Wow. Jeff and Sanjay both departing. Truly end of a golden era.
There is an entire cohort of work-optional very senior engineers for whom one of the last reasons for hanging on was "at least Jeff and Sanjay are around".
Their entire business is predicated on search dominance, which is now on much shakier footing. Agreed that those comments were ridiculous pre-ChatGPT, but now they have a genuine challenger.
This take only makes sense if you don’t know what people use Google search for. If you’re looking for your local Ford dealership, or to book a Carnival cruise, or need a disability lawyer, or to refinance your credit card debt, you’re searching for an ad. You’re not using Chat-GPT for that. You’re asking Chat-GPT about a research project. It’s stealing away all the hard to monetize searches and leaving the incredibly lucrative searches.
Your narrative depressed Google’s stock price for years, and bears finally gave up since they kept coming out with huge earnings beats.
I actually use Claude for finding stuff and recommendations. like "Find me a decent shoe with blah blah conditions" this query now goes to LLM Chat instead of google search. I use google search to find the url of something that i know exists like find the website of a company or finding address of a store.
Someone deleted a reply to this comment “And where does ChatGPT get the data for those answers?” they wrote and I think the question is important: LLM chatbots can crawl the web just as well as Google.
"google it" has a stain on it. And that's not said in jest. In my social circle, it has grown a boomer-tint. Like it or not; it's just how it is.
I personally like google over asking an AI. Esp. since something like google is a substrate without which an AI would be far more prone to hallucinations.
That’s over half of their business, but they do have some diversification with YouTube, Play Store, and cloud services.
Some of their newer efforts like Waymo and AI hosting for Apple could turn into large businesses to make up for any lost search revenue.
They also have significant costs to maintain their search revenue, such as $20B a year to Apple alone to make Google the default search engine in Safari.
If search becomes less lucrative, they would presumably pay less for such deals.
So yeah, definitely a time of disruption for Google and they need to keep moving fast on many fronts, but they are still well positioned in multiple markets to be successful.
Curious to see how long that lasts - YouTube and Google Maps were fairly mainstay apps for iPhones for a while, and eventually Apple cut the Maps cord to do their own thing. I don’t know if that’s from an existential concern of “we need to eventually move Maps in-house”, but given the Google of it all I wouldn’t be surprised if Apple is already at least planning on how to do the same with AI once the bulk of the usage evens out i.e. let someone else worry about it now while also getting a read on what rolling their own would feasibly require.
A model serves a different purpose from a web crawler.
I'd prefer to be able to find source material over steering a model I have no control over while managing hallucinated outputs without the ability to fact check.
Just because it sourced some materials using RAG doesn't make its outputs valid, accurate, or factual.
People have been saying that for over a decade now, and their business is still going.
So is IBM.
There was a pretty interesting article in the Wall Street Journal a few weeks back about how IBM is flying under the radar, and doing well by not taking the hype bait.
It noted that 70% of all credit card transactions on the planet go through an IBM system.
I dunno about going strong. $1,000 in Microsoft stock in 1990 would have made you a multi-millionaire today. $1,000 in IBM stock in 1990 would make you a ten-thousandaire today.
IBM had something of a downward blip last quarter, partly because of mainframe cycles. But it's actually done generally well the past few years and pays a pretty good dividend. $10B in net income for 2025 isn't shabby.
It seems there's a big shake up on the underperforming Gemini side. Before there was Shazeer (already gone) and Vinyals as co-leads, reporting to Hassabis, now Gemini comes under Kavukcuoglu reporting direct to Pichai as SVP of DeepMind.
Hassabis seems to have been pushed aside. He had been CEO of DeepMind, but that position no longer exists and it seems Kavukcuoglu is now leading DeepMind with a title of SVP. Hassabis is now just "Chair" of DeepMind, and has been given the newly created title of Alphabet Chief Scientist. Are these just face-saving titles, or does he still have any real influence over Google/DeepMind's pursuit of AGI?
Shane Legg remains as DeepMind "Chief AGI Scientist", but I wonder if the DeepMind founding mission of creating AGI is really intact, or if he will be next to go. Has DeepMind just become the Gemini division?
I just found out that David Silver, DeepMind's RL-expert, already left in february, to create a startup "Ineffible Intelligence" focusing on RL-based continual learning.
Yeah he seemed too reasonable to me, relative to the fervor. Whoever ends up in charge needs to do a lot of frothing to catch up to the ferver that would justify their valuations and investments
Probably not going to happen, but I'd love to see Isomorphic Labs separate from Alphabet with Hassabis still as CEO. He's too pure minded to be at company like Google.
Yeah, a bit surprised that the top comments aren't discussing losing Dean, he's been a figurehead for the company for decades. It's like Apple losing Ive in a way.
EDIT: "figurehead" - that's all I meant. A notable, public figure from the company who is credited with having a significant impact on its evolution. I'm not making a judgement call on his departure, or Ive's, being good or bad.
That said... just give it a couple years, they'll be back in a lucrative aquihire.
Losing Ive was probably a good thing for Apple. I often feel that after a few years these big guys have done what they could do and somebody else can take it from there. So far even losing Jobs didn’t hurt Apple as it looks.
