Perhaps an unpopular opinion: Google would greatly benefit from this AI bubble to pop and take down OpenAI and Anthropic.
These guys...
When Eric Schmidt became chairman or Ruth Porat became president, this was more a transition towards less involvement than an increase of impact.
The next chapter of our AI momentum
First, it is incredibly difficult to pivot a large organization because there are too many competing interests, too many fiefdoms people have built and too much organizational inertia. A new company has the advantage of a singularity of purpose. Google has to compete with internal interests about AI disrupting search, ad revenue and so on. This can slow you down and limit the resources you get. It's why companies get disrupted, particularly when they reach monopoly status. Steve jobs said it best [1].
Second, Gogole's path here (IMHO) is to make their own hardware. They've already done this with their TPUs but need to be able to compete with NVidia offerings. NVidia controlling the price, features and, most importantly, who gets to buy them is bad for Google. This is what Google should be pouring billions into.
The beauty of this is that success is easily measurable against metrics like price-per-petaflops, performance-per-Watt and so on. Throw money at some key Nvidia engineers and have them design silicon for you for TSMC to fab.
I still believe that Google is positioned to survive the (IMHO) inevitable AI bubble popping.
Demis tried to be google ceo by pumping out self aggrandizing 'documentaries' and highfalutin interviews where said a whole bunch of nothing.
I do wonder if anybody working at Google can give us an insight why the chaos and lack of competitiveness?
https://x.com/sundarpichai/status/2085033425736745093
So it's not stepping down, right?
So yes fancy title, but basically, someone who can move the stock price is quitting and we're trying to ease the perception of it.
This means Demis wanted a change and to work on other things, it's not Google wanting this.
A chief scientist can be super influential, or a guy who’s on the slow path to retirement but is keeping a paycheck to keep up appearances.
It's still the typical title as a stepping stone towards something else (often outside).
Edit: sibling story in front page links to this which explains better in its lede text than what I just wrote: https://www.nytimes.com/2026/08/05/technology/google-researc...
But Jeff was responsible for a lot more of what is actually used today than Demis.
Demis is responsible for a lot more of the hype though ;)
I think saying Jeff's contributions were a long time ago must represent some kind of lack of understanding of Jeff's recent contributions.
Jeff has still been focused more on infrastructure, and that is just more hidden most of the time.
I would say,if i was forced to pick someone whose vision to follow, it would definitely be Jeff and not Demis, even today.
Which is: When you think about Google, true, old, "don't be evil", tech excellence Google, you don't think about Demis. You think about Jeff's and Sanjay's geeky, technically uncompromising, faces.
But missing out right as AI coding agents become genuinely deeply capable and useful is just an immense failure.
Clayton Christensen [1] says (paraphrasing) it's a good idea to spin-off (or invest) in a startup that you've a say on, before the ecosystem sprouts a seemingly-non-entity and gently disrupts your market.
Interestingly (diff strategies i guess) Apple tends to acquire (Q.ai) rather than invest/venture (as google in OP).
[1] The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail.
This over-reaction is why people can't see over a long time horizon.
This is great news for Google as they realize that Sundar is the problem and he will soon leave Google for Demis to be the new CEO of Alphabet (Google) which I am predicting. [0]
Whatever happened to Prabhakar Raghavan? Got kicked upstairs and we barely hear from him nowadays.
It's only a matter of time before Demis leaves and joins Anthropic or OpenAI.
This is a promotion for Demis and this could be a path for Demis to be CEO of Alphabet in the future in the AI era.
Google already invested in Discovery Loop (Jeff Dean, Orol Vinyals, Quoc Le and Sanjay Ghemawat's company), so what is happening is still a win in investment terms.
The question is about Sundar's future at Google, he is a mobile era CEO at Alphabet and I would hazard a guess he will probably step down in less than 3 years.
Perhaps now there could be a shift back to investing in R&D to get fresh monopolies.
His current title is CEO of Google DeepMind. Becoming Chief Scientist of Alphabet seems to be a step away from the path to replacing Sundar, no?
P.S. I think his real interest is Isomorphic, and his new role will offer fewer distractions.
He was the guy branding Google an AI-first company back when they invented the transformer.
Looks like Gemini's sub-par performance is claiming heads
Something was definitely going down internally.
