world data from real-world projects, before cc / codex were a thing.

> We used reinforcement learning on difficult problems in realistic environments spanning both software engineering and broader knowledge work. These environments teach the model to investigate problems, use tools, recover from mistakes, and verify results.

> Many of these problems had to be designed to be difficult enough that even frontier models fail at them. As models improve, existing tasks stop teaching them anything new, and problems that once required extensive reasoning become routine.

> We developed a distributed agent system to construct these environments at scale. Engineers specify a problem and how a solution is verified, and large groups of agents construct, test, and refine each environment.

This is where scale comes in. You use the previous gen model to prepare datasets for the next model iteration. The better the models, the better the data, the better the next models. (they also have a comparison with their composer2.5 training run, for people still thinking chinese models are "close to SotA"...)

Reports of xAIs demise (after giving a lot of compute to Anthropic) were slightly exaggerated, it seems.

> Grok 4.5 was trained across tens of thousands of NVIDIA GB300 GPUs

I have never liked the various nerfs Anthropic has used to balance GPU (slowing down responses, quota variance, model optimizations etc) and it definitely has burned a lot of good-will.

But it has seemed that being able to look beyond the short term pitchforks has worked quite well.