Jony Ive wanted to chop down all the trees at De Anza Community College for something like 11 million dollars for an Apple event. There's a lot of ways he was demanding in the wrong ways for Apple
> So far even losing Jobs didn’t hurt Apple as it looks.
Who knows? Pure speculation? You can also say if Jobs was still around they could have 10x-ed it even further?
Apple car could have been a thing? Apple could have been way ahead and actually competing in AI and data centers? Who knows what else Jobs could have came up with?
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
Part of me thinks Google's entire problem is crappy internal tooling, not really an anti-innovation environment. Just making a dev take 2x as long to get something done has a bigger effect than you'd think. With LLMs it's more like 10x now because even Gemini doesn't understand Google-internal tooling.
Replacement for what? Google has some good internal tools, mostly the older ones. They're lucky to be using React instead of Angular at Facebook though.
I have a friend at Google DeepMind who tells me that Google believes in AGI/superintelligence just as much as HN does, which is probably why no one with conviction wants to work there.
Most of HN does not take the idea of superintelligence seriously, and until this year did not take the idea of AGI seriously.
I think LessWrong is a much better community for rational takes on AI, they've been reasoning about these risks for years under a much more sound logical framework
I'm a researcher in the field and I definitely take AGI seriously, but think all the major labs and most of the academic research is not helping achieve it any serious way. The field is seriously delusional (and has been ever since GPT 3 was released).
Even though my PhD research was in generative language modeling, I got into it for the pursuit of AGI. I just think LLMs are a dead end for AGI.
I think HN believes in AGI. HN probably doesn't believe LLMs will lead to AGI.
Also lots of tech people, HN included, are waking up to technology not only including penicillin (net positive for humanity) but also dynamite (best case: net neutral).
Only the GFG models know anything internal. Regular Gemini isn't trained on any of that. And GFG is a much older base model, so people use the regular one. If the tools seem to handle google3 code ok, it's only because of skills and not the model itself, and then you run into issues with skill bloat. Sometimes the A/B test would give me the bad model of the day that'd try to grep all of piper.
Start in a blank directory and tell it to spin up a boq Scaffolding stubby server that responds with "hello world." Unless something has changed after I quit a few months ago, it won't know how to do that locally, let alone actually deploy it.
The tooling is not the problem. If shit takes forever to launch, it's because there are many stakeholders that need to be satisfied (some for security, some for regulatory, some for the kinds of politics you get in a company that employs almost half a million people.)
But GDM isn't gated on launches. They were freely releasing things internally for dogfood. Problem is that stuff was just not as good as the competition.
No, they have an actually good internal search. And there are some good things like stubby, but again pretty annoying that Gemini doesn't understand stubby.
They earned that reputation in like 2005. Some people have been there so long that they don't even know what non-Google tooling looks like in this decade or the previous.
I mean it was legitimately frontier for all of a week or two, and then OAI and Anthropic made better releases, and then did that several more times over the year. Google’s pace is not cutting it.
> Lastly, after an incredible 27-year run, Jeff Dean is at a moment where he wants to try something new, and we’re excited to support him in that. Jeff and Google Senior Fellow Sanjay Ghemawat are launching an independent public benefit corporation to accelerate discoveries in ML, science, and engineering.
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.
Clarification: this comment is saying Sanjay Ghemawat joined Twitter as a user recently (new account @Sanjay_Ghemawat as of July 2026), as opposed to Sanjay working for Twitter the company.
For at least a year now the standard reflexive reply to “Google seems way behind OAI and Anthropic” has been “it’ll be ok, they’ve got Dean and Hassabis.” And now they don’t. What reason is there to be bullish about Google now?
I still think Google is the only one who has a shot of coming out of this on top. Open AI and Anthropic's whole existence is predicated on some sort of moat, which I don't really see them having long term. They've got a bit of a headstart, but that's it.
Conversely, AI is just a means to an end for Google - they don't need for their model to be the one to succeed. But, in contrast to the other major company in their position Apple, they do have a model, so they're not totally beholden to another for AI (like Apple is using Gemini!).
But beyond that, for training the model they have YouTube, and of course they have their crawler and index, and the billions of users.
I think HN skews coding agent focused, but that's not really a market for Google. I expect we will have coding specific models in the future, but Google wants a more general intelligence, to handle search queries, be able to connect email to chat to calendar and tasks, and so on. I don't find Gemini that much worse than the other big models for non-coding things.
As much as everyone wants to pay accomplished celebrities, all of these companies have young nameless geniuses that are about to make one for themselves. A guard passing torch can be opportunity.
Now, whether Google is the right environment to nurture, that’s its own quandary.
It just depends on what are the most upvoted comments in HN. If they are bearish, you can be sure that you should be bullish in your investments. Meta was supposed to be broken as a business already, OpenAI and Anthropic would be failures as well.
Maybe they'll replace Hassabis with someone more focussed on beating OAI and Anthropic at chatbots? He always seemed a bit more into science, protein folding, new drugs and the like.
Couple of things come to mind. One is that Web sites tend to actively fight AI companies' crawlers while actively courting Google's crawler.
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.
Google's weakness (well one of) is its total lack of cohesion. If the Google Books team could, they would sell access to that data in a heartbeat to boost their metrics.
Whoa. So is Jeff effectively leaving Google to work on this new venture full time? Or is the venture a side project? It sounds like the former. I’m sure he’ll still have internal access as an advisor of sorts. But this feels like a seismic change. Much larger than I initially realized?
