15 ms·

Google, Nvidia, and OpenAI

257 points by tambourine_man 11mo ago

aworks 11mo ago
"the naive approach to moats focuses on the cost of switching; in fact, however, the more important correlation to the strength of a moat is the number of unique purchasers/users."
- esafak 11mo ago
  I was not able to find any research that posits that moat strength is determined by customer diversity. I think customer diversity correlates instead with resilience.
  - deleted 11mo ago
    [deleted]
  - Jyaif 11mo ago
    I would say that customer diversity may be a marker of past resilience, and likely results in moat. Customer diversity says nothing about current or future resilience.
  - caminante 11mo ago
    Author isn't non-financial, but the "moat 2.0" doesn't feel right. > More than anything, though, I believe in the market power and defensibility of 800 million users, which is why I think ChatGPT still has a meaningful moat. It's 800M weekly active users according to ChatGPT. I keep hearing that once you segment paid and unpaid, daily ChatGPT users fall off dramatically (<10% for paid and far less for unpaid).
jasonjmcghee 11mo ago
Idk if I'm just holding it wrong, but calling Gemini 3 "the best model in the world" doesn't line up with my experience at all. It seems to just be worse at actually doing what you ask.
- cj 11mo ago
  It's like saying "Star Wars is the best movie in the world" - to some people it is. To others it's terrible. I feel like it would be advantageous to move away from a "one model fits all" mindset, and move towards a world where we have different genres of models that we use for different things. The benchmark scores are turning into being just as useful as tomatometer movie scores. Something can score high, but if that's not the genre you like, the high score doesn't guarantee you'll like it.
  - everdrive 11mo ago
    Outside of experience and experimentation, is there a good way to know what models are strong for what tasks?
    - grahamplace 11mo ago
      See: https://lmarena.ai/leaderboard https://lmarena.ai/leaderboard
      - jasonjmcghee 11mo ago
        Unless you overfit to benchmark style scenarios and are worse for real-world use.
    - jpollock 11mo ago
      Not really, it's like asking which C compiler was best back in the 90s. You had Watcom, Intel, GCC, Borland, Microsoft, etc. They all had different optimizations and different target markets. Best to make your tooling model agnostic. I understand that tuned prompts are model _version_ specific, so you will need this anyways.
- wrsh07 11mo ago
  It's a good model. Zvi also thought it was the best model until Opus 4.5 was announced a few hours after he wrote his post https://thezvi.substack.com/p/gemini-3-pro-is-a-vast-intelligence https://thezvi.substack.com/p/gemini-3-pro-is-a-vast-intelli...
cs702 11mo ago
The analysis fails to mention that if TPUs take market share from Nvidia GPUs, JAX's software ecosystem likely would also take market share from the PyTorch+Triton+CUDA software ecosystem.
- claytonjy 11mo ago
  not even google thinks this will happen, given their insistence on only offering TPU access through their cloud
  - cs702 11mo ago
    As the OP points out, Google is now selling TPUs to at least some corporate customers.
    - hackernewds 11mo ago
      they are not though
  - deleted 11mo ago
    [deleted]
RoddaWallPro 11mo ago
"advertising would make ChatGPT a better product." And with that, I will never read anything this guy writes again :)
- Groxx 11mo ago
  yeah... and it's (partly) based on the claim that it has network effects like how Facebook has? I don't see that at all, there's basically no social or cross-account stuff in any of them and if anything LLMs are the best non-lock-in system we've ever had: none of them are totally stable or reliable, and they all work by simply telling it to do the thing you want. your prompts today will need tweaking tomorrow, regardless of if it's in ChatGPT or Gemini, especially for individuals who are using the websites (which also keep changing). sure, there are APIs and that takes effort to switch... but many of them are nearly identical, and the ecosystem effect of ~all tools supporting multiple models seems far stronger than the network effect of your parents using ChatGPT specifically.
  - stanfordkid 11mo ago
    I’d argue that AI apis are nearly trivial to switch… the prompts can largely stay the same, and function calling pretty similar
- alecco 11mo ago
  Indeed. Why do people follow these clowns? They seem to read high level takes and spew out their nonsense theories. They fail to mention Google's edge: Inter-Chip Interconnect and the allegedly 1/3 of price. Then they talk about software moat and it sounds like they never even compiled a hello world in either architecture. smh And this comes out days after many in-depth posts like: https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-s... A crude Google search AI summary of those would be better than this dumb blogpost.
  - ebiester 11mo ago
    Why? It turns out that I try to read people who have a different perspective than I do. Why am I trying to read everything that just confirms my current biases? (Unless those writings are looking to dehumanize or strip people of rights or inflame hate - I'm not talking about propaganda or hate speech here.)
raw_anon_1111 11mo ago
I do all of my “AI” development on top of AWS Bedrock that hosts every available model except for OpenAIs closed source models that are exclusive to Microsoft. It’s extremely easy to write a library that makes switching between models trivial. I could add OpenAI support. It would be just slightly more complicated because I would have to have a separate set of API keys while now I can just use my AWS credentials. Also of course latency would be theoretically worse since with hosting on AWS and using AWS for inference you stay within the internal network (yes I know to use VPC endpoints). There is no moat around switching models unlike Ben says.
