Amazon has mostly sat out the AI talent war

2025-09-0119:04363660www.businessinsider.com

Amazon has struggled to recruit top AI talent. An internal document and company insiders reveal the reasons.

Amazon CEO Andy Jassy
Amazon CEO Andy Jassy Fortune/Reuters Connect

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  • Amazon struggles to attract AI talent due to its pay model and perception of falling behind others.
  • Amazon's compensation model has long caused complaints from employees.
  • Competitors like Meta and OpenAI offer more attractive packages for AI engineers.

As the AI talent war sweeps across Silicon Valley, Amazon has largely sat on the sidelines. A confidential internal document, and accounts from people familiar with the matter, reveal why.

The company has flagged its unique pay structure, lagging AI reputation, and rigid return-to-office rules as major hurdles. Now, the tech giant is being pushed to rethink its recruiting strategy as it scrambles to compete for top talent.

The document, from late last year, was written by the HR team covering Amazon's non-retail businesses, including Amazon Web Services, advertising, devices, entertainment, and the newly formed artificial general intelligence team.

"GenAI hiring faces challenges like location, compensation, and Amazon's perceived lag in the space," the document noted. "Competitors often provide more comprehensive and aggressive packages." Business Insider obtained a copy of the document.

Amazon's absence from recent splashy AI hires underscores those concerns. Meta has pulled in high-profile talent from ScaleAI, Apple, and OpenAI. Google and OpenAI continue to be top destinations for AI experts, while Microsoft has even drafted a wish list of Meta AI employees it hopes to recruit.

Amazon's spokesperson initially told BI that the company continues to "adapt our approach to remain highly competitive, maintaining flexibility in both our compensation packages and work arrangements to attract and retain the best AI talent in this dynamic market."

Hours later, the spokesperson updated the statement, saying the premise of the story was "wrong," without providing any specifics.

"We continue to attract and retain some of the best people in the world and they're building and deploying GenAI applications at a rapid clip. Our compensation is competitive, but we also want missionaries who are passionate about inventing things that will make a meaningful difference for customers — for those kinds of people, there's no better place in the world to build."

Door desks and 'egalitarian' pay

Amazon founder Jeff Bezos back in the 1990s

Amazon founder Jeff Bezos back in the 1990s TNS/ABACA via Reuters Connect

Amazon is famously frugal. One of its origin stories recounts how the company bought cheap doors from Home Depot and hacked them together as office desks. This became guiding symbol of Amazon's cautious spending, with founder Jeff Bezos still using door desks today.

This penny-pinching culture has smashed straight into an AI hiring battle that's being fueled by unprecedented spending, putting Amazon in a tricky situation.

The internal document described compensation as one of the "hotly debated topics" among Amazon recruiters, citing the company's strict use of fixed salary bands for each role. Amazon's "egalitarian philosophy" on pay leaves its offers "below par" compared with top rivals, it added.

"The lack of salary range increases for several key job families over the past few years does not position Amazon as an employer of choice for top tech talent," the document warned.

For Amazon, missing out on top AI talent is a potential risk. ​​The pool of top-tier AI researchers and engineers is limited, and without experts with deep knowhow, it's hard to compete at the frontier of the field. Indeed, Amazon has yet to find a blockbuster AI product like OpenAI's ChatGPT or Anthropic's Claude, although its Bedrock AI cloud service has made progress.

Amazon's pay structure has been a long-standing source of tension.

Several people who spoke to Business Insider cited the 2020 departure of Amazon robotics VP Brad Porter as evidence of the company's frugal approach hampering talent recruitment and retention. Porter left in part after Amazon refused to raise his pay band.

Amazon's stock vesting schedule is also heavily backloaded, a structure that can be less attractive to new hires. The policy extends even to top executives, who generally receive no cash bonuses.

'Voting with their feet'

Amazon CEO Andy Jassy

Amazon CEO Andy Jassy REUTERS/Brendan McDermid

In addition to highlighting Amazon's "perceived lag in the AI space," the internal document said generative AI has further intensified the competition for specialized talent, particularly individuals with expertise in large language models.

An August report from venture capital firm SignalFire shows Amazon is on the lower end of engineering retention, far below Meta, OpenAI, and Anthropic. Jarod Reyes, SignalFire's head of developer community, told Business Insider that Amazon rivals are making bigger strides in AI, across open models, foundational research, and developer tooling.

"Amazon hasn't clearly positioned itself as a leader in the generative AI wave," Reyes said. "Engineers are paying attention and they're voting with their feet."

SignalFire chart on engineering talent retention

SignalFire chart on engineering talent retention SignalFire

Some investors share that view. On Amazon's earnings call last month, Morgan Stanley analyst Brian Nowak pressed CEO Andy Jassy on Wall Street's "narrative right now that AWS is falling behind" in AI and fears of losing market share to rivals. Jassy's response fell flat, sending Amazon's stock lower during the call.

Amazon intends to tackle these concerns. According to the document, the company will refine its "compensation and location strategy" and host more events designed to highlight its generative AI capabilities. It also intends to set up dedicated recruiting teams for generative AI within business units like AWS to boost efficiency.

'Hubs' constrain talent

Amazon employees at company headquarters

Hundreds of tech workers gathered outside Amazon's headquarters in Seattle. REUTERS/Lindsey Wasson

Another point of contention is Amazon's aggressive return-to-office mandate, which has already caused logistical issues.

The company's new "hub" policy — which requires employees to relocate to a central office or risk termination — has further limited its access to "high-demand talent like those with GenAI skills," according to the internal document.

"Hubs constrain market availability," it stated.

Amazon is exploring ways to allow for more "location-flexible" roles, the document added.

Amazon's spokesperson told BI that the company is "always looking for ways to optimize our recruiting strategies and looking at alternate talent rich locations."

Amazon hasn't been entirely on the sidelines. Last year, it brought on Adept CEO David Luan as part of a licensing deal with the AI startup. Luan now heads Amazon's AI agents lab. But the company has also seen departures, including senior AI leaders like chip designer Rami Sinno and VP Vasi Philomin, who worked on Bedrock.

One Amazon recruiter told Business Insider that a growing number of job candidates started declining offers last year because of the company's RTO policy. Even if a competitor pays less, people are open to taking the job if they can stay remote, this person said.

"We are losing out on talent," this person added.

Indeed, Bloomberg reported recently that Oracle has hired away more than 600 Amazon employees in the past two years because Amazon's strict RTO policy has made poaching easier.

Staying the course

The internal Amazon document dates to late last year, leaving open the possibility that the company has since adjusted its compensation approach to make exceptions for top AI talent.

Still, multiple people familiar with the situation told Business Insider there haven't been any formal updates to internal pay guidelines. One current Amazon manager said it remains almost impossible for the company to enact sweeping changes, given its long track record of sticking to the existing system. The people who spoke with Business Insider asked not to be identified discussing sensitive matters.

"Based on how we run our business and what we have achieved, there are more risks than potential benefits from changing an approach that has been so successful for our shareholders over the past several decades," Amazon wrote this year about executive compensation in its annual proxy statement.

Of course, the AI talent war may end up being an expensive and misguided strategy, stoked by hype and investor over-exuberance.

Some of the high-profile recruits Meta recently lured have already departed.

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Comments

  • By Traster 2025-09-0211:5218 reply

    Zuckerberg rushing into every new fad with billions of dollars has somehow tricked people into thinking that's what big tech is about and all of them should be shovelling money into this.

    But actually every other company has been much more strategic, Microsoft is bullish because they partnered up with OpenAI and it pumps their share price to be bullish, Google is the natural home of a lot of this research.

    But actually, Amazon, Apple etc aren't natural homes for this, they don't need to burn money to chase it.

    So there we have it, the companies that have a good strategy for this are investing heavily, the others will pick up merges and key technological partners as the market matures, and presumably Zuck will go off and burn $XB on the next fad once AI has cooled down.

    • By JCM9 2025-09-0213:272 reply

      I generally agree with you, although Amazon is really paranoid about being behind here.

      On the last earnings call the CEO gave a long rambling defensive response to an analyst question on why they’re behind. Reports from the inside also say that leaders are in full blown panic mode, pressing teams to come up with AI offerings even though Amazon really doesn’t have any recognized AI leaders in leadership roles and the best talent in tech is increasingly leaving or steering clear of Amazon.

      I agree they should just focus on what they’re good at, which is logistics and fundamental “boring” compute infrastructure things. However leadership there though is just all over the map trying to convince folks their not behind vs just focusing on strengths.

      • By ericmcer 2025-09-0218:423 reply

        Doesn't Amazon have a huge lead just because of AWS? Every other player is scrambling for hardware/electricity while Amazon has been building out data centers for the last 20 years.

        • By dragonwriter 2025-09-0218:541 reply

          > Doesn't Amazon have a huge lead just because of AWS?

          They have huge exposure because of AWS; if the way people use computing shifts, and AWS isn't well-configured for AI workloads, then AWS has a lot to lose.

          > Every other player is scrambling for hardware/electricity while Amazon has been building out data centers for the last 20 years.

          Microsoft and Google have also been building out data centers for quite a while, but also haven't sat out the AI talent wars the way Amazon has.

          • By deanCommie 2025-09-0219:011 reply

            > AWS isn't well-configured for AI workloads

            What does that mean? Not enough GPUs?

            • By JCM9 2025-09-0219:32

              A few things, including:

              1. Price-performance has struggled to stay competitive. There’s some supply-demand forces at play, but the top companies consistently seem to strike better deals elsewhere.

              2. The way AWS is architected, especially on networking, isn’t ideal for AI. They’ve dug their heels on in their own networking protocols despite struggling to compete on performance. I personally know of several workloads that left AWS because they couldn’t compete on networking performance.

              3. Struggling on the managed side. On paper a service like Bedrock should be great but in practice it’s been a hot mess. I’d love to use Anthropic via Bedrock, but it’s just much more reliable when going direct. AWS has never been great at these sort of managed services at scale and they’re again struggling here.

        • By JCM9 2025-09-0219:36

          In theory they should, but it’s increasingly looking like they’re struggling to attract/retain the right talent to take advantage of that position. On paper they should be wiping the floor with others in this space. In practice they’re getting their *ss kicked and in a panic on what to do.

        • By dataking 2025-09-0218:54

          My understanding is that they fell behind on offering the latest gen Nvidia hardware (Blackwell/Blackwell Ultra) due to their focus on internally developed ASICs (Trainium/Inferentia gen 2).

      • By butlike 2025-09-0217:04

        Which bar raiser is going to raise the bar first??

    • By alexc05 2025-09-0214:464 reply

      I'd argue that Meta's income derives in no small part from their best in class ad targeting.

      Being on the forefront of

      (1) creating a personalized, per user data profile for ad-targeting is very much their core business. An LLM can do a very good job of synthesizing all the data they have on someone to try predicting things people will be interested in.

      (2) by offering a free "ask me anything" service from meta.ai which is tied directly to their real-world human user account. They gather an even more robust user profile.

      This isn't in-my-opinion simply throwing billions at a problem willy nilly. Figuring out how to apply this to their vast reams of existing customer data economically is going to directly impact their bottom line.