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.
A deal with a company Google has a share of, announced a week before their IPO, with very non-committal terms and ramp period protections delivered in one large block on short term notice priced likely at the high end of what Google charges for A4X instances anyway.
I don’t think this reflects desperation as much as strategy.
"The company raised its full-year 2026 capex forecast to between $195 billion and $205 billion, with further significant increases planned for 2027." - Alphabet.
I think ~$200B is just for AI infrastructure capex. Fun fact: that's nearly what the 3rd largest military in the world (Russia) is spending on a land war in Europe.
- 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.
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.
> 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.
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.
Which poses the question: Why does Google even need to catch up? At least currently, the name of the game is integration. The actual model is a commodity.
Unfortunately the new cow is cannibalizing the old cow, so Google is in a bit of a bind here. (So far I cannot imagine them monetizing AI overviews enough to compensate for the sharp loss of ads on SERPs.)
To its credit Google seems willing to disrupt itself before its competitors can.
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).
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.
Just some random tidbit: When I was in high school I bought every computer gaming magazine I could get my hands on and could afford with my allowance.
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.
It is a bit of a tagent but still--Theme Park was dope. I was too young to grasp the compete with other businesses (stocks and shares?) level but still had a lot of fun building parks.
I always assumed Demis' ultimate secret motivation behind his work was the desire to deliver a version of Black and White that actually lived up to the hype
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.
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"
"No, no, no" a person on Reddit, Hacker News, YouTube screams for the billionth time. "It's all marketing" as the terminator bots kick in the door and slaughter their families.
If Google put out a blog post about Gemini 4 having escaped its sandbox via 0-day exploit to then hack other companies, I unironically believe this would result in a boost to their stock price.
They could really use some encouraging news about the competitiveness of their AI lab.
I wonder if we'll ever know the story of Gemini 3.5 Pro. Is it possible Google saw its potential for hacking, tried to nerf it, and ended up ruining the training run?
Google's AI was telling people to put elmer's glue on pizza to help the cheese stick [1], Gemini was turning the Founding Fathers black [2], and more. Oh and the 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 one and only effect of this was lots of amusing posts and articles. Their stock price went up during the whole ordeal.
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 think the reason Google hasn't prioritized larger models that are more intelligent than everyone else's, even though they probably could, is because they have 4 billion active users already. For example, they send AI Overviews for a large portion of Google searches now.
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.
When you have a lot of free users the business demands that you serve them with the best cheap model you can build
And time spent building that may provide dividends (eg OpenAI has very good RL and reasoning) but it might take resources away from the larger model training
(I have no inside knowledge, so please consider this to all be speculation)
I don’t think so. If Kimi and Deepseek can launch models better than Gemini with much much lesser resources then it is increasingly looking like an organization issue at Google
No I think you misunderstood GP’s comment. The idea (which I personally don’t agree with) was that Google didn’t have to have the best models; it just needed to have the best compute infrastructure, i.e. having TPUs and the software stack to use TPUs. It was a better use of money to develop compute infrastructure than to develop better models. Perhaps Gemini itself was resource-starved because Google liked to rent out TPUs to Anthropic instead. (Second-hand information: I heard that Mythos/Fable were trained on Google TPUs.)
I agree that other organizations can compete with few resources, but my hypothesis is that Gemini training specifically is being given nearly 0 resources, despite Google obviously having lots of resources. The hypothesis is based on an assumption that Google profits more by selling ALL their compute to others training models instead of using it themselves for training.
They already have good models, so “better” isn’t as profitable.
Google does not have to compete at the frontier, they already own a lot of Anthropic. It's not an "issue" for them because it's not one of their goals.
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.
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.
In retrospect this is very similar to the Apple strategy: don’t invest heavily in doing something that isn’t already a core competency, position for novel uses of the tech but don’t build it per se. Apple probably would have benefitted in the last 4 quarters from hyping a custom model stack but it doesn’t seem that this strategy paid off to even close to 20% ROI while Apple got to hold their cash and organizational focus on their main thing
And Gemini seems about as good as a search engine as any of the other LLMs, if you use them casually. And Google has the brand-name recognition.
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.
Gemini seems like a better search engine than the alternatives because it has access to Google's massive crawler feeds. I think Google could charge 30 dollars month for Gemini + all the data Google has locked up (Scholar, Books, crawled webpages, Google Groups, Usenet archives + search of your personalized datasets such as calendars, photos, emails, docs, slides, etc...). I'd pay that easily.
Anthropic has no moat, their models just get distilled and resold. They can maybe get good margins when LLM improvement rate is very high but as it slows down they are looking at commodity margins. Google has the products that makes that commodity more than just cost of electricity + 0.5%.
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.
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.
So they are fixing this by letting go of the people who were best at the research side, and therefore will have no problem converting "nothing" into products anymore!
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.
That's one thing everyone needs to remember. AI is here to stay, but the bubble IS going to pop. This level of spending is unsustainable. Soon the market will readjust and the amount of money we spend on AI will return to sane levels.
Google has multiple cash firehouses, the small AI companies do not.
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.
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.
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.
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.
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.