- biophysboy 11mo ago
  Have you noticed any significant AND consistent differences between them when you switch? I frequently get a better answer from one vs the other, but it feels unpredictable. Your setup seems like a better test of this
  - raw_anon_1111 11mo ago
    For the most part, I don’t do chatbots except for a couple of RAG based chatbots. It’s more behind the scenes stuff like image understanding, categorization, nuanced sentiment analsys, semantic alignment, etc. I’ve created a framework that lets me test the quality in automated way between prompt changes and models and I compare costs/speed/quality. The only thing that requires humans to judge the qualify out of all those are RAG results.
    - biophysboy 11mo ago
      So who is the winner using the framework you created?
      - raw_anon_1111 11mo ago
        It depends. Amazon’s Nova Light gave me the best speed vs performance when I needed really quick real time inference for categorizing a users input (think call centers). One of Anthropics models did the best with image understanding with Amazon’s Nova Pro being slightly behind. For my tests, I used a customer’s specific set of test data. For RAG I forgot. But is much more subjective. I just gave the customer an ability to configure the model and modify the prompt so they could choose.
diavarlyani 11mo ago
2018 me: ‘Aggregation Theory is basically unbeatable’ 2025 me, watching OpenAI voluntarily stay in the top-right quadrant while Google happily camps bottom-left with infinite ammo: ‘…maybe there’s an asterisk’ Great update to the Moat Map
citizenpaul 11mo ago
Its a long article and one of the first points "google strikes back." Is completely wrong ime. Not only is Gemini much worse than all the other models. The latest release is now so bad it is almost useless half the time or more. Hard to read more with such a bad take what I've seen myself. I don't care what benchmarks it beats if it just churns out comically bad results to me.
- Crash0v3rid3 11mo ago
  Mind sharing some examples of bad results you've seen vs other LLMs?
  - citizenpaul 11mo ago
    1. Seems to forget its context about 20/80 of results now. It used to be decent but now I may make only two prompts and it forgets the previous one noticeably more. 2. Results are noticeably worse, much more prone to "cheating" outcomes like generating some logic then = true to all results so it always finishes regardless of conditions.
martin_drapeau 11mo ago
Most analysts seem to forget what actual consumers do. Normal people use ChatGPT. They accidentally use Gemini when they Google something. But I don’t know anyone non-technical who has ditched ChatGPT as their default LLM. For 99% of questions these days, it’s plenty good enough—there’s just no real reason to switch. OpenAI's strategy is to eventually overtake search. I'd be curious for a chart of their progress over time. Without Google trying to distort the picture with Gemini benchmark results and usage stats which are tainted by sheer numbers from traditional search and their apps.
- bloppe 11mo ago
  I don't think that's a distorted picture at all. Google is still handling billions of searches per day. A huge number of those include AI answers. To all those billions of people who still reach for the omnibar first, Gemini is becoming their LLM of first resort.
- bambax 11mo ago
  I like Google Search for simple searches and still use it all the time. But for "complex" searches that are more like research, ChatGPT is actually pretty good, and provides actual, working links whereas Gemini seems to hallucinate more (in my experience).
- knallfrosch 11mo ago
  > usage stats which are tainted by sheer numbers from traditional search and their apps. You're looking at this backwards. Being able to push Gemini into your face on Gmail, Gdocs, Google Search, Android, Android TV, Android Auto and Pixel devices sure is: Annoying, disruptive and unfair. But market-wise., it sure is a strength, not a weakness.
  - raw_anon_1111 11mo ago
    And it is “fair” that a company can gain market share while losing billions backed by VC funding?
- msabalau 11mo ago
dismalaf 11mo ago
At this point it's not even OpenAI vs Google. It's OpenAI vs themselves. They're burning through more money making the models than they can realistically hope to make. When their investors decide they've burned through enough money it's basically over. Google's revenue stream and structural advantages mean they can continue this forever and if another AI winter comes, they can chill because LLM-based AI isn't even their main product.
stanfordkid 11mo ago
I agree with his take on Googles enormous strategic advantages. I think he’s wrong that OpenAI can win this by upping the revenue engine through ads or through building a consumer behavior moat. At the end of the day these are chat bots. Nobody really cares about the url and the interface is simple. Google won search by having deeply superior search algorithms and capitalizing on user traffic data to improve and refine those algorithms. It didn’t win because of AdWords … it just got rich that way. The AI market is an undifferentiated oligopoly (IMO) and the only way to win is by having better algos trained on more data that give better results. Google can win here. It is already winning on video and image generation. I actually think OpenAI is (wrongly) following Ben’s exact advice — going to the edge and consumer interface through things like the acquisition of things like Jony Ives device company. This is a failing move and an area where Google can also easily win with Android. I agree with Ben that upping the revenue makes sense but they can’t do it at the cost of user experience. Too much at stake.
- outside1234 11mo ago
  Also, it is not like OpenAI is going to go and build ads infrastructure overnight. Google has DECADES of experience with this.
tyre 11mo ago
