[dzonga]

the reason - i'm a little bearish on AI is due to its cost. small companies won't innovate on models if they don't have billions to burn to train the models.

yet when you look back at history, things that were revolutionary, it was due to low cost of production. web, bicycles, cars, steam engine cars etc.

[rafaelmn]

> yet when you look back at history, things that were revolutionary, it was due to low cost of production.

Nuclear everything, rockets/satellites, tons of revolutionary things that are very expensive to produce and develop.

Also software scales differently.

[miguelaeh]

The first cars, networks, and many other things were not unexpensive. They became so with time and growing adoption.

Cost of compute will continue decreasing and we will reach that point where it is feasible to have AI everywhere. I think with this particular technology we have already reached a no return point

[Manuel_D]

I suspect that models will become smaller, getting pruned to focus on relevant tasks. Someone using an LLM to power tech support chat doesn't want, nor need, the ability to generate random short stories. In this sense, AI is akin to cars prior to assembly line manufacturing: expensive and bespoke machines, with their full potential tapped when they're later made in a more efficient manner.

[farco12]

I could see the cost of licensing data to train models increasing significantly, but the cost of compute for training models is only going to drop on a $/PFLOP basis.

[zwijnsberg]

yet if the weights are made public, smaller companies can leverage these pretrained models can't they?