[CWuestefeld]

I'm not at all an industry pundit. But I suspect there's a reason we're not seeing leading models from Google recently.

Judging from my own frustrating attempts to use Gemini for vibe-coding, it seems like Google is badly over-sold (i.e., under-provisioned).

From all those promos giving away their pro-level subscription with phones; spinning up a mid-level subscription to undercut other providers and (probably most significantly) putting AI queries into ever search response because their flagship search product had become useless; they're promising a lot more processing to customers than they can reliably deliver.

The recent iterations seem to be intended not to push the capabilities forward, but to deliver capabilities at the current level while consuming less resources. That will allow them to maintain their trajectory until (I'm expecting) they get the huge infusion of extra compute resources from Space X later this year.

If I'm right, then I expect we should see Google start pushing forward again (rather than more of this lateral stuff) by the end of the year.

[nateb2022]

> Judging from my own frustrating attempts to use Gemini for vibe-coding, it seems like Google is badly over-sold (i.e., under-provisioned).

Based on my experience lately with Codex, it seems the opposite. Gemini 3.6 Flash (high) in Antigravity CLI feels a LOT faster than GPT 5.6 Luna (high) in codex. In the order of 10-50x.

[addaon]

> putting AI queries into ever search response

There's no way these are using a significant amount of compute. I'm not 100% convinced they're actually LLM-generated rather than an old-school Markov model. Both the relevance and accuracy numbers of the responses flirt with 0%. It's possible they just have a few million stashed responses and choose one at random, from what I can tell as a user.