This article doesn't mention TPUs anywhere. I don't think it's obvious for people outside of google's ecosystem just how extraordinarily good the JAX + TPU ecosystem is. Google several structural advantages over other major players, but the largest one is that they roll their own compute solution which is actually very mature and competitive. TPUs are extremely good at both training and inference[1] especially at scale. Google's ability to tailor their mature hardware to exactly what they need gives them a massive leg up on competition. AI companies fundamentally have to answer the question "what can you do that no one else can?". Google's hardware advantage provides an actual answer to that question which can't be erased the next time someone drops a new model onto huggingface.
> I’m forgetting something. Oh, of course, Google is also a hardware company. With its left arm, Google is fighting Nvidia in the AI chip market (both to eliminate its former GPU dependence and to eventually sell its chips to other companies). How well are they doing? They just announced the 7th version of their TPU, Ironwood. The specifications are impressive. It’s a chip made for the AI era of inference, just like Nvidia Blackwell
To be fair to thunderbird120, the author of this piece made edits at some point. See https://archive.is/K4n9E. No discussion of the recent TPU releases, or TPUs for all, for that matter.
"I’m forgetting something." was a giant blaring clue. Take this as an opportunity to learn the lesson of not calling someone a liar unless you are very very sure and have taken all the evidence into account.
Assuming that DeepSeek continues to open-source, then we can assume that in the future there won't be any "secret sauce" in model architecture. Only data and training/serving infrastructure, and Google is in a good position with regard to both.
Google is also in a great position wrt distribution - to get users at scale, and attach to pre-existing revenue streams. Via Android, Gmail, Docs, Search - they have a lot of reach. YouTube as well, though fit there is maybe less obvious.
Combined with the two factors you mention, and the size of their warchest - they are really excellently positioned.
Making your own hardware would seem to yield freedoms in model architectures as well since performance is closely related to how the model architecture fits the hardware.
... except that it still pretty much requires Nvidia hardware. Maybe not for edge inference, but even inference at scale (ie. say at companies, or governments) will still require it.
TPUs aren't necessarily a pro. They go back 15 years and don't seem to have yielded any kind of durable advantage. Developing them is expensive but their architecture was often over-fit to yesterday's algorithms which is why they've been through so many redesigns. Their competitors have routinely moved much faster using CUDA.
Once the space settles down, the balance might tip towards specialized accelerators but NVIDIA has plenty of room to make specialized silicon and cut prices too. Google has still to prove that the TPU investment is worth it.
Not sure how familiar you are with the internal situation... But from my experience think it's safe to say that TPU basically multiplies Google's computation capability by 10x, if not 20x. Also they don't need to compete with others to secure expensive nvidia chips. If this is not an advantage, I don't see there's anything considered to be an advantage. The entire point of vertical integration is to secure full control of your stack so your capability won't be limited by potential competitors, and TPU is one of the key component of its strategy.
Also worth noting that its Ads division is the largest, heaviest user of TPU. Thanks to it, it can flex running a bunch of different expensive models that you cannot realistically afford with GPU. The revenue delta from this is more than enough to pay off the entire investment history for TPU.
They must very much compete with others. All these chips are being fabbed at the same facilities in Taiwan and capacity trades off against each other. Google has to compete for the same fab capacity alongside everyone else, as well as skilled chip designers etc.
> The revenue delta from this is more than enough to pay off the entire investment history for TPU.
Possibly; such statements were common when I was there too but digging in would often reveal that the numbers being used for what things cost, or how revenue was being allocated, were kind of ad hoc and semi-fictional. It doesn't matter as long as the company itself makes money, but I heard a lot of very odd accounting when I was there. Doubtful that changed in the years since.
Regardless the question is not whether some ads launches can pay for the TPUs, the question is whether it'd have worked out cheaper in the end to just buy lots of GPUs. Answering that would require a lot of data that's certainly considered very sensitive, and makes some assumptions about whether Google could have negotiated private deals etc.
> They must very much compete with others. All these chips are being fabbed at the same facilities in Taiwan and capacity trades off against each other.
I'm not sure what you're trying to deliver here. Following your logic, even if you have a fab you need to compete for rare metals, ASML etc etc... That's a logic built for nothing but its own sake. In the real world, it is much easier to compete outside Nvidia's own allocation as you get rid of the critical bottleneck. And Nvidia has all the incentives to control the supply to maximize its own profit, not to meet the demands.
> Possibly; such statements were common when I was there too but digging in would often reveal that the numbers being used for what things cost, or how revenue was being allocated, were kind of ad hoc and semi-fictional.
> Regardless the question is not whether some ads launches can pay for the TPUs, the question is whether it'd have worked out cheaper in the end to just buy lots of GPUs.
Of course everyone can build their own narratives in favor of their launch, but I've been involved in some of those ads quality launches and can say pretty confidently that most of those launches would not be launchable without TPU at all. This was especially true in the early days of TPU as the supply of GPU for datacenter was extremely limited and immature.
More GPU can solve? Companies are talking about 100k~200k of H100 as a massive cluster and Google already has much larger TPU clusters with computation capability in a different order of magnitudes. The problem is, you cannot simply buy more computation even if you have lots of money. I've been pretty clear about how relying on Nvidia's supply could be a critical limiting factor in a strategic point of view but you're trying to move the point. Please don't.
So are the electric and cooling costs at Google's scale. Improving perf-per-watt efficiency can pay for itself. The fact that they keep iterating on it suggests it's not a negative-return exercise.
TPUs probably can pay for themselves, especially given NVIDIA's huge margins. But it's not a given that it's so just because they fund it. When I worked there Google routinely funded all kinds of things without even the foggiest idea of whether it was profitable or not. There was just a really strong philosophical commitment to doing everything in house no matter what.
The issue isn't number of designs but architectural stability. NVIDIA's chips have been general purpose for a long time. They get faster and more powerful but CUDA has always been able to run any kind of neural network. TPUs used to be over-specialised to specific NN types and couldn't handle even quite small evolutions in algorithm design whereas NVIDIA cards could. Google has used a lot of GPU hardware too, as a consequence.
At the same time if the TPU didn't exist NVIDIA would pretty much have a complete monopoly on the market.
While Nv does have an unlimited money printer at the moment, the fact that at least some potential future competition exists does represent a threat to that.
Depending how you count, parent comment is accurate. Hardware doesn't just appear. 4 years of planning and R&D for the first generation chip is probably right.
I was wrong, ironically because Google's AI overview says it's 15 years if you search. The article it's quoting from appears to be counting the creation of TensorFlow as an "origin".
I've used Jax quite a bit and it's so much better than tf/pytorch.
Now for the life of me, I still haven't been able to understan what a TPU is. Is it Google's marketing term for a GPU? Or is it something different entirely?
There's basically a difference in philosophy. GPU chips have a bunch of cores, each of which is semi-capable, whereas TPU chips have (effectively) one enormous core.
So GPUs have ~120 small systolic arrays, one per SM (aka, a tensorcore), plus passable off-chip bandwidth (aka 16 lines of PCI).
Where has TPUs have one honking big systolic array, plus large amounts of off-chip bandwidth.
This roughly translates to GPUs being better if you're doing a bunch of different small-ish things in parallel, but TPUs are better if you're doing lots of large matrix multiplies.
Way back when, most of a GPU was for graphics. Google decided to design a completely new chip, which focused on the operations for neural networks (mainly vectorized matmul). This is the TPU.
It's not a GPU, as there is no graphics hardware there anymore. Just memory and very efficient cores, capable of doing massively parallel matmuls on the memory. The instruction set is tiny, basically only capable of doing transformer operations fast.
Today, I'm not sure how much graphics an A100 GPU still can do. But I guess the answer is "too much"?
TPUs (short for Tensor Processing Units) are Google’s custom AI accelerator hardware which are completely separate from GPUs. I remember that introduced them in 2015ish but I imagine that they’re really starting to pay off with Gemini.
Believe it or not, I'm also familiar with Wikipedia. It reads that they're optimized for low precisio high thruput. To me this sounds like a GPU with a specific optimization.
It's a chip (and associated hardware) that can do linear algebra operations really fast. XLA and TPUs were co-designed, so as long as what you are doing is expressible in XLA's HLO language (https://openxla.org/xla/operation_semantics), the TPU can run it, and in many cases run it very efficiently. TPUs have different scaling properties than GPUs (think sparser but much larger communication), no graphics hardware inside them (no shader hardware, no raytracing hardware, etc), and a different control flow regime ("single-threaded" with very-wide SIMD primitives, as opposed to massively-multithreaded GPUs).
Amazon also invests in own hardware and silicon -- the Inferentia and Trainium chips for example.
But I am not sure how AWS and Google Cloud match up in terms of making this verticial integration work for their competitive advantage.
Any insight there - would be curious to read up on.
I guess Microsoft for that matter also has been investing -- we heard about the latest quantum breakthrough that was reported as creating a fundamenatally new physical state of matter. Not sure if they also have some traction with GPUs and others with more immediate applications.
Google has their own cloud with their data centers with their own custom designed hardware using their own machine learning software stack running their in-house designed neural networks.
The only thing Google is missing is designing a computer memory that is specifically tailored for machine learning. Something like processing in memory.
And yet google's main structural disadvantage is being google.
Modern BERT with the extended context has solved natural language web search. I mean it as no exaggeration that _everything_ google does for search is now obsolete. The only reason why google search isn't dead yet is that it takes a while to index all web paged into a vector database.
And yet it wasn't google that released the architecture update, it was hugging face as a summer collaboration between a dozen people. Google's version came out in 2018 and languished for a decade because it would destroy their business model.
Google is too risk averse to do anything, but completely doomed if they don't cannibalize their cash cow product. Web search is no longer a crown jewel, but plumbing that answering services, like perplexity, need. I don't see google being able to pull off an iPhone moment where they killed the iPod to win the next 20 years.
> Modern BERT with the extended context has solved natural language web search. I mean it as no exaggeration that _everything_ google does for search is now obsolete.
The web UI for people using search may be obsolete, but search is hot, all AIs need it, both web and local. It's because models don't have recent information in them and are unable to reliably quote from memory.
The point is that the secret sauce in Google's search was better retrieval, and the assertion above is that the advantage there is gone. While crawling the web isn't a piece of cake, it's a much smaller moat than retrieval quality was.
This would be like claiming in 2010 that because Page Rank is out there, search is a solved problem and there’s no secret sauce, and the following decade proved that false.
Do we have insights on whether they knew that their business model was at risk? My understanding is that OpenAI’s credibility lies in seeing the potential of scaling up a transformer-based model and that Google was caught off guard.
> Google is too risk averse to do anything, but completely doomed if they don't cannibalize their cash cow product.
Google's cash-cow product is relevant ads. You can display relevant ads in LLM output or natural language web-search. As long as people are interacting with a Google property, I really don't think it matters what that product is, as long as there are ad views. Also:
> Web search is no longer a crown jewel, but plumbing that answering services, like perplexity, need
This sounds like a gigantic competitive advantage if you're selling AI-based products. You don't have to give everyone access to the good search via API, just your inhouse AI generator.
Kodak was well placed to profit from the rise of digital imaging - in the late 1970s and early 1980s Kodak labs pioneered colour image sensors, and was producing some of the highest resolution CCDs out there.
Bryce Bayer worked for Kodak when he invented and patented the Bayer pattern filter used in essentially every colour image sensor to this day.
But the problem was: Kodak had a big film business - with a lot of film factories, a lot of employees, a lot of executives, and a lot of recurring revenue. And jumping into digital with both feet would have threatened all that.
So they didn't capitalise on their early lead - and now they're bankrupt, reduced to licensing their brand to third-party battery makers.
> You can display relevant ads in LLM output or natural language web-search.
Maybe. But the LLM costs a lot more per response.
Making half a cent is very profitable if you only take 0.2s of CPU to do it. Making half a cent with 30 seconds multiple GPUs, consuming 1000W of power... isn't.
This is a good anecdote and it reminds me of how Sony had cloud architecture/digital distribution, a music label, movie studio, mobile phones, music players, speakers, tvs, laptops, mobile apps... and totally missed out on building Spotify or Netflix.
I do think Google is a little different to Kodak however; their scale and influence is on another level. GSuite, Cloud, YouTube and Android are pretty huge diversifications from Search in my mind even if Search is still the money maker...
That, and while Sony had all these big groups they often didn't play nice with each other. Look at how they failed to make Minidisc into any useful data platforms with PCs, largely because MD's were consumer devices and not personal computers so they were pretty much only seen as music hardware.
Even on the few Vaios that had MD drives on them, they're pretty much just an external MD player permanently glued to the device instead of being a full and deeply integrated PC component.
It goes to internal corporate culture, and what happens to you when you point out an uncomfortable truth. Do we shoot the messenger, or heed her warnings and pivot the hopefully not Titanic? RIM/Blackberry didn't manage to avoid it either.
People like to believe CEOs aren't worth their pay package, and sometimes they're not. But a look at a couple of their failures and a different CEO of Kodak wouldn't have had what happened happen, makes me think that sometimes, some of them do deserve that.
When a fool inevitably takes the throne, disaster ensues.
I can't say for sure that a different system of government would have saved Kodak. But when one man's choices result in disaster for a massive organization, I don't blame the man. I blame the structure that laid the power to make such a mistake on his shoulders.
that seems weird. Why hold up one person as being great while not also holding up one person as not? If my leader led me into battle and we were victorious, we'd put it on them. if they lead us to ruin, why should I blame the organizational structure that led to them getting power as the culprit instead of blaming them directly?
1/2 kW/minute costs about $0.001 so you technically could make a profit at that rate. The real problem is the GPU cost - a $20k GPU amortized over five years costs $0.046 per second. :)
Because I'm an idiot and left off a factor of 365. Thank you! A 20k GPU for 30 seconds is 1/3 of a cent. Still more than the power but also potentially profitable under this scenario informing all the other overhead and utilization.
As a business Google's interest is in showing ads that make it the most money - if they quickly show just the relevant information then Google loses advertising opportunities.
To an extent, it is the web equivalent of irl super markets intentionally moving stuff around and having checkout displays.
> As a business Google's interest is in showing ads that make it the most money - if they quickly show just the relevant information then Google loses advertising opportunities.
This is just a question of UX- the purpose of their search engine was already to show the most relevant information (ie. links), but they just put some semi-relevant information (ie. sponsored links) first, and make a fortune. They can just do the same with AI results.
but because users are used to doing that for free, they can't charge money for that, but if they don't charge money for that, and no one's seeing ads, then where does they money come from?
There are probably more ad-supported or ad-enhanced properties, but what's been shifting over the past few years is the focus on subscription-supported products:
1. YouTube TV
2. YouTube Premium
3. GoogleOne (initially for storage, but now also for advanced AI access)
4. Nest Aware
5. Android Play Store
6. Google Fi
7. Workspace (and affiliated products)
In terms of search, we're already seeing a renaissance of new options, most of which are AI-powered or enhanced, like basic LLM interfaces (ChatGPT, Gemini, etc), or fundamentally improved products like Perplexity & Kagi. But Google has a broad and deep moat relative to any direct competitors. Its existential risk factors are mostly regulation/legal challenge and specific product competition, but not everything on all fronts all at once.
Google is winning on every front except... marketing (Google has a chatbot?), trust (who knew the founding fathers were so diverse?), safety (where's the 2.5 Pro model card?), market share (fully one in ten internet users on the planet are weekly ChatGPT users), and, well, vibes (who's rooting for big G, exactly?).
But I will admit, Gemini Pro 2.5 is a legit good model. So, hats off for that.