      • By WtfRuSerious 2025-09-0215:53

        5 minutes on facebook being force-fed mesopotamian alien conspiracies is all you'll need to experience to fully understand just how BADLY they need some kind of intelligence for their content/advertising targeting, artificial or not...

      • By graemep 2025-09-0215:074 reply

        Obviously one is a very bad sample, but why are the ads I see on FB so badly targetted?

        • By Scaevolus 2025-09-0215:111 reply

          You probably don't spend enough time on their sites to have a good ad targeting model of you developed. The closer you are to normal users, with hundreds of hours of usage and many ad clicks, the more accurate the ads will be for you.

          • By butlike 2025-09-0217:11

            You mean the closer I am to the top of the bell curve, the more your ads "shooting from the hip" will land? Who would've thunk it?!

        • By agent327 2025-09-0216:03

          Did you block their tracking across the whole damn internet, by any chance?

        • By potro 2025-09-0216:22

          Same terrible experience for me while I was on FB. I was spending a lot of time there and I do shop a lot online. They couldn’t come with relevant ad targeting for me. For my wife they started to show relevant ads AFTER she went to settings and manually selected areas she is interested in. This is not an advanced technology everyone claim FB has.

        • By sharadov 2025-09-0216:40

          Instagram has killer ad targeting; no wonder all these direct-to-consumer brands flock there. FB not so much I agree.

      • By dylan604 2025-09-0215:571 reply

        >An LLM can do a very good job of synthesizing all the data they have on someone to try predicting things people will be interested in.

        Is synthesizing the right word here?

        • By veidr 2025-09-0216:07

          I think is absolutely is, LOL. Though a "very good job of synthesizing" might not actually good for much...

      • By idopmstuff 2025-09-0216:50

        People look at all the chaos in their AI lab but ignore the fact that they yet again beat on earnings substantially and directly cited better ad targeting as the reason for that. Building an LLM is nice for them, but applying AI to their core business is what really matters financially, and that seems like it's going just fine.

    • By HarHarVeryFunny 2025-09-0213:011 reply

      The largest LLMs are mostly going to be running in the cloud, so the general purpose cloud providers (Amazon, Microsoft, Google) are presumably going to be in the business of serving models, but that doesn't necessarily mean they need to build the models themselves.

      LLMs look to be shaping up as an interchangeable commodity as training datasets, at least for general purpose use, converge to the limits of the available data, so access to customers seems just as important, if not more, than the models themselves. It seems it just takes money to build a SOTA LLM, but the cloud providers have more of a moat, so customer access is perhaps the harder part.

      Amazon do of course have a close relationship with Anthropic both for training and serving models, which seems like a natural fit given the whole picture of who's in bed with who, especially as Anthropic and Amazon are both focused on business customers.

      • By GloriousMEEPT 2025-09-0213:301 reply

        Microsoft is building it's own in-house LLM's based on OpenAI's IP. Google builds it's own models.

        • By HarHarVeryFunny 2025-09-0215:411 reply

          Sure, but you can also sell something without having built it yourself, just as Microsoft Copilot supports OpenAI and Anthropic models.

          It doesn't have to be either/or of course - a cloud provider may well support a range of models, some developed in house and some not.

          Vertical integration - a cloud provider building everything they sell - isn't necessarily the most logical business model. Sometimes it makes more sense to buy from a supplier, giving up a bit of margin, than build yourself.

          • By GloriousMEEPT 2025-09-0216:22

            I'm just an observer. Microsoft has invested billions in OpenAI and can access their IP as a result. It might even be possible MS hopes that OpenAI fails and doesn't allow them to restructure to continue to acquire outside funding. You can go directly to the announcement of their in-house model offerings and they are clearly using this as a recruiting tool for talent. Whether it makes sense for the cloud providers to build their own models is not for me to say, but they may not have a choice given how quickly OpenAI/Anthropic are burning cash. If those two fail then they're essentially ceding the market to Google.

    • By jayd16 2025-09-0216:15

      I think this analysis is a bit shallow with regard to Metas product portfolio and how AI fits in.

      Much more than the others, metter runs a content business. Gen AI aides in content generation so it behooves them to research it. Even before the current explosion of chatbots, meta was putting this stuff into their VR framework. It's used for their headset tracking and speech to text is helpful for controlling a headset without a physical keyboard.

      You're making it sound like they'll follow anything that walks by but I do think it's more strategic than that.

    • By gus_massa 2025-09-0214:031 reply

      Zuckerberg bought Whatsapp and Instagram. For normal people, those replaced 90% of the internet here in Argentina

      (The other 10% is mostly Google Maps and MercadoLibre.)

      • By danieldk 2025-09-0214:162 reply

        But that didn't require deep insight. Both were already really popular and clearly a threat to Facebook. WhatsApp was huge in Europe before they bought (possibly other places as well).

        Buying competition is par for the course for near-monopolies in their niches. As long as the scale differences in value are still very large, you can avoid competition relatively cheaply, while the acquired still walk away with a lot of money.

        • By YetAnotherNick 2025-09-0214:241 reply

          Why does investing in AI require deep insight? ChatGPT is already huge, significantly bigger than Whatsapp when the deal was done. And while OpenAI is not for sale, he figured that their employees are. Also not to mention, investors are very positive for AI.

          • By PhunkyPhil 2025-09-0214:462 reply

            So far there hasn't been a transformative use case for LLMs besides the straightforward chat interface (Or some adjacent derivative). Cursor and IDE extensions are nice, but not something that generates billions in revenue.

            This means there's two avenues:

            1. Get a team of researchers to improve the quality of the models themselves to provide a _better_ chat interface

            2. Get a lot of engineers to work LLMs into a useful product besides a chat interface.

            I don't think that either of these options are going to pan out. For (1), the consumer market has been saturated. Laymen are already impressed enough by inference quality, there's little ground to be gained here besides a super AGI terminator Jarvis.

            I think there's something to be had with agentic interfaces now and in the future, but they would need to have the same punching power to the public that GPT3 did when it came out to justify the billions in expenditure, which I don't think it will.

            I think these companies might be able to break even if they can automate enough jobs, but... I'm not so sure.

            • By YetAnotherNick 2025-09-0216:561 reply

              Whatsapp had $10M revenue when it was acquired[1]. Lots of so called "chatgpt wrappers" has more revenue than that. While in hindsight Whatsapp acquisition at $19B seems no brainer, no concrete metric pointed to that compared to him investing $19B in AI now.

              [1]: https://www.sec.gov/Archives/edgar/data/1326801/000132680114...

              • By utyop22 2025-09-030:02

                Dude Zuckerberg bought whatsapp because FB Messenger was losing market share... nothing to do with Whatsapps revenue! Rather Zuckerbergs fear of FB products being displaced.

            • By bonsai_bar 2025-09-0215:061 reply

              > Cursor and IDE extensions are nice, but not something that generates billions in revenue.

              I mean Cursor is already at $500 million ARR...

              • By PhunkyPhil 2025-09-0215:24

                How many software engineers are there in the world? How many are going to stop using it when model providers start increasing token cost on their APIs?

                I could see the increased productivity of using Cursor indirectly generating a lot more value per engineer, but... I wouldn't put my money on it being worth it overall, and neither should investors chasing the Nvidia returns bag.

        • By therealdrag0 2025-09-0218:061 reply

          Pretty sure everyone was balking at the purchase prices at the time

          • By utyop22 2025-09-030:01

            In the UK it was an obvious purchase - whatsapp was the main mode of communicaton on a phone. Nobody used Messenger for instance.

    • By h1fra 2025-09-0212:053 reply

      Amazon strategy is to invest in the infrastructure, money is where the machines live. I think they just realized none of those companies have a moat, so why would they? But all of them will buy compute

      • By JCM9 2025-09-0214:171 reply

        Except they’re struggling here. The performance of their offerings is consistently behind competitors, particularly given their ongoing networking challenges, and they’re consistently undercut on pricing.

        For Amazon “renting servers” at very high margin is their cash cow. For many competitors it’s more of a side business or something they’re willing to just take far lower margin on. Amazon needs to keep the markup high. Take away the AWS cash stream and the whole of Amazon’s financials start to look ugly. That’s likely driving the current panic with its leadership.

        Culturally Amazon does really well when it’s an early mover leader in a space. It really struggles, and its leadership can’t navigate, when it’s behind in a sector as is playing out here.

        • By adventured 2025-09-0214:264 reply

          Under what scenario does Amazon lose the beast that is its high margin cloud service renting? It appears to be under approximately zero threat.

          Companies are not going to stop needing databases and the 307 other things AWS provides, no matter how good LLMs get.

          Cheaper competitors have been trying to undercut AWS since the early days of its public availability, it has not worked to stop them at all. It's their very comprehensive offering, proven track record and the momentum that has shielded AWS and will continue to indefinitely.

          • By geodel 2025-09-0215:37

            AWS is losing marketshare to Azure and GCP. This is big deal, it was unexpected after years of Google/Microsoft trying and failing.

            Further AWS is losing share at a time when GCP and Azure are becoming profitable businesses, so no longer losing money to gain market share.

          • By JCM9 2025-09-0214:32

            It’s already playing out. Just look at recent results. While once light years ahead competitors are now closing ranks and margins are under pressure. AWS clearly isn’t going away, but on the current trajectory its future as the leading cloud is very much not a certainty.

          • By tguedes 2025-09-0218:50

            Because if LLM inference is going to be a bigger priority for the majority of companies, they're going to go where they can get the best performance to cost ratio. AWS is falling behind on this. So companies (especially new ones) are going to start using GCP or Azure, and if they're already there for their LLM workloads, why not run the rest of the infrastructure there?

            It's similar to how AWS became the de-facto cloud provider for newer companies. They struggled to convince existing Microsoft shops to migrate to AWS, instead most of the companies just migrated to Azure. If LLMs/AI become a major factor in new companies deciding which will be their default cloud provider, they're going to pick GCP or Azure.

          • By breppp 2025-09-0219:09

            Except for spending cloud budgets on LLMs elsewhere like other mentioned, LLM coding will make it easier to convert codebases from being AWS dependent, easing their lock-in

      • By zaphirplane 2025-09-0213:44

        I would be surprised if a cloud market leader thinks winning on commodity vm rental is a strategy

      • By mhb 2025-09-0213:11

        And electricity.

    • By giancarlostoro 2025-09-0216:09

      Microsoft has the pleasure of letting you pay for your own hosted GPT models, Mixtral, etc

      Microsoft's in a sweet spot. Apple's another interesting one, you can run local LLM models on your Mac really nicely. Are they going to outcompete an Nvidia GPU? Maybe not yet, but they're fast enough as-is.

    • By malfist 2025-09-0214:492 reply

      > But actually, Amazon, Apple etc aren't natural homes for this, they don't need to burn money to chase it.

      Amazon is the biggest investor of AI of any company. They've already spent over $100b YTD on capex for AI infrastructure.

      • By ZiiS 2025-09-0218:25

        This is "shovels" they rent out; very different then research investment.

      • By dylan604 2025-09-0216:021 reply

        To do what for that money? Write summaries of product reviews? If they wanted to do something useful, they'd use the LLM to figure out which reviews are for a different product than what is currently being displayed.

