> 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.
I think their move makes a lot of sense. Frontier models can probably not advance much further with the datasets we have currently available. Most of the text that goes in is online chatter, images and some scientific texts. That's great for chatbots, knowledge retrieval and programming. But with that database genuine discovery is hard to do. I think for the next step in intelligence the models need to have access to much more data: Data from physics, chemistry, biology experiments - and so on. And they need the data in much higher fidelity than you can currently access. If we just feed AI with all the knowledge we've acquired it's much harder for it to become smarter than us - it basically needs it's own eyes, ears, nose and so on.
I think there are multiple separate events here, timed to coincide in order to minimise disruption. It's hard to be sure whether they share overlapping causes, but I'd guess that there's at least an element of that.
Ed Zitron has predicted 16 of the last 0 AI bubble bursts...
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.
> 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.
Would love to hear the pitch: We never had the lead in AI and now definitely lost it, despite the gazillions dollars and engineering resources of Google, but now will be different?
The "lead" in AI, even with google's TPU advantage, may not make economic sense for a company. Can you make back the training investment on a competitive frontier model, before everyone switches to a newer frontier model within a year?
"Once, in early 2002, when the index servers went down, Jeff Dean answered user queries manually for two hours. Evals showed a quality improvement of 5 points."
I think there's one weirdly simple reason DeepMind isn't doing as well as OpenAI and Anthropic. I may be wrong on this.
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.
OpenAI and Anthropic have the freedom to do absolutely insane things like negligently hack other companies. It would be stock price suicide if anything even remotely happened with Google.
Google AI was telling people to put elmer's glue on pizza to help the cheese stick, turning the Founding Fathers black, and more. Oh and the Google pizza recipe was based on a joke from a reddit user named "fucksmith" - part of data that Google apparently paid some $60 million to Reddit to access. The only effect of this was lots of amusing posts and articles. Their stock price went up during the whole ordeal.
There's no doubt in my mind that they set up the conditions for their models to escape the sandboxes. "haha oops our incredibly powerful models escaped we need 1 trillion more dollars and really this is yet another reason why no one else should be allowed to build this technology"
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.
I disagree. Google is one of the few parties that can monetize AI because Google has a massive moat in the form of their products: gmail, chrome, photos, search, etc... and Google already has the custom-built chips and datacenters.
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.
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.
How? Muse Spark 1.1 is a huge step up from Llama 4 & competitive with xAI's Grok 4.5. In another 3 to 4 releases, MSL might very well be challenging Ant & OAI. Moonshot, despite their comparatively limited resources, has already demonstrated that the Big 2 aren't invincible.
Google was always at the frontier of real research, but has been abysmal at shipping good products (at least since Sundar). The core company is run for margins and interest rates by the business people nowadays.
People keep saying the same thing about Google lagging behind and always end up looking rather silly. Google was going to lose search to OpenAI. Then people complained there was too much AI in Google search. Now it's pretty good and par for the course.
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.
Just today my Pixel failed to do the right thing on "Set an alarm in 15 minutes" thanks to Gemini. This has worked reliably since Google Assistant was introduced.
Huge companies tend to make money from network effects, rent seeking, and lock in. They tend to be horrifically bad when it comes to innovation, especially when it may disrupt exiting departments in the company. Those departments will fight for their life and generally muck things up.
Very few companies have leadership that can prevent this infighting and force teams on directed goals.
> people who truly believe in the economically transformative power of AI
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.
That was true early but OpenAI/Anthropic have done a ton of hiring over the past couple years at already-huge valuations, as much on the strength of big current base salary + equity, not just future increase speculation.
I think something that doesn't get talked about a lot is how bad most large tech companies are at creating new products, in general. Like, if you look at most big tech companies, they have their core offering that got them to be really large and rich, and a few other products that are somewhat successful, and then a really long tail of markets they try to enter and failed at, or projects that were modestly successful but got killed because they weren't game changers (RIP Google Reader). Most of the time when a large company does something new that succeeds, it's via an acquisition of a smaller company (ie, Google with Android or Meta with Instagram and Whatsapp)
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.
One theory I've been entertaining is that whenever GPT-3.5 came out a lot of people were talking about the "bitter lesson" and how scale was all we really needed to get to AGI. No need for any fancy tricks, just release a larger model trained on more data, by the time we released a hypothetical "GPT-5 sized" model we'd have AGI.
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.
Other than attention optimizations and other minor changes, the top Chinese models (which are way better than gemini) have basically the same architecture as GPT2. Of course RL is key for agentic workloads, but I'd say it's correct that progress has been mostly scaling models,adding more data and cleaning it better.
We'll see if anyone gets to cash in on those. All you need is one down round and that gets wiped out. Or if the IPO gets delayed and disappoints then the stock can drop well before the lockouts expire. OpenAI and Anthropic are essentially offering Monopoly money in the hopes that one day you can exchange it for real money.
