Today I saw one of the most talented engineers I know and worked with leave DeepMind and now I probably know part of the "why". Dark clouds hovering over Google's AI game.
It's not really clear what their gameplan is. From the outside, it looks like they're asleep at the wheel. Qwen/Deepseek/Kimi are crushing them from the cheap-and-open side, and they're not remotely competitive with Mythos/Sol or even plain-vanilla Opus on the "premium" tier.
Gemini does actually have its uses, but they're very very marginal and niche.
From day one everybody was saying that Google would eventually capture the AI market, but it looks more remote than ever. Maybe Hassabis' personal inclination towards AI-for-science, and physics/chemistry in particular -- as opposed to consumer AI and coding AI -- has hurt them commercially.
There's an old saying, if you judge a fish's smarts by how well it can ride a bicycle, it will always seem dumb.
The people who have risen to the top of at Google are built for a different environment than what's needed right now. They are good at playing their political games, sabotaging each other, etc.; i.e. all of the petty games that managers play in big companies. But the AI era demands a different skill set: how to bring together incredibly smart people and forge them into a battle group that will achieve victory in the ongoing battle for AGI! It's as if you have built an army of tanks, but the next battle is being fought on the high seas.
The group running the company, is the company.
For agentic work, but especially for web search, 3.6 Flash has an important leg up, its fast speed, that no other model comes close to matching. I guess nobody pays attention to it because it's not the one big flashy number that you compare to other models.
They are quietly trying to become the chatbot that everyone uses to look things up. That strikes me as an intelligent move - not everything has to be done by an expensive frontier model.
for all these reasons, is being 6 months behind the frontier actually a structural, long term disadvantage? some day the pace of improvement will slow, and google will vacuum up the market. they'll be able to compete with open-weight models just on pure cost advantage from their vertical integration
Whether this happened because they bet on "world models -> better reasoning" and that bet didn't pay off, or failed a frontier run for technical reasons like OpenAI did with 4.5, or something else went down? We don't know.
Will they bleed talent, fall further behind until they give up, or clean the organizational and infrastructural cobwebs and get back in the saddle? We don't know.
I think the question is whether that's even relevant.
If AI becomes a commodity (will it?) you're better off being Google than OpenAI.
Microsoft struggled to keep up with the mobile industry frontier and here they are, healthier than ever.
They may not capture enterprise use, but I don't think they have to. That's only one piece of the market. AI that's useful to consumers will still get delivered via a smartphone, and Google is in a great place to capture that.
I also don't think LLMs have to be a "winner takes all" situation. Value isn't going to come from having direct access to a chatbot or selling API inference, value is going to be in the form of a specific product (for most, devs aside here). Something a consumer, or a non-tech business can buy off the shelf and plug and play. A "ready made" customer service agent system, a "ready made" BI platform using AI, etc.
For consumers, that's probably going to look like whatever is bundled and tightly integrated into their mobile OS of choice.
Yeah, this is one of the things I find so weird on the discourse. If you get there negligeably later, but without astonishing spend and waste, you might even be better off in the long term.
- In a world where open source Chinese models decimate Frontier models ability to charge a high price, it's the operators of efficient inference data centers that will win. Like Google
- Google is probably the biggest provider of "free" AI because it's on Google.com. That forces them to focus on cost. And in a commodity market, low-cost providers are the ones that make the money.
10 years from now, its gonna be Google, Apple and Microsoft left standing in the AI game. Well, until the US wakes up and starts attempting to break the oligopoly like the EU has recently started to do.
This is in addition to bumping their CapEx spend to the extent their cash flow turned negative for the first time ever this quarter: https://arstechnica.com/google/2026/07/google-just-had-its-f...
The world doesn’t realize how desperately compute-crunched hyperscalers are to meet AI demand.
This is a better problem to have than SpaceX, which is renting out capacity obviously because it’s own AI products aren’t selling.
I don’t think this reflects desperation as much as strategy.
[1] https://www.mindstudio.ai/blog/sundar-pichai-google-compute-...
[2] https://www.cnbc.com/2025/11/21/google-must-double-ai-servin...
[3] https://www.bloomberg.com/news/articles/2026-07-22/google-sa...
[4] https://arstechnica.com/google/2026/07/google-just-had-its-f...
[5] https://thenextweb.com/news/google-caps-meta-gemini-compute-...
In any case, my point is that the SpaceX deal specifically likely has ulterior motives.
My point is that an ulterior motive is not necessary to assume when all their actions and statements point to them being severely crunched for compute.
