That is not a benefit. If you use a tool like this to try to compete with sophisticated actors (e.g. all major firms in the capital markets space) you will lose every time.
We come up with all sorts of things that are initially a step backwards, but that lead to eventual improvement. The first cars were slower than horses.
That's not to suggest that Renaissance is going to start using Chat GPT tomorrow, but maybe in a few years they'll be using fine tuned versions of LLMs in addition to whatever they're doing today.
Even if it's not going to compete with the state of the art models for something, a single model capable of many things is still useful, and demonstrating domains where they are applicable (if not state of the art) is still beneficial.
It seems to me that LLMs the metaphorical horse and specialized algorithms are the metaphorical car in this situation. A horse is a an extremely complex biological system that we barely understand and which has evolved many functions over countless iterations, one of which happening to be the ability to run quickly. We can selectively breed horses to try to get them to run faster, but we lack the capability to directly engineer a horse for optimal speed. On the other hand, cars have been engineered from the ground-up for the specific purpose of moving quickly. We can study and understand all of the systems in a car perfectly, so it's easy to develop new technology specialized for making cars go faster.
Far too much in the way of "maybe in a few years" LLM prediction relies on the unspoken assumption that there will not be any gains in the state of the art in the existing, non-LLM tools.
"In a few years" you'd have the benefit of the current, bespoke tools, plus all the work you've put into improving them in the meantime.
And the LLM would still be behind, unless you believe that at some point in the future, a radically better solution will simply emerge from the model.
That is, the bet is that at some point, magic emerges from the machine that renders all domain-specialist tooling irrelevant, and one or two general AI companies can hoover up all sorts of areas of specialism. And in the meantime, they get all the investment money.
Why is it that we wouldn't trust a generalist over a specialist in any walk of life, but in AI we expect one day to be able to?
> That is, the bet is that at some point, magic emerges from the machine that renders all domain-specialist tooling irrelevant, and one or two general AI companies
I have a slightly more cynical take: Those LLMs are not actually general models, but niche specialists on correlated text-fragments.
This means human exuberance is riding on the (questionable) idea that a really good text-correlation specialist can effectively impersonate a general AI.
Even worse: Some people assume an exceptional text-specialist model will effectively meta-impersonate a generalist model impersonating a different kind of specialist!
> Even worse: Some people assume an exceptional text-specialist model will effectively meta-impersonate a generalist model impersonating a different kind of specialist!
Specialists exist because the human generalist can no longer possibly learn and perfect all there is to learn in the world not because the specialist has magic powers the generalist does.
If there were some super generalist that could then the specialist would have no power.
