The reason LLMs are viable for use cases that Watson wasn't is their natural language and universal parsing strengths.
In the Watson era, all the front- and back-ends had to be custom engineered per use case. Read, huge IBM services implementation projects that the company bungled more often than not.
Which is where the Palantir comparison is apt (and differs). Palantir understood their product was the product, and implementation was a necessary evil, to be engineered away ASAP.
To IBM, implementation revenue was the only reason to have a product.
> Read, huge IBM services implementation projects that the company bungled more often than not
Well this is _not_ what they wanted to sell in that talk.
But the implementation shown was über vanilla, and once I got home the documentation was close to un existent (Or, at least, not even trying to be what the docs for such a technology should be).
In the Watson era, all the front- and back-ends had to be custom engineered per use case. Read, huge IBM services implementation projects that the company bungled more often than not.
Which is where the Palantir comparison is apt (and differs). Palantir understood their product was the product, and implementation was a necessary evil, to be engineered away ASAP.
To IBM, implementation revenue was the only reason to have a product.