Even in indirect ways. OpenAI itself was founded because Musk got fixated on "stopping" Hassabis.
Kinsley Gaffe: A mistake whereby a politician inadvertently says something truthful which they had not meant to reveal.
To me the biggest news is Jeff and Sanjay leaving Google... but then not really, since Google will be "an investor". I guess that's sorta their retirement plan? And is Discovery Loop actually part of Alphabet? So complicated.
Don’t understand how Sundar is still running things, though.
1) Repair search that had been broken by AI initiatives.
2) Include less invasive AI with ads for those that need to be spoon fed.
3) Pretend to work on AGI and data centers in space.
4) Sell shovels and TPUs to the gold diggers.
Where do real results live?
We'll know that we've reached the singularity when there's a mysterious and superintelligent AI entity with technology and motivations that we as humanity do not understand and have no control over. Not sure why we want that, exactly, but that'll be the big sign.
> Announcing Discovery Loop!
> I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor.
There are PetaBytes of important scientific data locked in archival file formats. The first step is to make this efficiently readable.
I’ve been reading this sentiment on HN since GPT4o, yet models got better and better
If Demis Hassabis got a Nobel prize for being a Project Manager, is Zitron up for the Nobel on Economy for excellence in economic forecast?
Uh oh...
ahahah golden.
Will be following their journey
I am sure VCs are fighting to invest and they will get 4B investment immediately with this team
This is probably the only time some prominent VCs would be active during August, only for Jeff Dean & Co's company.
I can see them now frantically negotiating, responding and firing off emails right now during their holidays to get an allocation in Jeff's new company.
So when Deepmind first made headlines I recalled an article in the UK magazine, Edge, which had an article about a game called Republic being developed by a team of former Bullfrog employees lead by one Demis Hassabis.
Out of interest, I just checked Internet Archive, and lo and behold I found it [0]
I see Wikipedia [1] also mentions that he was the lead programmer on Them park and worked with Peter Molyneaux at Lionhead during the development of Black & White.
It doesn't add much to the story under discussion, but it makes me think at the time I wanted to be a game programmer but my parents encouraged me to go study engineering instead.
[0]: https://archive.org/details/edge-issue-078-november-1999/pag... [1]: https://en.wikipedia.org/wiki/Demis_Hassabis
Instant nostalgia: https://youtu.be/tQJJ_rhHxIk?si=V8pOVwj3gYkw0xKB&t=188
OpenAI and Anthropic went from tiny startups to huge companies. As a consequence the stock options/RSU's offered to the employees paid off a far higher percentage ROI than any stock options a DeepMind (and thus Google) employee would get (since Google is already huge). This disincentivizes people who truly believe in the economically transformative power of AI to work at Google since their benefits will be capped by Google being large + having public company obligations.
Considering these are the best stats they could find, gemini usage+general situation must be really, really bleak.
High demand means nothing. A model being live is nothing to brag about. And gemma downloads also can be from auto CI pipelines etc. Nothing concrete
I said for a few years to many a downvote on HN, everyone wants AI, nobody wants to pay the true costs, the AI race will turn into a "race to the bottom" that is, who can give you the most compute for the lowest cost, and still remain profitable?
I keep seeing this but this line of thinking doesn't make any sense. What does it really mean?
There are expensive models that increase the probability of you doing your task under a lower cost. That means you can't use Gemma for coding your new compiler - it would just be overall costlier.
Heavier models are cheaper at more complicated tasks because they use fewer turns and fewer mistakes.
Cheaper models are more likely to be cheap at less complicated tasks. Like if you just ask Gemma "Hi" it would probably be cheaper than asking Opus.
So what does this statement really mean? People don't want to pay the extra for a more costly model? Why wouldn't you? It reduces your overall cost!
I have always found NPM download numbers truly suspect. Is no one caching? Are they estimating true number of downloads base on some estimate of cache hits?
Thank you for adding some clarity to this. When I calculated 900 million downloads divided by 8.3 billion people in the world, I came with a number that made it look like about one person in 10 were downloading this model.
The bigger deal is the departure of Jeff and Sanjay, rather than Demis moving into a different role.
That said... just give it a couple years, they'll be back in a lucrative aquihire.