        • By veidr 2025-09-0216:10

          "useful" means different things

    • By kcplate 2025-09-0213:021 reply

      > But actually, Amazon, Apple etc aren't natural homes for this, they don't need to burn money to chase it.

      I really liked the concept of Apple Intelligence with everything happening all on device, both process and data with minimal reliance off device to deliver the intelligence. It’s been disappointing that it hasn’t come to fruition yet. I am still hopeful the vapor materializes soon. Personally I wouldn’t mind seeing them burning a bit more to make it happen.

      • By mleo 2025-09-0213:13

        It will likely occur, just maybe not this year or next. If we look over the last eighty years of computing, the trend has been smaller and more powerful computers. No reason to think this won’t occur with running inference on larger models.

    • By burnte 2025-09-0219:00

      Exactly. Being a tech company doesn't mean you need to do everything any more than just because you're a family doctor you also should do trauma surgery, dentistry, and botox injections. Pick a lane, be an expert in it.

    • By buyucu 2025-09-0215:51

      Except that Amazon's AWS business is severely threatened by the rise of alternative cloud providers who offer much more AI-friendly environments. It's not an existential topic yet, but could easily turn into one.

    • By chaosbolt 2025-09-0213:581 reply

      Zuckerberg knows what he's doing, his net worth is >250 billion dollars now.

      Go all in the new fad, investors pile up on your stock, dump, repeat...

      • By aleph_minus_one 2025-09-0214:50

        > Zuckerberg knows what he's doing, his net worth is >250 billion dollars now.

        Does he have this net worth because what he is doing or despite what he is doing? :-)

        Correlation does not imply causation. Attribution is hard.

    • By throwawayq3423 2025-09-0216:54

      By that logic, a social media company shouldn't rush into it either, but they did anyway.

    • By DonsDiscountGas 2025-09-0214:55

      Zuckerbergs AI "strategy" seems to be to make it easy for people to generate AI slop and share it on FB thus keeping them active on the platform. Or to give people AI "friends" to interact with on FB, thus keeping them on the platform and looking at ads. It's horrifying but it does make business sense (IMHO) at least at first glance.

    • By idiomat9000 2025-09-0216:25

      They have out sensors though for any AGI, because AGI could subvert buisness fields and expertise moats. Thats what most AI teams are- vanity projects and a few experts calming the higher ups every now and then with a "its still just autocompletion on steroids, it can not yet do work and research alone."

    • By ath3nd 2025-09-0213:482 reply

      > Zuckerberg rushing into every new fad with billions of dollars has somehow tricked people into thinking that's what big tech is about and all of them should be shovelling money into this.

      Zuckerberg failed every single fad he tried.

      He's becoming more irrelevant every year and only the company's spoils from the past (earned not less by enabling, for example, a genocide to be committed in Myanmar https://www.pbs.org/newshour/world/amnesty-report-finds-face...) help carry them through to the series of disastrous idiotic decision Zuck is inflicting on them.

      - VR with Oculus. It never caught on, for most people who own one, it's just gathering dust.

      - Metaverse. They actually spend billions on that? https://www.youtube.com/watch?v=SAL2JZxpoGY

      - LLAMA is absolute trash, a dumpster fire in the world of LLMs

      Zuck is now trying to jump again on the LLM bandwagon and he's trying to...buy his way in with ridiculous pay packages: https://www.nytimes.com/2025/07/31/technology/ai-researchers.... Why is he so wrong to do that, you might ask?

      He is doing it at the worst possible moment: LLMs are stagnating and even far better players than Meta like Anthropic and OpenAI can't produce anything worth writing about.

      ChatGPT5 was a flop, Anthropic are struggling financially and are lowering token limits and preparing users for cranking up prices, going 180 on their promises not to use chat data for training, and Zuck, in his infinite wisdom, decides to hire top AI talent for premium price at a rapidly cooling market? You can't make up stuff like that.

      It would appear that apart from being an ass kisser to Trump, Zuck shares another thing with the orange man-child running the US: a total inability to make good, or even sane deals. Fingers crossed that Meta goes bankrupt just like Trump's 6 banrkruptcies and then Zuck can focus on his MMA career.

      • By code_for_monkey 2025-09-0214:122 reply

        I've been taking heat for years for making fun of the metaverse. I had hopeful digital landlords explain to me that theyll be charging rent in there! Who looked at that project and thought it was worth anything?

        • By williamdclt 2025-09-0215:131 reply

          > I've been taking heat for years for making fun of the metaverse

          I don't know in what circles you're hanging out, I don't know a single person who believed in the metaverse

          • By ath3nd 2025-09-0215:41

            > I don't know in what circles you're hanging out, I don't know a single person who believed in the metaverse

            Oh please, the world was full of hype journalists wanting to sound like they get it and they are in it, whatever next trash Facebook throws their way.

            The same way folks nowadays pretend like the LLMs are the next coming of Jesus, it's the same hype as the scrum crowd, the same as crypto, nfts, web3. Always ass kissers who cant think for themselves and have to jump on some bandwagon to feign competence.

            Look at what the idiots at Forbes wrote: https://www.forbes.com/councils/forbestechcouncil/2023/02/27...

            They are still very influential, despite having shit takes loke that.

            Accenture still think the Meta is groundbreaking: https://www.accenture.com/us-en/insights/metaverse

            What a bunch of losers!

            71% of executives seemed to be very excited about it: https://www.weforum.org/stories/2022/04/metaverse-will-be-go...

            Executives (like Zuck) are famous for being rather stupid so if they are claiming something, you bet its not gonna happen.

            Apparently, "The metaverse is slowly becoming the new generation’s digital engagement platform, but it’s making changes across enterprises, too."

            https://www.softserveinc.com/en-us/blog/the-promise-of-the-m...

        • By mring33621 2025-09-0215:18

          i don't care about virtual real estate, but VR mini golf sure is fun!

      • By HDThoreaun 2025-09-0216:171 reply

        meta made $62 billion dollars last year. Mark burns all this money because his one and only priority is making sure his company doesnt become an also ran. The money means nothing to him

        • By ath3nd 2025-09-037:55

          Yet his company and him are becoming rapidly irrelevant.

    • By physhster 2025-09-0212:002 reply

      So does Pichai... Every time there is something new, he forces Google to pivot, upending everything without much to show for it.

      • By rorads 2025-09-0214:071 reply

        Google basically invented modern AI (the 'T' in ChatGPT stands for Transformer), then took a very broad view of how to apply broadly neural AI - AlphaGo, AlphaGenome being the kind of non-LLM stuff they've done).

        A better way to look at it is that the absolute number 1 priority for google since they first created a money spiggot throguh monetising high-intent search and got the monopoly on it (outside of Amazon) has been to hold on to that. Even YT (the second biggest search engine on the internet other than google itself) is high intent search leading to advertising sales conversion.

        So yes, google has adopted and killed lots of products, but for its big bets (web 2.0 / android / chrome) it's basically done everything it can to ensure it keeps it's insanely high revenue and margin search business going.

        What it has to show for it is basically being the only company to have transitioned as dominent across technological eras (desktop -> web2.0 -> mobile -> (maybe llm).

        As good as OpenAI is as a standalone, and as good as Claude / Claude Code is for developers, google has over 70% mobile market share with android, nearly 70% browser market share with chrome - this is a huge moat when it comes to integration.

        You can also be very bullish about other possible trends. For AI - they are the only big provider which has a persistent hold on user data for training. Yes, OpenAI and Grok have a lot of their own data, but google has ALL gmail, high intent search queries, youtube videos and captions, etc.

        And for AR/VR, android is a massive sleeping giant - no one will want to move wholesale into a Meta OS experience, and Apple are increasingly looking like they'll need to rely on google for high performance AI stuff.

        All of this protects google's search business a lot.

        Don't get me wrong, on the small stuff google is happy to let their people use 10% time to come up with a cool app which they'll kill after a couple of years, but for their big bets, every single time they've gone after something they have a lot to show for it where it counts to them.

        • By msabalau 2025-09-0214:36

          Yeah, and Google has cared deeply about AI as a long term play since before they were public. And have been continuously invested there over the long haul.

          The small stuff that they kill is just that--small stuff that was never important to them strategically.

          I mean, sure, don't heavily invest (your attention, time, business focus, whatever) in something that is likely to be small to Google, unless you want to learn from their prototypes, while they do.

          But to pretend that Google isn't capable of sustained intense strategic focus is to ignore what's clearly visible.

      • By 42lux 2025-09-0212:133 reply

        When did Google ever pivot?

        • By chubot 2025-09-0212:432 reply

          I haven't followed that closely, but Gemini seems like a pivot based on ChatGPT's market success

          Google is leading in terms of fundamental technology, but not in terms of products

          They had the LLambda chatbot before that, but I guess it was being de-emphasized, until ChatGPT came along

          Social was a big pivot, though that wasn't really due to Pichai. That was while Larry Page was CEO and he argued for it hard. I can't say anyone could have known beforehand, but in retrospect, Google+ was poorly conceived and executed

          ---

          I also believe the Nth Google chat app was based on WhatsApp success, but I can't remember the name now

          Google Compute Engine was also following AWS success, after initially developling Google App Engine

          • By itsoktocry 2025-09-0213:05

            >I haven't followed that closely, but Gemini seems like a pivot based on ChatGPT's market success

            "AI" in it's current form is already a massive threat to Google's main business (I personally use Google only a fraction of what I used to), so this pivot is justified.

          • By sabas123 2025-09-0213:03

            Is it really such a pivot when they invested a lot in AI already?

            They bought DeepMind in 2014 and always showed of a ton of AI research.

          • By devin 2025-09-0213:491 reply

            None of these are pivots. The core business has always been the core business.

            • By swiftcoder 2025-09-0214:132 reply

              If you are defining "pivot" as "abandon all other lines of business", then no, none of the BigTechs have ever pivoted.

              By more reasonable standards of "pivot", the big investment into Google Plus/Wave in the social media era seems to qualify. As does the billions spent building out Stadia's cloud gaming. Not to mention the billions invested in their abandoned VR efforts, and the ongoing investment into XR...

              • By msabalau 2025-09-0215:04

                G+ was a significant effort that was abandoned.

                I'd personally define that as Google hedging their bet's and being prepared in case they needed to truly pivot, and then giving up when it became clear that they wouldn't need to. Sort of like "Apple Intelligence" but committing to the bit, and actually building something that was novel, and useful to some people, who were disappointed when it went away.

                Stadia was always clearly unimportant to Google, and I say that as a Stadia owner (who got to play some games, and then got refunds.) As was well reported at the time, closing it was immaterial to their financials. Just because spending hundreds of millions of dollars or even a few billion dollars is significant to you or I doesn't mean that this was ever part of their core business.

                Regardless, the overall sentimentality on HN about Google Reader and endless other indisputably small projects says more about the lack of strategic focus from people here, than it says anything about Alphabet.

              • By veidr 2025-09-0216:151 reply

                Well, "pivot" implies the core business has failed and you're like "oh shit, let's do X instead".

                Stadia was just Google's New Coke, Apple's Mac Cube, or Microsoft's MSNBC (or maybe Zune.

