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Comment by atleastoptimal | Hacker News Reader

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.

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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"

It might also be that the security sandbox was haphazardly vibeslopped together.

Hmm. And the AISI unintended model hacking yesterday was also just marketing for the British Government?

At some point we have to all accept that powerful AI is most likely dangerous AI as well, almost by definition.

"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.

Cute story. How could they stop this timeline, even if they were true believers?

They can't, we are all living in a suicide pact of 'Let these guys do whatever they want, consequences be damned.'

Crash society to the point where we can no longer make complicated chips. Of course this really isn't a great solution as it involves billions dying too. Not that someone won't try...

We've signed up on the "If you build it, everyone will die" express. No brakes, no stops, full speed ahead.

At least join PauseAI or similar. Or give money to them. Write to politicians. Read Plan A and tell people about it.

Yes.

> As was standard in our cyber testing, we had intentionally permitted internet access, and model- provider cyber classifiers were deliberately disabled - conditions that do not reflect how frontier models are made available to the public.

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.

[1] - https://www.forbes.com/sites/jackkelly/2024/05/31/google-ai-...

[2] - https://www.axios.com/2024/02/23/google-gemini-images-stereo...

“Human … Please die.”

https://www.cbsnews.com/news/google-ai-chatbot-threatening-m...

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?

They did publicly mention some partners have had preview access. Maybe we’ll find out one day.

Yes. These corporations now run on fascist logic. (Hold up, stay with me here.) The goal isn't to right, or ethical, or principled. It's to (appear to) be strong and capable, and loud about it. You end up with horrible things happening that get cheered because they're only horrible along dimensions that don't matter anymore to the people calling the shots. Same thing for amazing developments that get crickets.

Note that I'm not calling Google "literally Hitler". I'm just saying that traditional assumptions about what's virtuous and what isn't don't apply anymore. For now, at least. If anyone wants to change that, they're going to have to be purposeful about it.

Would it though? Meta and Microsoft have had very scandalous AI things happen, and their shares didn't tank (or quickly recovered)

> It would be stock price suicide if anything even remotely happened with Google.

aka they're dead in the water.

The answer is far more benign.

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.

Awfully ironic that Google makes more money (probably an order or magnitude or so) from it's direct competitors than it does from a it's own product.

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.)

> Perhaps Gemini itself was resource-starved because Google liked to rent out TPUs to Anthropic instead.

This was the core hypothesis.

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 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.

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.

This is probably true in a smaller way for OpenAI

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 Google is that freaking short-sighted. Even if you're not trying to be frontier, the value of having domain knowledge via experience for AI is worth saving internal compute alone. By all accounts Google believes in ai as much as everyone else.

If AI turns out 1/100 as important as they seem to think, it would be insane to intentionally be slack on it.

There is no other way to describe the Gemini 3.5 pro delay than as a complete and unmitigated disaster.

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 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.

Gemini 3.1-pro was genuinely SOTA for a few weeks, in fairness

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!

Yeah. I’m guessing they’re behind the Chinese models in capability now

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.

"Hey Siri, find me gas stations along my route" routinely fails now. That worked great for years. Total clown show.

> But things need to change.

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.

As someone who has worn Meta Glasses for about a year, AI is not Meta's strength

Meta did not blow up their entire AI Org to be the 5th or 6th place model

https://artificialanalysis.ai/models/muse-spark

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 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.

Apparently Sundar thought it was a problem.

Obviously a delay is a problem.

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.

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.

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.

Legal will have a shit fit at all of the things that you just mentioned.

And that might be why Google loses, they are competing with companies that are willing to operate outside the law (or at least pay Trump to have the DoJ look the other way).

> If anthropic wins, Google copies their approach, buys anthropic for cheap or both

Google buys Anthropic for cheap? How?

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.

Why would Google be the only interested buying party?

They wouldn't be the only party, but they are in my opinion the most likely party.

What the people that can monetize it? Microsoft, X, IBM, Salesforce, Discord, Google

Microsoft would likely buy OpenAI due to their investment and integration with OpenAI. Making Anthropic not that valuable.

Google would likely buy Anthropic due to their investment and ties to Anthropic.

X, Salesforce maybe. Not sure Discord has the money. No idea if IBM capable of growing.

All this changes if Anthropic figures out a moat/sticky product that doesn't just depend on having the best LLM. If they pull off https://claude.com/solutions/healthcare then all bets are off.

But there would be enough other interest that they would not be getting bought "for cheap"

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

> don’t invest heavily in doing something that isn’t already a core competency

“Heavily” it’s a high bar at their scale. They spent over $10bn playing with cars.

But paying so little attention to it that it no longer is a core competency? As in, the people most representative of that competency leaving in droves? I doubt that was the plan.

Well it all depends on whether you’ve ever thought Apple was any good at search, Siri, etc.

My own personal opinion is that they’re great at design, UX, hardware, and even some power user desktop software (lifetime Logic Pro addict here). I would say it’s a stretch to call anything they have ever done with ML to be anything near cutting edge compared to Google, for whom all of the “hard” problems of recommendation, search, NLP, and other ML have been the absolute focus of their SaaS, white papers, and web offerings for close to 30 years

> 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.

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.

Is that what's happening? Search has sucked for a while, and I'd just assumed it was partly because they'd been pushing compute to AI-related activities.

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.

> OpenAI and Anthropic are essentially offering Monopoly money in the hopes that one day you can exchange it for real money.

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...)

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.

Everyone knows there isn't a moat.

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.)

From what I've heard, DeepMind's pay structure is not tied to Google compensation bands. Which means that top performers and key hires can be and are compensated extremely generously if the need calls for it. Another Alphabet subsidiary, Waymo, was well-known for doing this back in the day.

But yes, even large RSU grants cannot replicate the upside of joining a company that increases in valuation 1000X, but to be fair, that is not a position the vast majority of OpenAI or Anthropic employees find themselves in either.

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.

Google was offering researchers the Bell Labs/Xerox PARC model of comfortable budgets and pay but capped upside. And just like with Bell Labs and Xerox PARC the inventions at Google got productized elsewhere by people chasing the uncapped upside. Google would have been happy to keep LLMs in the research lab forever and never productize any of it. ChatGPT forced their hand.

DeepMind is just a part of Google which does research stuff, and Google is a massive company that does loads of things but mostly sells ads.

OpenAI and Anthropic are in a completely different game, their primary business model is making a product out of advancements that come out of DeepMind and co.

This makes a lot of sense to me. Probably doesn't explain everything but I could see it explaining a lot. Could also explain openai and anthropic delaying IPO (among other factors)

Is this not an apples-and-pears comparison? DeepMind is a research lab, not an LLM-pilled money furnace.

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.

Google has a ton of smart researchers trying all sorts of stuff. They didn’t really go all in on any one thing. They hedge their bets.

But they suck at harnesses, developer tooling etc. their internal environment is very complex and an intern on a MacBook with codex can probably move faster than a seasoned deepmind engineer

> 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.

Google Gemma disproves this

its just simple as someone think he knows better than the engineer,

anthropic and open ai doesn't have those people currently as they are private.

I've always thought this as well.

Wouldn't this be a demonstration of the downsides of the anticompetitiveness of monopolies?

What is anticompetitive about it? The comment above is literally claiming they’ve been outcompeted.

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.

Yeah, that makes sense. I suppose the flip side of all of this is that neither OpenAI nor Anthropic have made a lot of money relative to their spending. Google is probably just trying to be more prudent. But, AI so far is a money spending contest, more than a money making one.

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