I mean sure, if they had a choice between say, CoreWeave and SpaceX, they’d choose the latter for the nice bump to SpaceX’s financials and their stake… but not just for that, not when it contributes to their cash flow turning negative and their own stock taking a hit.
> The RPO can be anywhere from 3 - 6 years, sure, but even on an annual basis that’s like a hundred billion now.
OpenAI and Anthropic deals are mostly 5 year and Anthropic’s starts in 2027, accordingly these RPOs are sized for projected compute needs and run rate in 2027 not today.
The only way either lab could pay 1 year of RPOs today (~80B for anthropic and ~150B for OpenAI) is with a lot more debt or circular financing, the former of which is difficult in this market.
Everyone spending crazy money on capex right now says they have a crushing backlog and need more compute to protect their share price. I highly doubt the demand exists today at current prices if the big 3 hyperscalers magically had an extra 2-3GW of compute. It’s not like Anthropic and OpenAI are turning away customers offering to pay API pricing..
> Everyone spending crazy money on capex right now says they have a crushing backlog and need more compute to protect their share price. I highly doubt the demand exists today at current prices if the big 3 hyperscalers magically had an extra 2-3GW of compute.
I don't get this though: The theory is all these hyperscalers are simultaneously spending buttloads of money on CapEx to the extent it affects their stock price, and then they would lie about the demand to protect their share price. Why would they do all that when they could just do nothing and keep their firehoses of existing business revenue untouched and maintain their stock prices on the upward trajectory they already were -- like Apple?
> It’s not like Anthropic and OpenAI are turning away customers offering to pay API pricing..
We don't know, but clearly Anthropic has been struggling to keep Claude's 9's better than GitHub's 9's even after paying through the nose for capacity from competitors like SpaceX and Google.
I suspect OpenAI is managing only because Altman scrounged for compute like a madman way in advance, and most of its traffic is free users who can be arbitrarily bumped down to weaker models whenever compute is low. Whenever Anthropic does that Claude Code degrades and people complain.
This doesn’t mean more compute at any price is worthwhile. It also doesn’t mean that the SpaceX compute deal would even be offered to Meta.
> I don't get this though: The theory is all these hyperscalers are simultaneously spending buttloads of money on CapEx to the extent it affects their stock price, and then they would lie about the demand to protect their share price.
It’s not lying - it’s optimistic revenue projections. Your Sam Altman point is an example, these RPOs are real but what’s questionable is whether the AI labs can generate enough premium token API revenue to actually pay those commitments. Today’s OpenAI annualized revenue estimate is only 40B. Will they actually be able to 10x that to pay those RPOs? I’m skeptical especially with offloading inference to cheaper models.
> Why would they do all that when they could just do nothing and keep their firehoses of existing business revenue untouched and maintain their stock prices on the upward trajectory they already were -- like Apple?
The hyperscalers with proven revenue streams and strong financials (Amazon, MSFT, Google, arguably Meta) benefit from making the game more expensive than everyone, will get at least 50% of their capex back from this peak supply/demand mismatch and maybe other than AWS could easily use any excess compute for internal needs.
Edit. Google invested 900 million in 2015 for roughly 5% of the company which comes out to 71.5 Billion dollars at 1.45 Trillion dollar current valuation. That's an 80x increase.
I think Larry Page individually might also have a very large stake as well.
In order get that back by “pumping” SpaceX stock, SpaceX market cap would have had to increase $200B based on that investment, and then Google would need to sell the stock.
Also, for better or worse, SpaceX did increase by about 200B today.
For all it's faults, it is forming a massive military and telecom monopoly that is nearly unassailable. In the next few years, it can start directly competing with Verizon. The Ukrainian military and civilian population heavily relies on those satellites
I agree, which is just one more reason why the original reason you proposed for Google’s investment is very likely not correct. More likely is that Google got a good deal on compute from them. Normal business reasons.
> Also, for better or worse, SpaceX did increase by about 200B today.
The relevant question is how much it would have increased without Google’s investment of an additional $12B. If the answer is “more than $0” then the fact that it only increased $200B means it was a bad investment to pump the stock.
my take on this is eventually the money is going to run out and there's going to be acquisitions and consolidation. I think that's when Google will come out on top.
Edit: hmm, no, they seem to be mentioning AGI and frontier models in https://blog.google/company-news/inside-google/message-ceo/n...
model training and inference is the opposite. people switch LLMs like people change clothes in the morning. there are popular services (openrouter) that make moving as easy as changing a model string.
To its credit Google seems willing to disrupt itself before its competitors can.
No, the top talent is clearly at Anthropic and OpenAI
They need to route all browser search strings to an LLM, and slowly begin to charge where people will pay. Likely ad space.