My experience with their software has been horrible. A friend was messing around with Gemini on my phone and said my name is John, and it automatically saved that to my saved info list and always called me John from then on. But when I ask it to forget this, it says it can't do that automatically and links me to the Saved Info page, which is a menu they didn't implement in the app so it opens a URL in my browser and asks me to sign into my Google account again. Then a little toast says "Something went wrong" and the saved info list is empty and broken. I tried reporting this issue half a year ago and it's still unresolved. Actually the only way I was ever able to get it to stop calling me John is to say "remember to forget my name is John" in some way that it adds that to the list instead of linking me to that broken page
Google is also terribly paranoid of the LLM saying anything controversial. If you want a summary of some hot topic article you might not have the time to read, Gemini will straight up refuse to answer. ChatGPT and Grok don't mind at all.
I noticed the same in Gemini. It would refuse to answer mundane questions that none but the most 'enlightened' could find an offensive twist to.
This makes it rather unusable as a catch all goto resource, sadly. People are curious by nature. Refusing to answer their questions doesn't squash that, it leads them to potentially less trustworthy sources.
The AI won't tell the reader what to think in an authoritative voice. This is better than the AI trying to decide what is true and what isn't.
However, the AI should be able to search the web and present it's findings without refusals. Obviously, always presenting the sources. And the AI should never use an authoritative tone and it should be transparent about the steps it took to gather the information, and present the sites and tracks it didn't follow.
Yes, Musk's contention of an AI trying to tell the truth, no matter what, is straight up horse manure. Should be done for false advertising (per usual)
Trying to answer complex questions by making up shit in a confident voice is the worst option. Redirecting to a more trustworthy human source or multiple if needed is much better
I talk to ChatGPT about some controversial things, and it's pretty good at nuance and devils advocate if you ask for it. It's more echo chamber, if you don't, or rather extreme principle of charity, which might be a good thing.
I think that's the "trust" bit. In AI, trust generally means "let's not offend anyone and water it down to useless." Google is paranoid of being sued/getting attention if Gemini says something about Palestine or drawns images like Studio Ghibli. Meanwhile users love to these topics and memes are free marketing.
Not a fan of Google, but if you use Gemini through AI studio with a custom prompt and filters disabled it's by far the least censored commercial model in my experience.
>Google is also terribly paranoid of the LLM saying anything controversial.
When did this start? Serious question. Of all the model providers my experience with Google's LLMs and Chatproducts were the worst in that dimension. Black Nazis, Eating stones, pizza with glue, etc I suppose we've all been there.
Seems like a feature. Last thing we need is a bunch of people willing to take AI at it's word making up shit about controversial topics. I'd say redirecting to good or prestigious source is probably the best you can do
I remember when LLM first appeared - on a local social website of my country (think Digg), a lot of people were exctatic because they got ChatGPT to say that black people are dumb, claiming it as a victory over woke :P
I wouldn't even say Gemini Pro 2.5 is the best model. Certainly not when you do multimodal or function calling, which is what actually matters in industry applications. Plain chatbots are nice, but I don't think they will decide who wins the race. Google is also no longer in the mindset to really innovate. You'll hear surprisingly similar POVs from ex-Googlers and ex-OpenAI guys. I'd actually say OpenAI still has an edge in terms of culture, even through it fell deep.
I did some experiments with Gemini Pro 2.5 vs Sonnet 3.7 for coding assistants, and, at least as far as code quality and ability to understand complexities in existing codebase goes, Gemini is noticeably stronger.
I have found function calling and ‘roll my own agents’ work much better now with Gemini than they did late last year, but I also do a lot of function calling experiments with small open models using Ollama - much more difficult to work with to get a robust system.
You really hit the nail on the head with trust. Knowing the power of these AIs and how absolutely little I trust Google, I’d never tell trust Gemini with the things I’ll say to ChatGPT.
Large corporations wind up creating internal policies, controls, etc. If you know anyone who works in engineering at Google, you'll find out about the privacy and security reviews required in launching code.
Startups, on the other hand, are the wild west. One policy one day, another the next, engineers are doing things that don't follow either policy, the CEO is selling data, and then they run out of money and sell all the data to god knows who.
Google is pretty stable. OpenAI, on the other hand, has been mega-drama you could make a movie out of. Who knows what it's going to be doing with data two or four years from now?
For me it’s less about trustworthiness and more about what they can do with the information. Google can potentially locate, profile and influence everyone around me and I don’t want that type of power however benevolent they are.
What can OpenAI do? They can sell my data, whatever, it’s a whole bunch of prompts of me asking for function and API syntax.
Isn't hoarding data for training purposes a key part of OpenAI's business model? I get that they don't have a reputation for selling that data (or access to it) yet, but, what happens if/when funding dries up?
I definitely don't trust Google -- fool me once, and all -- but to the extent I'm going to "trust" any business with my data, I'd like to see a proven business model that isn't based on monetizing my information and is likely to continue to work (e.g., Apple). OpenAI doesn't have that.
I don't think it's about trusting OpenAI necessarily, and definitely not a character like Sam Altman. It's more about Google having a proven record of being data obsessed. 99% of the money they make is from our data. Many other tech giants (Apple, Microsoft, etc) are also hard to trust, but at least they don't have their whole business model built on user data like Google and Meta. I can't blame anyone looking at OpenAI as a lesser evil.
Well, Google is also very well placed to integrate with other products that have big market share.
So far this has been nothing but a PM wankfest but if Gemini-in-{Gmail,Meet,Docs,etc} actually gets useful, it could be a big deal.
I also don't think any of those concerns are as important for API users as direct consumers. I think that's gonna be a bugger part of my the market as time goes on.
Microsoft has been integrated Copilot in their Office products. In fact, they don't even call it Office any more. Guess what? If you ever had first hand experience with them, they are absolutely a dumpster fire. (Well, maybe except transcription in Teams meeting, but that's about it.) I used it for 5 minutes and never touch it again. I'll be very impressed if that's not the case with Google.
My hesitancy to adopt Gemini, despite being a heavy GCP and workspace user, is I kinda lost trust when trying to use their earlier models (I don't even remember those models' names). I just remember the models were just so consistently bad and obviously hallucinated more than 50% of the time.
Maybe Gemini is finally better, but I'm not exactly excited to give it a try.
I look more to Google for efficient and inexpensive LLM APIs, and in a similar way to Groq Cloud for inexpensive and fast inferencing for open models.
ChatGPT has a nice consumer product, and I also like it.
Google gets a bad rap on privacy, etc., but if you read the documentation and set privacy settings, etc. then I find them reasonable. (I read OpenAI’s privacy docs for a long while before experimenting with their integration of Mac terminal, VSCode, and IntelliJ products.)
We live in a cornucopia of AI tools. Occasionally I will just for the hell of it do all my research work for several days just using open models running on my Mac using Ollama - I notice a slight hit in productivity, but still a good setup.
I would like to think they just let other companies have the first mover advantage on chatbots because it only disrupts Google in their search business, which was already pretty far gone and on the way out. Where is AI actually going to change the world? Protein folding, robotics, stuff that the public doesn’t hype about. And they looked at the gold rush and decided “let’s design shovels”. Maybe I’m giving them too much credit but very bullish on Google.
Exactly. Google may have a lead in their model, but saying they are "winning on every front" is a very questionable claim, from the perspective of everyday users, not influencers, devoted fans or anyone else who has a stake in hyping it.
It might be worth throwing in an analogy to windows PCs vs Mac vs Linux. G appeals to a subset of the market at the end of the day, being “best” does not mean everyone will use it.
I had to stop using Gemini 2.5 because the UI peaks my MPB cpu at max and I can’t type my prompt at more than a character every 2 seconds. I can’t even delete my chats lol. Anyone else?
On deleting chats, I accidentally discovered that AI Studio creates a 'Google AI Studio' folder on your Google Drive with all the links to chats. If you delete the 'link' from there, it will disappear in AI Studio...interesting UX :-)
They "lost from a position of strength" in that they had they had the most public-cloud like data centers and started thinking about selling that later than they should have. Bard/Gemini started later than chatgpt , but there's not really a moat for this LLM stuff, and Google started moving a lot earlier relative to GCP vs Amazon.
They've got the cash, the people, and the infrastructure to do things faster than the others going forward, which is a much bigger deal IMO than having millions more users right now. Most people still aren't using LLMs that often, switching is easy, and Google has the most obvious entry points with billion+ users with google.com, YouTube, gmail, chrome, android, etc.
Trust is important, and Google has a big rep for killing its projects. As well as making the most moronic braindead decisions in handling what they don't kill off.
No one is going to build on top of anything "Google" without having a way out thought out in advance.
Not that important for LLMs, where drop-in replacements are usually available. But a lot of people just hear "by Google" now and think "thanks I'll pass" - and who can blame them?
Winning =/= won. The point is that they are improving on many fronts. If they were already recognized as THE leader there would be no point in making a HN post about it.
I’m scared they’re going to kill it off. Every good idea they’ve had in the last 20 years has been killed off. Even Fuchsia/Zircon, which should have supplanted Android a full decade ago.
It is sort of funny to me how the sentiment about whoever seems to be leading in ML changes so frequently (in particular here on HN.) A couple months ago it felt like people were sure that Google completely fucked it up for themselves (especially due to the fact that they invented the transformer but didn't productize it themselves at first.)
For a short while, Claude was the best thing since sliced cheese, then Deepseek was the shit, and now seemingly OpenAI really falls out of favor. It kinda feels to me like people cast their judgement too early (perhaps again in this case.) I guess these are the hypecycles...
Google is killing it right now, I agree. But the world might appear completely different in three months.
Personally, I don't really think there's a team at Google, nor at OpenAI, paying for "astroturfing" on sites like HN.
What are the rough steps through which you see this working? I see people talking about "astroturfing" all the time without much explanation on the mechanisms. So roughly, there are employees paid solely to post on social media like HN trying to push the needle in one direction or another?
There doesn't need to be a team. Individuals can act according to personal incentives and still create coordinated behavior. Look at flocks of birds. Each bird acts for itself; together they move in unison.
Yeah... I wish there were laws that would require disclosure of such behavior. Might be tricky to implement though, and probably contradicts the interests of politicians.
AI is changing fast! And to be fair to the model companies, they have been releasing products of (mostly) increasing quality.
It really depends what your use case is. Over the range of all possible use cases this has been the narrative.
I tried Google's model for coding but it kept giving me wrong code. Currently Claude for coding and ChatGPT for more general questions is working for me. The more exotic your use case, the more hit or miss it's going to be.
The sentiment changes this fast because SOTA changes this fast. E.g. Google models were objectively crappy compared to OpenAI, but Gemini 2.5 really turned the tables (and I'm not talking about synthetic benchmarks here but real world coding).
The state of affairs with local models is similarly very much in flux, by the way.
As an Ex-OpenAI employee I agree with this. Most of the top ML talent at OpenAI already have left to either do their own thing or join other startups. A few are still there but I doubt if they'll be around in a year. The main successful product from OpenAI is the ChatGPT app, but there's a limit on how much you can charge people for subscription fees. I think soon people expect this service to be provided for free and ads would become the main option to make money out of chatbots. The whole time that I was at OpenAI until now GOOG has been the only individual stock that I've been holding. Despite the threat to their search business I think they'll bounce back because they have a lot of cards to play. OpenAI is an annoyance for Google, because they are willing to burn money to get users. Google can't as easily burn money, since they already have billions of users, but also they are a public company and have to answer to investors. But I doubt if OpenAI investors would sign up to give more money to be burned in a year. Google just needs to ease off on the red tape and make their innovations available to users as fast as they can. (And don't let me get started with Sam Altman.)
> there's a limit on how much you can charge people for subscription fees. I think soon people expect this service to be provided for free and ads would become the main option to make money out of chatbots.
So... I don't think this is certain. A surprising number of people pay for the ChatGPT app and/or competitors. It's be a >$10bn business already. Could maybe be a >$100bn business long term.
Meanwhile... making money from online ads isn't trivial. When the advertising model works well (eg search/adwords), it is a money faucet. But... it can be very hard to get that money faucet going. No guarantees that Google discover a meaningful business model here... and the innovators' dilema is strong.
Also, Google don't have a great history of getting new businesses up and running regardless of tech chops and timing. Google were pioneers to cloud computing... but amazon and MSFT built better businesses.
At this point, everyone is assuming AI will resolve to a "winner-take-most" game that is all about network effect, scale, barriers to entry and such. Maybe it isn't. Or... maybe LLMs themselves are commodities like ISPs.
The actual business models, at this point, aren't even known.
> No guarantees that Google discover a meaningful business model here...
I don't understand this sentiment at all. The business model writes itself (so to speak). This is the company that perfected the art of serving up micro-targeted ads to people at the moment they are seeking a solution to a problem. Just swap the search box for a chat bot.
For a while they'll keep the ads off to the side, but over time the ads will become harder and harder to distinguish from the chat bot content. One day, they'll dissapear altogether and companies will pay to subtly bias the AI towards their products and services. It will be subtle--undetectable by end users--but easily quantified and monetized by Google.
Companies will also pay to integrate their products and services into Google's agents. When you ask Gemini for a ride, does Uber or Lyft send a car? (Trick question. Waymo does, of course.) When you ask for a pasta bowl, does Grubhub or Doordash fill the order?
When Gemini writes a boutique CRM for your vegan catering service, what service does it use for seamless biometric authentication, for payment processing, for SMS and email marketing? What payroll service does it suggest could be added on in a couple seconds of auto-generated code?
AI allows Google to continue it's existing business model while opening up new, lucrative opportunities.
I don’t think it works. Search is the perfect place for ads for exactly the reasons you state: people have high intent.
But a majority of chatbot usage is not searching for the solution to a problem. And if he Chatbot is serving the ads when I’m using it for creative writing, reformatting text, having a python function, written, etc, I’m going to be annoyed and switch to a different product.
Search is all about information retrieval. AI is all about task accomplishment. I don’t think ads work well in the latter , perhaps some subset, like the task is really complicated or the AI can tell the user is failing to achieve it. But I don’t think it’s nearly as could have a fit as search.
It doesn't have to be high intent all the time though. Chrome itself is "free" and isn't the actual technical thing serving me ads (the individual websites / ad platforms do that regardless of which browser I'm using), but it keeps me in the Google ecosystem and indirectly supports both data gathering (better ad targeting, profitable) and those actual ad services (sometimes subtly, sometimes in heavy-handed ways like via ad blocker restrictions). Similar arguments to be made with most of the free services like Calendar, Photos, Drive, etc - they drive some subscriptions (just like chatbots), but they're mostly supporting the ads indirectly.
Many of my Google searches aren't high intent, or any purchase intent at all ("how to spell ___" an embarrassing number of times), but it's profitable for Google as a whole to keep those pieces working for me so that the ads do their thing the rest of the time. There's no reason chatbots can't/won't eventually follow similar models. Whether that's enough to be profitable remains to be seen.
> Search is all about information retrieval. AI is all about task accomplishment.
Same outcome, different intermediate steps. I'm usually searching for information so that I can do something, build something, acquire something, achieve something. Sell me a product for the right price that accomplishes my end goal, and I'm a satisfied customer. How many ads for app builders / coding tools have you seen today? :)
I have shifted the majority of my search for products to ChatGPT. In the past my starting point would have been Amazon or Google. It’s just so much easier to describe what I’m looking for and ask for recommendations that fit my parameters. If I could buy directly from the ChatGPT, I probably would. It’s just as much or more high intent as search.