And all the prominent names Google gained: NULL
Combined with no gemini frontier GA release in about 14 months. You have to have created an environment pretty hostile to innovation for this to happen
Oof. Good for Jeff and Sanjay (who just joined Twitter), bu that is a big loss for Google. Google stock is down 5%. It might not be much of an exaggeration to say these two are worth ~$200 billion.
Scientist-heavy orgs that want to solve everything in token space may overtook tool use; meanwhile Anthropic has been super focused on MCP, Claude Code etc for over a year
And most likely this is a calculated step to avoid freaking people out, even though he is effectively leaving.
To me it looks like he’ll be in a position to actually be unhobble DeepMind - reminder DM was sitting on a ChatGPT product for a whole year before ChatGPT got released (LMChat) and Google didn’t let them release it.
https://www.reuters.com/business/google-shakes-up-ai-leaders...
>We’ve got amazing talent, world-class compute and products…
Products are third on the list. Google is an incubator for talent first and foremost. Products are an afterthought
Nowadays, every company—even huge ones—prefers to be seen as a "growth opportunity", so they are trying to play up their ability to create new products which will somehow be so incredible that the line keeps moving up forever.
That said, there is definitely a correlation between companies chasing new products and leaving old ones to become crap.
Founders are first. Ideas are second.
1) the simplest explanation whenever a bunch of people leave [x] at a company at the same time, is that [x] is becoming less important to that company moving forward. It's not proof, but you know, everyone is trying really hard to say that's not what's happening here. Sometimes the simplest explanation is correct.
2) rumor is that Sergey Brin, co-founder of Google, got bored during pandemic lockdown and eventually returned to Google in a lower profile role, related to AI. I have to think that he still has a lot of say in what goes on in Google relative to his interests.
3) Yahoo Finance article says Hassabis "has long prioritized research over profits", so his replacement might indicate that Google has decided it is time for this stuff to pay for itself?
There is another universe where Google is hard AGI forward, freeing up as much compute as possible for Deepmind, turning away OAI and Anthropic, and offering the uncontested premier AI research lab, by probably an order of magnitude or more compute. Gemini 3.5 ultra is limited availability with unreal abilities.
But their balance sheet becomes terrifying, and it's all or nothing that this plays, er pays, out.
Right now though Google is pretty well hedged. Even Chinese models likely just mean more compute sold.
Seems like a good way to spend investor dollars without consequences while maintaining control. Maybe a bit cynical but i can't figure out why they'd form a PBC over anything else.
In most cases, it doesn't really matter. The board + management is still in charge, and they have significant legal leeway regardless of the structure. But there's little additional cost to opt for a PBC, and it does give you more legal defensibility to be truly mission driven. Standard C Corps weren't really intended for mission driven companies (see the shareholder primacy norm).
I think it's popular for AI startups, because many great researchers understand the risks involved, and they don't want what they build to be controlled solely for shareholder benefit.
While non-profits are also an option for a mission driven org, it's harder to raise the large amounts of cash that some AI startups need, and laws around deferred compensation and private inurement (e.g. options-like structures) make employee compensation harder.
[1] - https://www.forbes.com/sites/jackkelly/2024/05/31/google-ai-...
[2] - https://www.axios.com/2024/02/23/google-gemini-images-stereo...
They could really use some encouraging news about the competitiveness of their AI lab.
They getting a higher ROI renting their TPUs to Anthropic et al instead of performing training and serving their own models. Google cloud has insane backlog, and has rapidly expanded to satisfy it. While those DCs get built, they’re cannibalizing their own products for it.
This makes sense because (1) they are investors in Anthropic, so they still win and (2) they can always catch up on model training later when the profit opportunity shifts, or abandon it if there is no way to recapture that value.
So I think they have to prioritize scaling for their models to a higher degree than other groups. Being within say 5% or so in most cases is probably adequate and matters more overall for their user base than being the absolute best coder. So they may be setting compute constraints for training or inference that are firmer than other teams.
People who truly care about becoming rich*
DeepMind made enormous transformative discoveries, for instance in the world of protein folding. But that will just save human lives, not let CEOs fire their people to grab a larger piece of cake for themselves.
It's a huge company.
It's unlikely there is any one person to blame (and entirely possible he has none of it). But things need to change.