> OpenAI’s refusal to launch and iterate an ads product for ChatGPT — now three years old — is a dereliction of business duty, particularly as the company signs deals for over a trillion dollars of compute. I think this is intentional by Altman. He’s a salesman, after all. When there is infinite possibility, he can sell any type of vision of future revenue and margins. When there are no concrete numbers, It’s your word against his. Once they try to monetize, however, he’s boxed in. And the problem with OpenAI vs. Google in the earlier days is that he needs money and chips now. He needs hundreds of billions of dollars. Trillions of dollars. Ad revenue numbers get in the way. It will take time to optimize; you’ll get public pushback and bad press (despite what Ben writes, ads will definitely not be a better product experience.) It might be the case that real revenue is worse than hypothetical revenue.
- antiloper 11mo ago
  Absolute silicon valley logic: https://www.youtube.com/watch?v=BzAdXyPYKQo https://www.youtube.com/watch?v=BzAdXyPYKQo
  - gundmc 11mo ago
    "If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!"
  - tyre 11mo ago
    I’m not advocating for it! But it’s real.
  - dehrmann 11mo ago
    Pre-revenue gets weird when you're in the cuatro commas club.
    - zerosizedweasle 11mo ago
mackross 11mo ago
An often overlooked extra advantage to Google is their massive existing ad inventory. If LLMs do end up being ad supported and both products are roughly the same, Google wins. The large supply of ads direct from a diverse set of advertisers means they can fill more ad slots with higher quality ads, for a higher price, and at a lower cost. They’re also already staffed with an enormous amount of talent for ad optimization. Just thus advantage would translate into higher sustained margins (even assuming similar costs), but given TPU it might be even greater. This plus the gobs of cash they already spin off, and their massive war chest means they can spend an ungodly amount on user acquisition. It’s their search playbook all over again.
- mackross 11mo ago
  Guess my edit didn’t work…
mackross 11mo ago
An often overlooked extra advantage to Google is their massive existing ad inventory. If LLMs do end up being ad supported and both products are roughly the same, Google wins. The large supply of ads direct from a diverse set of advertisers means they can fill more ad slots with higher quality ads, for a higher price, and at a lower cost. They’re also already staffed with an enormous amount of talent for ad optimization. Just this advantage would translate into higher sustained margins (even assuming similar costs), but given TPU it might be even greater. This plus the gobs of cash they already spin off, and their massive war chest means they can spend an ungodly amount on user acquisition. It’s their search playbook all over again.
tsunamifury 11mo ago
I have never seen a more poorly informed and badly written article on this blog. Advertising is not easy and not automatic money. This seems to be written by a teenager unfamiliar with anything.
- deleted 11mo ago
  [deleted]
- Slartie 11mo ago
  > This seems to be written by a teenager unfamiliar with anything. Overly confident, but poorly informed articles aren't commonly written by teenagers anymore, but by LLMs.
rhubarbtree 11mo ago
In football (soccer), a team leading the match but conceding a late goal is then very likely to concede again and lose the game. The reason is momentum: the challenger is highly motivated and opportunity driven, whilst the incumbent typically drops deep defensively and is loss averse. So it is in business. It is very, very, difficult for an incumbent terrified of losing to the upstart. The momentum is with the challenger. They are faced with nothing but opportunity. Panic sets in. And the people responsible for the original win at the incumbent have either left (been substituted? Stretching my analogy) or don’t have the energy for the new battle. Google will lose to OpenAI because it is a huge bureaucratic monster that has enshittified its main product. It deserves to die. Google will survive, but it will become a shadow of its former self. And it deserves to wither, because it has ruined its own product.
- bloppe 11mo ago
  I read most of your comment thinking OAI was the incumbent and Google was the underdog lol
  - rhubarbtree 11mo ago
    Only on HN!
  - zoro2024 11mo ago
    That was exactly me, a bit confusing considering Google was the last one to score in the football analogy.
gherkinnn 11mo ago
> that it is a nearly perfect representation of the hero’s journey. Absolutely. It's a shame Return of the Jedi ruined the arc with those silly teddy bears.
code51 11mo ago
Actually Sourcegraph's "Amp Code" is testing out a free ad-supported coding agent. Here is a video showing how it works: https://ampcode.com/news/amp-free https://ampcode.com/news/amp-free "Supported by ads from developer tool partners we’ve carefully chosen" It's not trying to secretly insert tools into LLM output but directly present the product offering inside the agent area. At one point, I speculate that Cursor will test this out as well, probably in a more covert way so that tool use paths get modified. Once the industry realizes tool-use-ads, then we're toast.
w10-1 11mo ago
> changing the habits of 800 million+ people who use ChatGPT every week, however, is a battle that can only be fought individual by individual That's the basis for his conclusions about both OpenAI and Google, but is it true? It's precisely because uptake has been so rapid that I believe it can change rapidly. I also think worldwide consumers no longer view US tech as some savior of humanity that they need to join or be left behind. They're likely to jump to any local viable competitor. Still the adtech/advertiser consumers who pay the bills are likely to stay even if users wander, so we're back to the battle of business models.
- mike_hearn 11mo ago