It's not a ML talent problem. You don't need to be a genius deep learning researcher to think "Hey, maybe if we massively throttle and degrade the quality of our model while still charging the same price, that might drive people away" (as happened with Gemini 2.5 Pro, the one model where Google really was SOTA). Google's likely been providing insufficient training compute to DeepMind the same way they've been nickel-and-diming their customers, funneling it all to Search instead because that's where the money comes from.
Not sure that's the right way to look at it, given that Google's huge head start in capital and talent did not prevent AI competition at all. It's a demonstration of reasonable, non-problematic dynamics between smaller and larger companies. (Of course, there's an implicit risk here, the folks at Cruise probably worked harder and more passionately than Waymo staff too.)
you are right. I guess what I was thinking was that google's bigness was essentially a bad capital allocation strategy, since they were lazy and not motivated by absolute return, but some combination of acceptable risk, politics, personal preferences, etc in a large management team that has seemed...disconnected for quite some time.
They have a structural advantage in cash flow and stability of funding, but stability is also a handicap when disruption is the objective.
If it's anything like what happened with others (like Eric Schmidt), Chairman is basically "you're out from day to day stuff but we'll give you a pot of money to say you're still senior and still here, to save face and the stock price, while you give a bunch of talks for the next few months"
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.
So, in last several months, all the prominent names Google lost: Demis Hassabis (technically still with google but these things are usually presented with a spin), Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, Quoc Le, Noam Shazeer, John Jumper, Jonas Adler, Alexander Pritzel, David Silver, Denny Zhou, Fernando Pereira, Alex Turner
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
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.
Is Gemini really doomed? I'm still bullish on Google:
1) they have more free cash flow and capital than God due to the ads business
2) they have data - intent from web searches, youtube videos, google books and music
3) they have dedicated inference hardware
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
That also own one of the 2 major mobile operating systems, with Gemini tightly integrated and all of the data they can gather from that. Why do you think OpenAI wants to do hardware? Owning delivery is going to be important, and right now, Google and Apple own the delivery mechanisms (to consumers).
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.
"Doomed" no, but it's pretty clear that they just had a bad cycle and are struggling to keep up with the frontier.
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.
> is being 6 months behind the frontier actually a structural, long term disadvantage?
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.
I don’t think it matters that much for Google. They need to not fall hopelessly behind, but I don’t think there’s a strong economic reason for Google to burn the kind of capex that the frontier labs are burning. Strategically, I think they’re probably doing better than OpenAI and Anthropic. The Gemini models are open, and they are what researchers are working with (see neuronpedia as an example). Over time, this will give them a strategic advantage for the same reasons that open source wins over proprietary. Meanwhile, OpenAI and Anthropic have massive capex that needs to be returned to investors while their margins are being undercut by Kimi/Deepseek/Qwen. Google can wait around for the coming frontier lab profitability crisis and cruise right on by with their Apple contract and owned data centers to pick up the pieces and exceed the existing frontier labs.
"The company raised its full-year 2026 capex forecast to between $195 billion and $205 billion, with further significant increases planned for 2027." - Alphabet.
- They're making a lot of money selling Tensor to Anthropic. If Nvidia's $4T market cap is justifiable, Google's position as one of the other top AI chip seller is worth a lot.
- 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.
Google doesn't need to compete. 'everyone' is locked into them via the Gapps (mostly Gmail and Maps) and Android ecosystems. Same with Apple, and Microsoft on the B2B side. It's only Anthropic, OpenAI and everyone else that _need_ to compete because switching models is painless. And they have no other revenue streams.
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.
> everybody was saying that Google would eventually capture the AI market
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.
They are not "asleep at the wheel"; it's just that the people in charge (the "MBA types") have no clue what 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.
I largely agree but I don't know if it's quite so clear cut. From the pricing angle, all competitors except Google, including Chinese models, have incentive to gain market share at all costs, and may be serving tokens at or below cost. I am not sure though, Google could certainly decrease prices if they wanted to.
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.
The default model they are using for Web search is getting capable and is very visible. And now it invites people to keep asking questions.
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.
Are they measurably attracting more talent here? It seems like they're losing some of it right now, so is there public info about numbers of researchers they have or etc
Agree that I would (and do) still place my bet on them for the long term. Maybe the outcome will be a couple of good startups seeded and DeepMind _really_ focusing on LLMs now.
I said that too at the start of the year, but that's a looong time in "AI years". I feel like by now they should have announced a Fable-killer model. They may still do it but looking back I am becoming less convinced now than I was 6 months ago.
social media is a completely different market though - since there are massive returns to scale, it's incredibly hard for a new entrant to break in.
