The entry costs have always been so high that LLMs as a service was going to be a winner-take-most marketplace.

I think the best hope is to see other major players enter the space either commercially or as FOSS. I think the former is more likely, and I was really hoping that we would see PaLM on GCP or even something crazier like a Meta-Amazon partnership for LLaMa on AWS.

Unfortunately, I don't think any of those orgs will pivot fast enough until some damage is done.

Glad to see them make ChatGPT accessible via API and go back to update their documentation to be more clear on which model is which.

I had an exhausting number of conversations with confused product managers, engineers and marketing managers on "No, we're not using ChatGPT".

Let's just slap what's effectively a reskinned version of ChatGPT in a sidebar is certainly a choice ...

I like how this might be the spark that gets Product Management and UX at-large to finally start understanding how to work with ML-based functionality in their products. However, I think we're going to look back and facepalm at a lot of design decisions we see over the next 6-ish months as companies rush to get something (anything) out the door faster than their competitors.

Seems that the academia tangential AI research openings are going to be virtually non-existent for the near future, and existing jobs may be at risk.

Those that are closer to the direct value chain (i.e. a data scientist who can point to their A/B test increasing revenue by $X) are going to be safer until the market is on another upswing.

Most of them are really just CV padding to some 1st or 2nd year grad student. If you look into them more, it's usually just as trivial as being the first to publish a paper about using a model that came out 12 months ago on a less common dataset.

It's really more about the grad student's advisor doing them a solid in terms of building their CV than actually adding useful literature to the world.