  Takeup of Google was rapid but nobody managed to obviate their advantage. It wasn't even a first mover advantage. The problem for alternatives is they have to answer the question of why they are better than ChatGPT. ChatGPT only had to answer the question of why it was better than <anything before AI> and for most people that was obvious.
seu 11mo ago
Is this seriously trying to portray OpenAI/Altman or Nvidia/Huang as unlikely everyday dudes who reluctantly take up a challenge and rise to become heroes? I never stop being amazed at how people love to present rich, well connected, people as underdogs and turn them into heroes.
- eurekin 11mo ago
  The point is to cover your tracks and making sure no one will be able to repeat that. They want to send you in a random direction, even, if you only want to try.
- FrustratedMonky 11mo ago
  Unfortunately we do like rich people. We do hold them up as hero's. We fantasize about being rich. Watch shows about their lives, etc... Bruce Wayne was rich. Those fancy suits and cars aren't cheap. Tony Stark was rich. Fancy robot tech isn't cheap. It takes money to actually do anything. Super Hero stories about rich people. We idolize making a difference with the money.
  - omnicognate 11mo ago
    If by "we" you mean US popular culture, yes. It's not universal, and definitely varies by country. In the UK we don't tend to idolise the rich so much. Not to say it doesn't happen, but in popular culture positive depictions tend to be limited to period portrayals of idealised aristocracy (and even then it's rarely shown as heroic), with contemporary wealth usually treated as a dubious virtue.
    - FrustratedMonky 11mo ago
      Yes. Good point. This is a pretty US specific cultural phenomenon. Probably rooted in the 'self made man', 'rugged individualism'. Go West to make your fortune. We forget the US is pretty young, and still has a lot of culture based on colonizing the west, taming the wilderness to find your riches.
- 11mo ago
throwawayffffas 11mo ago
> advertising would make ChatGPT a better product. Stuck 20 years ago.
gizmo 11mo ago
Google is the favorite to win AI by a mile. Not only do they have some of the best AI people, they have absolute unmatched distribution with youtube, search, gmail, docs, chrome, and android. As impressive as OpenAI is they don't have anything except for their brand. It should be clear by now that nobody has a strong lead in training. Nobody has a strong lead in access to compute. Nobody has a killer app because the interface is just chat or voice. And what happens when you can't compete on product? Distribution wins. And Google's advantage here is almost insurmountable. Google can fumble the next 2 years and still end up on top.
- Quarrel 11mo ago
  Google has another massive advantage over OpenAI. It makes serious revenue outside the AI bubble. Google has (much) more money in cash on hand than OpenAI has raised. Of course, there are a few other MegaCorps out there, who make money in other places, while having a serious stake in doing well out of AI, but I'm with you. Google FTW. Nvidia selling shovels to the miners is great, but the analogy falls down if the gold mines are bottomless, and the cost of the tools to mine them trend to zero.
- guerrilla 11mo ago
  Who's sitting there talking to Gemini though? Nobody I know's even heard of it. Everyone talks to ChatGPT, everyone. Habits are everything. Google will be swimming against the current and be seen as just another company forcing AI down everyone's throat, while everyone is still talking to ChatGPT. We'll see though, maybe I'm wrong. Maybe Google is clever and can do integration well and make something useful out of it though. They have never succeeded in that way before though and in fact seem terrible at it as an organization, so I very much doubt that.
  - roxolotl 11mo ago
    They aren’t talking about Gemini because Google is the brand name people know. “Oh Google told me this”. “Google planned my day.” Or maybe “Google’s AI said X” Gemini is less a consumer brand name and a more a brand name for those of us who care about models.
kashyapc 11mo ago
"... the way to commoditize suppliers and internalize network effects is by having a huge number of unique users. And, by extension, the best way to monetize that user base — and to achieve a massive user base in the first place — is through advertising" Urgh. There we go, advertising as the panacea. How about a decent product that people actually want to pay for?
bjackman 11mo ago
> A useful analogy here is the rise of AMD in the datacenter. [...] Large hyperscalers found it worth their time and effort to rewrite extremely low level software to be truly agnostic between AMD and Intel As someone who works on such low-level software at a hyperscaler I am skeptical of this comparison. The difference between AMD and Intel is really not that great, and in the biggest areas, open source software (e.g. kernel (especially KVM) and compilers) is already fully agnostic, in large part thanks to Intel and AMD themselves. Nobody in this space is gonna buy an x86 CPU without full upstream Linux+KVM+LLVM support. If breaking down the CUDA wall was the same order of magnitude a challenge as Intel vs AMD CPUs, I would think we would already have broken down that wall by now? Plus, I don't see any sign of Nvidia helping out with that. I don't know anything about CUDA though so maybe I'm overestimating the barrier here and the real reason is just that people haven't been sufficiently motivated yet.
- kccqzy 11mo ago
  What they mean is that they are rewriting low level synchronization primitives in order not to penalize AMD CPUs. For example on the AMD Rome CPUs, the cross-CCD latency of atomic instructions could be as high as 200 nanoseconds even when the instructions supposedly access a memory location already in the cache. Common code patterns like multiple cores atomically incrementing a single counter would have borderline acceptable performance on Intel but terrible performance on AMD. Or consider things like CPU core allocators, which now need to be CCD-aware when allocating cores within a CPU to a container.