                When they can't sell ads anymore, they'll have to pivot.

                • By swiftcoder 2025-09-0217:061 reply

                  > Well, "pivot" implies the core business has failed and you're like "oh shit, let's do X instead".

                  I mean, Facebook's core business hasn't actually failed yet either, but their massive investments in short-form video, VR/XR/Metaverse, blockchain, and AI are all because they see their moat crumbling and are desperately casting around for a new field to dominate.

                  Google feels pretty similar. They made a very successful gambit into streaming video, another into mobile, and a moderately successful one into cloud compute. Now the last half a dozen gambits have failed, and the end of the road is in sight for search revenue... so one of the next few investments better pay off (or else)

                  • By devin 2025-09-0314:18

                    The link you posted has a great many very insignificant investments included in it, and nothing I've seen Google doing has felt quite like the desperation of Facebook in recent years.

                    I didn't really see it at first, but I think you are correct to point out that they kind of rhyme. However to me, I think the clear desperation of Facebook makes it feel rather different from what I've seen Google doing over the years. I'm not sure I agree that Google's core business is in jeopardy in the way that Facebook's aging social media platform is.

        • By ethbr1 2025-09-0212:181 reply

          Social. User-facing AI.

          • By earth2mars 2025-09-0212:37

            Social: YouTube User facing AI: Gemini, Google photos, NotebookLM and plenty of products.

    • By spjt 2025-09-0212:331 reply

      I suppose you could argue that Amazon does have one special thing going for it here, idle compute resources in AWS. However that is not the sort of thing that requires "AI talent" to make use of.

      • By swiftcoder 2025-09-0213:01

        They also have made pretty big investments in cloud VMs with GPUs attached, so they are making money off the AI craze regardless

  • By whatever1 2025-09-024:1011 reply

    The evidence shows that there is no methodological moat for LLMS. The moat of the frontier folks is just compute. xAI went in months from nothing to competing with the top dogs. DeepSeek too. So why bother with splurging billions in talent when you can buy GPUs and energy instead and serve the compute needs of everyone?

    Also Amazon is in another capital intensive business. Retail. Spending billions on dubious AWS moonshots vs just buying more widgets and placing them across the houses of US customers for even faster deliveries does not make sense.

    • By cedws 2025-09-027:406 reply

      A lot of C-suite people seem to have an idea that if they just throw enough compute at LLMs that AGI will eventually emerge, even though it's pretty clear at this point that LLMs are never going to lead to general intelligence. In their view it makes sense to invest massive amounts of capital because it's like a lottery ticket to being the future AGI company that dominates the world.

      I recall Zuckerberg saying something about how there were early signs of AI "improving itself." I don't know what he was talking about but if he really believes that's true and that we're at the bottom of an exponential curve then Meta's rabid hiring and datacenter buildout makes sense.

      • By hliyan 2025-09-0212:023 reply

        In early 2023, I remember someone breathlessly explaining that there are signs that LLMs that are seemingly good at chess/checkers moves may have a rudimentary model of the board within them, somehow magically encoded into the model weights through the training. I was stupid enough to briefly entertain the possibility until I actually bothered to develop a high level understanding of the transformer architecture. It's surprising how much mysticism this field seems to attract. Perhaps it being a non-deterministic, linguistically invoked black box, triggers the same internal impulses that draw some people to magic and spellcasting.

        • By pegasus 2025-09-0212:471 reply

          Just because it's not that hard to reach a high-level understanding of the transformer pipeline doesn't mean we understand how these systems function, or that there can be no form of world model that they are developing. Recently there has been more evidence for that particular idea [1]. The feats of apparent intelligence LLMs sometimes display have taken even their creators by surprise. Sure, there's a lot of hype too, that's part and parcel of any new technology today, but we are far from understanding what makes them perform so well. In that sense, yeah you could say they are a bit "magical".

          [1] https://the-decoder.com/new-othello-experiment-supports-the-...

          • By ath3nd 2025-09-0214:011 reply

            > Just because it's not that hard to reach a high-level understanding of the transformer pipeline doesn't mean we understand how these systems function

            Mumbo jumbo magical thinking.

            They perform so well because they are trained on probabilistic token matching.

            Where they perform terribly, e.g math, reasoning, they are delegating to other approaches, and that's how you get the illusion that there is actually something there. But it's not. Faking intelligence is not intelligence. It's just text generation.

            > In that sense, yeah you could say they are a bit "magical"

            Nobody but the most unhinged hype pushers are calling it "magical". The LLM can never ever be AGI. Guessing the next word is not intelligence.

            > there can be no form of world model that they are developing

            Kind of impossible to form a world model if your foundation is probabilistic token guessing which is what LLMs are. LLMs are a dead end in achieving "intelligence", something novel as an approach needs to be discovered (or not) to go into the intelligence direction. But hey, at least we can generate text fast now!

            • By whalee 2025-09-0216:011 reply

              > LLMs are a dead end in achieving "intelligence"

              There is no evidence to indicate this is the case. To the contrary, all evidence we have points to these models, over time, being able to perform a wider range of tasks at a higher rate of success. Whether it's GPQA, ARC-AGI or tool usage.

              > they are delegating to other approaches > Faking intelligence is not intelligence. It's just text generation.

              It seems like you know something about what intelligence actually is that you're not sharing. If it walks, talks and quacks like a duck, I have to assume it's a duck[1]. Though, maybe it quacks a bit weird.

              [1] https://en.wikipedia.org/wiki/Solipsism

              • By ath3nd 2025-09-033:47

                > There is no evidence to indicate this is the case

                Burden of proof is on those trying to convince us to buy into the idea of LLMs as being "intelligence".

                There is no evidence of the Flying Spaghetti monster or Zeus or God not existing either, but we don't take seriously the people who claim they do exist (and there isn't proof because these concepts are made up).

                Why should we take seriously the tolks claiming LLMs are intelligence without proof (there can't be proof, of course, because LLMs are not intelligence)?

        • By ericmcer 2025-09-0218:46

          Is there something we are all missing? Using Claude feels like magic sometimes, but can't everyone see the limitation now that we are 4 years and 100s of billions down the road?

          Are they still really hoping that they are gonna tweak a model and feed it an even bigger dataset and it will be AGI?

        • By momojo 2025-09-0216:43

          I'm not a fan of mysticism. I'm also with you that these are simply statistical machines. But I don't understand what happened when understood transformers at a high-level.

          If you're saying the magic disappeared after looking at a single transformer, did the magic of human intelligence disappear after you understood human neurons at a high level?

      • By stuaxo 2025-09-028:139 reply

        Its insane really, anyone who has worked with LLMs for a bit and has an idea of how they work shouldn't think its going to lead to "AGI".

        Hopefully some big players, like FB bankrupt themselves.

        • By IanCal 2025-09-0210:077 reply

          Tbh I find this view odd, and I wonder what people view as agi now. It used to be that we had extremely narrow pieces of AI and I remember being on a research project about architectures and just very basic “what’s going on?” was advanced. Understanding that someone asked a question, that would be solved by getting a book and being able to then go and navigate to the place the book was likely to be was fancy. Most systems could solve literally one type of problem. They weren’t just bad at other things they were fundamentally incapable of anything but an extremely narrow use case.

          I can throw wide ranging problems at things like gpt5 and get what seem like dramatically better answers than if I asked a random person. The amount of common sense is so far beyond what we had it’s hard to express. It used to be always pointed out that the things we had were below basic insect level. Now I have something that can research a charity, find grants and make coherent arguments for them, read matrix specs and debug error messages, and understand sarcasm.

          To me, it’s clear that agi is here. But then what I always pictured from it may be very different to you. What’s your image of it?

          • By whizzter 2025-09-0211:19

            It's more that "random" people are dumb as bricks (but we've in the name of equality and historic measurement errors decided to forgo that), add to it that AI's have a phenomenal (internet sized) memory makes them far more capable than many people.

            However, even "dumb" people can often make judgements structures in a way that AI's cannot, it's just that many have such a bad knowledge-base that they cannot build the structures coherently whereas AI's succeed thanks to their knowledge.

            I wouldn't be surprised if the top AI firms today spend an inordinate amount of time to build "manual" appendages into the LLM systems to cater to tasks such as debugging to uphold the facade that the system is really smart, while in reality it's mostly papering up a leaky model to avoid losing the enormous investments they need to stay alive with a hope that someone on their staff comes up a real solution to self-learning.

            https://magazine.sebastianraschka.com/p/understanding-reason...

          • By adwn 2025-09-0210:221 reply

            I think the discrepancy between different views on the matter mainly stems from the fact that state-of-the-art LLMs are better (sometimes extremely better) at some tasks, and worse (sometimes extremely worse) at other tasks, compared to average humans. For example, they're better at retrieving information from huge amounts of unstructured data. But they're also terrible at learning: any "experience" which falls out of the context window is lost forever, and the model can't learn from its mistakes. To actually make it learn something requires very many examples and a lot of compute, whereas a human can permanently learn from a single example.

            • By andsoitis 2025-09-0212:41

              > human can permanently learn from a single example

              This, to me at least, seems like an important ingredient to satisfying a practical definition / implementation of AGI.

              Another might be curiosity, and I think perhaps also agency.

          • By Yoric 2025-09-0210:451 reply

            I think it's clear that nobody agrees what AGI is. OpenAI describes it in terms of revenue. Other people/orgs in terms of, essentially, magic.

            If I had to pick a name, I'd probably describe ChatGPT & co as advanced proof of concepts for general purpose agents, rather than AGI.

            • By delecti 2025-09-0213:10

              > I think it's clear that nobody agrees what AGI is

              People selling AI products are incentivized to push misleading definitions of AGI.

          • By boppo1 2025-09-0210:573 reply

            Human-level intelligence. Being able to know what it doesn't know. Having a practical grasp on the idea of truth. Doing math correctly, every time.

            I give it a high-res photo of a kitchen and ask it to calculate the volume of a pot in the image.

            • By tomaskafka 2025-09-0211:19

              You discover truth by doing stuff in real world and observing the results. Current LLM have enough intelligence, but all the inputs they have are the “he said she said” by us monkeys, including all omissions and biases.

            • By snapcaster 2025-09-0212:29

              But many humans can't do a lot of those things and we still consider them "generally intelligent"

            • By 293984j29384 2025-09-0211:201 reply

              None of what you describe would I label within the realm of 'average'

              • By swiftcoder 2025-09-0213:08

                It's not about what the average human can do - it's about what humans as a category are capable of. There will always be outliers (in both directions), but you can, in general, teach a human a variety of tasks, such as performing arithmetic deterministically, that you cannot teach to, for example, a parrot.

          • By audunw 2025-09-037:05

            I don’t have a very high expectation of AGI at all. Just an algorithm or system you can put onto a robot dog, and get a dog level general intelligence. You should be able to live with that robot dog for 10 years and it should be just as capable as a dog throughout that timespan.

            Hell, I’d even say we have AGI if you could emulate something like a hamster.

            LLMs are way more impressive in certain ways than such a hypothetical AGI. But that has been true of computers for a long time. Computers have been much better at Chess than humans for decades. Dogs can’t do that. But that doesn’t mean that a chess engine is an AGI.

