[aleksiy123]

I’m starting to think that googles strategy is a bit different then the other frontier providers.

Focusing more on performance to compute efficiency over pure performance. And maybe that’s why Gemini is (seemingly) lagging behind?

Other providers hitting capacity and hitting the limits subsidising their inference.

Google strategy seems to be about scaling and distributing these models to their existing billions of users.

[nilkn]

I don't view Gemini as falling behind. I actually view it as a somewhat distinct type of intelligence compared to the latest iterations of GPT5 and Claude. The latter are, increasingly, very focused on productivity and automation of work tasks. They're optimized for long, agentic, self-correcting reasoning loops. Gemini is very different: it feels to me like a much smarter baseline model, with much deeper intuition (especially its Deep Think mode), but it's not nearly as good at long-range self-corrective agentic loops. For months now my workflow has been to use Gemini for creative leaps and insights, while preferring Codex or Claude or GPT5.5 Pro for routine or precision work.

[mark_l_watson]

I like Google’s business model more than the other frontier model providers: sustainable. One thing I don’t like with Gemini Ultra is no visibility into token use or what the cost would be. I have been planning on letting my Ultra subscription expire and go with OpenCode with a fast inference provider to get this visibility, but this discussion thread gave me the idea of also trying the paid APIs with AntiGravity instead of a subscription. When I sit down to do a specific task I want accurate token usage and $$ data as I work.

[leecommamichael]

Isn't that where everyone's strategy is shifting?

[aleksiy123]

Yes, but I think Google was playing that strategy from essentially day 1 or very early in this AI race, where as the others are there now because of their lack of access of compute.

The general narrative I would read on HN/others, was that Google would be able to outlast/outcompete OpenAI and Anthropic because Google had both more money and more compute. Playing the game of subsidizing their most capable models to capture market share longer than the VCs could.

But instead I feel like Google opted out of that much earlier. Shifting their focus on efficiency and scaling much much earlier. Flash and Gemma being where Google was actually ahead of the competition while everyone was focused on bigger more capable models.

In the last month the environment has changed, compute is constrained, costs for consumers are way higher than expected. Copilot pretty much imploded, and I'm guessing both Anthropic and OpenAI are starting to feel the squeeze.

My personal opinion was this was necessary because integrating AI into products like AI overview, search meant scaling to billions of users was a requirement right out of the gate. And theres not enough money/compute no matter who you are to use frontier models for that.

[throwaway219450]

It benefits Google's bottom line to have very capable small models that can cheaply cache results for search queries, even if they're frequently wrong. But I wonder if they use Gemini for the top X% of search terms to try and get better retention? Also the TPU vertical gives a good advantage here. I've never been super impressed with Gemini out of the box, but surely, surely, Google is best positioned here.

As a consumer, 24-32 GB VRAM is affordable ($1-2 k) and that's the frontier I'm most interested in. It's very "two papers down the line". Those models are also feasible to fine-tune, unlike the O(100+B) behemoths. The 4000 Pro Blackwell has very good TDP compared to people insisting on using 300-600W gaming cards. If I was freelancing, I would definitely consider getting a 6000 for work.

[scottyah]

They also just have the resources- both in $$ to spend time optimizing, but the people like Jeff Dean who have already been focused on AI efficiency for a long time.

[chakintosh]

> Google strategy seems to be about scaling and distributing these models to their existing billions of users.

Yeah, part of that is installing a model in chrome to millions of users without consent.