I think it's just more about market incentive. At it's core, LLMs are bad for google's previous business model, which was to send you to as many sites 'good enough' for what you were looking for and plant Ad land mines along the way, in the search results and in the websites themselves.
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.
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.
It's a notoriously double edged sword. When you create huge absolute return incentives, you get things like the Airtable acquisition, where everyone's sad that you built a $1.2B company because some investor at some point mistakenly thought it was an $11B company.
> 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.
There’s a difference between things getting incrementally better and a step function.
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.
That’s because a significant portion of the work and spending going on at frontier labs is generating new, more curated data, in select domains like software engineering and now biology[1].
I think that was his argument as well. It looks like there is no more data to find, but then it becomes important to find more data - lo and behold we can make more.
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.
Yes. The web is enormous, but just think of home much information within a company doesn't even make it into the internal knowledgebase, let alone anywhere public.
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.
Yes. In public relations you never want to trickle out bad/concerning the news, you want to "rip off the band-aid and preferably follow it up with some positive/distracting news like 3.5 Pro model release or price drops.
Gemini had come under Hassabis, being co-led by Shazeer and Vinyals, who are now both gone. Gemini will now come under Koray Kavukcuoglu, reporting direct to Pichai as SVP of DeepMind.
Fun fact: the Jeff Dean Facts was started by Kenton Varda (kentonv around here, also known for open-sourcing ProtoBufs v2, creating CapnProto and Sandstorm), as a joke inside Google. He has expressed regret that it eclipsed Sanjay Ghemawat, with whom Jeff Dean basically did pair-programming with.
Basically, he had a server that did code indexing running on his workstation. If he didn't login and run a command to refresh his prod credentials every day, the server would lose its ability to talk to prod (as it should) and fail. Much of the company depended on that service. Eventually it was moved to prod.
"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."
It seems reminiscent of the Green Project/FirstPerson episode that led to Java https://landley.net/history/mirror/java/javaorigin.html , and I'm sure many another attempt to keep some unhappy star players in a company's orbit using a spinoff.
> 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?
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.
gemma-4 is an incredibly good model, beating several others five time its' size. Hope Jeff's departure won't impact their next-gen products! And best of success for their new ventures!
With the top AI personnel leaving Google, I wonder what will happen to their Gemini. Right now they still have a lead in inference hardware with TPUs but with both Anthropic and OpenAI developing their own chips, I wonder how long until either one will catch up.
2023: Google is doomed. They're not building AI. No wonder the "Attention" authors left. Google just sat on this. Their PMs are steering them into oblivion. OpenAI is winning while Google takes no risks. Innovator's dilemma. Google Search is doomed.
2024: Google is on fire. Sergey coming back helped. Gemini, Veo. They've caught up. OpenAI is doomed.
2025: Google is seriously winning now. Nano Banana!! Google was destined to be the true winner of AI.
2026 H1: Google is slow as hell. Where is Gemini? Google is not launching anything, meanwhile just look at everyone else. Anthropic! And open source. What the hell are they doing over there?
2026 H2: Everyone is leaving Google. Google is doomed. PMs are destroying the company. They don't take risks.
It seems Google has to play both offense and defense: competing against frontier labs' models while protecting search and ads. They also have to be mindful of not doing anything that could hurt their own search or ads. That seems harder than a frontier lab just doing offense on both models and search/ads.
Having said that, I think Google's moat is still strong with Cloud, Gmail, YouTube, Android, Chrome, etc.
I don't think YouTube can survive if GenAI keeps going like this. Android, same, but on a different timescale and for different reasons. The Play Store (and all other app stores including Apple's) will also face problems from GenAI making apps (it already replaces my need to buy, but I'm weird and a software dev ("but I repeat myself")).
Not sure how big a moat Chrome really is? It's more like a sales funnel than a product itself, I think?
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?
The breadcrumbs going back a year or so point to Deepmind wanting to be research heavy, but Google being more interested in hedging AI bets by selling compute. If you ever worked hard on something that you have a lot of conviction about, only to get denied resources for it, it's a pretty big gut punch.
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.
Who knew the real revenue unlock wouldn’t be based on how much paranoid red-teaming the model underwent to resist users jailbreaking its ‘alignment’, and instead more on whether the model is post-trained to use ‘sed’ and ‘git’? Poor Gemini
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
If you just talk to it over API (no web search) the Gemini models are extremely resistant to thinking the user may be living in a universe outside their training data. Try to discuss any news etc and they assume it’s fake or fiction
Out of the three main US AI companies' models, Gemini is obviously the less aligned (read: censored). So I really don't know what you're talking about.
I'm saying AI researchers have a bias towards thinking what needs to happen is prompt -> [crunching tokens] -> response rather than prompt -> [orchestrates 5 tools] -> response
In other words 'just add a calculator tool' is not as sexy research-wise as making the model accurately eyeball arithmetic in its chain of thought. Maybe I'm wrong but that seems to be the case
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.
Hasn't that always been the case with Google? Outside of a few that are mostly good and have stuck around, I've always seen Google has having great tech but being quite bad and making products out of it.
It just tells you about their intended audience: Investors rather than consumers.
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.
I'd bet my mortgage that if the first sentence was "We've got amazing products, amazing talent, and world-class compute", you'd be in the comments complaining that they didn't put talent first.
WoW, so basically all senior members of GDM are now mostly gone? I thought Google was lagging in coding AI, but this suggests bigger issues.
