New AI optimization framework beats Claude Code and Codex by 2.5x on the same compute budget

Oh, brilliant — another day, another 'breakthrough' that promises to squeeze more intelligence out of the same compute wattage. Researchers at Renmin University of China and Microsoft Research have unveiled Arbor, a framework that apparently outperforms Claude Code and Codex by a factor of 2.5x on the same compute budget. Because what we really need is AI that's cheaper and faster, not safer or more aligned.

Let's pause to appreciate the maths: if your model was costing $100 to run before, now it costs $40 — and you can spend the savings on... more model runs, presumably. The paper, no doubt destined for a prestigious conference, will be cited by everyone who wants to claim they're doing 'efficient AI' while the actual deployment risks remain unaddressed. The researchers, bless their hearts, have optimised the algorithm but not the ethics.

So here we are: Arbor prunes computational branches, but the tree of AI harms still grows wild. Microsoft's involvement ensures the framework will be productised before anyone asks whether we should be running 2.5x more inference in the first place. The only thing 2.5x faster is the sprint towards a future where we've optimised every last flop except the ones that matter. Cheers.