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Even with efficiency breakthroughs, it would just afford packing more agents per unit of memory. Scaling compute and data keeps paying off, leading to smarter m
by bionhoward 11d ago
Even with efficiency breakthroughs, it would just afford packing more agents per unit of memory. Scaling compute and data keeps paying off, leading to smarter models, and smarter models have more demand even at higher prices because they can accomplish more work at higher quality. Sovereign AI hasn’t even really taken off yet to anywhere near the level it could. That’s going to dramatically increase the number of massive-scale users of AI agents. So no, IMHO they will “never” have “enough.”
- epicureanideal 11d agoAt some point though, won't someone be able to extrapolate the demand growth curve, and invest some colossal amount of money into making and selling more RAM chips?
- fragmede 11d agoYes. China's done just that. Expect those factories to come online within 2-3 years.
- cyanydeez 11d agoWeve hit the sigmoid. Whats scalling is ancillary to the model. The cry for a slowdown is because the open weight models demonstrate the cost of parameter pacling is not work neither inference nor training. The assumption about the singularity simply is a delusion with LLMs. However, the models do provide a means to improve the harness universe, so that residual will continue to improve perception. Parameter cpunt will stagnate and training wont be justifiable from every angle.