257 points by tambourine_man 11mo ago

aworks 11mo ago

"the naive approach to moats focuses on the cost of switching; in fact, however, the more important correlation to the strength of a moat is the number of unique purchasers/users."

esafak 11mo ago
I was not able to find any research that posits that moat strength is determined by customer diversity. I think customer diversity correlates instead with resilience.
- deleted 11mo ago
  [deleted]
- Jyaif 11mo ago
  I would say that customer diversity may be a marker of past resilience, and likely results in moat. Customer diversity says nothing about current or future resilience.
- caminante 11mo ago
  Author isn't non-financial, but the "moat 2.0" doesn't feel right. > More than anything, though, I believe in the market power and defensibility of 800 million users, which is why I think ChatGPT still has a meaningful moat. It's 800M weekly active users according to ChatGPT. I keep hearing that once you segment paid and unpaid, daily ChatGPT users fall off dramatically (<10% for paid and far less for unpaid).

jasonjmcghee 11mo ago

Idk if I'm just holding it wrong, but calling Gemini 3 "the best model in the world" doesn't line up with my experience at all. It seems to just be worse at actually doing what you ask.

cj 11mo ago
It's like saying "Star Wars is the best movie in the world" - to some people it is. To others it's terrible. I feel like it would be advantageous to move away from a "one model fits all" mindset, and move towards a world where we have different genres of models that we use for different things. The benchmark scores are turning into being just as useful as tomatometer movie scores. Something can score high, but if that's not the genre you like, the high score doesn't guarantee you'll like it.
- everdrive 11mo ago
  Outside of experience and experimentation, is there a good way to know what models are strong for what tasks?
  - grahamplace 11mo ago
    See: https://lmarena.ai/leaderboard https://lmarena.ai/leaderboard
    - jasonjmcghee 11mo ago
      Unless you overfit to benchmark style scenarios and are worse for real-world use.
  - jpollock 11mo ago
    Not really, it's like asking which C compiler was best back in the 90s. You had Watcom, Intel, GCC, Borland, Microsoft, etc. They all had different optimizations and different target markets. Best to make your tooling model agnostic. I understand that tuned prompts are model _version_ specific, so you will need this anyways.
wrsh07 11mo ago
It's a good model. Zvi also thought it was the best model until Opus 4.5 was announced a few hours after he wrote his post https://thezvi.substack.com/p/gemini-3-pro-is-a-vast-intelligence https://thezvi.substack.com/p/gemini-3-pro-is-a-vast-intelli...

cs702 11mo ago

The analysis fails to mention that if TPUs take market share from Nvidia GPUs, JAX's software ecosystem likely would also take market share from the PyTorch+Triton+CUDA software ecosystem.

claytonjy 11mo ago
not even google thinks this will happen, given their insistence on only offering TPU access through their cloud
- cs702 11mo ago
  As the OP points out, Google is now selling TPUs to at least some corporate customers.
  - hackernewds 11mo ago
    they are not though
- deleted 11mo ago
  [deleted]

RoddaWallPro 11mo ago

"advertising would make ChatGPT a better product." And with that, I will never read anything this guy writes again :)

Groxx 11mo ago
yeah... and it's (partly) based on the claim that it has network effects like how Facebook has? I don't see that at all, there's basically no social or cross-account stuff in any of them and if anything LLMs are the best non-lock-in system we've ever had: none of them are totally stable or reliable, and they all work by simply telling it to do the thing you want. your prompts today will need tweaking tomorrow, regardless of if it's in ChatGPT or Gemini, especially for individuals who are using the websites (which also keep changing). sure, there are APIs and that takes effort to switch... but many of them are nearly identical, and the ecosystem effect of ~all tools supporting multiple models seems far stronger than the network effect of your parents using ChatGPT specifically.
- stanfordkid 11mo ago
  I’d argue that AI apis are nearly trivial to switch… the prompts can largely stay the same, and function calling pretty similar
alecco 11mo ago
Indeed. Why do people follow these clowns? They seem to read high level takes and spew out their nonsense theories. They fail to mention Google's edge: Inter-Chip Interconnect and the allegedly 1/3 of price. Then they talk about software moat and it sounds like they never even compiled a hello world in either architecture. smh And this comes out days after many in-depth posts like: https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-swing-at-the https://newsletter.semianalysis.com/p/tpuv7-google-takes-a-s... A crude Google search AI summary of those would be better than this dumb blogpost.
- ebiester 11mo ago
  Why? It turns out that I try to read people who have a different perspective than I do. Why am I trying to read everything that just confirms my current biases? (Unless those writings are looking to dehumanize or strip people of rights or inflame hate - I'm not talking about propaganda or hate speech here.)

raw_anon_1111 11mo ago

I do all of my “AI” development on top of AWS Bedrock that hosts every available model except for OpenAIs closed source models that are exclusive to Microsoft. It’s extremely easy to write a library that makes switching between models trivial. I could add OpenAI support. It would be just slightly more complicated because I would have to have a separate set of API keys while now I can just use my AWS credentials. Also of course latency would be theoretically worse since with hosting on AWS and using AWS for inference you stay within the internal network (yes I know to use VPC endpoints). There is no moat around switching models unlike Ben says.