            I would also say we have a special form of AGI if the AI can pass an extended Turing test. We’ve had chat bots that can fool a human for a minute for a long time. Doesn’t mean we had AGI. So time and knowledge was always a factor in a realistic Turing test. If an AGI can fool someone who knows how to properly probe an LLM, for a month or so, while solving a bunch of different real world tasks that require stable long term memory and planning, then I’d day we’re in AGI territory for language specifically. I think we have to distinguish between language AGI and multi-modal AGI. So this test wouldn’t prove what we could call “full” AGI.

            These are some of the missing components for full AGI: - Being able to act as a stable agent with a stable personality over long timespans - Capable of dealing with uncertainties. Having a understanding of what it doesn’t know - One-shot learning, with long term retention, for a large number of things - Fully integrated multi-modality across sound, vision, and other inputs/outputs we may throw at it.

            The last one is where we may be able to get at the root of the algorithm we’re missing. A blind person can learn to “see” by making clicks and using their ears to see. Animals can do similar “tricks”. I think this is where we truly see the full extent of the generality and adaptability of the biological brain. Imagine trying to make a robot that can exhibit this kind of adaptability. It doesn’t fit into the model we have for AI right now.

          • By homarp 2025-09-0211:321 reply

            my picture of AGI is 1) autonomous improvement 2) ability to say 'i don't know/can't be done'

            • By dmboyd 2025-09-0212:511 reply

              I wonder if 2) is a result of published bias for positive results in the training set. An “I don’t know” response is probably ranked unsatisfactory by human feedback and most published scientific literature are biased towards positive results and factual explanations.

              • By InitialLastName 2025-09-0217:28

                In my experience, the willingness to say "I don't know" instead of confabulate is also down-rated as a human attribute, so it's not surprising that even an AGI trained on the "best" of humanity would avoid it.

          • By AlienRobot 2025-09-0210:41

            Nobody is saying that LLM's don't work like magic. I know how neural networks work and they still feel like voodoo to me.

            What we are saying is that LLM's can't become AGI. I don't know what AGI will look like, but it won't look like an LLM.

            There is a difference between being able to melt iron and being able to melt tungsten.

        • By thaawyy33432434 2025-09-0211:38

          Recently I realized that US are very close to a centrally planned economy. Meta wasted 50B on metaverse, which like how much Texas spends on healthcare. Now the "AI" investments seems dubious.

          You could fund 1000+ projects with this kinds of money. This is not an effective capital allocation.

        • By amelius 2025-09-029:23

          The only way we'll have AGI is if people get dumber. Using modern tech like LLMs makes people dumber. Ergo, we might see AGI sooner than expected.

        • By menaerus 2025-09-029:45

          > ... and has an idea of how they work shouldn't think its going to lead to "AGI"

          Not sure what level of understanding are you referring to but having learned and researched about the pretty much all LLM internals I think this has led me exactly to the opposite line of thinking. To me it's unbelievable what we have today.

        • By janalsncm 2025-09-0212:51

          I think AI research is like anything else really. The smartest people are heads down working on their problems. The people going on podcasts are less connected to day to day work.

          It’s also pretty useless to talk about whether something is AGI without defining intelligence in the first place.

        • By foobarian 2025-09-0213:58

          I think something like we saw in the show "Devs" is much more likely, although what the developers did with it in the show was bonkers unrealistic. But some kind of big enough quantum device basically.

        • By guardian5x 2025-09-028:25

          Just scaling them up might not leat to "AGI", but they can still lead to AGI as a bridge.

        • By meowface 2025-09-0211:58

          This is not and has not been the consensus opinion. If you're not an AI researcher you shouldn't write as if you've set your confidence parameter to 0.95.

          Of course it might be the case, but it's not a thing that should be expressed with such confidence.

        • By blackhaz 2025-09-028:272 reply

          Is it widely accepted that LLMs won't lead to AGI? I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly:

          1) LLMs as simple "next token predictors" so they just mimicry thinking: But can it be argued that current models operate on layers of multiple depth and are able to actually understand by building concepts and making connections on abstract levels? Also, don't we all mimicry?

          2) Grounding problem: Yes, models build their world models on text data, but we have models operating on non-textual data already, so this appears to be a technical obstacle rather than fundamental.

          3) Lack of World Model. But can anyone really claim they have a coherent model of reality? There are flat-earthers, yet I still wouldn't deny them having AGI. People hallucinate and make mistakes all the time. I'd argue hallucinations is in fact the sign of an emerging intelligence.

          4) Fixed learning data sets. Looks like this is now being actively solved with self-improving models?

          I just couldn't find a strong argument supporting this claim. What am I missing?

          • By globnomulous 2025-09-028:501 reply

            Why on earth would you copy and paste an LLM's output into a comment? What does that accomplish or provide that just a simply stated argument doesn't accomplish more succinctly? If you don't know something, simply don't comment on it -- or ask a question.

            • By blackhaz 2025-09-0218:301 reply

              None of the above is AI.

              • By globnomulous 2025-09-0411:14

                > I've asked Gemini, so it came up with four primary arguments for this claim, commenting on them briefly:

                This line means, and literally says, that everything that follows is a summary or direct quotation from an LLM's output.

                There's a more charitable but unintuitive interpretation, in which "commenting on them briefly" is intended to mean "I will comment on them briefly:". But this isn't a natural interpretation. It's one I could be expected to reach only after seeing your statement that 'none of the above is AI.' But even this more charitable interpretation actually contradicts your claim that it's not AI.

                So now I'm even less sure I know what you meant to communicate. Either I'm missing something really obvious or the writing doesn't communicate what you intended.

          • By welferkj 2025-09-029:36

            Fur future reference, pasting llm slop feels exactly as patronizing as back when people pasted links to google searches in response to questions they considered beneath their dignity to answer. Except in this case, no-one asked to begin with.

      • By qcnguy 2025-09-0210:234 reply

        > I don't know what he was talking about

        There's a bunch of ways AI is improving itself, depending on how you want to interpret that. But it's been true since the start.

        1. AI is used to train AI. RLHF uses this, curriculum learning is full of it, video model training pipelines are overflowing with it. AI gets used in pipelines to clean and upgrade training data a lot.

        2. There are experimental AI agents that can patch their own code and explore a tree of possibilities to boost their own performance. However, at the moment they tap out after getting about as good as open source agents, but before they're as good as proprietary agents. There isn't exponential growth. There might be if you throw enough compute at it, but this tactic is very compute hungry. At current prices it's cheaper to pay an AI expert to implement your agent than use this.

        • By Eggpants 2025-09-0216:50

          So have an AI with a 40% error rate judge an AI with an 40% error rate…

          AGI is a complete no go until a model can adjust its own weights on the fly, which requires some kind of negative feedback loop, which requires a means to determine a failure.

          Humans have pain receptors to provide negative feedback and we can imagine events that would be painful such as driving into a parked car would be painful without having to experience it.

          If current models could adjust its own weights to fix the famous “how many r’s in strawberry” then I would say we are on the right path.

          However, the current solution is to detect the question and forward it to a function to determine the right answer. Or attempt to add more training data the next time the model is generated ($$$). Aka cheat the test.

        • By mitjam 2025-09-0215:20

          I think LLM as a toolsmith like demonstrated in the Voyager paper (1) is another interesting approach to creating a system that can learn to do a task better over time. (1) https://arxiv.org/abs/2305.16291

        • By Yoric 2025-09-0210:421 reply

          > There are experimental AI agents that can patch their own code and explore a tree of possibilities to boost their own performance. However, at the moment they tap out after getting about as good as open source agents, but before they're as good as proprietary agents.

          Interesting. Do you have links?

        • By franktankbank 2025-09-0212:54

          I'm skeptical that RLHF really works. Doesn't it just patch the obvious holes so it looks better on paper? If it can't reason then it will continue to get 2nd and 3rd order difficulty problems wrong.

      • By abraxas 2025-09-0219:20

        > it's pretty clear at this point that LLMs are never going to lead to general intelligence.

        It is far from clear. There may well be emergent hierarchies of more abstract thought at much higher numbers of weights. We just don't know how a transformer will behave if one is built with 100T connections - something that would finally approach the connectome level of a human brain. Perhaps nothing interesting but we just do not know this and the current limitation in building such a beast is likely not software but hardware. At these scales the use of silicon transistors to approximate analog curve switching models just doesn't make sense. True neuromorphic chips may be needed to approach the numbers of weights necessary for general intelligence to emerge. I don't think there is anything in production at the moment that could rival the efficiency of biological neurons. Most likely we do not need that level of efficiency. But it's almost certain that stringing together a bunch of H100s isn't a path to the scale we should be aiming for.

      • By epolanski 2025-09-0210:091 reply

        I don't get it, I really don't.

        Even assuming a company gets to AGI first this doesn't mean another one will follow.

        Suppose that FooAI gets to it first: - competitors may get there too in a different or more efficient way - Some FooAI staff can leave and found their own company - Some FooAI staff can join a competitor - FooAI "secret sauce" can be figured out, or simply stolen, by a competitor

        At the end of the day, it really doesn't matter, the equation AI === commodity just does not change.

        There is no way to make money by going into this never ending frontier model war, price of training keeps getting higher and higher, but your competitors few months later can achieve your own results for a fraction of your $.

        • By cedws 2025-09-0211:411 reply

          Some would say that the race to AGI is like the race to nuclear weapons and that the first to get there will hold all the cards (and be potentially able to stop others getting there.) It's a bit too sci-fi for me.

          • By Yossarrian22 2025-09-0214:55

            If AGI is reached it would be trivial for the competing superpowers to completely quarantine themselves network wise by cutting undersea cables long enough to develop competing AGI

      • By CrossVR 2025-09-028:151 reply

        I don't know if AGI will emerge from LLM, but I'm always reminded of the Chinese room thought experiment. With billions thrown at the idea it will certainly be the ultimate answer as to whether true understanding can emerge from a large enough dictionary.

        • By torginus 2025-09-028:595 reply

          Please stop refering to the Chinese Room - it's just magical/deist thinking in disguise. It postulates that humans have way of 'understanding' things that is impossible to replicate mechanically.

          The fact that philosophy hasn't recognized and rejected this argument based on this speaks volumes of the quality of arguments accepted there.

          (That doesn't mean LLMs are or will be AGI, its just this argument is tautological and meaningless)

          • By armada651 2025-09-029:261 reply

            That some people use the Chinese Room to ascribe some magical properties to human consciousness says more about the person drawing that conclusion than the thought experiment itself.

            I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.

            If the answer is no, that shouldn't lead to a deist conclusion. It can just as easily lead to the conclusion that a non-deterministic Turing machine is required.

            • By torginus 2025-09-0212:322 reply

              I'd appreciate if you tried to explain why instead of resorting to ad hominem.

              > I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.

              Since the real world (including probabilistic and quantum phenomena) can be modeled with deterministic computation (a pseudorandom sequence is deterministic, yet simulates randomness), if we have a powerful enough computer we can simulate the brain to a sufficient degree to have it behave identically as the real thing.