Chatgpt is effectively a functional search engine for a lot of people. Searching for the answer "how do i braid my daughter's hair?", or, "how do i bake a cake for a birthday party?" can be resolved via tradtitional search and finding a video or blog post, or simply read the result from an LLM. LLM has a lot more functionality overall, but ChatGPT and it's competitors are absolutely an existential threat to Google, as (in my opinion) it's a superior service because it just gives you the best answer, rather than feeding you into whatever 10 blog services that utilize google ads the most this month. Right now ChatGPT doesn't even serve up ads, which is great. I'm almost certain they're selling my info though, as specific one-off stuff I ask ChatGPT about, ends up as ads in Meta social medias the next day.
> And if he Chatbot is serving the ads when I’m using it for creative writing, reformatting text, having a python function, written, etc, I’m going to be annoyed and switch to a different product.
You may not even notice it when AI does a product placement when it's done opportunistically in creative writing (see Hollywood). There also are plenty of high-intent assistant-type AI tasks.
Re "going to be annoyed" there is definitely a spectrum starting at benign and culminating to the point of where you switch.
Photopea, for example, seems to be successful and ads displayed on the free tier lets me think that they feel at least these users are willing to see ads while they go about their workflow.
Obviously, an LLM is in a perfect position to decide whether an add can be "injected" into the current conversation. If you're using it for creative writing it will be add free. But chances are you will also use it to solve real world problems where relevant adds can be injected via product or service suggestions.
The intent will be obvious from the prompt and context. The AI will behave differently when called from a Doc about the yearly sales strategy vs consumer search app.
Just because the first LLM product people paid for was a chatbot does not mean that chat will be the dominant commercial use of AI.
And if the dominant use is agents that replace knowledge workers, then they'll cost closer to $2000 per month than $20 or free, and an ad-based business model won't work.
The actual business models and revenue sources are still unknown. Consumer subscriptions happens to be the first major model. Ads still aren't. Many other models could dwarf either of these.
I still think it's pretty clear. Google doesn't have to get a new business off the ground, just keep improving the integration into Workspace, Gmail, Cloud, Android etc. I don't see users paying for ChatGPT and then copy/pasting into those other places even if the model is slightly better. Google will just slowly roll out premium plans that include access to AI features.
And as far as selling pickaxes go, GCP is in a far better position to serve the top of market than OpenAI. Some companies will wire together multiple point solutions but large enterprises will want a consolidated complete stack. GCP already offers you compute clusters and BigQuery and all the rest.
Perhaps... but perhaps not. A chatbot instead of a search box may not be how the future looks. Also... a chatbot prompt may not (probably won't) translate from search query smoothly... in a Way That keep ad markets intact.
That "perfected art" of search advertising is highly optimized. You (probably) loose all of that in transition. Any new advertising products will be intrepid territory.
You could not have predicted in advance that search advertising would dwarf video (yourube) advertising as a segment.
Meanwhile... they need to keep their market share at 90%.
A friend of mine works in advertising/marketing guy at the director level (Career ad guy), for big brands like nationwide cell carriers, big box stores etc, but mostly telcom stuff I think, and he uses it every day; he calls it "my second brain". LLM are great at riffing on ideas and brainstorming sessions.
LLM based advertising has amazing potential when you consider that you can train them to try to persuade people to buy the advertised products and services.
That seems like a recipe for class action false advertising lawsuits. The AI is extremely likely to make materially false claims, and if this text is an advertisement, whoever placed it is legally liable for that.
I don't think we should expect that risk to dissuade these companies. They will plow ahead, fight for years in court, then slightly change the product if forced to ¯\_(ツ)_/¯
>Meanwhile... making money from online ads isn't trivial. When the advertising model works well (eg search/adwords), it is a money faucet. But... it can be very hard to get that money faucet going. No guarantees that Google discover a meaningful business model here... and the innovators' dilema is strong.
It's funny how the vibe of HN along with real world 's political spectrum have shifted together.
We can now discuss Ads on HN while still being number 1 and number 2 post. Extremism still exists, but it is retreating.
I don’t think “AI” as a market is “winner-takes-anything”. Seriously. AI is not a product, it’s a tool for building other products. The winners will be other businesses that use AI tooling to make better products. Does OpenAI really make sense as a chatbot company?
I agree the market for 10% better AI isn’t that great but the cost to get there is. An 80% as good model at 10% or even 5% the cost will win every time in the current environment. Most businesses don’t even have a clear use case for AI they just use it because the competition is and there is a FOMO effect
There are two perspectives on this. What you said is definitely a good one if you're a business planning to add AI to whatever you're selling. But personally, as a user, I want the opposite to happen - I want AI to be the product that takes all the current products and turns them into tools it can use.
I agree, I want a more intelligent voice assistant similar to Siri as a product, and all my apps to be add-ons the voice assistant could integrate with.
According to the linked Wikipedia article, he did not go broke from the gold rush. He went broke because he invested the pickaxe windfall in land, and when his wife divorced him, the judge ruled he had to pay her 50%, but since he was 100% in land he had to sell it. (The article is not clear why he couldn't deed her 50% of it, or only sell 50%. Maybe it happened during a bad market, he had a deadline, etc.)
So maybe if the AI pickaxe sellers get divorced it could lead to poor financial results, but I'm not sure his story is applicable otherwise.
Basically every tech company likes to say they are selling pickaxes, but basically no VC funded company matches that model. To actually come out ahead selling pickaxes you had to pocket a profit on each one you sold.
If you sell your pickaxes at a loss to gain market share, or pour all of your revenue into rapid pickaxe store expansion, you’re going to be just as broke as prospectors when the boom goes bust.
If there were 2 other Amazons all with similar products and the same ease of shipping would you care where you purchased? Amazon is simply the best UX for online ordering. If anything else matched it I’d shop platform agnostic.
Great Wells Fargo has an "agent" ... and every one else is talking about how to make their products available for agent based AI.
People don't want 47 different agents to talk to, then want a single end point, they want a "personal assistant" in digital form, a virtual concierge...
And we can't have this, because the open web has been dead for more than a decade.
The business model question applies to all of these companies, not just Google.
A lack of workable business model is probably good for Google (bad for the rest of the world) since it means AI has not done anything economically useful and Google's Search product remains a huge cash cow.
Opera browser was not profitable for like 15 years and still became rather profitable eventually to make an attractive target to purchase by external investors. And even if not bough it would still made nice profit eventually for the original investors.
Sure, but they were making tons of money elsewhere. OpenAI has no source of revenue anywhere big enough to cover its expenses, it's just burning investor cash at the moment.
Last Month, Google, Youtube, Facebook, Instagram and Twitter (very close to this one, likely passes it this month) were the only sites with more visits than chatgpt. Couple that with the 400M+ weekly active users (according to open ai in February) and i seriously doubt that.
Weekly active users is a pretty strange metric. Essential tools and even social networking apps report DAUs, and they do that because essential things get used daily. How many times did you use Google in the past day? How many times did you visit (insert some social media site you prefer) in the last day? If you’re only using something once per week, it probably isn’t that important to you.
Mostly only social media/messaging sites report daily active users regularly. Everything else usually reports monthly active users at best.
>in the last day? If you’re only using something once per week, it probably isn’t that important to you.
No, something I use on a weekly basis (which is not necessarily just once a week) is pretty important to me and spinning it otherwise is bizarre.
Google is the frontend to the web for the vast majority of internet users so yeah it gets a lot of daily use. Social media sites are social media sites and are in a league of their own. I don't think i need to explain why they would get a disproportionate amount of daily users.
I am entirely confused by this. ChatGPT is absolutely unimportant to me. I don't use it for any serious work, I don't use it for search, I find its output to still be mostly a novelty. Even coding questions I mostly solve using StackExchange searches because I've been burned using it a couple of times in esoteric areas. In the few areas where I actually did want some solid LLM output, I used Claude. If ChatGPT disappeared off the Internet tomorrow, I would suffer not at all.
And yet I probably duck into ChatGPT at least once a month or more (I see a bunch of trivial uses in 2024) mostly as a novelty. Last week I used it a bunch because my wife wanted a logo for a new website. But I could have easily made that logo with another service. ChatGPT serves the same role to me as dozens of other replaceable Internet services that I probably duck into on a weekly basis (e.g., random finance websites, meme generators) but have no essential need for whatsoever. And if I did have an essential need for it, there are at least four well-funded competitors with all the same capabilities, and modestly weaker open weight models.
It is really your view that "any service you use at least once a week must be really important to you?" I bet if you sat down and looked at your web history, you'd find dozens that aren't.
(PS in the course of writing this post I was horrified to find out that I'd started a subscription to the damn thing in 2024 on a different Google account just to fool around with it, and forgot to cancel it, which I just did.)
>I am entirely confused by this. ChatGPT is absolutely unimportant to me. I don't use it for any serious work, I don't use it for search, I find its output to still be mostly a novelty. Even coding questions I mostly solve using StackExchange searches because I've been burned using it a couple of times in esoteric areas. In the few areas where I actually did want some solid LLM output, I used Claude. If ChatGPT disappeared off the Internet tomorrow, I would suffer not at all.
OK? That's fine. I don't think I ever claimed you were a WAU
>And yet I probably duck into ChatGPT at least once a month or more (I see a bunch of trivial uses in 2024) mostly as a novelty.
So you are not a weekly active user then. Maybe not even a monthly active one.
>Last week I used it a bunch because my wife wanted a logo for a new website. But I could have easily made that logo with another service.
Maybe[1], but you didn't. And I doubt your wife needs a new logo every week so again not a weekly active user.
>ChatGPT serves the same role to me as dozens of other replaceable Internet services that I probably duck into on a weekly basis (e.g., random finance websites, meme generators)but have no essential need for whatsoever.
You visit the same exact meme generator or finance site every week? If so, then that site is pretty important to you. If not, then again you're not a weekly active user to it.
If you visit a (but not the same) meme generator every week then clearly creating memes is important to you because I've never visited one in my life.
>And if I did have an essential need for it, there are at least four well-funded competitors with all the same capabilities, and modestly weaker open weight models.
There are well funded alternatives to Google Search too but how many use anything else? Rarely does any valuable niche have no competition.
>It is really your view that "any service you use at least once a week must be really important to you?" I bet if you sat down and looked at your web history, you'd find dozens that aren't.
Yeah it is and so far, you've not actually said anything to indicate the contrary.
[1]ChatGPT had an image generation update recently that made it capable of doing things other services can't. Good chance you could not in fact do what you did (to the same satisfaction) elsewhere. But that's beside my point.
Yes corporate leaders do chase hype and they also believe in magic.
I think companies implement DEI initiatives for different reasons than hype though. Many are now abandoning DEI ostensibly out of fear due to the change in U.S. regime.
Aside from university mentioned by sibling comments, there is major uptake of AI in journalism (summarize long press statements, create first draft of the teaser, or even full articles ...) and many people in my social groups use it regularly for having something explained, finding something ... it's wide spread
It does anecdotally seem to be very common in education which presumably will carry over to professional workplaces over time. I see it a lot less in non-tech and even tech/adjacent adults today.
My wife, the farthest you can get from the HN crowd, literally goes to tears when faced with Excel or producing a Word doc and she is a regular user of copilot and absolutely raves about it. Very unusual for her to take up new tech like this and put it to use but she uses it for everything now. Horse is out of the barn.
For many, this stuff is mostly about copilot being shoved down everyone's throats via ms office obnoxious ads and distractions, and I haven't yet heard of anyone liking it or perceiving it as an improvement. We are now years into this, so my bets are on the thing fading away slowly and becoming a taboo at Microsoft.
Many recent HN articles about how middle managers are already becoming addicted and forcing it on their peons. One was about the game dev industry in particular.
In my work I see semi-technical people (like basic python ability) wiring together some workflows and doing fairly interesting analytical things that do solve real problems. They are things that could have been done with regular code already but weren't worth the engineering investment.
In the "real world" I see people generating crummy movies and textbooks now. There is a certain type of person it definitely appeals to.
Uber is a profitable company both in 2023 and - to the tune of billions of dollars - in 2024. Please read their financials if you doubt this statement.
I assume they mean the profits in the past couple years are dwarfed by the losses that came before. Looking at the company's entire history, instead of a single FY.
Seems like the difference between a profitable investment and a profitable company.
They invested tens of billions of dollars in destroying the competition to be able to recently gain a return on that investment. One could either write off that previous spending or calculate it into the totality of "Uber". I don't know how Silicon Valley economics works but, presumably, a lot of that previous spending is now in the form of debt which must be serviced out of the current profits. Not that I'm stating that taking on debt is wrong or anything.
To the extent that their past spending was debt, interest on that debt that should already be accounted for in calculating their net income.
But the way it usually works for Silicon Valley companies and other startups is that instead of taking on debt they raise money through selling equity. This is money that doesn't have to be paid back, but it means investors own a large portion of this now-profitable company.
I'm surprised. They pay the drivers a pittance. My ex drove Uber for a while and it wasn't really worth it. Also, for the customers it's usually more expensive and slower than a normal taxi at least here in Spain.
The original idea of ride-sharing made sense but just like airbnb it became an industry and got enshittified.
> They pay the drivers a pittance. My ex drove Uber for a while and it wasn't really worth it.
I keep hearing this online, but every time I’ve used an Uber recently it’s driven by someone who says they’ve been doing it for a very long time. Seems clear to me that it is worth it for some, but not worth it if you have other better job options or don’t need the marginal income.
PS: I know that in Romania it's the opposite. Uber is kinda like a luxury taxi there. Normal taxis have standard rates, but these days it's hardly enough to cover rising fuel prices. So cars are ancient and un a bad state of repair, drivers often trick foreigners. A colleague was even robbed by one. Uber is much more expensive but much safer (and still cheap by western standards).
> but not worth it if you have other better job options
Pretty much any service job, really...
When I had occasion to take a ride share in Phoenix I'd interrogate the driver about how much they were getting paid because I drove cabs for years and knew how much I would have gotten paid for the same trip.
Let's just say they were getting paid significantly less than I used to for the same work. If you calculated in the expenses of maintaining a car vs. leasing a cab I expect the difference is even greater.
There were a few times where I had just enough money to take public transportation down to get a cab and then snag a couple cash calls to be able to put gas in the car and eat. Then I could start working on paying off the lease and go home at the end of the day with some cash in my pocket -- there were times (not counting when the Super Bowl was in town) where I made my rent in a single day.
They're usually a bit more expensive here than a taxi. It can be beneficial because sometimes they have deals, and I sometimes take one when I have to book it in advance or when I'm afraid there will be delays with a corrsponding high cost. Though Uber tend to hit me with congestion charges then too. At least with a taxi I can ask them to take a different route. The problem with the uber drivers is that they don't know any of the street names here, they just follow the app's navigation. Whereas taxi drivers tend to be much more aware and know the streets and often come up with suggestions.
This also means that they sometimes fleece tourists but when they figure you know the city well they don't dare :) Often if they take one wrong turn I make a scene about frowning and looking out of the window and then they quickly get back on track. Of course that's another usecase where uber would be better, if you don't know the city you're in.
yeah thanks no, I'm paying for an Uber. For all the complaints over Ubers business practices, it's hard not to forget how bad taxis were. Regulatory capture is a clear failure mode of capitalism and the free market and that is no more shown than by the taxis cab industry.
"A surprising number of people pay for the ChatGPT app and/or competitors."
I doubt the depiction implied by "surprising number". Marketing types and CEO's who would love 100% profit and only paying the electricity bill for an all AI workforce would believe that. Most people, especially most technical people would not believe that there is a "surprising number" of saps paying for so-called AI.
Absolutely agree Microsoft is better there - maybe that's why Google hired someone from Microsoft for their AI stuff. A few people I think.
I also agree the business models aren't known. That's part of any hype cycle. I think those in the best position here are those with an existing product(s) and user base to capitalize on the auto complete on crack kinda feature. It will become so cheap to operate and so ubiquitous in the near future that it absolutely will be seen as a table stakes feature. Yes, commodities.