biophysboy 11mo ago
Have you noticed any significant AND consistent differences between them when you switch? I frequently get a better answer from one vs the other, but it feels unpredictable. Your setup seems like a better test of this
- raw_anon_1111 11mo ago
  For the most part, I don’t do chatbots except for a couple of RAG based chatbots. It’s more behind the scenes stuff like image understanding, categorization, nuanced sentiment analsys, semantic alignment, etc. I’ve created a framework that lets me test the quality in automated way between prompt changes and models and I compare costs/speed/quality. The only thing that requires humans to judge the qualify out of all those are RAG results.
  - biophysboy 11mo ago
    So who is the winner using the framework you created?
    - raw_anon_1111 11mo ago
      It depends. Amazon’s Nova Light gave me the best speed vs performance when I needed really quick real time inference for categorizing a users input (think call centers). One of Anthropics models did the best with image understanding with Amazon’s Nova Pro being slightly behind. For my tests, I used a customer’s specific set of test data. For RAG I forgot. But is much more subjective. I just gave the customer an ability to configure the model and modify the prompt so they could choose.

diavarlyani 11mo ago

2018 me: ‘Aggregation Theory is basically unbeatable’ 2025 me, watching OpenAI voluntarily stay in the top-right quadrant while Google happily camps bottom-left with infinite ammo: ‘…maybe there’s an asterisk’ Great update to the Moat Map

citizenpaul 11mo ago

Its a long article and one of the first points "google strikes back." Is completely wrong ime. Not only is Gemini much worse than all the other models. The latest release is now so bad it is almost useless half the time or more. Hard to read more with such a bad take what I've seen myself. I don't care what benchmarks it beats if it just churns out comically bad results to me.

Crash0v3rid3 11mo ago
Mind sharing some examples of bad results you've seen vs other LLMs?
- citizenpaul 11mo ago
  1. Seems to forget its context about 20/80 of results now. It used to be decent but now I may make only two prompts and it forgets the previous one noticeably more. 2. Results are noticeably worse, much more prone to "cheating" outcomes like generating some logic then = true to all results so it always finishes regardless of conditions.

martin_drapeau 11mo ago

Most analysts seem to forget what actual consumers do. Normal people use ChatGPT. They accidentally use Gemini when they Google something. But I don’t know anyone non-technical who has ditched ChatGPT as their default LLM. For 99% of questions these days, it’s plenty good enough—there’s just no real reason to switch. OpenAI's strategy is to eventually overtake search. I'd be curious for a chart of their progress over time. Without Google trying to distort the picture with Gemini benchmark results and usage stats which are tainted by sheer numbers from traditional search and their apps.

bloppe 11mo ago
I don't think that's a distorted picture at all. Google is still handling billions of searches per day. A huge number of those include AI answers. To all those billions of people who still reach for the omnibar first, Gemini is becoming their LLM of first resort.
bambax 11mo ago
I like Google Search for simple searches and still use it all the time. But for "complex" searches that are more like research, ChatGPT is actually pretty good, and provides actual, working links whereas Gemini seems to hallucinate more (in my experience).
knallfrosch 11mo ago
> usage stats which are tainted by sheer numbers from traditional search and their apps. You're looking at this backwards. Being able to push Gemini into your face on Gmail, Gdocs, Google Search, Android, Android TV, Android Auto and Pixel devices sure is: Annoying, disruptive and unfair. But market-wise., it sure is a strength, not a weakness.
- raw_anon_1111 11mo ago
  And it is “fair” that a company can gain market share while losing billions backed by VC funding?
msabalau 11mo ago

dismalaf 11mo ago

At this point it's not even OpenAI vs Google. It's OpenAI vs themselves. They're burning through more money making the models than they can realistically hope to make. When their investors decide they've burned through enough money it's basically over. Google's revenue stream and structural advantages mean they can continue this forever and if another AI winter comes, they can chill because LLM-based AI isn't even their main product.

stanfordkid 11mo ago

I agree with his take on Googles enormous strategic advantages. I think he’s wrong that OpenAI can win this by upping the revenue engine through ads or through building a consumer behavior moat. At the end of the day these are chat bots. Nobody really cares about the url and the interface is simple. Google won search by having deeply superior search algorithms and capitalizing on user traffic data to improve and refine those algorithms. It didn’t win because of AdWords … it just got rich that way. The AI market is an undifferentiated oligopoly (IMO) and the only way to win is by having better algos trained on more data that give better results. Google can win here. It is already winning on video and image generation. I actually think OpenAI is (wrongly) following Ben’s exact advice — going to the edge and consumer interface through things like the acquisition of things like Jony Ives device company. This is a failing move and an area where Google can also easily win with Android. I agree with Ben that upping the revenue makes sense but they can’t do it at the cost of user experience. Too much at stake.

outside1234 11mo ago
Also, it is not like OpenAI is going to go and build ads infrastructure overnight. Google has DECADES of experience with this.