              The original 'Chinese Room' experiment describes a book of static rules of Chinese - which is probably not the way to go, and AI does not work like that. It's probabilistic in its training and evaluation.

              What you are arguing is that constructing an artificial consciousness lies beyond our current computational ability(probably), and understanding of physics (possibly), but that does not rule out that we might solve these issues at some point, and there's no fundamental roadblock to artificial consciousness.

              I've re-read the argument (https://en.wikipedia.org/wiki/Chinese_room) and I cannot help but conclude that Searle argues that 'understanding' is only something that humans can do, which means that real humans are special in some way a simulation of human-shaped atoms are not.

              Which is an argument for the existence of the supernatural and deist thinking.

              • By CrossVR 2025-09-0212:481 reply

                > I'd appreciate if you tried to explain why instead of resorting to ad hominem.

                It is not meant as an ad hominem. If someone thinks our current computers can't emulate human thinking and draws the conclusion that therefore humans have special powers given to them by a deity, then that probably means that person is quite religious.

                I'm not saying you personally believe that and therefore your arguments are invalid.

                > Since the real world (including probabilistic and quantum phenomena) can be modeled with deterministic computation (a pseudorandom sequence is deterministic, yet simulates randomness), if we have a powerful enough computer we can simulate the brain to a sufficient degree to have it behave identically as the real thing.

                The idea that a sufficiently complex pseudo-random number generator can emulate real-world non-determinism enough to fully simulate the human brain is quite an assumption. It could be true, but it's not something I would accept as a matter of fact.

                > I've re-read the argument (https://en.wikipedia.org/wiki/Chinese_room) and I cannot help but conclude that Searle argues that 'understanding' is only something that humans can do, which means that real humans are special in some way a simulation of human-shaped atoms are not.

                In that same Wikipedia article Searle denies he's arguing for that. And even if he did secretly believe that, it doesn't really matter, because we can draw our own conclusions.

                Disregarding his arguments because you feel he holds a hidden agenda, isn't that itself an ad hominem?

                (Also, I apologize for using two accounts, I'm not attempting to sock puppet)

                • By torginus 2025-09-0212:571 reply

                  What are his arguments then?

                  >Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in the normal sense of the word.

                  This is the only sentence that seems to be pointing to what constitutes the specialness of humans, and the terms of 'understanding' and 'intentionality' are in air quotes so who knows? This sounds like the archetypical no true scotsman fallacy.

                  In mathematical analysis, if we conclude that the difference between 2 numbers is smaller than any arbitrary number we can pick, those 2 numbers must be the same. In engineering, we can reduce the claim to 'any difference large about to care about'

                  Likewise if the difference between a real human brain and an arbitrarily sophisticated Chinese Room brain is arbitrarily small, they are the same.

                  If our limited understanding of physics and engineering makes the practical difference not zero, this essentially becomes a bit of a somewhat magical 'superscience' argument claiming we can't simulate the real world to a good enough resolution that the meaningful differences between our 'consciousness simulator' and the thing itself disappear - which is an extraordinary claim.

                  • By CrossVR 2025-09-0213:081 reply

                    > What are his arguments then?

                    They're in the "Complete Argument" section of the article.

                    > This sounds like the archetypical no true scotsman fallacy.

                    I get what you're trying to say, but he is not arguing only a true Scotsman is capable of thought. He is arguing that our current machines lack the required "causal powers" for thought. Powers that he doesn't prescribe to only a true Scotsman, though maybe we should try adding bagpipes to our AI just to be sure...

                    • By torginus 2025-09-0213:581 reply

                      Thanks, but that makes his arguments even less valid.

                      He argues that computer programs only manipulate symbols and thus have no semantic understanding.

                      But that's not true - many programs, like compilers that existed back when the argument was made, had semantic understanding of the code (in a limited way, but they did have some understanding about what the program did).

                      LLMs in contrast have a very rich semantic understanding of the text they parse - their tensor representations encode a lot about each token, or you can just ask them about anything - they might not be human level at reading subtext, but they're not horrible either.

                      • By CrossVR 2025-09-0216:49

                        Now you're getting to the heart of the thought experiment. Because does it really understand the code or subtext, or is it just really good at fooling us that it does?

                        When it makes a mistake, did it just have a too limited understanding or did it simply not get lucky with its prediction of the next word? Is there even a difference between the two?

                        I would like to agree with you that there's no special "causal power" that Turing machines can't emulate. But I remain skeptical, not out of chauvinism, but out of caution. Because I think it's dangerous to assume an AI understands a problem simply because it said the right words.

              • By dahart 2025-09-0214:141 reply

                > I cannot help but conclude that Searle argues that ‘understanding’ is only something that humans can do, which means…

                Regardless of whether Searle is right or wrong, you’ve jumped to conclusions and are misunderstanding his argument and making further assumptions based on your misunderstanding. Your argument is also ad-hominem by accusing people of believing things they don’t believe. Maybe it would be prudent to read some of the good critiques of Searle before trying to litigate it rapidly and sloppily on HN.

                The randomness stuff is very straw man, definitely not a good argument, best to drop it. Today’s LLMs are deterministic, not random. Pseudorandom sequences come in different varieties, but they model some properties of randomness, not all of them. The functioning of today’s neural networks, both training and inference, is exactly a book of static rules, despite their use of pseudorandom sequences.

                In case you missed it in the WP article, most of the field of cognitive science thinks Searle is wrong. However, they’re largely not critiquing him for using metaphysics, because that’s not his argument. He’s arguing that biology has mechanisms that binary electronic circuitry doesn’t; not human brains, simply physical chemical and biological processes. That much is certainly true. Whether there’s a difference in theory is unproven. But today currently there absolutely is a difference in practice, nobody has ever simulated the real world or a human brain using deterministic computation.

                • By torginus 2025-09-0214:361 reply

                  If scientific consensus is that he's wrong why is he being constantly brought up and defended - am I not right to call them out then?

                  Nobody brings up that light travels through the aether, that diseases are caused by bad humors etc. - is it not right to call out people for stating theory that's believed to be false?

                  >The randomness stuff is very straw man,

                  And a direct response to what armada651 wrote:

                  >I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.

                  > He’s arguing that biology has mechanisms that binary electronic circuitry doesn’t; not human brains, simply physical chemical and biological processes.

                  Once again the argument here changed from 'computers which only manipulate symbols cannot create consciousness' to 'we don't have the algorithm for consiousness yet'.

                  He might have successfully argued against the expert systems of his time - and true, mechanistic attempts at language translation have largely failed - but that doesn't extend to modern LLMs (and pre LLM AI) or even statistical methods.

                  • By dahart 2025-09-0216:411 reply

                    You’re making more assumptions. There’s no “scientific consensus” that he’s wrong, there are just opinions. Unlike the straw man examples you bring up, nobody has proven the claims you’re making. If they had, then the argument would go away like the others you mentioned.

                    Where did the argument change? Searle’s argument that you quoted is not arguing that we don’t have the algorithm yet. He’s arguing that the algorithm doesn’t run on electrical computers.

                    I’m not defending his argument, just pointing out that yours isn’t compelling because you don't seem to fully understand his, at least your restatement of it isn’t a good faith interpretation. Make his argument the strongest possible argument, and then show why it doesn’t work.

                    IMO modern LLMs don’t prove anything here. They don’t understand anything. LLMs aren’t evidence that computers can successfully think, they only prove that humans are prone to either anthropomorphic hyperbole, or to gullibility. That doesn’t mean computers can’t think, but I don’t think we’ve seen it yet, and I’m certainly not alone there.

                    • By torginus 2025-09-036:061 reply

                      >most of the field of cognitive science thinks Searle is wrong.

                      >There’s no “scientific consensus” that he’s wrong, there are just opinions.

                      • By dahart 2025-09-0316:38

                        And? Are you imagining that these aren’t both true at the same time? If so, I’m happy to explain. Since nothing has been proven, there’s nothing “scientific”. And since there’s some disagreement, “consensus” has not been achieved yet. This is why your presumptive use of “scientific consensus” was not correct, and why the term “scientific consensus” is not the same thing as “most people think”. A split of 60/40 or 75/25 or even 90/10 counts as “most” but does not count as “consensus”. So I guess maybe be careful about assuming what something means, it seems like this thread was limited by several incorrect assumptions.

          • By globnomulous 2025-09-0211:121 reply

            > The fact that philosophy hasn't recognized and rejected this argument based on this speaks volumes of the quality of arguments accepted there.

            That's one possibility. The other is that your pomposity and dismissiveness towards the entire field of philosophy speaks volumes on how little you know about either philosophical arguments in general or this philosophical argument in particular.

            • By torginus 2025-09-0212:353 reply

              Another ad hominem, I'd like you to refute my claim that Searle's argument is essentially 100% magical thinking.

              And yes, if for example, medicine would be no worse at curing cancer than it is today, yet doctors asserted that crystal healing is a serious study, that would reflect badly on the field at large, despite most of it being sound.

              • By dahart 2025-09-0213:441 reply

                Searle refutes your claim that there’s magical thinking.

                “Searle does not disagree with the notion that machines can have consciousness and understanding, because, as he writes, "we are precisely such machines". Searle holds that the brain is, in fact, a machine, but that the brain gives rise to consciousness and understanding using specific machinery.”

                • By torginus 2025-09-0214:141 reply

                  But the core of the original argument is that programs only manipulate symbols and consciousness can never arise just through symbol manipulation - which here then becomes 'we have not discovered the algorithms' for consciousness yet.

                  It's just a contradiction.

                  • By dahart 2025-09-0216:54

                    When you say something that contradicts his statements, it doesn’t mean he’s wrong, it most likely means you haven’t understood or interpreted his argument correctly. The Wikipedia page you linked to doesn’t use the word “algorithm”, so the source of the contradiction you imagine might be you. Searle says he thinks humans are biological machines, so your argument should withstand that hypothesis rather than dismiss it.

              • By globnomulous 2025-09-0216:37

                Why on earth do you take it as an ad hominem attack? Do you really think your comment isn't dismissive or pompous?

              • By malfist 2025-09-0214:54

                Another ad hominem, just like you calling anyone who talks about the chinese room thought experiment a deist?

          • By simianparrot 2025-09-029:041 reply

            It is still relevant because it hasn’t been disproven yet. So far all computer programs are Chinese Rooms, LLM’s included.

            • By IanCal 2025-09-0210:09

              If you’re talking about it being proven or disproven you’re misunderstanding the point of the thought experiment.

          • By herculity275 2025-09-0210:281 reply

            "Please stop referring to this thought experiment because it has possible interpretations I don't personally agree with"

            • By torginus 2025-09-0212:45

              Please give me an interpretation that is both correct an meaningful (as in possible to disprove)

          • By welferkj 2025-09-029:372 reply

            The human way of understanding things can be replicated mechanically, because it is mechanical in nature. The contents of your skull are an existence proof of AGI.

            • By hiatus 2025-09-0210:04

              The A stands for artificial.

            • By armada651 2025-09-0210:031 reply

              The contents of my skull are only a proof for AGI if your mechanical machine replicates all its processes. It's not a question about whether a machine can reproduce that, it's a question about whether we have given our current machines all the tools it needs to do that.