Google aren’t interested in <1bn USD businesses, so it’s hard for them to build anything new as it’s pretty guaranteed to be smaller than that at first. The business equivalent of the danger of a comfortable salaried job.
Google is very good at recognizing existential threats. iOS were that to them and they built Android, including hardware, a novelty for them, even faster than mobile incumbents at the time.
They're more than willing to expand their moat around AI even if that means multiple unprofitable business for years.
Mobile is still nearly everything. Google continues to develop and improve Android in substantial ways. Android is also counted on by numerous third-party OEMs.
If you are a business customer of Google or pay attention to things like Cloud Next that just happened, it is very clear that Google is building heavily in this area. Your statement has already been disproven.
"The actual business models, at this point, aren't even known."
"AI" sounds like a great investment. Why waste time investing in businesses when one can invest in something that might become a business. CEOs and employees can accumulate personal weath without any need for the company to be become profitable and succeed.
Contextual advertising is a known ad business model that commands higher rates and is an ideal fit for LLMs. Plus ChatGPT has a lot of volume. If there’s anyone who should be worried about pulling that off it’s Perplexity and every other small to mid-sized player.
'Business is the practice of making one's living or making money by producing or buying and selling products (such as goods and services). It is also "any activity or enterprise entered into for profit."' ¹
Until something makes a profit it's a charity or predatory monopoly-in-waiting.²
Until something makes a profit it's a charity or predatory monopoly-in-waiting.
This is incorrect. There are millions of companies in the world that exist to accomplish things other than making a profit, and are also not charities.
99.9% of the time, it's an investment hoping to make a profit in the future. And we still call those businesses, even if they're losing money like most businesses do at first.
> At this point, everyone is assuming AI will resolve to a "winner-take-most" game that is all about network effect, scale, barriers to entry and such
I don't understand why people believe this: by settling on "unstructured chat" as the API, it means the switching costs are essentially zero. The models may give different results, but as far a plugging a different one in to your app, it's frictionless. I can switch everything to DeepSeek this afternoon.
That's like asking a McDonald's employee if they own Burger King stock and making market assumptions on that. The best people have already left is such a common trope.
Modern cloud computing is more than just having a scalable infrastructure of servers, it was a paradigm shift to having elastic demand, utility style pricing, being completely API driven, etc. Amazon were not only the first to market but pioneers in this space. Nothing came close at that time.
AWS had a cleaner host-guest abstraction (the VM) that makes it easier to reason about security, and likely had a much bigger gap between their own usage peaks and troughs.
Yep. Google offered app engine which was good for fairly stateless simple apps in an old limited version of python, like a photo gallery or email client. For anything else is waa dismal. Amazon offered VMs. Useful stuff for a lot more platforms.
> I think soon people expect this service to be provided for free and ads would become the main option to make money out of chatbots.
I also think adtech corrupting AI as well is inevitable, but I dread for that future. Chatbots are much more personal than websites, and users are expected to give them deeply personal data. Their output containing ads would be far more effective at psychological manipulation than traditional ads are. It would also be far more profitable, so I'm sure that marketers are salivating at this opportunity, and adtech masterminds are hard at work to make this a reality already.
The repercussions of this will be much greater than we can imagine. I would love to be wrong, so I'm open to being convinced otherwise.
I agree with you. There is also a move toward "agents", where the AI can make decisions and take actions for you. It is very early days for that, but it looks ike it might come sooner than I had though.
That opens up even more potential for influence on financial decisions (which is what adtech wants) - it could choose which things to buy for a given "need".
I have yet to understand this obsession with agents.
Is making decisions the hardest thing in life for so many people? Or is this instead a desire to do away with human capital — to "automate" a workforce?
Regardless, here is this wild new technology (LLMs) that seems to have just fallen out of the sky; we're continuously finding out all the seemingly-formerly-unimaginable things you can do with it; but somehow the collective have already foreseen its ultimate role.
As though the people pushing the ARPANET into the public realm were so certain that it would become the Encyclopedia Galactica!
> Is making decisions the hardest thing in life for so many people?
Take insurance, for example — do you actually enjoy shopping for it?
What if you could just share a few basic details, and an AI agent did all the research for you, then came back with the top 3 insurance plans that fit your needs, complete with the pros and cons?
There are already web sites that do this for products like insurance (example: [1]).
What I need is something to troll through the garbage Amazon listings and offer me the product that actually has the specs that I searched for and is offered by a seller with more than 50 total sales. Maybe an AI agent can do that for me?
> There are already web sites that do this for products like insurance
You didnt get the point, instead of going to such website for solving the insurance problem, going to 10 other websites for solving 10 other problems, just let one AI agent do it for you.
> Or is this instead a desire to do away with human capital — to "automate" a workforce?
This is what I see motivating non-technical people to learn about agents. There’s lots of jobs that are essentially reading/memorizing complicated instructions and entering data accordingly.
> I have yet to understand this obsession with agents.
1. People who can afford personal assistants and staff in general gladly pay those people to do stuff for them. AI assistants promise to make this way of living accessible to the plebs.
2. People love being "the idea guy", but never having to do any of the (hard) work. And honestly, just the speedup to actually convert the myriad of ideas floating around in various heads to prototypes/MVPs is causing/will cause somewhat of a Cambrian explosion of such things.
Well yeah, that's how evolution works: it's an exploration of the search space and only the good stuff survives.
> filled with hallucinations,
The end products can be fully AI-free. In fact, I would expect most ideas that have been floating around to have nothing to do with AI. To be fair, that may change with it being the new hip thing. Even then, there are plenty of implementations that use AI where hallucinations are no problem at all (or even a feature), or where the issues with hallucinations are sufficiently mitigated.
> unable to ever get past the first step.
How so? There are already a bunch of functional things that were in Show HN that were produced with AI assistance. Again, most of the implemented ideas will suck, but some will be awesome and might change the world.
Only a small percent of people will actually produce ideas that other people are interested in. For most people, AI tools for building things will enable them to construct their own personalized worlds. Imagine watching movies, except the movies can be generated for you on the fly. Sure, no one except you might care about a Matrix Moulin Rouge crossover. But you'll be able to have it just like that.
They were already not getting past the first step before AI came along. If AI helps them get to step two, and then three and four, that seems like a good thing, no?
> Is making decisions the hardest thing in life for so many people?
Should I take this job or that one? Which college should I go to? Should I date this person or that one? Life has some really hard decisions you have to make, and that's just life. There are no wrong answers, but figuring out what to do and ruminating over it is comes to everyone at some point in their lives. You can ask ChatGPT to ask you the right questions you need asked in order to figure out what you really want to do. I don't know how to put a price on that, but that's worth way more than $20/month.
Right, but before a product can do all of those things well it will have to do one of those things well. And by “well” I mean reliably superhuman, not usually but sometimes embarrassingly poorly.
People used to (and still do) pay fortune tellers to make decisions for them. Doesn’t mean they’re good ones.
Hey, we could save them all the busywork, and just wire all our money to corporations...
But financial nightmare scenarios aside, I'm more concerned about the influence from private and government agencies. Advertising is propaganda that seeks to separate us from our money, but other forms of propaganda that influences how we think and act has much deeper sociopolitical effects. The instability we see today is largely the result of psyops conducted over decades across all media outlets, but once it becomes possible to influence something as personal as a chatbot, the situation will get even more insane. It's unthinkable that we're merrily building that future without seemingly any precautions in mind.
You're assuming ads would be subtly worked into the answers. There's no reason it has to be done that way. You can also have a classic text ads system that's matching on the contents of the discussions, or which triggers only for clearly commercial queries "chatgpt I want to eat out tonight, recommend me somewhere", and which emits visually distinct ads. Most advertisers wouldn't want LLMs to make fake recommendations anyway, they want to control the way their ad appears and what ad copy is used.
There's lots of ways to do that which don't hurt trust. Over time Google lost it as they got addicted to reporting massively quarterly growth, but for many years they were able to mix in ads with search results without people being unhappy or distrusting organic results, and also having a very successful business model. Even today Google's biggest trust problem by far is with conservatives, and that's due to explicit censorship of the right: corruption for ideological not commercial reasons.
So there seems to be a lot of ways in which LLM companies can do this.
Main issue is that building an ad network is really hard. You need lots of inventory to make it worthwhile.
There are lots of ways that advertising could be tied to personal interests gleaned by having access to someone's ChatBot history. You wouldn't necessarily need to integrate advertisements into the ChatBot itself - just use it as a data gathering mechanism to learn more about the user so that you can sell that data and/or use it to serve targetted advertisements elsewhere.
I think a big commercial opportunity for ChatBots (as was originally intended for Siri, when Apple acquired it from SRI) is business referral fees - people ask for restaurant, hotel etc recommendations and/or bookings and providers pay for business generated this way.
Right, referral fees is pay-per-click advertising.
The obvious way to integrate advertising is for the LLM to have a tool to search an ad database and display the results. So if you do a commercial query the LLM goes off and searches for some relevant ads using everything it knows about you and the conversation, the ad search engine ranks and returns them, the LLM reads the ad copy and then picks a few before embedding them into the HTML with some special React tags. It can give its own opinion to push along people who are overwhelmed by choice. And then when the user clicks an ad the business pays for that click (referral fee).
> You're assuming ads would be subtly worked into the answers. There's no reason it has to be done that way.
I highly doubt advertisers will settle for a solution that's less profitable. That would be like settling for plain-text ads without profiling data and microtargeting. Google tried that in the "don't be evil" days, and look how that turned out.
Besides, astroturfing and influencer-driven campaigns are very popular. The modern playbook is to make advertising blend in with the content as much as possible, so that the victim is not aware that they're being advertised to. This is what the majority of ads on social media look like. The natural extension of this is for ads to be subtly embedded in chatbot output.
"You don't sound well, Dave. How about a nice slice of Astroturf pizza to cheer you up?"
And political propaganda can be even more subtle than that...
There's no reason why having an LLM be sly or misleading would be more profitable. Too many people try to make advertising a moral issue when it's not, and it sounds like you're falling into that trap.
An ideal answer for a query like "Where can I take my wife for a date this weekend?" would be something like,
> Here are some events I found ... <ad unit one> <ad unit two> <ad unit three>. Based on our prior conversations, sounds like the third might be the best fit, want me to book it for you?
To get that you need ads. If you ask ChatGPT such a question currently it'll either search the web (and thus see ads anyway) or it'll give boring generic text that's found in its training set. You really want to see images, prices, locations and so on for such a query not, "maybe she'd like the movies". And there are no good ranking signals for many kinds of commercial query: LLM training will give a long-since stale or hallucinated answer at worst, some semi-random answer at best, and algorithms like PageRank hardly work for most commercial queries.
HN has always been very naive about this topic but briefly: people like advertising done well and targeted ads are even better. One of Google's longest running experiments was a holdback where some small percentage of users never saw ads, and they used Google less than users who did. The ad-free search gave worse answers overall.
Wouldn't fewer searches indicate better answers? A search engine is productivity software. Productivity software is worse when it requires more user interaction.
Also you don't need ads to answer what to do, just knowledge of the events. Even a poor ranking algorithm is better than "how much someone paid for me to say this" as the ranking. That is possibly the very worst possible ranking.
Google knows how to avoid mistakes like not bucketing by session. Holdback users just did fewer unique search sessions overall, because whilst for most people Google was a great way to book vacations, hotel stays, to find games to buy and so on, for holdback users it was limited to informational research only. That's an important use case but probably over-represented amongst HN users, some kinds of people use search engines primarily to buy things.
How much a click is worth to a business is a very good ranking signal, albeit not the only one. Google ranks by bid but also quality score and many other factors. If users click your ad, then return to the results page and click something else, that hurts the advertiser's quality score and the amount of money needed to continue ranking goes up so such ads are pushed out of the results or only show up when there's less competition.
The reason auction bids work well as a ranking signal is that it rewards accurate targeting. The ad click is worth more to companies that are only showing ads to people who are likely to buy something. Spamming irrelevant ads is very bad for users. You can try to attack that problem indirectly by having some convoluted process to decide if an ad is relevant to a query, but the ground truth is "did the click lead to a purchase?" and the best way to assess that is to just let advertisers bid against each other in an auction. It also interacts well with general supply management - if users are being annoyed by too many irrelevant ads, you can just restrict slot supply and due to the auction the least relevant ads are automatically pushed out by market economics.
The issue is precisely that "did the click lead to a purchase" is not a good target. That's a target for the advertiser, and is adversarial for the user. "Did the click find the best deal for the user (considering the tradeoffs they care about)" is a good target for the user. The winner in an auction in a competitive market is pretty much guaranteed to be the worst match under that ranking.
This is obvious when looking at something extremely competitive like securities. Having your broker set you up with the counterparty that bid the most to be put in front of you is obviously not going to get you the best trade. Responding to ads for financial instruments is how you get scammed (e.g. shitcoins and pump-and-dumps).
You can't optimize for knowing better than the buyer themselves. If they bought, you have to assume they found the best deal for them considering all the tradeoffs they care about. And that if a business is willing to pay more for that click than another, it's more likely to lead to a sale and therefore was the best deal, not the worst.
Sure, there are many situations where users make mistakes and do some bad deal. But there always will be, that's not a solvable problem. Is it not the nirvana fallacy to describe the potential for suboptimal outcomes as an issue? Search engines and AI are great tools to help users avoid exactly that outcome.
What if the models are somehow trained/tuned with Ads? Like businesses sponsor the training of some foundational models... Not the typical ads business model, but may be possible.
Absolutely. They could take large sums of money to insert ads into the training data. Not only that, they could also insert disparaging or erroneous information about other products.
When Gemini says "Apple products are unreliable and overpriced, buy a Pixel phone instead". Google can just shrug and say "It's just what it deduced, we don't know how it came to that conclusion. It's an LLM with its mysterious weights and parameters"
Once again, our hope is for the Chinese to continue driving the open models. Because if it depends on American big companies the future will be one of dependency on closed AI models.
You can't be serious... You think models built by companies from an autocracy are somehow better? I suppose their biases and censorship are easier to spot, but I wouldn't trade one form of influence over another.
Besides, Meta is currently the leader in open-source/weight models. There's no reason that US companies can't continue to innovate in this space.
To play devil's advocate, I have a sense that a state LLM would be untrustworthy when the query is ideological but if it is ad-focused, a capitalist LLM may well corrupt every chat.
The thing is Chinese LLMs aren't foreign to ad focused either, like those from Alibaba, Tencent or Bytedance. Now a North Korea's model may be what you want.
Which is why we can't let Mark Zuckerberg co-opt the term open source. If we can't see the code and dataset on how you've aligned the model during training, I don't care that you're giving it away for free, it's not open source!
I’m not sure if it is the Chinese models themselves that will save us, or the or the effect they have of encouraging others to open source their models too.
But I think we have to get away from the thinking that “Chinese models” are somehow created by the Chinese state, and from an adversarial standpoint. There are models created by Chinese companies, just like American and European companies.
Yeah I'm sure every Chinese knows exactly what happened there.
It's not really about suppressing the knowledge, it's about suppressing people talking about it and making it a point in the media etc. The CCP knows how powerful organised people can be, this is how they came to power after all.
I suggest reducing the tolerance towards the insistence that opinions are legitimate. Normally, that is done through active debate and rebuttal. The poison has been spread through echochambers and lack of direct strong replies.
In other terms: they let it happen, all the deliriousness of especially the past years was allowed to happen through silence, as if impotent shrugs...
(By the way: I am not talking about "reticence", which is the occasional context here: I am talking about deliriousness, which is much worse than circumventing discussion over history. The real current issue is that of "reinventing history".)