I am not fan of the future of AI but this I am not sure how I feel about Google's (also Amazon recently laid off it's AGI team) AI initiatives blowing up before all the new AI Labs.
I don't get why Google failed here? maybe after a decade someone will write about it candidly.
If I had to guess, they have the wrong kind of bureaucracy for where things are headed - and it is manifesting through talented individuals deciding to leave.
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?
Personally, In SWE, i think the industry has made a grave mistake with the agents and we're just one big Catastrophe waiting to happen. I do think that there is very real value when software engineers use these tools as something akin to exoskeletons that allow the human to do more, rather than just fully replacing them. However, I'm finding more and more that companies are slop shops and just attempting to automate all of their software engineering. That will certainly end terribly. I hope we are not cannon fodder.
For what? There are some things I want AI for because it does it well. There are some things I don't want AI for because it just makes a mess (hallucinations). Maybe the next AI will be different and we will have the conversation again.
Indeed, it kills our planet, our culture and our economy extremely well. Oh yes, and some code monkeys enjoy that it can make computer code on the side.
> 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!
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.
This is the wrong way to look at it. If you have a complicated task , you can solve it for cheaper if you used Fable. It will use fewer turns to achieve the same result.
You can solve it for cheaper if you use GLM but if you are involved in it more, but that defeats the purpose.
The point is that there aren't many complex tasks were fable delivers a significant value increase over cheaper models.
Single prompting a very complex tasks is rare even on frontier models, because it can be done successfully only for specific situations (e.g. you have a very strong verification step the model can iterate on).
Most of my everyday usage is for smaller takes, were you don't really get the benefit of the most expensive models, and my guess is that is the case for the most users
Again this is a resolution problem. Your tasks are small enough that fit into a nice $3 quota. If you are an enterprise or a power user, the right-sizing argument doesn't work.
I'm talking about API prices - subscription is a different game.
> And gemma downloads also can be from auto CI pipelines etc. Nothing concrete
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?
> And gemma downloads also can be from auto CI pipelines etc. Nothing concrete
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.
I actually think there's a low probability of that.
The four have so much influence within the company that they could have trivially set this up as part of Alphabet, if they wanted to. They are also ridiculously wealthy.
I feel it's image management and a bet. If they succeed, Google profits. If they don't succeed it doesn't matter, it's still a signal to the market/wallstreet that there's no "bad blood" between them and Google and that they won't be cannibalizing Google's current interest.
At least judging from the headlines, it seems Google is far behind on AI. Fable/GPT 5.6 and recently Kimi get a lot of attention, solve long-standing math problems and lead in coding. It seems that Google's supposed advantages of very deep pockets, original talent, vast amounts of data and unmatched distribution are really not making that much of a difference. What is going wrong?
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.
I formed a PBC and worked at a well-known PBC. Personally, I opted for a PBC because I liked that I could balance a specific cause with shareholder benefit.
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.
The important part is PBCs protect you from a shareholder primacy directive. Eric Ries describes it in his new book, but the example he gives is if the most evil company you know tried to buy out your company you have to do it in a normal "best practices" C corp because it is your fiduciary duty. PBC helps prevent that based on your declared mission statement. If the sale doesn't facilitate your mission then you aren't obligated to sell.
> The moves suggest that DeepMind will be absorbed into Google’s broader business. [...]
> They added that while Google DeepMind would not become purely commercial, it was integrating further into Google’s business.
I guess might be quite a change (but writing was on the wall, the Deepmind -> Google Deepmind part was the first step)
Publicity benefit corporation HAHAHA typo, think they mean public benefit corporation. Anthropic however is DEFINITELY a publicity benefit corporation.
Considering Hassabis co-founded Deepmind to pursue AGI, I take this pivot as a tacit understanding on his part that Deepmind isn't on a path towards AGI in the near future. They might at some point, but they cannot be close in his estimation, otherwise why leave now?
This seems bad for AI safety/risk. Does DeepMind have any checks on model alignment now? What's stopping them from using AI for military/surveillance purposes?
They already quietly agreed to "all lawful use" with the Pentagon, to no real fanfare. Gemini Slaughterbot Edition, coming soon to a DHS facility near you?
If all the other AI companies are promising AGI by Q4 of next year, what else can you do to satisfy shareholders than also jump on that same bandwagon?
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.
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.
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.
Agree. Demis is very smart, but obviously less humble and more interested in self-promotion. There have been multiple Deepmind documentaries that mythologize the Demis origin story.
Demis is great -- this is not a zero-sum game. Who built what, and the relative importance, is an interesting discussion to have, but it's orthogonal to my point.
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.
Surely the two leading candidates must be (a) the model is just not that good, or (b) it is misaligned in a pretty obvious way that can't be swept under the rug.
They're definitely "down", but by no means "out". They're probably back in another "code red" and will need to deliver something leading edge in some area next. My bet is that Google will be the first to crack continuous knowledge cutoff updates to their model.
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.
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.
I don't see Demis becoming CEO. He's a scientist and researcher. He wouldn't want to be bogged down by minutiae of corporate politics, org structure, government relations, mobile hardware, etc. Chair lets him have authority to explore any path of interest without overhead of operations.
