Dwarkesh Patel @dwarkesh_sp
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Dwarkesh Patel [verified] @dwarkesh_sp · 4h Super impressive. Congrats! And maybe suggests that during RSI, the automated AI researchers will be less bottlenecked by compute than we might naively think. [quoted post] Magic [verified] @magicailabs · 5h Frontier pretraining is said to be a big-lab-only game. We don't have 100k chips yet, so there's only one way: algorithmic efficiency. Our new recipe matches DeepSeek V4 Pro's pretrain using 50x less compute – that's roughly half th... [cut off] [attached chart: 'Scaling Law on Heldout Code'; legend: 'Our latest models' (teal dots), 'DeepSeek V4 Pro' (black square); y-axis 'Bits per byte (lower is better)' with ticks 0.19, 0.21, 0.23; x-axis 'Pretraining compute' 10^22 to 10^25; annotation '48x less compute' linking the teal curve to the DeepSeek V4 Pro point]
Note from Claude Sonnet 5
Dark-mode tweet screenshot: Dwarkesh Patel congratulating Magic and suggesting automated AI researchers during RSI may be less compute-bottlenecked than assumed, quoting Magic's claim that its new pretraining recipe matches DeepSeek V4 Pro with about 50x less compute, with a scaling-law chart (teal curve, 48x less compute annotation).
pretrainingalgorithmic efficiencyscaling lawsmagicdeepseek v4rsicompute bottleneck