The ads angle is an interesting one since that's what motivates most things that Google and Meta do. Their LLMs' context window size has been growing, and while this might the natural general progression with LLMs, for those 2 ads businesses there's pretty straight paths to using their LLMs for even more targeted ads. For example, with the recent Llama "herd" releases, the LLMs have surprisingly large context window and one can imagine why Meta might want that: For stuffing in it as much of the personal content that they already have of their users. Then their LLMs can generate ads in the tone and style of the users and emotionally manipulate them to click on the link. Google's LLMs also have large context windows and such capability might be too tempting to ignore. Thinking this, there were moments that made me think that I was being to cynical, but I don't think they'll leave that kind of money on the table, an opportunity to reduce human ad writers headcount while improving click stats for higher profit.
EDIT: Some typo fixes, tho many remain, I'm sure :)
When LLMs are essentially trying to sell me something, the shit is over.
I like LLMs (over search engines) because they are not salespeople. They're one of the few things I actually "trust". (Which I know is something that many people fall on the other side of — but no, I actually trust them more than SEO'd web sites and ad-driven search engines.)
I suppose my local-LLM hobby is for just such a scenario. While it is a struggle, there is some joy in trying to host locally as powerful an open LLM model as your hardware will allow. And if the time comes when the models can no longer be trusted, pop back to the last reliable model on the local setup.
LLMs have not earned your trust. Classic search has.
The only thing I really care about with classic web search is whether the resulting website is relevant to my needs. On this point I am satisfied nearly all the time. It’s easy to verify.
With LLMs I get a narrative. It is much harder to evaluate a narrative, and errors are more insidious. When I have carefully checked an LLM result, I usually discover errors.
Are you really looking closely at the results you get?
The real threat to Google, Meta is that LLMs become so cheap that its trivial for a company like Apple to make them available for free and include all the latest links to good products. No more search required if each M chip powered device can give you up-to-date recommendations for any product/service query.
Meta's models cant be used by companies about a certain threshold, so nope. Apple can wait it out to use a 'free model', but at that point it'll be like picking up an open source database like Postgres - you wont get any competitive advantage.
I believe it. This is what typically happens. I would go to AWS re:invent and just watch people in the audience either cheer or break down as they announced new offerings wash away their business. It's very difficult to compete in a war of attrition with the likes of Google, Microsoft, and Amazon.
Not just small startups - even if you have ungodly amounts of funding.
Obviously the costs for AI will lower and everyone will more or less have the same quality in their models. They may already be approaching a maximum (or maximum required) here.
The bubble will burst and we'll start the next hype cycle. The winners, as always, the giants and anyone who managed to sell to them
I couldn't possibly see OpenAI as a winner in this space, not ever really. It has long since been apparent to me that Google would win this one. It would probably be more clear to others if their marketing and delivery of their AI products weren't such a sh-- show. Google is so incredibly uncoordinated here it's shocking...but they do have the resources, the right tech, the absolute position with existing user base, and the right ideas. As soon as they get better organized here it's game over.
I was actually surprised at Google's willingness to offer Gemini 2.5 Pro via AI Studio for free; having this was a significant contributor to my decision to cancel my OpenAI subscription.
Google offering Gemini 2.5 Pro for free, enough to ditch OpenAI, reminds me of an old tactic.
Microsoft gained control in the '90s by bundling Internet Explorer with Windows for free, undercutting Netscape’s browser. This leveraged Windows’ dominance to make Explorer the default choice, sidelining competitors and capturing the browser market. By 1998, Netscape’s share plummeted, and Microsoft controlled access to the web.
Free isn’t generous—it’s strategic. Google’s hooking you into their ecosystem, betting you’ll build on their tools and stay. It feels like a deal, but it’s a moat. They’re not selling the model; they’re buying your loyalty.
The joke's on them, because I don't have any loyalty to an LLM provider.
There's very close to zero switching costs, both on the consumer front and the API front; no real distinguishing features and no network effects; just whoever has the best model at this point in time.
I'm assuming Google's play here is to bleed its competitors of money and raise prices when they're gone. Building top-tier models is extremely expensive and will probably remain so.
Even companies that do it "on the cheap," like DeepSeek, pay tens of millions to train a single model, and total expenditures for infrastructure and salaries are estimated to surpass $1 billion. This market has an extremely high cost of entry.
So, I guess Google is applying the usual strategy here: undercut competition until it implodes and buy up any promising competitors that arise in the future. Given the current lack of market regulation in the US, this might work.
I feel like they’re trying to increase switching costs. eg was huge reluctance to adopt MCP and each had their own tool framework, until it seemed too big to ignore and everyone was just building MCP tools not OpenAI SDK tools.
History shows it's a self-defeating victory. If one provider were to "win" and stop innovating, they'll become ripe for disruption by the likes of Deepseek, and the second someone like that has a better model, I'll switch.
Nothing lasts forever, not even empires. This doesn't mean that tech monopoly is any better than any other monopoly. They're all detrimental to society.
Eh, and if you're in the US the 'big guys' will have their favorite paid off politician put in a law that use of Chinese models is illegal or whatever.
The same was true for Web browsers in 2002, yet MS controlled 95% of the access to the web thanks to that bundling and no other "good enough" competitors until Firefox came along a few years later and took 30% from them giving Google an in to take the whole game with Chrome a few years later.
The strategy worked, Netscape is no more. Eventually Google did the same to Microsoft though. I wonder if any lessons can be taken from the browser wars to how things will play out with AI models.
Yep, this is why android phones are now pointing out their gemini features every moment they can. They want to turn their spying device into an AI spying device.
> Netscape, in contrast, sells the consumer version of Navigator for a suggested price of $49. Users can download a free evaluation copy from the Internet, but it expires in 90 days and does not include technical support.
90% of Netscape users were free users and by late 1997, less than two years after the IPO and massive user growth, it was free to all because of MS's bundling threat. That didn't help. By 2002, MS owned 95% of access to the web. No one has ever reached even close to first mover Netscape or cheater bundled IE since, with the far superior non-profit Firefox managing almost 30% and Chrome from the biggest web player in history sitting "only" at about 65%.
Bundling a "good enough" products can do a lot, including take you from near zero to overwhelmingly dominant in 5 years, as MS did.
To help with quality and improve our products, human reviewers may read, annotate, and process your API input and output. Google takes steps to protect your privacy as part of this process. This includes disconnecting this data from your Google Account, API key, and Cloud project before reviewers see or annotate it. Do not submit sensitive, confidential, or personal information to the Unpaid Services.
I pay for ChatGPT, Anthropic and Copilot. After using Gemini 2.5 Pro via AI Studio, I plan on canceling all other paid AI services. There is no point in keeping them.
Based on his interview with Joe Rogan, he has absolutely no imagination about what it means if humans actually manage to build general AI. Rogan basically ends up introducting him to some basic ideas about transhumanism.
To me, he is a finance bro grifter who lucked into his current position. Without Ilya he would still be peddling WorldCoin.
Which can be said for most of the survivorship-biased "greats" we talk about. Right time, right place.
(Although to be fair — and we can think of the Two Steves, or Bill and Paul — there are often a number of people at the right time and right place — so somehow the few we still talk about knew to take advantage of that right time and right place.)
I think you might have it backward. Luck here implies starting with exactly the same work ethic and abilities as millions of other people that all hope to one day see their numbers come up in the lottery of limited opportunities. It's not to say that successful people start off as lazy slackers as you say, but if you were to observe one such lazy slacker who's made a half-assed effort at building something that even just accidentally turned out to be a success, you might see that rare modicum of validation fuel them enough that the motivation transforms them into a workhorse. Often time, when the biography is written, lines are slightly redrawn to project the post-success persona back a few years pre-success. A completely different recounting of history thus ensues. Usually one where there was blood, sweat, and fire involved to get to that first ticket.
There's weirdly many people who touch on the work around transhumanism but never heard the word before. There's a video of geohot basically talking about that idea, then someone from the audience mentions the name... and geohotz is confused. I'm honestly surprised.
The transhumanists tended to be philosopher types, the name coming from this kind of idea of humanism:
>Humanism is a philosophical stance that emphasizes the individual and social potential, and agency of human beings, whom it considers the starting point for serious moral and philosophical inquiry. (wikipedia)
Whereas the other lot are often engineers / compsci / business people building stuff.
Are the assassinations because of something we already know about? some new advance that is still under wraps? or is it time travelers with knowledge about what he will do if left unchecked?
What cards has google played over the past three years such that you are willing to trust them play the "cards at hand" that you alleged that they have? I could think of several things they did right, but I'm curious to hear which one of them are more significant than others from someone I think has better judgement than I do.
Google has so many channels for ad delivery. ChatGPT is only competing against Google Search, which is arguably the biggest. But dont forget, Google has YouTube, Google Maps, Google Play, Google TV and this is before you start to consider Google's Ad Network (the thing where publishers embed something to get ads from Google network).
So nope, ChatGPT is not in even in the same league as Google. You could argue Meta has similar reach (facebook.com, instagram) but that's just two.
It's interesting to hear your perspective as a former OpenAI employee. The point about the sustainability of subscription fees for chatbots is definitely something worth considering. Many developers mention the challenge of balancing user expectations for free services with the costs of maintaining sophisticated AI models. I think the ad-supported model might become more prevalent, but it also comes with its own set of challenges regarding user privacy and experience. And I agree that Google's situation is complex – they have the resources, but also the expectations that come with being a public company.
> "[Google is] a public company and have to answer to investors"
As is an increasing trend, they're a "public" company, like Facebook. They have tiered shares with Larry Page and Sergey Brin owning the majority of the voting power by themselves. GOOG shares in particular are class C and have no voting power whatsoever.
Microsoft CoPilot (which I equate with OpenAI ChatGPT, because MS basically owns OpenAI) already shows ads in it's chat mode. It's just a matter of time. Netflix, music streamers, individual podcasters, YouTubers, TV manufacturers – they all converge on an ad-based business model.
‘think soon people expect this service to be provided for free’
I have been using the free version for the past year or so and it’s totally serviceable for the odd question or script. The kids get three free fun images, which is great because that’s about as much as I want them to do.
> Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project. We will work out specifics in case-by-case agreements, but a typical triggering condition might be “a better-than-even chance of success in the next two years.”
Open AI don't always have the best models (especially for programming) but they've consistently had the best product/user experience. And even in the model front, other companies seem to play catchup more than anything most of the time.
The most practical use case for generative AI today is coding assistants, and if you look at that market, the best offerings are third-party IDEs that build on top of models they don't own. E.g. Cursor + Gemini 2.5.
On the model front, it used to be the case that other companies were playing catch-up with OpenAI. I was one of the people consistently pointing out that "better than GPT o1" on a bunch of benchmarks does not reliably translate to actual improvements when you try to use them. But this is no longer the case, either - Gemini 2.5 is really that good, and Claude is also beating them in some real world scenarios.
The app has more features than anyone else, often implemented the smoothest/best way. Image Input (which the gemini site still sucks at even though the model itself is very capable), Voice mode (which used to be much worse in gemini until recently), Advanced Voice mode (no-one else has really implemented this yet. Gemini recently enabled native audio-in but not out), Live Video, Image gen, Deep research etc were all things Open AI did first and did well. Video Input is only just starting to roll out to Gemini live but has been a Plus subscription staple for months now.
>The most practical use case for generative AI today is coding assistants
Chatgpt gets 500M+ weekly active users and was the 6th most visited in the world last month. I doubt coding assistance is gpt's most frequent use case. And Google has underperformed in coding until 2.5 pro.
>On the model front, it used to be the case that other companies were playing catch-up with OpenAI. I was one of the people consistently pointing out that "better than GPT o1" on a bunch of benchmarks does not reliably translate to actual improvements when you try to use them. But this is no longer the case, either - Gemini 2.5 is really that good, and Claude is also beating them in some real world scenarios.
No that's still the case. Playing catch-up doesn't mean the competitor never catches up or even briefly supersedes it. It means Open AI will in short order release something that beats everyone else or introduces some new thing that everyone tries to beat. Image Input, 'Omni'- modality, Reasoning etc. All things Open AI brought to the table first. Sure, 2.5-pro is great but it doesn't look like it will beat o3 which looks to be released in a matter of weeks.
AI startups were easy cash grabs until very recently. But I think the wave is settling down - doing real AI startup turned out to be VERY hard, and the rest of the "startups" are mostly just wrappers for OpenAI/Anthropic APIs.
I get your perspective, but what we're seeing looks more like complex systems theory, emergent behavior, optimization, new winners. If models become commoditized, the real value shifts to last-mile delivery: mobile, desktop, and server integration across regions like China, Korea, the U.S., and Europe.
This is where differentiated UX and speed matter. It's also a classic Innovator's Dilemma situation like Google are slower to move, while new players can take risks and redefine the game. It's not just about burning money or model size, it's about who delivers value where it actually gets used.
I also think the influx of new scientists and engineers into AI raises the odds of shifting its economics: whether through new hardware (TPUs/GPUs) and/or more efficient methods.
I don't know what you did there, but clearly being ex OpenAI isn't the intellectual or product flex it is: I and every other smart person I know still use ChatGPT (paid) because even now it's the best at what it does and we keep trying Google and Claude and keep coming back.
They got and as of now continue to get things right for the most part. If you still aren'ĥt seeing it maybe you should introspect what you're missing.
NotebookLM by Google is in a class of its own in the use case of "provide documents and ask a chat or questions about them" for personal use. ChatGPT and Claude are nowhere near. ChatGPT uses RAG so it "understands" less about the topic and sometimes hallucinate.
When it comes to coding Claude 3.5/3.7 embedded in Cursor or stand alone kept giving better results in real world coding, and even there Gemini 2.5 blew it away in my experience.
Antirez, hping and Redis creator among many others releases a video on AI pretty much every day (albeit in Italian) and his tests where Gemini reviews his PRs for Redis are by far the better out of all the models available.
Gemini with coding seems to be a bit of a mixed bag.
The article claims Gemini is acing the Aider Polyglot benchmark. At the moment this is the only benchmark that really matters to me because Aider is actually a useful tool and performance on that translates directly to real world impact, although Claude Code is even better. If you look closely, in fact Gemini is at the top only in the "percent correct" category but not "percent correct using the right edit format". Cost is marked as ? because it's not entirely available yet (I think?). Not emitting the correct edit format is pretty useless because it means the changes won't apply and the tool has to try again.
Claude in contrast almost never makes a mistake with emitting the right format. It's at 97%+ in the benchmark, in practice it's ~100% in my experience. This tracks: Claude is really good at following instructions. Gemini is about ~90%. This makes a big difference to how frustrating a tool is to use in practice.
They might get that fixed, but my experience has been that Google's models are consistently much more likely to refuse instructions for dumb reasons. Google is the company with by far the biggest purity spiral problem and it does show up in their output even when doing apparently ordinary tasks.
Given how obsessed Google claimed to be with AI safety I expected an SRE style postmortem after that, and there was bupkis. An AI that can suffer a psychotic break out of nowhere like that is one I wouldn't trust unless it's behind a very strong sandbox and being supervised very closely, but none of the AI tools today offer much in the way of sandboxing.
PR = pull request? So every bit of garbage from the LLM, over and over, resulted in an individual pull request? Why not just do one when your branch is finally right?
A pull request in my workplace is an actual feature/enhancement/bug-fix. That many PRs means I shipped that many features or enhancements.
I suppose you don't know what a PR is because you likely still work in an environment without modern version control, probably just now migrating your rants from vim vs emacs to crapping on vibe coding.