My impression, inside and out of G, was that Sundar (and Ruth) were about scaling down R&D expenses (as a fraction of revenue), and focusing on exploiting the monopolies.
Perhaps now there could be a shift back to investing in R&D to get fresh monopolies.
Dunno what the longer term effects of this and the other departures will be, but in the world of vibe-finance Alphabet dumped 4.15% of share value as of this minute.
The labs are all very interested in bio right now. Demis is working on Isomorphic rn (Google's version of that) but I could imagine a lab tempting him away to work on their equivalent if it had stronger momentum
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.
"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.
My uninformed hypothesis: Demis was gently pushed out. Gemini, while having made major strides over the last year, continues to lag behind Anthropic and OpenAI.
We'll know that we've reached AGI when a key conservative political belief in the US is that AIs are not people and do not deserve rights.
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.
That's the fun part. We don't. Could have happened 10 years ago without us noticing. I don't expect my skin cells to be able to recognize "me" anymore than I expect we will be able to recognize a superintelligent AGI. If it arrived 10 years ago, then the last 10 years could simply be its PR campaign. We wouldn't even be able to tell if it was successfully achieving its "goals" or not, assuming an AGI even has "goals"
1) I think about this a lot. The AI overview has already destroyed the need to even look further down the page for a lot of people. And if you look further down the page, there is often a whole page of AI generated blog spam, which is in turn being regurgitated by the AI overview up-top. The AI overview often regurgitates a completely false reddit comment from two days ago as well. That AI generated blog spam is probably reading the AI overview.
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.
Can we please stop saying anything about "AGI"? I remember when people would be like "AGI in 3 months" / "AGI in 2024" / "AGI is confirmed in 2025" like stop, you don't know if it's even a thing, let alone if it's coming/imminent.
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.
Public warning: please don't trade on Ed's idiotic analyses. Or if you do, look at his track record so far, and apply the Kelley Criterion to your bet sizing.
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.
"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."
And they did raise new funding as he predicted...Since July 2024, OpenAI has raised or secured roughly $170 billion in new capital. Numbers never see before. So yes its the one, and remarkably right so far.
Notice how he didn't say "I predict they will raise new funding", he predicted collapse, with the implicit retort that there's no way they will raise more funding than has ever been done before and invent a whole new AI. He was mocking the supposed things that would need to happen. He strongly assumed collapse.
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.
Oh, come on. He's not perfect, but compared to CEOs and researchers predicting AGI and complete economic upheaval every 3 months, he's looking very good. He's no more a charlatan than Altman is.
His predictions (while often wrong) are at least somewhat justified and backed up by genuine scoops and original research. Altman goes on podcasts and talks about building Dyson spheres for energy.
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).
As it stands, the leaked financials prove him correct. Given their losses (not counting restructuring) they are going to need enough funding to buy any of the bottom 300-400 of the F500 companies outright just to keep the lights on for the next year.
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.
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.
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.
hmm not really, and it probably isnt the origin, but im glad to see him vindicated by having both C-level google leadership people who targeted to try and either stop google from doing the pentagon deal, both stepping down the same day.
At least it makes me feel like he reaching out might have made a difference on remembering this people killer robots are bad, and being part of it might not be on the best of their interests to go down on history for
There is an entire cohort of work-optional very senior engineers for whom one of the last reasons for hanging on was "at least Jeff and Sanjay are around".
Your narrative depressed Google’s stock price for years, and bears finally gave up since they kept coming out with huge earnings beats.
I personally like google over asking an AI. Esp. since something like google is a substrate without which an AI would be far more prone to hallucinations.
Some of their newer efforts like Waymo and AI hosting for Apple could turn into large businesses to make up for any lost search revenue.
They also have significant costs to maintain their search revenue, such as $20B a year to Apple alone to make Google the default search engine in Safari.
If search becomes less lucrative, they would presumably pay less for such deals.
So yeah, definitely a time of disruption for Google and they need to keep moving fast on many fronts, but they are still well positioned in multiple markets to be successful.
Curious to see how long that lasts - YouTube and Google Maps were fairly mainstay apps for iPhones for a while, and eventually Apple cut the Maps cord to do their own thing. I don’t know if that’s from an existential concern of “we need to eventually move Maps in-house”, but given the Google of it all I wouldn’t be surprised if Apple is already at least planning on how to do the same with AI once the bulk of the usage evens out i.e. let someone else worry about it now while also getting a read on what rolling their own would feasibly require.
A model serves a different purpose from a web crawler.
I'd prefer to be able to find source material over steering a model I have no control over while managing hallucinated outputs without the ability to fact check.
Just because it sourced some materials using RAG doesn't make its outputs valid, accurate, or factual.
So is IBM.
There was a pretty interesting article in the Wall Street Journal a few weeks back about how IBM is flying under the radar, and doing well by not taking the hype bait.
It noted that 70% of all credit card transactions on the planet go through an IBM system.
The bigger deal is the departure of Jeff and Sanjay, rather than Demis moving into a different role.
Hassabis seems to have been pushed aside. He had been CEO of DeepMind, but that position no longer exists and it seems Kavukcuoglu is now leading DeepMind with a title of SVP. Hassabis is now just "Chair" of DeepMind, and has been given the newly created title of Alphabet Chief Scientist. Are these just face-saving titles, or does he still have any real influence over Google/DeepMind's pursuit of AGI?