Google spending money on competing head to head with your LLMs is a waste of money for Google. If anthropic wins, Google copies their approach, buys anthropic for cheap or both. All the investors throwing money into OpenAI, Anthropic, are just accidentally subsidizing Google's product development. Google shouldn't spend its AI research capital in an arms race with Anthropic but instead should invest in AI approaches that no one else is investigating at scale. That way Google can hedge against LLMs hitting a wall.
There is a real but small danger to Google that Anthropic replaces Google as a search engine, but that is an uphill fight for Anthropic. Google has massive brand recognition, network effects with gmail and chrome, Anthropic can't just copy what works from Google. On the other hand Google can copy what works for Anthropic. Google would have to play poorly to lose that fight.
Probably the worse case for Google is that software becomes so cheap and easy to create and maintain that all of Google's product offerings become commoditized. Even in that world Google has a lock on infrastructure. Perhaps ASI software creation completely removes that as well? If so we are living in a post-singularity world and probably the stockmarket doesn't exist anymore either.
Not all change is good, as proven by Zuckerberg's response after the lackluster Llama 4 release. The radical restructuring appears to have made things worse.
Yes it's a huge company.
So they are slower. Then they will surface it across their massive product base and keep generating cash. While having a hand in Anthropic and others via investment anyway.
Google doesn't need to offer you the bleeding edge at startup pace. They're playing a different game. When the bubble pops they will be well positioned really no matter the outcome to continue to capitalize as their competitors implode or get absorbed.
The idea a delay is a "complete and unmitigated disaster" is just laughable. People have been saying this about Google since ChatGPT first invaded the public consciousness. Google will continue to do well, the histrionics of people like you aside.
Very few companies have leadership that can prevent this infighting and force teams on directed goals.
It's funny, because I think the company that's going to be best positioned coming out of this bubble is in fact google, because they have the expertise and the capital. But I honestly can't tell you right now what their AI product even is -- I've seen so many things go into the graveyard a few months after its launched that I'm utterly confused what their offering even is at this point.
Anyway, the actual theory is that Google and Meta have fallen behind because they've been playing by this playbook of focusing on scale and training data, whereas OpenAI and Anthropic have done so well because they are likely doing much more interesting things to improve their models over time. It makes sense when you realize that one of Google's key strengths, besides talent, is that they have an incredible amount of data they can use for training due to being both the world's leading search engine as well as having all that video data from YouTube. Scaling the training data makes more sense to them than it does to Anthropic and OpenAI, who are both relatively data-disadvantaged.
You can kind of see this when you look at the Gemini 3 scorecard when it came out (https://blog.google/products-and-platforms/products/gemini/g...) and notice that while it wasn't as good as Claude And GPT at coding, it scored higher on a bunch of other non-coding benchmarks, and I think the reason why is simply because of Google's data advantage.
If true, I feel even more vindicated for believing that the "scale is all we need" narrative was bullshit.
I thought employees have already had opportunities to cash out (there's enough funding rounds for that).
(Tho how much you can sell was limited, iirc to double digit millions...)
The new paradigm is you ask the llm a question, get the answer and cutout the middle man. (yes the answer may or may not be as good as the old google result, but for the sake of the argument lets say it is), Google was in danger of simply getting their arm cut off. so they focused on scaling so they could add LLMs to the search, which they largely have. You can't offer an opus like model on something as big as search (and which is offered for 'free'), so they focused on that model, and the infrastructure to run it, because they cannot afford to lose search.
Meanwhile, they know the power of frontier models, they are working to have the infrastructure to be a huge player in them and I'm sure they will have a frontier capable model, eventually. They are playing a longer game, because they can, and I think it's going to work out very well for them.
Now, whether Google is the right environment to nurture, that’s its own quandary.
Another is that they hold the key Transformer architecture patent. If it is still relevant (which I'm not personally clueful about) and if they start enforcing it, then we may see a reprise of the situation where Microsoft made money for years every time an Android phone was sold. Regardless of that particular patent, it's probably safe to say they'll be better-positioned than anyone else if AI companies start lobbing patent nukes at each other.
A third factor that shouldn't be discounted is that Google has access to warehouses of training data that other companies don't. Google Books alone is an Alexandria-scale archive that the courts forced them to keep to themselves. Those restrictive copyright decisions may turn out to be a blessing in disguise for Google because no one else will have been able to scrape the data.