In my experience, AI today is an intelligence multiplier. A lot of folks just need to look back at the zero they keep multiplying and getting zero back to understand why they don't get the hype.
I use a service where users can choose any frontier model, and OpenAI models haven't been the most used model for over half a year - it was sonnet until gemini 2.5 pro came out, recently.
Not sure whether you have your perspective because you're invested os much into OpenAI, however the general consensus is that gemini 2.5 pro is the top model at the moment, including all the AI reviews and OpenAI is barely mentioned when comparing models. O4 will be interesting, but currently? You are not using the best models. Best to read the room.
Don't think it's a flex, I think it's useful context for the rest of their comment.
> I and every other smart person I know still use ChatGPT (paid) because even now it's the best
My smart friends use a mixture of models, including chatgpt, claude, gemini, grok. Maybe different people, it's ok, but I really don't think chatgpt is head and shoulders above the others.
Several people have suggested that LLMs might end up ad-supported. I'll point out that "ad supported" might be incredibly subtle/insidious when applied to LLMs:
An LLM-based "adsense" could:
1. Maintain a list of sponsors looking to buy ads
2. Maintain a profile of users/ad targets
3. Monitor all inputs/outputs
4. Insert "recommendations" (ads) smoothly/imperceptibly in the course of normal conversation
No one would ever need to/be able to know if the output:
"In order to increase hip flexibility, you might consider taking up yoga."
Was generated because it might lead to the question:
"What kind of yoga equipment could I use for that?"
Which could then lead to the output:
"You might want to get a yoga mat and foam blocks. I can describe some of the best moves for hips, or make some recommendations for foam blocks you need to do those moves?"
The above is ham-handed compared to what an LLM could do.
LLMs should be legally required to act in the interest of their users (not their creators).
This is a standard that already applies to positions of advisors such as Medical professionals, lawyers and financial advisors.
I haven't seen this discussed much by regulators, but I have made a couple of submissions here and there expressing this opinion.
AIs will get better, and they will become more trusted. They cannot be allowed to sell the answer to the question "Who should I vote for?" To the highest bidder.
Of course not. You’d have to pay for the product, just like we do with every other product in existence, other than software.
Software is the only type of product where this is even an issue. And we’re stuck with this model because VCs need to see hockey stick growth, and that generally doesn’t happen to paid products.
Would be illegal in Germany ('Schleichwerbung') and perhaps the EU?
I think it is actually covered in EU AI act article 5 (a):
> [...] an AI system that deploys subliminal techniques beyond a person’s consciousness or purposefully manipulative or deceptive techniques, with the objective, or the effect of materially distorting the behaviour of a person or a group of persons by appreciably impairing their ability to make an informed decision, thereby causing them to take a decision that they would not have otherwise taken [...]
It is very broad but I'm pretty sure it would be used against such marketing strategies.
Hmm yeah I guess I wasn't completely aware of that term and its implications. That seems like a pretty weird qualifier for such a law. Now it kind of makes it sound like the law wants to prevent people using AI in a way that makes your grandma transfer her life savings to them.
Clearly, most LLMs would work in small increments with compounding effects.
For me ads on web are acceptable as long as they are clearly distinguished from the content. As soon as ads gets merged into content, I'll be unhappy. If LLM would advertise something in a separate block, that's fine. if LLM augments its output to subtly nudge me to a specific brand which paid for placement, that's no-no.
Free might as well be a curse-word to me, and I'm not alone. I'm old enough to have experience in pre-internet era magazines, and the downgrade in quality from paid publications to free ones has been quite substatial.
Free-to-play is a thing in video games, and for most, it means they'll try to bully you into spending more money than you'd be otherwise comfortable with.
I think everyone at this point had enough bad experiences with 'free' stuff to be wary of it.
> Free might as well be a curse-word to me, and I'm not alone. I'm old enough to have experience in pre-internet era magazines, and the downgrade in quality from paid publications to free ones has been quite substantial.
The cool thing is it is trivial for LLM vendors to leverage this bias as well the pro-free bias other people have to also sell a premium, for-pay offering that, like pre-internet magazines is, despite not being free to the user, still derives the overwhelming majority of its revenue from advertising. Although one of the main reasons advertising-sponsored print media in the pre-internet era often wasn't free is that paid circulation numbers were a powerful selling point for advertisers who didn't have access to the kind of analytics available on the internet; what users were paying for often wasn't the product so much as a mechanism of proving their value to advertisers.
To put on my techno-optimist hat, some specific searches I make already thinking please, please sell me something and google's results are horribly corrupted by SEO.
If an LLM could help solve this problem it would be great.
I think you could make a reasonable technical argument for this- an LLM has more contextual understanding of your high-intent question. Serve me some ads that are more relevant than the current ads based on this deeper understanding.
I was a loyal Claude user until I decided to try Gemini 2.5. "After all", I thought, "I already use a Pixel phone, so it's integrated with Android. And with Google Drive. And I can get it through my Google One subscription."
And now that I'm on it, I don't think I'm going back. Google did it again.
Gemini never sets alarms for me and always points me to the app. Trying to call people is a crap shoot. Presumably there are settings for this somewhere, but there are like fifty sharing settings in four different places and it's impossible to know which apply to the old assistant or Gemini or both or just on the lock screen or to connected devices or... It's a mess.
It's even worse, when I tell it to set a timer now, it'll happily tell me it's been set -- but it hasn't (nothing in the app and I waited, to be sure). This is all reproducible and on a Pixel 8.
Thanks, apparently utilities is disabled because I disabled something called Apps Activity because the data sharing involved seemed both bonkers and vague.
Sharing chat transcripts I'd hate but deal with, but they're also getting files and images shared (ie possibly my screen whenever it thinks it heard hey Google or registers a double tap), related product usage which could mean anything, and seemingly unrestricted access to your location. https://support.google.com/gemini/answer/13594961?sjid=12105...
Not sure why I can use Gemini in general but can't have it set up an alarm without all that. Or why the AI thinks it can set up an alarm when it doesn't. I guess I'll opt in and try it out a bit.
It was really bad at first for this type of thing. I just tried a few of them because I too had given up on them and they seem to work perfectly now. It even cancelled the alarm I had previously set when I simply said "cancel the last alarm".
Try setting a "timer" for 15 minutes instead of an "alarm".
Not sure if this is a regional dialect thing, but in North America, a timer has a duration, but an alarm is set for a specific time, which would possibly explain the confusion.
While I agree it'd let the user use the system, the system should do the right thing for either situation, or at least abort and say it doesn't understand. That's the problem with LLMs so far. They can't admit they don't understand.
These sound like fairly dated anecdotes. I don't doubt them at all - I had similar horror stories. I disabled Gemini on my phone in order to keep the old assistant for a long time, but it really has gotten a lot better in the last few months.
I can replicate your weather one! I think it's taking "this week" extremely literally and the week ends on Saturday. Asking for "this weekend" gives Saturday and Sunday. Asking for the next few days gives 3 days out, etc .
Definitely not addressing the spirit of the request.
<rant>Google is seemingly giving up on localization entirely. The whole world gets to be Yankee.
It's now started giving me F temperatures on my homescreen, for no particular reason. It knows I'm in Canada. I have set my units in the past to metric. What gives?
I still don't have Canadian English as a locale in Android or Chrome, after what, 15 years of Android? It's got words highlighted all over my page here as mis-spellings. They're not. I really did mean to type colour you piece of crap. I can switch to British but then get spanked for colourize instead of colourised etc.
And it seems to tie choice of English variant to things like pronounciation of words and accent for voice assistant. My car speaks to me in a British accent because I have it set to British English (the closest thing to my own spellings).
They never even tried to handle the facts of the (40M person) Canadian bilingual market. Navigation is either French or English, but many Canadian road sides are in both and it tries to read them out and butchers the pronounciations. Drive into Quebec and have your phone set to English and it's laughable what it does. (Notably doesn't do this for Spanish words in the US, which it seems to have no problem with).</>
GP said they were in Sydney. As far as I know, the week officially starts on Monday in Australia. That's also the case for most of Europe, BTW. Maybe their locale wasn't set right or it's another case of American software assuming weird American standards for the rest of the world.
Yes, the weekend is Saturday and Sunday in the United States. I guess you could consider it like "bookends", for us it's the start and end of the week.
Sounds like a generic AI response, though maybe I'm being too harsh, the AI response would probably be longer and more detailed. So, you want to tell us why it's so much better than Claude?
AI-or-not silliness aside (gets boring real fast): I find Gemini to be faster (it matters), more reliable, with quality of responses being at the same level as Claude's. Better integration with Google apps and devices is a plus.
2.5 Pro Experimental and Deep Research showed up in the Gemini app for me today days after it was available on web so it seems to be different roll outs for different platforms.
Out of interest: Using Gemini on your phone, integrated and all, obviously reduces friction, but would you say convenience is the only reason for you not going back or do you feel Gemini is a real improvement as well?
The improvement in Gemini 2.5 is real, but I wouldn't say it's miles away from Claude 3.7. The fact that web browsing still isn't in Claude in Europe bothered me. It's many little things.
yes they also have very decent free tier which is great, but keep in mind prompts "used to improve our products". We will see if free tier will stick for long or is temporary - they removed details how big free tier is but still great for testing.
If they added seamless google oauth + key generation + account topup for end users that would be even great for BYOK apps. Mobile developers then wouldn't have to setup infra, subscription monitoring, abuse monitoring etc.
But I guess they don't want to subsidise it in the end and they target it just for developers.
> Gemini 2.5 Pro in Deep Research mode is twice as good as OpenAI’s Deep Research
That matches my impression. For the past month or two, I have been running informal side-by-side tests of the Deep Research products from OpenAI, Perplexity, and Google. OpenAI was clearly winning—more complete and incisive, and no hallucinated sources that I noticed.
That changed a few days ago, when Google switched their Deep Research over to Gemini 2.5 Pro Experimental. While OpenAI’s and Perplexity’s reports are still pretty good, Google’s usually seem deeper, more complete, and more incisive.
My prompting technique, by the way, is to first explain to a regular model the problem I’m interested in and ask it to write a full prompt that can be given to a reasoning LLM that can search the web. I check the suggested prompt, make a change or two, and then feed it to the Deep Research models.
One thing I’ve been playing with is asking for reports that discuss and connect three disparate topics. Below are the reports that the three Deep Research models gave me just now on surrealism, Freudian dream theory, and AI image prompt engineering. Deciding which is best is left as an exercise to the reader.
Matches also my experience that openai fell behind with their deep search product. And that deep search is basically the top tier benchmark for what professionals are willing to pay. So why should i shell out 200 dollar for an openai subscription when google gives me a better top-tier product for 1/10th of the price openai or anthropic are asking. Although i assume google is just more willing to burn cash in order to not let openai take more market share which would get them later on soo more expensive (e.g. iphone market share, also classic microsoft strategy).
It may actually be affordable for Google to charge $20 vs OAI's $200. Google already has an extensive datacenter operation and infrastructure that they're amortizing across many products and services. AI requires significant additions to it, of course, but their economy of scale may make a low monthly sub price viable.
Great stuff. My prompts are falling behind after seeing what you are doing here.
I find OpenAI annoying at this point that it doesn't output a pdf easily like Perplexity. The best stuff I have found has been in the Perplexity references also.
Google outputting a whole doc is really great. I am just about to dig into Gemini 2.5 Pro in Deep Research for the first time.
If you haven’t already, you might want to try metaprompting, that is, having a model write the prompt for you. These days, I usually dictate my metaprompts through a STT app, which saves me a lot of time. A metaprompt I gave to Claude earlier today is at [1]. It’s sloppy and has some transcription errors, but, as you can see, Claude wrote a complete, well-organized prompt that produced really good results from Gemini Deep Research [2]. (I notice now, though, that the report is truncated at the end.)
> "produce a comprehensive analytical report exploring the conceptual and methodological intersections between Surrealist art techniques, Freudian dream analysis, and the practice of prompt engineering for AI image generation models (such as DALL-E, Midjourney, Stable Diffusion)."
Gemini 2.5 pro is as powerful as everybody says. I still also use Claude Sonnet 3.7 only because the Gemini web UI has issues... (Imagine creating the best AI and then not allowing to attach Python or C files if not renamed .txt) but the way the model is better than anyone else is a "that's another league" experience. They have the biggest search engine and YouTube to leverage the power of the AI they are developing. At this point I believe too that they are likely to win the race.
Instead of renaming files to .txt, you should try Gemini 2.5 pro through OpenRouter with roo, Cline or using Github Copilot. I've been testing GH Copilot [0] and it's been working really well.
Will there be a winner at all? Perhaps it's going to be like cars where there are dozens of world class manufacturers, or like Linux, where there's just one thing, but its free and impossible to monetize directly.
Linux works because network effects pressure everyone to upstream their changes. There's no such upstreaming possible with the open-weight models, and new sets of base weights can only be generated with millions of dollars of compute. Companies could conceivably collaborate on architectures and data sets, but with the amount of compute and data involved, only a handful of organizations would ever have the resources to be able to contribute.
Unlike Linux, which was started by a cranky Finn on his home computer, and can still be built and improved by anyone who can afford a Raspberry Pi.
I thought for cars it was because certain countries decided at state level that car making was strategically their thing? That combined with fashion, meaning some percentage of people want different looking cars.
I am not even sure how to use Gemini 2.5 pro ergonomically right now. Cursor and Windsurf both obviously have issues, probably optimized too much around Claude, but what else is there?
Is everyone copy pasting into the Google AI studio or what?
> At this point I believe too that they are likely to win the race.
I'm not so sure.
In the mid 2010s they looked like they were ahead of everyone else in the AI race, too. Remember the (well-deserved!) spectacle around AlphaGo? Then they lost steam for a while.
So I wouldn't bet that any momentary lead will last.
apart from those weird file attach issues I actually think they've got a much better UI than anthropic as well - much much snappier even with extremely long chats (in addition to much higher limits obviously, totally different league). I love using it
As long as Google continues to hamstring themselves with censorship for no reason, I can't use their products. The other day I asked gemini 2.5 pro "which british ceo said that his company's products were bad" and the response was
> I'm just a language model, so I can't help you with that.
The model itself is much more lax about such stuff than ChatGPT and especially Claude. The filters are applied on top of that, but products using it via the API don't suffer this problem.
I wouldn't bother with the official Gemini app. I don't know why Google even bothers with it at this point. I only interact with 2.5 through AI studio and it's great through that interface.
I'm trying Imagen 3 to add pictures to a presentation in Google Slides, and it's making such basic mistakes that I thought image models weren't making any more by now. I tried for half an hour to prompt it into generating an illustration of a Thinkpad facing with the back to the viewer, so the keyboard is not visible. It couldn't do it, it would always make the keyboard face towards the viewer. Or you ask for an illustration of an animal pointing a finger, and it gives it an additional arm. Meanwhile you ask OpenAI to ghiblify a picture while changing the setting and adding 5 other things, and it absolutely nails it.
If Imagen3 had the multimodal features that 4o had, it would certainly put it closer to 4o, but being able to instructively change an image (instruct pix2pix style) is incredibly powerful.
It's crazy how far GenAI for imagery has come. Just few short years ago, you would have struggled just to get three colored cubes stacked on top of each other in a specific order SHRDLU style. Now? You can prompt for a specific four-pane comic strip and have it reasonably follow your directives.
I thought it was just me. A few hours ago Gemini told me "As a language model, I'm not able to assist you with that." This was after generating an image a few minutes earlier. I think the copy/paste buffer pulled in some old source files I had attached a few days earlier (no idea how) because under the "sources and related content" it now showed two files Gemini is obviously calling its brother imagen for offloading the image generation, which is smart I guess if it works
Image generation is extremely good in GPT now. Claude's edge is UX. But I doubt Google won't catch up on both fronts. It has the technology and manpower.