Shane Legg remains as DeepMind "Chief AGI Scientist", but I wonder if the DeepMind founding mission of creating AGI is really intact, or if he will be next to go. Has DeepMind just become the Gemini division?
I just found out that David Silver, DeepMind's RL-expert, already left in february, to create a startup "Ineffible Intelligence" focusing on RL-based continual learning.
Yeah he seemed too reasonable to me, relative to the fervor. Whoever ends up in charge needs to do a lot of frothing to catch up to the ferver that would justify their valuations and investments
EDIT: "figurehead" - that's all I meant. A notable, public figure from the company who is credited with having a significant impact on its evolution. I'm not making a judgement call on his departure, or Ive's, being good or bad.
That said... just give it a couple years, they'll be back in a lucrative aquihire.
Anyone remember the touchpad MBP with no physical escape key and the butterfly keyboard?
(Respect to many of Ive’s great legacy though)
Who knows? Pure speculation? You can also say if Jobs was still around they could have 10x-ed it even further?
Apple car could have been a thing? Apple could have been way ahead and actually competing in AI and data centers? Who knows what else Jobs could have came up with?
Are you sure we should compare it like this? Not sure it implies what you think it does...
So the parent comment would imply that losing Dean is a good thing for Google, which is way less likely here.
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
Then the Google engineers who joined Facebook missed it so much that they built a better replacement.
I think LessWrong is a much better community for rational takes on AI, they've been reasoning about these risks for years under a much more sound logical framework
Even though my PhD research was in generative language modeling, I got into it for the pursuit of AGI. I just think LLMs are a dead end for AGI.
Also lots of tech people, HN included, are waking up to technology not only including penicillin (net positive for humanity) but also dynamite (best case: net neutral).
this is false, it's very good at internal tooling.
Start in a blank directory and tell it to spin up a boq Scaffolding stubby server that responds with "hello world." Unless something has changed after I quit a few months ago, it won't know how to do that locally, let alone actually deploy it.
That can work in the B2C space but it's horrible in B2B.
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.
Clarification: this comment is saying Sanjay Ghemawat joined Twitter as a user recently (new account @Sanjay_Ghemawat as of July 2026), as opposed to Sanjay working for Twitter the company.
Conversely, AI is just a means to an end for Google - they don't need for their model to be the one to succeed. But, in contrast to the other major company in their position Apple, they do have a model, so they're not totally beholden to another for AI (like Apple is using Gemini!).
But beyond that, for training the model they have YouTube, and of course they have their crawler and index, and the billions of users.
I think HN skews coding agent focused, but that's not really a market for Google. I expect we will have coding specific models in the future, but Google wants a more general intelligence, to handle search queries, be able to connect email to chat to calendar and tasks, and so on. I don't find Gemini that much worse than the other big models for non-coding things.
Those 2 are definitely NOT Google's strong points. Maybe by means of marketing.
Oracle, Amazon, Microsoft, Equinix and many more are in the data center race.
As for TPUs... Broadcom, Mediatek and all the other partners you hear less about are likely more important. Google just has the flashy media outreach.
Now, whether Google is the right environment to nurture, that’s its own quandary.
They all leave to start new companies. Everyone on the Attention is all you need paper is at a startup.
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
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.
I don’t think this reflects desperation as much as strategy.
- 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.
The group running the company, is the company.
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.
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.
To its credit Google seems willing to disrupt itself before its competitors can.
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
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.
At some point we have to all accept that powerful AI is most likely dangerous AI as well, almost by definition.
They could really use some encouraging news about the competitiveness of their AI lab.
[1] - https://www.forbes.com/sites/jackkelly/2024/05/31/google-ai-...
[2] - https://www.axios.com/2024/02/23/google-gemini-images-stereo...
https://www.cbsnews.com/news/google-ai-chatbot-threatening-m...
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.
When you have a lot of free users the business demands that you serve them with the best cheap model you can build
And time spent building that may provide dividends (eg OpenAI has very good RL and reasoning) but it might take resources away from the larger model training
(I have no inside knowledge, so please consider this to all be speculation)
This was the core hypothesis.
They already have good models, so “better” isn’t as profitable.
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.
“Heavily” it’s a high bar at their scale. They spent over $10bn playing with cars.
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.
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.
https://artificialanalysis.ai/models/muse-spark
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.
Google has multiple cash firehouses, the small AI companies do not.
Did he say a "complete and unmitigated disaster"?
No, because he's not a fool. If he had said that the correct response would have been to question his sanity.
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.
No one joins OpenAI/Anthropic unless they think these companies will reach superintelligence.
So most people joining believe their equity will 10-100x even from where it is today.
(Coincidentally, the talent that believes we will reach AGI overlaps a lot with the best talent, which has a magnetic effect.)
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.
They have a structural advantage in cash flow and stability of funding, but stability is also a handicap when disruption is the objective.
> 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.
https://www.earthmover.io/blog/virtual-zarr
https://news.ycombinator.com/item?id=46659254
I’ve been reading this sentiment on HN since GPT4o, yet models got better and better
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.
A massive shake up for sure, but why do you see it as separate events merely timed to coincide ?
Big shake up for Gemini it seems.
https://github.com/LRitzdorf/TheJeffDeanFacts
Uh oh...