tyre 11mo ago

> OpenAI’s refusal to launch and iterate an ads product for ChatGPT — now three years old — is a dereliction of business duty, particularly as the company signs deals for over a trillion dollars of compute. I think this is intentional by Altman. He’s a salesman, after all. When there is infinite possibility, he can sell any type of vision of future revenue and margins. When there are no concrete numbers, It’s your word against his. Once they try to monetize, however, he’s boxed in. And the problem with OpenAI vs. Google in the earlier days is that he needs money and chips now. He needs hundreds of billions of dollars. Trillions of dollars. Ad revenue numbers get in the way. It will take time to optimize; you’ll get public pushback and bad press (despite what Ben writes, ads will definitely not be a better product experience.) It might be the case that real revenue is worse than hypothetical revenue.

antiloper 11mo ago
Absolute silicon valley logic: https://www.youtube.com/watch?v=BzAdXyPYKQo https://www.youtube.com/watch?v=BzAdXyPYKQo
- gundmc 11mo ago
  "If you show revenue, people will ask 'HOW MUCH?' and it will never be enough. The company that was the 100xer, the 1000xer is suddenly the 2x dog. But if you have NO revenue, you can say you're pre-revenue! You're a potential pure play... It's not about how much you earn, it's about how much you're worth. And who is worth the most? Companies that lose money!"
- tyre 11mo ago
  I’m not advocating for it! But it’s real.
- dehrmann 11mo ago
  Pre-revenue gets weird when you're in the cuatro commas club.
  - zerosizedweasle 11mo ago

mackross 11mo ago

An often overlooked extra advantage to Google is their massive existing ad inventory. If LLMs do end up being ad supported and both products are roughly the same, Google wins. The large supply of ads direct from a diverse set of advertisers means they can fill more ad slots with higher quality ads, for a higher price, and at a lower cost. They’re also already staffed with an enormous amount of talent for ad optimization. Just thus advantage would translate into higher sustained margins (even assuming similar costs), but given TPU it might be even greater. This plus the gobs of cash they already spin off, and their massive war chest means they can spend an ungodly amount on user acquisition. It’s their search playbook all over again.

mackross 11mo ago
Guess my edit didn’t work…

mackross 11mo ago

An often overlooked extra advantage to Google is their massive existing ad inventory. If LLMs do end up being ad supported and both products are roughly the same, Google wins. The large supply of ads direct from a diverse set of advertisers means they can fill more ad slots with higher quality ads, for a higher price, and at a lower cost. They’re also already staffed with an enormous amount of talent for ad optimization. Just this advantage would translate into higher sustained margins (even assuming similar costs), but given TPU it might be even greater. This plus the gobs of cash they already spin off, and their massive war chest means they can spend an ungodly amount on user acquisition. It’s their search playbook all over again.

tsunamifury 11mo ago

I have never seen a more poorly informed and badly written article on this blog. Advertising is not easy and not automatic money. This seems to be written by a teenager unfamiliar with anything.

deleted 11mo ago
[deleted]
Slartie 11mo ago
> This seems to be written by a teenager unfamiliar with anything. Overly confident, but poorly informed articles aren't commonly written by teenagers anymore, but by LLMs.

rhubarbtree 11mo ago

In football (soccer), a team leading the match but conceding a late goal is then very likely to concede again and lose the game. The reason is momentum: the challenger is highly motivated and opportunity driven, whilst the incumbent typically drops deep defensively and is loss averse. So it is in business. It is very, very, difficult for an incumbent terrified of losing to the upstart. The momentum is with the challenger. They are faced with nothing but opportunity. Panic sets in. And the people responsible for the original win at the incumbent have either left (been substituted? Stretching my analogy) or don’t have the energy for the new battle. Google will lose to OpenAI because it is a huge bureaucratic monster that has enshittified its main product. It deserves to die. Google will survive, but it will become a shadow of its former self. And it deserves to wither, because it has ruined its own product.

bloppe 11mo ago
I read most of your comment thinking OAI was the incumbent and Google was the underdog lol
- rhubarbtree 11mo ago
  Only on HN!
- zoro2024 11mo ago
  That was exactly me, a bit confusing considering Google was the last one to score in the football analogy.

gherkinnn 11mo ago

> that it is a nearly perfect representation of the hero’s journey. Absolutely. It's a shame Return of the Jedi ruined the arc with those silly teddy bears.

code51 11mo ago

Actually Sourcegraph's "Amp Code" is testing out a free ad-supported coding agent. Here is a video showing how it works: https://ampcode.com/news/amp-free https://ampcode.com/news/amp-free "Supported by ads from developer tool partners we’ve carefully chosen" It's not trying to secretly insert tools into LLM output but directly present the product offering inside the agent area. At one point, I speculate that Cursor will test this out as well, probably in a more covert way so that tool use paths get modified. Once the industry realizes tool-use-ads, then we're toast.

w10-1 11mo ago

> changing the habits of 800 million+ people who use ChatGPT every week, however, is a battle that can only be fought individual by individual That's the basis for his conclusions about both OpenAI and Google, but is it true? It's precisely because uptake has been so rapid that I believe it can change rapidly. I also think worldwide consumers no longer view US tech as some savior of humanity that they need to join or be left behind. They're likely to jump to any local viable competitor. Still the adtech/advertiser consumers who pay the bills are likely to stay even if users wander, so we're back to the battle of business models.