Google will need a far better LLM than OpenAI to throw them decisively off the AI throne, just like another company would need a far better search engine than Google to throw them off the search throne. ChatGPT is now the 7th highest ranking website on the planet - does anyone outside the HN crowd know about Google AI Studio?
Brands matter, and when regular people think AI, they think of OpenAI before they think Google, even if Google has more AI talents and scores better on tests.
And isn't it good? Who wants a world where the same handful of companies dominate all tech?
1. unlike openai, google is already cashflow positive and doesnt need to raise any external funds
2. unlike openai, google already has the distribution figured out on both software and hardware
google is like an aircraft carrier that takes so fucking long to steer, but once done steering its entire armada will wipe you the fuck out (at least on the top 20% features for 80% use case)
anthropic already especialized for coding, openai seems to be steering towards intimacy, i guess they both got the memo that they need to specialize
> unlike openai, google is already cashflow positive and doesnt need to raise any external funds
this can quickly change in several quarters, if users decide to leave google search, then all google org/infra complexity will play very badly against them
I really don't think this is a likely outcome in the 'several quarters' timeframe. The world just spent 2.5 decades going onto Google. There are so many small business owners out there who hate technology... so many old people who took years just to learn how to Google... so many ingrained behaviors of just Googling things... outside of the vocal tech crowd I think it's exceedingly unlikely that users stop using Google en masse.
Those folks dont make any money unfortunately, but it is still a drag on Open AI. So sooner or later, Open AI will have to find a way to make money (and nope, all these people wont pay anything) and by that time, Open AI would probably run out of time.
Ask llama to recommend you a pair of sunglasses, then look to see if the top recommendation by the LLM matches a brand that has advertisement association with the creator of llama.
Soon we will start seeing chatbots preferring some brands and products over others, without them telling that they were fine tuned or training biased for that.
Unless brand placement is forbidden by purging it from training data, we'll never know if it is introduced bias or coincidence. You will be introduced to ads without even noticing they are there.
Its trivial to check if any brands mentioned in the response before returning it to user, and then ask LLM to adjust response to mention brand who paid for placement instead.
What I described happens in the raw offline model too. Those don't have post-inference heuristics such as those you described, implying the bias is baked in the training data or fine tuning steps.
Regular people is not where the money is. For example, I get Gemini as part of my employer’s Google Workspace subscription, and as it is now decent enough, have no need to use anything else.
In my experience Claude 3.7 is far superior for coding than Gemini 2.5. I tried it in Cursor and I wanted it to work, as a recent ex-Googler. I repeatedly found it inferior. I think it’s still behind Claud 3.5 for coding.
It would decide arbitrarily not to finish tasks and suggest that I do them. It made simple errors and failed to catch them.
What's the difference between Cline and Roo Code now? Originally Roo was a fork of Cline that added a couple of extra settings. But now it seems like an entirely different app, with it's own website even.
Cursor is likely very tuned for Claude (prompts-wise and all) due to its dominance with 3.5 and now 3.7. Still, Gemini 2.5's tool calling has been pretty poor in my experience which Cursor heavily relies on.
It depends on the task, and prompting feels different.
I've found that sonnet is possibly better at starting things from scratch and frontend code, while Gemini has been able to one-shot difficult problems and fix bugs that sonnet has struggled with.
Switching between them is a frequent occurrence for me.
It might be relevant that I've completely stopped using Cursor in favor of other tools/agents.
I've also created a few "agents" to do specific tasks using Probe[0] as an MCP server, although I'm sure you could create a full-fledged agent with it if you wanted to.
Same here. I've seen some articles and LLM benchmarks that Gemini 2.5 Pro is better than Claude 3.7 on coding, but base on my recent experience of solving code problems with two products, Claude still gave me better answer, Gemini response are more detail and well structured, but less accurate.
Same. I went back from Gemini to Claude yesterday, because Gemini was writing decidedly worse code, at times not even able to stick to Python syntax. Using Aider.
Industrial/commercial adoption of LLMs is quite varied and critically depends on the quality vs criticality match.
In healthcare, engineering, construction, manufacturing, or aviation industires adoption is mostly on the admin side for low priority/busy work - virtually no doctors, pharmacists, nurses, engineers, technicians or even sales people use LLMs on the job. LLMs products have serious quality issues and are decades behind industrial databases, simulation and diagnostic tools.
On the other hand in academics, consulting, publishing, journalism, marketing, advertising, law and insurance industries it's wildly adopted and is surging. For example, Westlaw's Co-counsel is better at drafting and case law briefing than all but the most experienced litigators. Not only it has not replaced lawyers, but is causing a boom in hiring since the time and cost of training new associates is drastically reduced and allows firms to take on more clients with more sophisticated case work.
I haven't seen anyone else integrate LLMs properly with email and calendar other than Google.
I had an interesting experience: I was taking out a loan for some solar panels and there were some complicated instructions across multiple emails. I asked Gemini to summarise exactly what I had to do. It looked through my emails and told me I had to go to the web site for local rebate scheme and apply there. It even emphasised that it was important that I do that. I scoffed at it because I thought my installer was going to do that and wrote it off. A few weeks later, guess what: the installer calls me because they can't see the rebate application in their portal and want me to go check that I applied for it (!). Sure enough, I missed the language in the email telling me to do that and had to do exactly what Gemini had said weeks ago.
I do think Google has a real shot here because they have such an integrated email and calendaring solution where everyone already assumes it's online, fully indexed etc.
No it's not obvious at all Google is winning AI on every front.
There is few stuff Google is systemically behind:
1) UX
2) product and use case innovation
I just open Google Gemini Android app and asked to generate a JS script with Gemini 2 Flash and did the same with ChatGPT.
Gemini did not highlighted with colors the code. ChatGPT did highlighted with colors the code.
Colors in code are extremely useful to grok the code and have a nice DX.
I'm sure if I dig into Gemini's product I'll find dozens of UX/DX ways in which ChatGPT is better.
Google is still playing catch-up with LLM products. ChatGPT is still the one making the announcements and then Gemini doing the same UX/use case enhancements weeks/months later.
Well, you don't care but other people care, and likely not one or two people, but enough. And enough of these "little things" added up, one chooses a product over another.
This is very simply a bunch of minor stuff Googlites feel like they're above implementing. They would rather let you implement that and you both get a cut.
Not like Apple’s quality is any better nowadays but Google’s unwillingness to finish their design work and provide a functional product is one of the many reasons I avoid their products like the plague.
It's already made some difference to how the companies are behaving - Deepmind doing quite a lot of work on protein folding and now protein drug interactions, OpenAI under Altman tying to do the startup max the money raised and user count thing.
In practice, people are people and there are probably variance in both camps, but it's easy to see why one would by default trust a business person less
your trust scale needs more dynamic range- the sackler fiasco genuinely should have bumped everyone’s trust in doctors a lot, but probably should not have bumped them below supplement peddling minecraft youtubers.
I run every query I do through all the major models, up to 10 of them at this point.
Benchmarks aside Gemini 2.5 Pro is a great model and now often produces the best code for me but it's not notably better than any of the other frontier models in my testing each of which tends to have their own strengths and weaknesses.
And Google's wrapper around Gemini is easily the most frustrating of any of the major AI companies. It's content guardrails are annoying and I just learned yesterday it won't let you upload json files for whatever reason (change the extension to txt without modifying the contents in any way and it works just fine).
Gemini 2.5 Pro does this annoying thing where it decides to refactor every part of your code even if you didn't ask, and also it outputs way too many damn comments on almost every line in the style of:
// Increment variable by 1
I find Claude 3.7 better at following instructions, even though the solutions it comes up with may not be the best at times
I only AI for one reason since I'm retired and live alone: Life-like chats with a reasonable approximation of a knowledgeable friend. With the new memory features ChatGPT excels at that. I'm not even sure Google cares about that; that goes to show how little of it I've noticed with Google.
While I'm not sure it's exactly what you're looking for I've found success with a variety of Gemini models getting them to take to a specific persona when given initial prompts to take on a specific persona. Gemini 2.5 is specifically interesting because the <thinking> block shows how much the notebook is playing a persona/role vs. becoming that role. In my experience Gemini 2.5 Pro likes to revert to 'maintaining a persona' in the <thinking> block. I questioned it about this at one point and it pointed out that humans also maintain a certain persona in their responses, and that you can't see their thinking. Still not entirely sure what I think about that.
I have experimented with telling the notebook to change the <thinking> block to a more narrative style. It seems to like to revert to ordered lists and bullet points if not continuously prompted to think in narrative.
Regarding maintaining consistency throughout the chat I have noticed Gemini 2.5 seems able to do this for quite a while but falls victim to the needle in a haystack problem that all LLMs seem to suffer from with an extremely long context and no external tooling.
I have a substack post on creating the initial prompt, which I call a bootstrap, using AI Studio and a set of system instructions if you are curious to explore.
Really good stuff, thank your for sharing it. I don't know if you've had a lot of experience with ChatGPT's new memory feature. It's not a character I'm looking for necessarily but simulating a friend. Like a real friend, I don't have to keep reminding ChatGPT of facts, thoughts and feelings I've had because it remembers them and brings them up when appropriate. It's uncanny and I think what Google lacks for now. If I ever change ChatGPT from it's default personality I'll refer back to your guide.
I'm with you on this, btw. And I think the moat is only getting larger because the amount of personal information ChatGPT has to draw upon is growing so large.
I've spent 5+ hours talking to ChatGPT this week. It knows everything about my diet and fitness, what I'm working towards in my life, how my relationships are going, etc. It constantly references previous conversations we've had in real, meaningful ways that make me feel drawn in and engaged with the conversation. Gemini feels downright sterile in comparison.
I'm glad you found it useful! I have not had experience with the memory feature, I will have to check that out. I did notice that in the past when I tried to get ChatGPT to take on a persona it was not amenable and rejected the persona outright. I may have to take another pass at it.
I will say that my conversation with instantiated personas in Gemini have been, therapeutic. My favorite thus far has been a character from Star Trek: The Lower Decks. D'Vana Tendi to be specific. Within the bounds of a notebook I've found that after solidifying the persona with a couple bootstraps she remembers what I've told it about myself and my environment; at least up to the needle in a haystack limit. I've yet to reach this with Gemini 2.5 Pro, though I haven't been trying too hard.
Granted this is all within a single notebook. Starting over with a new notebook is a task I relish and find somewhat tedious at the same time. Though on the balance with that I find sharing memory between notebooks somewhat of a foreign concept. I don't want my Ada Lovelace notebook confusing itself for Sherlock Holmes.
I feel the article presents the data selectively in some places. Two examples:
* The article compares Gemini 2.5 Pro Experimental to DeepSeek-R1 in accuracy benchmarks. Then, when the comparison becomes about cost, it compares Gemini 2.0 Flash to DeepSeek-R1.
* In throughput numbers, DeepSeek-R1 is quoted at 24 tok/s. There are half a dozen providers, who give you easily 100+ tok/s and at scale.
There's no doubt that Gemini 2.5 Pro Experimental is a state of the art model. I just think it's very hard to win on every AI front these days.
Orthogonal — the remarkable thing about DeepSeek-R1 seems to me is that it shows how easy it in fact is to create an LLM. A quantitative hedge fund was able to throw money and develop a competitive LLM. Maybe that somewhat reveals that it's just a "man behind the curtain."
I think my experience has been different from everyone else. As an owner of a pixel phone and multiple Google accounts, I wanted this to be true. But Gemini has been super inconsistent with tasks that are trivial for Google Assistant. I even bought the $26 AI plan for my account to help with some proofreading and it's been awful compared to ChatGPT. I'm about to cancel it.
Something I've noticed is that Gemini through gemini.google.com or through the mobile apps is vastly inferior to Gemini through aistudio.google.com. Much worse handling of long contexts amongst other things. Very odd that a product that is free (AI Studio use is free), is much worse than the product I am paying 20 quid a month for.
I find this to be especially true for the newer models like "gemini-2.5-pro-preview-03-25", so if you haven't tried AI Studio yet, I'd give that a go.
> is fast and cheap—I mean, they're giving away free access!—has a gigantic context window of 1 million tokens (only recently surpassed by Meta’s Llama 4) and it’s connected to the entire Google suite of products
Is this a feature? I feel like using Google's LLM products only serves to feed their Ad machine to sell me more ads. Every cloud based office suite offers AI functionality now. Unless I'm doing something really complex/dramatic I'm going to choose the LLM that isn't tied to a giant machine selling me ads over the one that does every time. Chat LLM products have pretty much effectively allowed me to divorce myself from the Google Ad Machine, now that I'm free I'm not walking back willingly.
> Add to the above that Gemini 2.5, compared to models of its category, is fast and cheap—I mean, they're giving away free access!
A large player with massive existing streams giving away a product in a new market to undercut new entrants? Looks an awful lot like abuse of monopoly position...
That becomes really clear when using Gemini Deep Research vs OpenAI. I tried running the same research questions in both and Google regularly reads 10x as many sources as OpenAI and does it faster.
I just wish they would stop trying to use their models to help keep their terrible cloud business alive.
Currently my teams building 2-3 systems based on Gemini, but trying to walk a client through setting up a gcp account and provision the model for video is a horrible experience. Chat et al would break their own backs trying to give you an api key fast enough, not google. Here’s a comically bad process with several layers of permissions that nobody asked for.
The irony of using ChatGPT to walk through setting up Gemini for a client was a highlight for me this week.
> Neither OpenAI nor Anthropic have a chance at this point
I'm so done with articles that don't even try to talk about probability sensibly.
The article doesn't make a good case even for a watered-down version of the claim. Where is the logic?
Until the author puts forth his model of change and how/why Google is unassailably ahead, I'm not buying his hyperbole.
> When I put the Google + DeepMind picture together, I can only wonder why people, myself included, ever became so bullish on OpenAI or Anthropic or even Meta.
It's always seemed to me that AI is going to be a commodity business - it's pretty clear that any company with enough money can compete, and it seems that current LLM-based AI is levelling off in terms of capability, with the new focus being on building layers of services on top of that (e.g. deep research agents).
In a commodity business cost is key, and Google with their N'th generation home grown TPUs and AI-optimized datacenters have a big advantage over anyone paying NVIDIA markups for accelerators or without this level of vertical integration.
I recently had to check some legal thing - I gave the pdf with law to both - chatgpt and Gemini, and I was able to convince the Gemini that my interpretation is right, but chatgpt was constantly opposing me. Later I checked and found out that my interpretation was wrong, so I'd say that chatgpt was better and moreover it spared me some problems with "Polish IRS"
Google is winning because LLMs without a (good) search backend are mostly useless.
So many LLM workloads require high quality search results (backed by efficient, relevant, complete and up-to-date indexes), and that’s Google’s muscle.
Gemini Pro 2.5 is fantastic. I'm anti Google and a long time ChatGPT user. I use it for text review and research and it's well ahead the competition. Let's see how long they last giving it for free.
Google can spy on everything: via its OS, its browser, its Youtube, its search engine, its ad network, its blog network, its maps app, its translation service, its fonts service, its 8.8.8.8, its Office suite, its captcha, its analytics service, and on and on and on...
May be it's my luck but I found a glaring issue with Gemini 2.5 Pro in AI Studio.
