> Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?

One possible explanation: because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible - combined with some willingness to burn money so that they don't strongly fall behind on their AI models.

On the other hand, OpenAI and Anthropic at least formerly concentrated on building and providing the best models that they could with concerns about financial feasibility taking a backseat.

Just to be clear: I do have the impression that by now (likely because of pressure from investors) OpenAI and Anthropic take these financial concerns more seriously, but nevertheless Google's vs OpenAI's/Anthropic's "DNAs" concerning on what to focus on differ.

And also Google is public listed company and the other two (for now) are private. I think that fact does have a strong bearing on how they operate.

I think this is the correct answer.

Google never really has to outpace the competitors (other than to have some relevance) but they have a very large group of business customers using them for business process work in Gmail, Docs, etc.

Clearly they will win when the models are close enough to frontier to be good enough, but are long-term cheap for buy. I.e. they will aim to make it a commodity.

In theory MS has the same opportunity (plus they have GitHub so, you know, dev eco system too) but seem be blowing the strategy.

Anthropic and OpenAI are having to race to the top on ability entirely to keep their name in the media and in front of us all (which costs: hence more recently trying to pivot away from model releases and more into controversy/danger). The main cost is in training and so this strategy is much much more expensive and this will play out either as a huge cost hike or a forced slow down in pace.

I believe essentially Google is betting on that & I think it's probably the right strategy.

> Google is a little bit more frugal and focuses on how to make providing AI models financially feasible

There’s no magic there. You get an account executive and a call with a systems architect to find out what you’re doing.

Clouds gonna cloud, this is the reason they rolled deepmind into gcp and arguably the inverse is true, the labs are trying to become clouds

> because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible

That feels right. It's not as if they've been missing out on great profits.