Yep. And that's the real value add that is happening right now.
HN concentrates on the hype but ignores the massive growth in startups that are applying commoditized foundational models to specific domains and applications.
Early Stage investments are made with a 5-7 year timeline in mind (either for later stage funding if successful or acquisition if less successful).
People also seem to ignore the fact that foundational models are on the verge of being commoditized over the next 5-7 years, which decreases the overall power of foundational ML companies, as applications become the key differentiator, and domain experience is hard to build (look at how it took Google 15 years to finally get on track in the cloud computing world)
I notice that a lot of people seem to only focus on the things that AI can't do or the cases where it breaks, and seem unwilling or incapable of focusing on things it can do.
The reality is that both things are important. It is necessary to know the limitations of AI (and keep up with them as they change), to avoid getting yourself in trouble, but if you ignore the things that AI can do (which are many, and constantly increasing), you are leaving a ton of value on the table.
Same with consultancy. There is a huge amount of automation that can be done with current gen LLMs, as long as you keep their shortcomings in mind. The "stochastic parrot" crowd seems an over correction to the hype bros.
alephnerd|1 year ago
HN concentrates on the hype but ignores the massive growth in startups that are applying commoditized foundational models to specific domains and applications.
Early Stage investments are made with a 5-7 year timeline in mind (either for later stage funding if successful or acquisition if less successful).
People also seem to ignore the fact that foundational models are on the verge of being commoditized over the next 5-7 years, which decreases the overall power of foundational ML companies, as applications become the key differentiator, and domain experience is hard to build (look at how it took Google 15 years to finally get on track in the cloud computing world)
MostlyStable|1 year ago
The reality is that both things are important. It is necessary to know the limitations of AI (and keep up with them as they change), to avoid getting yourself in trouble, but if you ignore the things that AI can do (which are many, and constantly increasing), you are leaving a ton of value on the table.
skybrian|1 year ago
A trend towards domain-specific tools makes sense, though.
danielbln|1 year ago
Workaccount2|1 year ago
hanniabu|1 year ago