              • By torginus 2025-09-0212:41

                The theory of special relativity does not say 'you can't exceed the speed of light(unless you have a really big rocket)'. It presents a theoretical limit. Likewise the Chinese room doesn't state that consciousness is an intractable engineering problem, but an impossibility.

                But the way Searle formulates his argument, by not defining what consciousness is, he essentially gives himself enough wiggle room to be always right - he's essentially making the 'No True Scotsman' fallacy.

    • By bhl 2025-09-026:442 reply

      The moat is people, data, and compute in that order.

      It’s not just compute. That has mostly plateaued. What matters now is quality of data and what type of experiments to run, which environments to build.

      • By sigmoid10 2025-09-027:222 reply

        This "moat" is actually constantly shifting (which is why it isn't really a moat to begin with). Originally, it was all about quality data sources. But that saturated quite some time ago (at least for text). Before RLHF/RLAIF it was primarily a race who could throw more compute at a model and train longer on the same data. Then it was who could come up with the best RL approach. Now we're back to who can throw more compute at it since everyone is once again doing pretty much the same thing. With reasoning we now also opened a second avenue where it's all about who can throw more compute at it during runtime and not just while training. So in the end, it's mostly about compute. The last years have taught us that any significant algorithmic improvement will soon permeate across the entire field, no matter who originally invented it. So people are important for finding this stuff, but not for making the most of it. On top of that, I think we are very close to the point where LLMs can compete with humans on their own algorithmic development. Then it will be even more about who can spend more compute, because there will be tons of ideas to evaluate.

        • By DrScientist 2025-09-029:171 reply

          To put that into a scientific context - compute is capacity to do experiments and generate data ( about how best to build models ).

          However I do think you are missing an important aspect - and that's people who properly understand important solvable problems.

          ie I see quite a bit "we will solve this x, with AI' from startup's that don't fundamentally understand x.

          • By sigmoid10 2025-09-0210:57

            >we will solve this x, with AI

            You usually see this from startup techbro CEOs understand neither x nor AI. Those people are already replacable by AI today. The kind of people who think they can query ChatGPT once with "How to create a cutting edge model" and make millions. But when you go in on the deep end, there are very few people who still have enough tech knowledge to compete with your average modern LLM. And even the Math Olympiad gold medalists high-flyers at DeepSeek are about to have a run for their money with the next generation. Current AI engineers will shift more and more towards senior architecture and PM roles, because those will be the only ones that matter. But PM and architecture is already something that you could replace today.

        • By bhl 2025-09-033:531 reply

          > Originally, it was all about quality data sources.

          It still is! Lots of vertical productivity data that would be expensive to acquire manually via humans will be captured by building vertical AI products. Think lawyers, doctors, engineers.

          • By sigmoid10 2025-09-059:17

            That's literally what RLAIF has been doing for a while now.

      • By ActionHank 2025-09-0214:131 reply

        People matter less and less as well.

        As more opens up in OSS and academic space, their knowledge and experience will either be shared, rediscovered, or become obsolete.

        Also many of the people are coasting on one or two key discoveries by a handful of people years ago. When Zuck figures this out he gonna be so mad.

        • By bhl 2025-09-033:52

          Not all will become OSS. Some will become products, and that requires the best people.

    • By ml-anon 2025-09-028:291 reply

      Lets not pretend this is strategy. Amazon has been trying and failing to hire top AI people. No-one in their right minds would join. Even Meta has to shell out 8-9 figures for top people, who with any modicum of talent or self respect would go to Amazon rather than Anthropic, OAI, GDM? They bought Adept, everyone left.

      AWS is also falling far behind Azure wrt serving AI needs at the frontier. GCP is also growing at a faster rate and has a way more promising future than AWS in this space.

      • By mikert89 2025-09-0217:18

        AWS is very far behind, its already impacting the stock. Without a winning AI offering, all new cloud money is going to GCP and Azure. They have a huge problem

    • By Lyapunov_Lover 2025-09-027:381 reply

      > The evidence shows that there is no methodological moat for LLMS.

      Does it? Then how come Meta hasn't been able to release a SOTA model? It's not for a lack of trying. Or compute. And it's not like DeepSeek had access to vastly more compute than other Chinese AI companies. Alibaba and Baidu have been working on AI for a long time and have way more money and compute, but they haven't been able to do what DeepSeek did.

      • By postexitus 2025-09-0210:572 reply

        They may not have been leading (as in, releasing a SOTA model), but they definitely can match others - easily, as shown by llama 3/4, which proves the point - there is no moat. With enough money and resources, you can match others. Whether without SOTA models you can make a business out of it is a different question.

        • By Lyapunov_Lover 2025-09-0211:301 reply

          Meta never matched the competition with their Llama models. They've never even come close. And Llama 4 was an actual disaster.

          • By postexitus 2025-09-0212:201 reply

            I am not a daily user, so only rely on reviews and benchmarks - actual experience may be different.

            • By YetAnotherNick 2025-09-0214:27

              Even in reviews and benchmark, Llama wasn't close to frontier models. Also Llama 2/3 lead in open weight models wasn't more than few months.

        • By ath3nd 2025-09-0214:48

          > but they definitely can match others - easily, as shown by llama 3/4

          Are we living in the same universe? LLAMA is universally recognized as one of the worst and least successful model releases. I am almost certain you haven't ever tried a LLAMA chat, because, by the beard of Thor, it's the worst experience anyone could ever had, with any LLM.

          LLAMA 4 (behemoth, whatever, whatever) is an absolute steaming pile of trash, not even close to ChatGPT 4o/4/5/, Gemini(any) and even not even close to cheaper ones like DeepSeek. And to think Meta pirated torrents to train it...

          What a bunch of criminal losers and what a bunch of waste of money, time and compute. Oh, at least the Metaverse is a success...

          https://www.pcgamer.com/gaming-industry/court-documents-show...

          https://www.cnbc.com/2025/06/27/the-metaverse-as-we-knew-it-...

    • By karterk 2025-09-025:068 reply

      > The moat of the frontier folks is just compute.

      This is not really true. Google has all the compute but in many dimensions they lag behind GPT-5 class (catching up, but it has not been a given).

      Amazon itself did try to train a model (so did Meta) and had limited success.

      • By empiko 2025-09-025:292 reply

        I switched to Gemini with my new phone and I literally couldn't tell a difference. It is actually crazy how small the cost of switching is for LLMs. It feels like AI is more like a commodity than a service.

        • By lelanthran 2025-09-026:06

          > I switched to Gemini with my new phone and I literally couldn't tell a difference. It is actually crazy how small the cost of switching is for LLMs. It feels like AI is more like a commodity than a service.

          It is. It's wild to me that all these VCs pouring money into AI companies don't know what a value-chain is.

          Tokens are the bottom of the value-chain; it's where the lowest margins exist because the product at that level is a widely available commodity.

          I wrote about this already (shameless plug: https://www.rundata.co.za/blog/index.html?the-ai-value-chain )

        • By physicsguy 2025-09-028:09

          On top of that, the on-device models have got stronger and stronger as the base models + RL has got better. You can do on your laptop now what 2 years ago was state of the art.

      • By gnfargbl 2025-09-025:491 reply

        Which dimensions do you see Google lagging on? They seem broadly comparable on the usual leaderboard (https://lmarena.ai/leaderboard) and anecdotally I can't tell the difference in quality.

        I tend personally to stick with ChatGPT most of the time, but only because I prefer the "tone" of the thing somehow. If you forced me to move to Gemini tomorrow I wouldn't be particularly upset.

        • By motorest 2025-09-026:53

          > Which dimensions do you see Google lagging on? They seem broadly comparable on the usual leaderboard (https://lmarena.ai/leaderboard) and anecdotally I can't tell the difference in quality.

          Gemini holds indeed the top spot, but I feel you framed your response quite well: they are all broadly comparable. The difference in the synthetic benchmark from the top spot and the 20th spot was something like 57 points on a scale of 0-1500

      • By Keyframe 2025-09-025:463 reply

        " in many dimensions they lag behind GPT-5 class " - such as?

        Outside of computer, "the moat" is also data to train on. That's an even wider moat. Now, google has all the data. Data no one else has or ever will have. If anything, I'd expect them to outclass everyone by a fat margin. I think we're seeing that on video however.

        • By ivape 2025-09-026:451 reply

          You think Chinese companies are short on data and people? Google doesn’t have an advantage there until the CCP takes on a more hands on approach.

          Tin foil hat time:

          - If you were a God and you wanted to create an ideal situation for the arrival of AI

          - It would make sense to precede it with a social media phenomena that introduces mass scale normalization of sharing of personal information

          Yes, that would be ideal …

          People can’t stop sharing and creating data on anything, for awhile now. It’s a perfect situation for AI as an independent, uncontrollable force.

          • By rusk 2025-09-027:002 reply

            > People can’t stop sharing and creating data on anything

            Garbage in. Garbage out.

            There has never been a better time to produce an AI that mimics a racist uneducated teenager.

            • By Loudergood 2025-09-0214:10

            • By ivape 2025-09-028:081 reply

              Do you want to model the world accurately or not? That person is part of our authentic reality. The most sophisticated AI in the world will always include that person(s).

              • By rusk 2025-09-028:16

                Not in the slightest. I want useful information services that behave in a mature and respectable fashion.

        • By willvarfar 2025-09-025:532 reply

          not according to google: “We have no moat, and neither does OpenAI”: the big memo and the big HN thread on same https://news.ycombinator.com/item?id=35813322

          • By Keyframe 2025-09-025:583 reply

            a bit weird to think about it since google has literally internet.zip in multiple versions over the years, all of email, all of usenet, all of the videos, all of the music, all of the user's search interest, ads, everything..

            • By lelanthran 2025-09-026:15

              > a bit weird to think about it since google has literally internet.zip in multiple versions over the years, all of email, all of usenet, all of the videos, all of the music, all of the user's search interest, ads, everything..

              Yeah, Google totally has a moat. Them saying that they have no moat doesn't magically make that moat go away.

              They also own the entire vertical which none of the competitors do - all their competitors have to buy compute from someone who makes a profit just on compute (Nvidia, for example). Google owns the entire vertical, from silicon to end-user.

              It would be crazy if they can't make this work.

            • By rvba 2025-09-029:08

              That's why robots make so much traffic now. Those other companies are trying to get data.

              Google theoretically has reddit access. I wonder if they have sort of an internet archive - data unpolutted by LLMs

              On a side note, funny how all the companies seem to train on book archivr which they just downloaded from the internet

            • By lrem 2025-09-029:351 reply

              > all of the videos, [...], all of the user's search interest, ads, everything..

              And privacy policies that are actually limiting what information gets used in what.

          • By IncreasePosts 2025-09-0215:09

            That's one person's opinion that works for Google.

        • By seunosewa 2025-09-0211:02

          counterpoint: with their aggressive crawlers, most AI companies can have as much data as google...

      • By motorest 2025-09-026:49

        > This is not really true. Google has all the compute but in many dimensions they lag behind GPT-5 class (catching up, but it has not been a given).

        I don't know what you are talking about. I use Gemini on a daily basis and I honestly can't tell a difference.

        We are at a point where training corpus and hallucinations makes more of a difference than "model class".