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.
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
- 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.
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.
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.
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.
Edit: hmm, no, they seem to be mentioning AGI and frontier models in https://blog.google/company-news/inside-google/message-ceo/n...
They need to route all browser search strings to an LLM, and slowly begin to charge where people will pay. Likely ad space.
No, the top talent is clearly at Anthropic and OpenAI
Because real Fable usage starts at $20/month, and has oppressive usage limits even at that (ridiculous) monthly price.
Compared to my $3/month GLM-5.2 subscription, I have never felt like I was leaving capabilities on the table by refusing to cough up $20 for 15 minutes of Fable use per day.
You can solve it for cheaper if you use GLM but if you are involved in it more, but that defeats the purpose.
Heard this multiple time, to me this is pure history rewriting and post-rationalization. OpenAI also had a internal chat app before chatgpt, Microsoft had multiple, a bunch of other players also had internal chatgpt equivalents + many startups built some using OAI API.
The breakthrough of ChatGPT wasnt because OAI were the first to think of that (absolutely obvious and basic) product, it was because they were the first to get a model strong enough to be actually useful to talk to, vs a mere fun novelty.
There is no evidence whatsoever that DM ever had such a model that they decide not to talk about/release.
Everyone else was scared to unleashed it on the general public because it was too powerful with too many unknowns (in ~2022, hindsight is different)
Altman didn't give a fuck, first mover was more important to him.
He won’t have any authority in the company other than leading and voting in board meetings.
At some point we have to all accept that powerful AI is most likely dangerous AI as well, almost by definition.
Right now AI companies are competing to make LLMs better at graduate-school level tasks. They all can already competently tell you what the weather is going to be like tomorrow or when the first Led Zeppelin album was released.
Google buys Anthropic for cheap? How?
My case is based on the assumption that Anthropic will not hit RSI or if it does RSI rapidly hits a wall. Faster your growth curve, the faster you eat all the low hanging fruit and s-curve. I could be wrong here, maybe RSI will cause a hard takeoff singularity by 2030 and just keep going, but if that happens the world fundamentally changes.
Google has multiple cash firehouses, the small AI companies do not.
They have a structural advantage in cash flow and stability of funding, but stability is also a handicap when disruption is the objective.
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.
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.
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.
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.
The group running the company, is the company.
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.
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.
LLMs will obviously keep getting incrementally better, but in order to get the kind on jump that LLMs themselves were, the sentiment is that we need something more.
[1] https://www.synbiobeta.com/read/anthropic-is-hiring-biologis...
Sounds like the oil scare from 90' - we thought we were gonna run out of oil. But as oil gets more expensive it pays to dig further down to find the stuff that didn't make sense to dig up before.
But people want what AI does for them. It's a tragedy of the commons situation.
His accomplishments are far beyond project manager. https://en.wikipedia.org/wiki/Demis_Hassabis
But more seriously, this has to do with DeepMind falling way behind the frontier in intelligence and cost per task. There's basically no reason to use a Gemini model today.
"Based on estimates of their burn rate and historic analyses, I hypothesize that OpenAI will collapse in the next 12-24 months unless it raises more funding than in the history of the valley and creates an entirely new form of AI."
-July 29, 2024
2 years these charlatans have had their "imminent collapse" predictions repeatedly wrong, meanwhile all valuations, AGI progress in unsolved problems, and frontier leadership have been repeatedly vindicated that we are beginning ASI / the singularity.
Open weights will drop costs, but distribution matters. OpenAI has that.
Costs will come down. Deep entrenchment will not.
To say that’s an outrageous amount isn’t overstating in the slightest.
Given they are already being forced to drop inference prices in an attempt to compete with Chinese providers, I don’t know if billionaire investors will be willing to hand out that much money this time around if they can’t generate enough value to break even despite the hype.
Each generation of model is more expensive to train and run than the last.
More efficient hardware? That means throwing out billions of dollars worth of existing hardware and buying billions more in hardware. The cheapest hardware is the stuff they already have because spending $70B in hardware to halve a $2-4B electricity bill just doesn’t make financial sense (and that’s if doubling hardware efficiency can happen before they go under).
If they cut costs by using smaller models, they are then racing to the bottom vs China which seems hard to do (especially with China’s cheap solar energy).