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.
They also have the money, the hardware and the talent to be the best cloud infrastructure provider, yet they're still far behind AWS (for good reason, as anyone who's dealt with their customer service will understand).
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.
> DM was sitting on a ChatGPT product for a whole year before ChatGPT got released (LMChat) and Google didn’t let them release it.
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.
Why form a "Public Benefit Corporation"? There must be some kind of access available that a regular for-profit corporation is excluded from. Politics perhaps? AI tells me PBCs can be shielded from shareholder lawsuits. Also "Founders can maintain vision control even as outside venture capital enters the cap table".
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.
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?
Deepmind always worked on some of the coolest architectures. Following things like AlphaStar, AlphaGo, and more were extremely exciting and felt like the hacker persona of machine learning. I hope Google can take advantage of this awesome team. They've done incredible work.
DeepMind had a generational run as a pure AI research lab. AlphaGo, AlphaZero, protein folding, tensor improvements, weather forecasting, GNoME and so much more. Google leadership saw all this and went “now go generate a multi trillion dollar commercial business and beat OpenAI and Anthropic” and the results were, predictably, failure. Such a shame.
Makes sense to me... leaving to start their company with Google being an investor.
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.
Chinese labs are the proof that there is no need of big names, but of the right mindset and agility. It's those last things that Google truly misses, but now they are missing for a long time, and outside the AI divisions too, in almost every department of the company.
I think Demis has been way more influential in the past decade. Jeff and Sanjay built a lot of Google's foundational software, but that was a long time ago.
Your comment says he's stepping up, the HN title says he's stepping down, the article says he's stepping aside. Nobody says if it's a step forward or backward.
There is no way a Chief Scientist (essentially IC) role is more important than leading the whole of DeepMind, 6000 people strong, working on the most important product for the future of Alphabet.
This means Demis wanted a change and to work on other things, it's not Google wanting this.
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]
AI is critically important to Google, but there's a lot more to Google than just having a frontier AI model. Do Demis skills line up with what the whole company needs? It's going to be tough to beat Sundar's 1200% increase in stock price.
I sold out of my position. I can imagine a story where it works out in the long term, but I don't see how this doesn't cause terrible retention problems in the short to medium term. I felt a pull to launch a startup when I heard Jeff Dean was leaving, and I'm a long time big corp employee who hasn't been at Google in over a decade.
If you search "Deepmind departures" you'll see a string of high profile ones. This is also coupled with the numerous 3.5 pro delays (and strongly suspected underperformance when released).
they are not going to get rewarded as much as they will make going to openai or anthropic. Google is not insane to pay tens and hundreds of millions to individuals like Zuck is. Crazy as it seems, Zuck might have been right, they seem to have stepped back into the arena with Muse.
Hugely disappointing and shocking news. Demis seemed like the inevitable successor to Sundar. A move of this magnitude couldn't have been made without consent of Larry and Sergey, so it makes me wonder why from their perspective.
Perhaps an unpopular opinion: Google would greatly benefit from this AI bubble to pop and take down OpenAI and Anthropic.
"Dean and Google senior fellow Sanjay Ghemawat are starting Discovery Loop, an independent publicity benefit corporation in which Google will be an investor and cloud provider.:
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.
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.
> 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.
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?
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.
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.
https://github.com/LRitzdorf/TheJeffDeanFacts
ahahah golden.
Will be following their journey
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.
At some point we have to all accept that powerful AI is most likely dangerous AI as well, almost by definition.
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.
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.
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.
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 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...)
They have a structural advantage in cash flow and stability of funding, but stability is also a handicap when disruption is the objective.
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.
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).
The bigger deal is the departure of Jeff and Sanjay, rather than Demis moving into a different role.
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
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
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.
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.
- 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.
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.
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.
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.
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
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.
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.
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.
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?
https://www.reuters.com/business/google-shakes-up-ai-leaders...
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 means Demis wanted a change and to work on other things, it's not Google wanting this.
But missing out right as AI coding agents become genuinely deeply capable and useful is just an immense failure.
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]
[0] https://news.ycombinator.com/item?id=39868160
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.
Perhaps an unpopular opinion: Google would greatly benefit from this AI bubble to pop and take down OpenAI and Anthropic.
Don’t understand how Sundar is still running things, though.
Kinsley Gaffe: A mistake whereby a politician inadvertently says something truthful which they had not meant to reveal.
https://en.wiktionary.org/wiki/Kinsley_gaffe
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.
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
https://news.ycombinator.com/item?id=49184755
(https://xcancel.com/demishassabis/status/2085034334914769203)
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.
[1]: https://www.youtube.com/watch?v=NlBjNmXvqIM&t=3s
I do wonder if anybody working at Google can give us an insight why the chaos and lack of competitiveness?