      • By jorisboris 2025-09-026:451 reply

        Yes, or Apple who with all the talent don’t manage to pull off anything useful in AI

        xAI seems to be the exception, not the rule

        • By rusk 2025-09-027:03

          Given Apple’s moat is their devices, their particular spin on AI is very much edge focussed, which isn’t as spectacular as the current wave of cloud based LLM. Apple’s cloud stuff is laughably poor.

      • By jeanloolz 2025-09-026:27

        Depending on how you look at it I suppose but I believe Gemini surpasses OpenAI on many levels now. Better photo and video models. The leaderboard for text and embeddings are also putting Google on top of Openai.

      • By ebonnafoux 2025-09-027:441 reply

        gemini-2.5-pro is ranked number 1 in llmarena (https://lmarena.ai/leaderboard) before gpt-5-high. In the Text-to-Video and Image-to-video, google also have the highest places, OpenAI is nowhere.

        • By IX-103 2025-09-0211:211 reply

          Yes, but they're also slower. As LLMs start to be used for more general purpose things, they are becoming a productivity bottle-neck. If I get a mostly right answer in a few seconds that's much better than a perfect answer in 5 minutes.

          Right now the delay for Google's AI coding assistant is high enough for humans to context switch and do something else while waiting. Particularly since one of the main features of AI code assistants is rapid iteration.

          • By janalsncm 2025-09-0214:14

            Anecdotally, Gemini pro is way faster than GPT 5 thinking. Flash is even faster. I have no numbers though.

      • By paulddraper 2025-09-025:46

        It doesn’t guarantee success, but the point stands about X and Deepseek

    • By DrScientist 2025-09-029:12

      I think I'm right in saying that AWS, rather than deliveries, is by far the most profitable part of Amazon.

      Also a smart move is to be selling shovels in a gold rush - and that's exactly what Amazon is doing with AWS.

    • By jojobas 2025-09-025:15

      Amazon retail runs on ridiculously low margins compared to AWS. Revenue-wise retail dwarfs AWS, profit-wise it's vice-versa.

    • By StopDisinfo910 2025-09-029:00

      The barriers to entry for LLM are obvious: as you pointed, the field is extremely capital intensive. The only reason there are seemingly multiple players is because the amount of capital thrown at it at the moment is tremendous but that's unlikely to last forever.

      From my admittely poorly informed point of view, strategy-wise, it's hard to tell how wise it is investing in foundational work at the moment. As long as some players release competitive open weight models, the competitive advantage of being a leader in R&D will be limited.

      Amazon already has the compute power to place itself as a reseller without investing or having to share the revenue generated. Sure, they won't be at the forefront but they can still get their slice of the pie without exposing themselves too much to an eventual downturn.

    • By abtinf 2025-09-028:04

      The idea that models are copyrightable is also extremely dubious.

      So there probably isn’t even a legal moat.

    • By energy123 2025-09-0211:07

      There's not much of an architectural moat, but there is a methodological moat, such as with RL synthetic data.

    • By VirusNewbie 2025-09-026:362 reply

      Are you arguing anthropic has more compute than Amazon?

      Are you saying the only reason Meta is behind everyone else is compute????

      • By benterix 2025-09-026:48

        Think well: why should a platform provider get into a terribly expensive and unprofitable business when they can just provide hardware for those with money to spend? This was AWS strategy for years and it's been working well for them.

      • By motorest 2025-09-026:451 reply

        > Are you arguing anthropic has more compute than Amazon?

        I wouldn't be surprised if the likes of Anthropic wasn't paying AWS for its compute.

        As the saying goes, the ones who got rich from the gold rush were the ones selling shovels.

        • By ospray 2025-09-027:09

          I wouldn't be surprised if Amazon just buys Anthropic or another lab rather than competing for individuals.

  • By lizknope 2025-09-0122:425 reply

    Does Amazon want to be an AI innovator or an AI enabler?

    AWS enables thousands of other companies to run their business. Amazon has designed their own Graviton ARM CPUS and their own Trainium AI chips. You can access these through AWS for your business.

    I think Amazon sees AI being used in AWS as a bigger money generator than designing new AI algorithms.

    • By DoesntMatter22 2025-09-0123:381 reply

      Also I think that they realize this is just a money losing proposition right now for the most part. And they're not going to have a problem getting in later when there's a clear solution. Why fight it out? I don't think they're going to miss much because they can use any models they need and as you said some of that stuff may be run on their servers

      • By coredog64 2025-09-0214:14

        I can make a case: Building their own models like Nova and Titan allow them to build up expertise in how to solve hyperscaler problems. Think of it like Aurora, where they have a generally solved problem (RDBMS) but it needs to be modified to work with the existing low-level primitives. Yes, it can be done in the open, but if I'm AWS, I probably want to jealously guard anything that could be a key differentiator.

    • By PartiallyTyped 2025-09-020:472 reply

      Reading comments from the appropriate VPs will illuminate the situation.. Swami is looking to democratise AI, and the company is geared towards that more than anything else.

      Disclaimer; I work for amzn, opinions my own.

      https://aws.amazon.com/blogs/machine-learning/aws-and-mistra...

      • By mips_avatar 2025-09-024:042 reply

        I don't know what democratizing AI means, AWS doesn't have the GPU infrastructure to host inference or training on a large scale.

        • By lizknope 2025-09-0214:121 reply

          I started this part of the thread and mentioned Trainium but the person you replied to gave a link. Follow that and you can see Amazon's chips that they designed.

          Amazon wants people to move away from Nvidia GPUs and to their own custom chips.

          https://aws.amazon.com/ai/machine-learning/inferentia/

          https://aws.amazon.com/ai/machine-learning/trainium/

          • By mips_avatar 2025-09-0219:18

            TBH I was just going off of that I've heard AWS is a terrible place to get h100 clusters at scale. And for the training I was looking at we didn't really want to consider going off CUDA.

        • By PartiallyTyped 2025-09-026:111 reply

          Huh? That’s quite the assertion. They provide the infrastructure for Anthropic, so if that’s not large scale idk what is.

          • By ZeroCool2u 2025-09-0214:38

            They have to use GCP as well, which is arguably a strong indictment of their experience with AWS. Coincidentally, this aligns with my experience trying to train on AWS.

      • By JCM9 2025-09-0214:531 reply

        It’s unclear why Swami is put in charge of this stuff. He’s not a recognized leader in the space and hasn’t delivered a coherent strategy. However, per the article Amazon is struggling to hire and retain the best talent and thus it may just be the best they have.

        • By code4tee 2025-09-0215:16

          Who is “Swami?” Although I suppose that’s just making the point that Amazon’s folks aren’t recognized leaders in this space.

    • By justinator 2025-09-023:431 reply

      Selling pick axes vs. mining for gold yet again!

      • By dangus 2025-09-024:201 reply

        I'm glad this analogy is at the top. I think that some large companies like AWS really should not try to blow money on AI in ways that only make a lot more sense for companies like Meta, Google, and Apple. AWS can't trap you in their AI systems with network effects that the other competitors can.

        Companies like OpenAI and Anthropic are still incredibly risky investments especially because of the wild capital investments and complete lack of moat.

        At least when Facebook was making OpenAI's revenue numbers off of 2 billion active users it was trapping people in a social network where there were real negative consequences to leaving. In the world of open source chatbots and VSClone forks there's zero friction to moving on to some other solution.

        OpenAI is making $12 billion a year off of 700 million users [1], or around $17 per user annually. What other products that have no ad support perform that badly? And that's a company that is signing enterprise contracts with companies like Apple, not just some Spotify-like consumer service.

        [1] This is almost the exact same user count that Facebook had when it turned its first profit.

        • By jsnell 2025-09-024:523 reply

          > OpenAI is making $12 billion a year off of 700 million users [1], or around $17 per user annually. What other products that have no ad support perform that badly?

          That's a bit of a strange spin. Their ARPU is low because they are choosing not to monetize 95% of their users at all, and for now are just providing practically limitless free service.

          But monetising those free users via ads will pretty obviously be both practical and lucrative.

          And even if there is no technical moat, they seem to have a very solid mind share moat for consumer apps. It isn't enough for competitors to just catch up. They need to be significantly better to shift consumer habits.

          (For APIs, I agree there is no moat. Switching is just so easy.)

          • By chii 2025-09-028:231 reply

            > They need to be significantly better to shift consumer habits.

            i am hoping that a device local model would eventually be possible (may be a beefy home setup, and then an app that connects to your home on mobile devices for use on the go).

            currently, hardware restrictions prevent this type of home setup (not to mention the open source/free models aren't quite there and difficulty for non-tech users to actually setup). However, i choose to believe the hardware issues will get solved, and it will merely be just time.

            The software/model issue, on the other hand is harder to see solved. I pin my hopes onto deepseek, but may be meta or some other company will surprise me.

            • By dangus 2025-09-0321:591 reply

              I think you're super wrong about the local model issue and that's a huge risk for companies like OpenAI.

              Apple products as an example have an excellent architecture for local AI. Extremely high-bandwidth RAM.

              If you run an OSS model like gpt-oss on a Mac with 32GB of RAM it's already very similar to a cloud experience.

              • By chii 2025-09-045:171 reply

                i dont have the hardware to run or try them, but from the huggingfaces discussion forums, gpt-oss seems to be pretty hard censored. I would not consider it as being a viable self-hosted LLM except for the very narrowest of domains (like coding for example).

                • By dangus 2025-09-0410:481 reply

                  I'm not sure where censorship comes in with this discussion, it seems like cloud models are censored as well? And local models are frequently created that are abliterated? Correct me if I'm wrong or misunderstanding you.

                  Either way, it's just an example model, plenty of others to choose from. The fact of the matter is that the base model MacBook Air currently comes with about half as much RAM as you need for a really really decent LLM model. The integrated graphics are fast/efficient and the RAM is fast. The AMD Ryzen platform is similarly well-suited.

                  (Apple actually tells you how much storage their local model takes up in the settings > general > storage if you're curious)

                  We can imagine that by 2030 your base model Grandma computer on sale in stores will have at least 32GB of high-bandwidth RAM to handle local AI workflows.

                  • By chii 2025-09-0413:56

                    which is why i made the claim that hardware "problem" will be solved in the near future (i don't consider it solved right now, because even the apple hardware is too expensive and insufficient imho), but the more difficult problem of model availability is much, much harder to solve.

          • By 8n4vidtmkvmk 2025-09-025:52

            There does seem to be a mind share mote, but all you have to do is piss off users a little bit when there's a good competitor. See Digg to Reddit exodus.

          • By hiatus 2025-09-0212:03

            Which advertisers would risk having their product advertised by models that have encouraged kids to commit suicide?

    • By Mars008 2025-09-024:521 reply

      This is not mutually exclusive. They have home made robots and let others sell robots on their website. The same way they want to use AI and have resources to make their own. One way to use is to drive those robots. Another to enhance their web site. Current version sucks. I recently return the item because their bot told it has functionality while in fact it didn't.

      • By rswail 2025-09-026:44

        AWS is very much not the same as Amazon the product selling website.

        The two are effectively separate businesses with a completely separate customer base.

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