Comment by master_crab
1 month ago
by concentrating on creating a very domain specific model
I don’t disagree with this from an economics perspective (it’s expensive running an FM to handle domain specific queries). But the most accurate domain knowledge always tends to involve internal data. And then it becomes the issue raised above: a people problem involving internal knowledge and data management.
Incumbent hyperscalers and vendors like MS, Amazon, etc (and even third party data managers like snowflake) tend to have more leverage when it becomes this type of data problem.
>Startups can outcompete the Foundational Model companies by concentrating on creating a very domain specific model, and providing support and services that comes out of having expertise in that specific domain.
Well-put because the business is focused and to-the-point from the beginning.
For those applications where this gets you in the door to the domain, or gets you in sooner, this can be a competitive advantage. I think Lukas is pointing out the longer-term limitations of the approach though. I thought this would extend from 1980s electronics myself.
You could edit this however:
>Startups can [prosper] by concentrating on creating a very domain specific model, and providing support and services that comes out of having expertise in that specific domain.
And it may hold true anyway and you may have a lifetime of work ahead of you whether or not the more-generalized capabilities catch up or not. You don't always have to actually be competitive with capitalized corporations in the market if you are adding real value to begin with, and the sky can still be the limit.
>the most accurate domain knowledge always tends to involve internal data.
>Incumbent hyperscalers . . . tend to have more leverage when it becomes this type of data
That can help as a benchmark to gauge when a person or small team actually can occasionally outperform a billion-dollar corporation in some way or another.
I'm no Mr. Burns, but to this I have slowly said to myself "ex-cel-lent" similarly for decades.
It's good to watch AI approaches come and go and even better to be adaptable over time.