mike_hearn 11mo ago
Takeup of Google was rapid but nobody managed to obviate their advantage. It wasn't even a first mover advantage. The problem for alternatives is they have to answer the question of why they are better than ChatGPT. ChatGPT only had to answer the question of why it was better than <anything before AI> and for most people that was obvious.

seu 11mo ago

Is this seriously trying to portray OpenAI/Altman or Nvidia/Huang as unlikely everyday dudes who reluctantly take up a challenge and rise to become heroes? I never stop being amazed at how people love to present rich, well connected, people as underdogs and turn them into heroes.

eurekin 11mo ago
The point is to cover your tracks and making sure no one will be able to repeat that. They want to send you in a random direction, even, if you only want to try.
FrustratedMonky 11mo ago
Unfortunately we do like rich people. We do hold them up as hero's. We fantasize about being rich. Watch shows about their lives, etc... Bruce Wayne was rich. Those fancy suits and cars aren't cheap. Tony Stark was rich. Fancy robot tech isn't cheap. It takes money to actually do anything. Super Hero stories about rich people. We idolize making a difference with the money.
- omnicognate 11mo ago
  If by "we" you mean US popular culture, yes. It's not universal, and definitely varies by country. In the UK we don't tend to idolise the rich so much. Not to say it doesn't happen, but in popular culture positive depictions tend to be limited to period portrayals of idealised aristocracy (and even then it's rarely shown as heroic), with contemporary wealth usually treated as a dubious virtue.
  - FrustratedMonky 11mo ago
    Yes. Good point. This is a pretty US specific cultural phenomenon. Probably rooted in the 'self made man', 'rugged individualism'. Go West to make your fortune. We forget the US is pretty young, and still has a lot of culture based on colonizing the west, taming the wilderness to find your riches.
11mo ago

throwawayffffas 11mo ago

> advertising would make ChatGPT a better product. Stuck 20 years ago.

gizmo 11mo ago

Google is the favorite to win AI by a mile. Not only do they have some of the best AI people, they have absolute unmatched distribution with youtube, search, gmail, docs, chrome, and android. As impressive as OpenAI is they don't have anything except for their brand. It should be clear by now that nobody has a strong lead in training. Nobody has a strong lead in access to compute. Nobody has a killer app because the interface is just chat or voice. And what happens when you can't compete on product? Distribution wins. And Google's advantage here is almost insurmountable. Google can fumble the next 2 years and still end up on top.

Quarrel 11mo ago
Google has another massive advantage over OpenAI. It makes serious revenue outside the AI bubble. Google has (much) more money in cash on hand than OpenAI has raised. Of course, there are a few other MegaCorps out there, who make money in other places, while having a serious stake in doing well out of AI, but I'm with you. Google FTW. Nvidia selling shovels to the miners is great, but the analogy falls down if the gold mines are bottomless, and the cost of the tools to mine them trend to zero.
guerrilla 11mo ago
Who's sitting there talking to Gemini though? Nobody I know's even heard of it. Everyone talks to ChatGPT, everyone. Habits are everything. Google will be swimming against the current and be seen as just another company forcing AI down everyone's throat, while everyone is still talking to ChatGPT. We'll see though, maybe I'm wrong. Maybe Google is clever and can do integration well and make something useful out of it though. They have never succeeded in that way before though and in fact seem terrible at it as an organization, so I very much doubt that.
- roxolotl 11mo ago
  They aren’t talking about Gemini because Google is the brand name people know. “Oh Google told me this”. “Google planned my day.” Or maybe “Google’s AI said X” Gemini is less a consumer brand name and a more a brand name for those of us who care about models.

kashyapc 11mo ago

"... the way to commoditize suppliers and internalize network effects is by having a huge number of unique users. And, by extension, the best way to monetize that user base — and to achieve a massive user base in the first place — is through advertising" Urgh. There we go, advertising as the panacea. How about a decent product that people actually want to pay for?

bjackman 11mo ago

> A useful analogy here is the rise of AMD in the datacenter. [...] Large hyperscalers found it worth their time and effort to rewrite extremely low level software to be truly agnostic between AMD and Intel As someone who works on such low-level software at a hyperscaler I am skeptical of this comparison. The difference between AMD and Intel is really not that great, and in the biggest areas, open source software (e.g. kernel (especially KVM) and compilers) is already fully agnostic, in large part thanks to Intel and AMD themselves. Nobody in this space is gonna buy an x86 CPU without full upstream Linux+KVM+LLVM support. If breaking down the CUDA wall was the same order of magnitude a challenge as Intel vs AMD CPUs, I would think we would already have broken down that wall by now? Plus, I don't see any sign of Nvidia helping out with that. I don't know anything about CUDA though so maybe I'm overestimating the barrier here and the real reason is just that people haven't been sufficiently motivated yet.

kccqzy 11mo ago
What they mean is that they are rewriting low level synchronization primitives in order not to penalize AMD CPUs. For example on the AMD Rome CPUs, the cross-CCD latency of atomic instructions could be as high as 200 nanoseconds even when the instructions supposedly access a memory location already in the cache. Common code patterns like multiple cores atomically incrementing a single counter would have borderline acceptable performance on Intel but terrible performance on AMD. Or consider things like CPU core allocators, which now need to be CCD-aware when allocating cores within a CPU to a container.