Abhishek Nagaraj
@abhishekn
Associate Prof @berkeleyhaas, NBER RA. Dad to 2 girls. he/him/his.
Interested in using tech, AI and data to drive progress.
Using ideas as my maps.
6.5K Following 13.2K Followers
This is such a great grounded quantitative (and qualitative) case study of one bleeding edge organization as it adapts to using agents everywhere.
Must read if you want a glimpse of the organization of the future.
Well done @natewilmers and team! (Loved the UAW reference from the 1950s)
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@Afinetheorem It’s here!!
By my calculations, just the compute here (300 BILLION output tokens) would cost a regular user $20-$30 million in tokens. How do we sustain this level of investment in science once the AI companies have decided that its no longer worth their time/effort to go after everything thats not a millenium problem.
also -- totally 🤯 - going to be hard to focus on another other research seminar/topic for a while.
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the results? the best models in automate mode are not always the best models in augment mode.
for example - Opus and Sonnet are great on performing tasks on their own, but not in proving assistance, while GPT-5-Mini was great on both dimensions.
Gemini models were stronger as assistants than as automators.
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