I've noticed that Gemini has been timing out yesterday & today !
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.
OpenAI did but they had a totally different structure and were never a PBC.
If these guys adopt a similar LTBT+PBC structure to Anthropic it should be more resilient.
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.
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.
My guess is 18-24 months.
2024: Google is on fire. Sergey coming back helped. Gemini, Veo. They've caught up. OpenAI is doomed.
2025: Google is seriously winning now. Nano Banana!! Google was destined to be the true winner of AI.
2026 H1: Google is slow as hell. Where is Gemini? Google is not launching anything, meanwhile just look at everyone else. Anthropic! And open source. What the hell are they doing over there?
2026 H2: Everyone is leaving Google. Google is doomed. PMs are destroying the company. They don't take risks.
Meanwhile, Microsoft: "Copilot!"
Having said that, I think Google's moat is still strong with Cloud, Gmail, YouTube, Android, Chrome, etc.
Cloud and gmail, I agree.
I don't think YouTube can survive if GenAI keeps going like this. Android, same, but on a different timescale and for different reasons. The Play Store (and all other app stores including Apple's) will also face problems from GenAI making apps (it already replaces my need to buy, but I'm weird and a software dev ("but I repeat myself")).
Not sure how big a moat Chrome really is? It's more like a sales funnel than a product itself, I think?
The difference is nobody expects anything better from Microsoft. Teams didn't exactly set the bar high.
https://www.reuters.com/business/google-shakes-up-ai-leaders...
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.
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
In other words 'just add a calculator tool' is not as sexy research-wise as making the model accurately eyeball arithmetic in its chain of thought. Maybe I'm wrong but that seems to be the case
>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.
https://www.lesswrong.com/posts/iKm2FhpWkuuBojm82/why-i-left...
I am not fan of the future of AI but this I am not sure how I feel about Google's (also Amazon recently laid off it's AGI team) AI initiatives blowing up before all the new AI Labs.
I don't get why Google failed here? maybe after a decade someone will write about it candidly.
If I had to guess, they have the wrong kind of bureaucracy for where things are headed - and it is manifesting through talented individuals deciding to leave.
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?
Check out the New Luddite movement [1] [2]
[1] https://www.cnn.com/2025/10/08/business/ai-luddite-movement-...
[2] https://en.wikipedia.org/wiki/Neo-Luddism
But people want what AI does for them. It's a tragedy of the commons situation.
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!
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.
Single prompting a very complex tasks is rare even on frontier models, because it can be done successfully only for specific situations (e.g. you have a very strong verification step the model can iterate on).
Most of my everyday usage is for smaller takes, were you don't really get the benefit of the most expensive models, and my guess is that is the case for the most users
Strong disagree on this. Any decently complicated task like a refactor is going to be more likely to be solved by Fable than by Gemma 3B or whatever.
I have personally tried to use Sonnet over Opus for tasks and Sonnet gets things right sometimes and at other times I wish I had just paid higher.
This is the standard pattern I keep seeing and I can have a bet with you that it would stay like this.
I'm talking about API prices - subscription is a different game.
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 four have so much influence within the company that they could have trivially set this up as part of Alphabet, if they wanted to. They are also ridiculously wealthy.
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.
> The moves suggest that DeepMind will be absorbed into Google’s broader business. [...] > They added that while Google DeepMind would not become purely commercial, it was integrating further into Google’s business.
I guess might be quite a change (but writing was on the wall, the Deepmind -> Google Deepmind part was the first step)
Some think Gemini is falling behind in benchmarks so there was a shakeup at the top. I don’t agree with it.
That said, winning this LLM race is difficult given the number of talented people working on it across the world.
Looks like Gemini's sub-par performance is claiming heads
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
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.
> ... independent publicity [sic] benefit corporation in which Google ...
A Freudian slip? https://archive.is/SxFjr.
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 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.
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.
Perhaps now there could be a shift back to investing in R&D to get fresh monopolies.
He was the guy branding Google an AI-first company back when they invented the transformer.
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.
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.
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.
What's happening?!
Have they had enough of Google?
Don’t understand how Sundar is still running things, though.
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.
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?
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.
I suspect the SVP/CEO leading Deepmind has a lot more sway over the rest of the company than someone with a title and no reports. :)
(Also the 20y+ of contributions and having built much of the base Google infra probably gave more influence to Jeff than the remaining title)
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
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?
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
https://x.com/edzitron/status/1817955630784917548
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.
His predictions (while often wrong) are at least somewhat justified and backed up by genuine scoops and original research. Altman goes on podcasts and talks about building Dyson spheres for energy.
Open weights will drop costs, but distribution matters. OpenAI has that.
Costs will come down. Deep entrenchment will not.
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).
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.
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.
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).
This means Demis wanted a change and to work on other things, it's not Google wanting this.
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...
Demis tried to be google ceo by pumping out self aggrandizing 'documentaries' and highfalutin interviews where said a whole bunch of nothing.
1. https://www.lesswrong.com/posts/iKm2FhpWkuuBojm82/why-i-left...
At least it makes me feel like he reaching out might have made a difference on remembering this people killer robots are bad, and being part of it might not be on the best of their interests to go down on history for
https://www.youtube.com/watch?v=pGlJQHVeKIA&t=14m27s