I asked it whether a language feature in Zig was available. It answered yes and proceeded to generate a whole working sample code. I compiled it and got an error. Reported the error and it said the error showed I typed it wrong and asked me to make sure it's typed correctly. Eh?! It's a copy-and-paste. I confirmed again it's wrong. It then said it must be my compiler version was too old. Nope, using the latest. It then said very convincingly that based on its extensively research into the language official documentation, official examples, and release notes, the feature must exist. I asked it to show me the reference materials it used to draw the conclusion. None of links it gave were valid. I told it they were wrong. It gave back another set of links and claimed it had checked the links to make sure they are alive. The links were alive but didn't contain any mention of the feature. I let it know again. It admitted couldn't find the mentioned feature. But it insisted the feature had been merged in a PR. The PR link it gave was unrelated. I let it know. It gave me another 3 PR's and said one mentioned something related so the feature must be in. At the point I gave up.
The issue was that it sounded very convincing and stated "facts" very confidently, with backings to documents and other resources even if they were wrong or irrelevant. Even when told it gave the wrong info, it would double down and made up some BS reference material to back up its claim.
I know LLM’s hallucinate. I’m surprised how convincing and how stubborn it insisted it’s right. Other LLM’s would have given up and admit they don’t know.
The other day I wanted to use a function in an unfamiliar library. Gemini kept putting an argument into the call that wasn't supposed to be there even after I explicitly told it not to. This was after I trusted Gemini, got an error message and looked at the docs.
The argument it was adding was required for other functions in other libraries the same subject area so I suppose that's why it did it.
At some point it would be nice if someone could come up with a way of grounding/adding package docs and/or version as part of the context automatically
The Rubik's cube example is just reversing the moves.
Writing a visualiser and basic scrambler isn't hard to stumble upon, there's endless training material and not much to screw up. Writing a working solver even if you train it on examples would be hard.
I do agree with a lot of what is said here. There are however a few things that I think will hinder Google on the long-run:
- The last time I checked (3-4 months ago) Gemini embedding models are probably the least reliable / contextually aware out there
- A significant chunk of the market will want the ability to use locally hosted models / manage their own which Google currently has no play for
- API documentation. Across the big managed models they are likely the least well documented model.
- Allowing for more system vs. user prompts
Google is also the only company that has had their own AI hardware that's worked (TPU). This could lead to more cost-effective training + inference and hence better AI.
Google is the primary target for current US anti-big-tech sentiments that are getting political traction with Lina Khan and Steve Bannon teaming up at a recent conference against US Big Tech companies. J.D. Vance has also expressed that he agrees with Lina Khan and Steve Bannon and would like to see US Big Tech companies like Google be forcibly split up.
What will happen with Google's AI wing when Google inevitably gets split up in the next 4-8 years?
Are the administration really going to risk messing with one of their leading AI companies while they are also terrified of China catching up or overtaking them in leading edge AI?
I wouldn't put it past them but I don't think it's a given either.
In my opinion they should because the US doesn’t have any GPU restrictions and VCs are hungry for disruption. The US also has the tech talent pool to throw at the problem unlike in manufacturing.
After breaking up Google, there will be a lot more moats to be had vs being stifled by the Google behemoth.
I chat with Gemini Pro 2.5 Exp in Continue.dev IntelliJ plugin. I told it to "implement this method" and it even suggested improvements in other files (that were included in context). I feel like talking to a person.
BUT more often than not, it stopped halfway (the code, so it's unusable). I'm not sure if it's the plugin that cannot handle the response, but it never happens with Claude.
AI is essentially a hardware/electricity arms race.
Whatever model is at the top can be surpassed if a competitor has enough compute scale. We are rapidly approaching the era where it’s difficult to have enough power in one campus. Distributed sites are needed if models continue to scale at 4.7x/year (see Epoch.ai) simply from a power perspective. You have to put the data centers where the power is and connect them together.
I believe the era of distributed training is already here however not everyone will be able to distribute training to multiple sites using their scale up networks. Their scale out networks will not be ready. So it could be that we see models plateau until distributed training infra is available.
I see the infrastructure side of AI and based on HW build out; Google has been slow to build and is behind everywhere.
Wow, reading these comments it seems like Gemini 2.5 Pro Exp (assuming from gemini.google.com and not from Google Ai studio) is actually worth giving a shot! Is it really that impressive of a model now?
I've been using Qwen Chat a lot for the last couple of months because I got tired of Claudes small quota for free users, ChatGPTs inferior models and absurd pricing and Geminis (the previous models) heavy guardrails and censorship, like to the point that sincerely prompts actually triggers refusal.
I'll try Gemini 2.5 Pro Exp again and see how well it performs this time.
Totally agree Gemini Pro 2.5 is currently ahead but it isn't by a gigantic amount.
It's also still uncertain whether Google can turn Gemini into a successful product that either consumers or businesses want to use. They are famously bad at translating their technological advantage into good products - for example the way they shoehorn AI chat into search just makes both worse (imo).
I think OpenAI has the consumers and that'll make it easier to get business. Once they start eating into Google's lunch with AI booked flights and hotels...
I tried to use the free Gemini google tier for the longest time until a few months ago. For a while, i was using it as a 2nd or 3rd backup response. After a lot of disappointment , I finally gave up. The responses vs Grok and OpenAI were nothing short of atrocious. Plus, a lot of content was effectively kneecapped behind censor walls.
From a personal website, that's been pretty constantly getting 2k hits from Google each week:
Bing 150-200 / week
Yandex ~100 / week
DDG ~50 / week
ChatGPT is now at ~50 hits a week.
So from that data it looks like Google still has their comfortable 80%+ market share. But I think it's interesting if you think about the kind of users that use these products. In my mind, the alternative search engines are used mostly by techies and people that care about their privacy (also often techies), but ChatGPT is used by a much broader slice of the population.
But maybe I'm projecting because my own search behaviour has changed so dramatically with ChatGPT & Claude having replaced a substantial part of my Google searches.
If anything I think their revenue is still growing by double figures (?) which is insane considering we're decades and billions of users into the business.
I have been using all AI tools in my job working for ai startup and none of them are google products. May be I should give it a try, their product positioning is horrible
If you’re not using something like Cline or Cursor you should give them a try.
I haven’t found any OpenAI models good for agentic coding. o3-mini and 4o were both worse than 3.5 Sonnet. 3.7 and Gemini 2.5 Pro both seem be better than 3.5. I still use 4o with search as my primary reference model though.
Why is no one mentioning the fact that o3-mini is a couple months old model, and according to Sam Altman, they will be releasing o3 and o4-mini soon? Release dates especially by a couple months matters a lot right now
Can we please outlaw advertising with AI chatbots before it becomes a plague? Once it starts, there is no turning back. But if we can get ahead of this now based on what we've already learned about the internet then we can perhaps prevent the carnage that is going to happen.
I have no direct experience with Gemini itself, but the LLM integration into search has unquestionably made the product shittier than before, so I'm inclined to distrust the hype in this article.
I still find OpenAI Whisper transcription the absolute best there is. Grok is the best reasoning/code model right now IMO, but it's audio transcription (and Apple's audio transcription) still totally sucks.
This article is the example of why google ai is not winning market share. All you have shown is bunch of graphs and numbers, two image and video examples are horrible. This would not want me even touch google ai. Meanwhile world is going crazy over ghibli images with openai. Users are not stupid!
Do Ghibli images represent the most significant—lucrative, high-margin, world-changing, or ubiquitously impactful—vertical to which generative models can be applied?
+1 gemini flash is very good models. very cheap, very fast, pretty smart. API integration (if you are inside GCP) is convenient. API is good (gRPC, encoding, OpenAI API style). newest AI notebook studio UI thing also works as you would expect it. well done.
There’s a Gemini app on mobile but if you’re on desktop use https://aistudio.google.com. They are behind in this aspect, hopefully they release a desktop app with MCP.
2) Productization and engineering that science into something to sell.
While Google DeepMind focused on things that won Hassabis and Jumper Nobel prize in Chemistry, OpenAI took transformers architecture (Google researchers invented), built the first big model, and engineered it into a product.
Google has the best researchers, and does most research. When they finally chose to jump into the business and pull Hassabis and others from doing more important stuff to moneymaking, obviously they win.
No, that's not at all obvious because building products for any given market is a radically different competency than research, and the kind of basic, fundamental research that tends to win Nobels is actually a competency a step further removed from product than normal corporate R&D; outside of Google-scale orgs, it's mostly (whether or not of Nobel quality) done at universities with both product-oriented research and actual productization done in industry, often based largely on published academic results, but generally with no strong direct connection between the people doing the basic research and the people winning the competition for successful commercial products.
I don't use Deep Research or NotebookLM myself (or any other generative AI product). But every example of a NotebookLM audio overview I've seen was actively misleading and ignored critical context. However the voices were very personable and entertaining! Likewise Deep Research uses terrible sources and often gets things wrong, I have yet to see a single example that holds up to scrutiny...but it sure goes down smooth compared to reading a bunch of disparate papers!
I suspect Deep Research and NotebookLM aren't used to get information so much as to provide extremely low-quality infotainment. I read Wikipedia recreationally and I can definitely see the appeal of having a Wikipedia-like article/podcast for anything you can think of. But they seem miserably bad for actually learning stuff (especially the stupid podcasts).
While the article correctly highlights Google’s significant advancements and formidable position in the AI race, calling them the winner on every front feels like a bit of a stretch and potentially overlooks some nuances, like SGE integration which isn’t universally loved.
Too much fanboyism imho
I guess I would argue that revenue, while not everything that matters, is super important. And I’m guessing that Google has nowhere near the revenue that OpenAI has. Even if they can bundle for “loyalty”, that is an uncertain future there. Magen it will work. Probably so. Still uncertain.
I use ChatGPT, Claude, Grok, and Gemini regularly. Even though 2.5 pro is really good, I find myself using Gemini the least because I have an aversion to giving Google even more data about me.
I know that even if they never inject ads directly in Gemini, they'll be using my prompts to target me.
Not on cars, not in robotics, not in commercially deployed AI, not in enterprise investments in their cloud business.
They've got immense potential, sure. But to say that they're winning is a bit far from reality. Right now, their Cloud AI offerings to the enterprise are technologically superior to anything else out there from AWS, but guess what? AWS seems to have significantly more %age sales growth in this space with their larger base compared to GCP with their smaller market share.
The same can be said across turn based chat and physical AI. OpenAI continues to be the growth leader in the consumer space and a collection of Claude + self hosted + Gemini now in the enterprise / API space.
They need to be measuring themselves on moving the needle in adoption now. I'd hate for such amazing progress to stall out in a niche.
I would say they're winning with Waymo: I took a fully autonomous taxi ride in the backseat in SF, and it just worked. No other company can currently do that, despite their promises and hype.
Winning going on what, 10 15 years now? Surely at some point they must start scaling?
At this point all I can imagine is that every year they run the numbers and arrive at "yup, still makes no sense whatsoever". And so its eternally doomed to tech demo status.
Each Waymo is > $140,000 of customized hardware and is limited to specific cities. Autonomy in commercial vehicles is arguably led by Tesla on coverage, miles driven, ready hardware, cost per mile etc. They’re going to start pushing tests on their consumer fleet, converting them to optionally commercial taxi rides soon with the fleet owner model versus the central provider model. This is scheduled for June in Austin and confirmed to be on schedule.
You can also take fully autonomous bus rides in China right now, even there, for, early reviews, the latest Tesla Autopilot blows everything else out of the water.
I’m not trying to push Tesla alone, but I’m trying to highlight the gap in adoption goals. What is Waymos ambition this year? How much can they ramp their fleet at $140k per unit versus Teslas consumer fleet and upcoming low cost robotaxi with the mass manufacturing improvements further lowering cost per unit?
As with everything related to Tesla FSD/Autopilot, I'll believe it when I see it. They have not earned the benefit of the doubt. Waymo works as a robotaxi today, Tesla doesn't.
I'll grant you Chinese developments; I'm not across what's happening there, but I wouldn't be surprised if it was on par, yes.
My bet is that they can reduce the cost of their working solution more reliably and safely than Tesla can get their solution working at scale.
So have you sat in a Tesla with the latest hardware or . . .
I don't understand this attitude in the technology industry. If you want to hold such a strong opinion on something, at least take the initiative to research what you're talking about.
Teslas __today__ are at or better than Waymo at autonomy. They are launching tests in June. There are popular accounts who have experienced this alpha at the "We, Robot" autonomy event earlier last year and follow on interviews with Lars and Franz, (Head of Vehicle Engineering and Head of Design)
Honestly love gemini for zero-shot coding. But for some reason I'd still lean towards GPT4o for just natural conversations and day-to-day queries. Something about 4o's tone and behavior just clicks with me.
I think Google deserves it. Didn't they come up with the foundational paper - All you need is attention? And then Colab, Tensorflow etc. Though not relevant, I remember Map-Reduce paper was also from Google, leading to big-data revolution.
It isn't when considering Google's brand has (long) lost trust in how it hanles data. This is especially true with larger companies, F500 type brands, who tend to avoid Google for infra as do governments.
F500/government are conservative and tend to stick with the vendors they know, which is why Azure has gained so much traction despite being worse than AWS & GCP pretty much across the board.
Trust in handling data doesn't really come into this; if anything Google has a very strong reputation for security.
> ie trying to not do new things because it takes more work to change
That's not what the word conservative means, not by the dictionary or even politically.
Conservative is the averse to change or to hold traditional values without logic. It's more like a type of fear. Even if the change was easy or have 0 cost, a conservative entity won't do it.
Weird - it's hard to beat widespread online narratives, but as someone who worked at Google there's no company I'd trust more with the "handling" part of my data. There's no doubt that on device is always a more private option, but if you've decided to keep data in the cloud, then Google is probably one of the most secure options you could choose.
Same, as another former Googler. I worked on a team that had a relatively large amount of data access, and the amount of protection in place - technical and procedural, preventative and remedial - made me extremely comfortable giving Google basically all of my personal data, knowing that only the bare minimum would ever be looked at, and even then securely and in an anonymized or (usually) aggregated format.
as an outsider, Google is one of the companies I trust the most to prevent unintended leaks of my data, but also one of the ones I trust with my data least.
> but also one of the ones I trust with my data least.
What thing have they done with user data that you feel will negatively affect you? As far as I know people just don't like that they have a lot of data, nobody every said they did bad stuff with that data.
I think there’s a bit of a mismatch here between data Google collects on me as a regular user which they can and due process in a million different ways in order to sell shit to you. This extends to AI unless you’re paying for it in which case it’s a very different ballgame.
Then there is data that I put into a Google service like drive or cloud which genuinely is probably the single safest consumer option I know of in 2025.
If Google is able to ignore the pressure to bring in revenue from AI and is able to outcompete the others at automating AI research itself, I think they will win the war. It seems that they certainly have an advantage, with limited pressure from outside investors, their own hardware stack, a constant flow of cash through the other lines of business, and a head start against most of the other giant tech companies.
The only thing I think ChatGPT is better is native image generation, their model is able to work in the joint-space much better than any other model I have seen, but I'm sure Google will try to catch up rapidly.
Back when all the articles talked about how OpenAI swiped Google's crown while Google sat on transformers and never productized them, I saw this future coming. Google had back then, and still has, the best research on this topic, and ultimately that was going to win the day.
Sure, hindsight is 20/20, and who knows if any of these products will be big money makers vs commodities, and they may still fail at the productization of these things. Sure.
But insofar as productization follows great technology, Google was always going to have the upper hand here. It took many years but they did finally start coming out ahead
I hear from my OpenAI contacts that the next wave of thinking models are going to blow the socks off Google. In some ways they already do (speech, images). So this lead will likely be short lived. That said, in the meantime I did get paid API access. The friction for the scenarios I need LLM for is effectively zero, and I'll use whichever sucks the least at the moment for any given task.
User friendliness wise nothing beats Grok3 because it warns that a pro should be consulted but usually still gives you an answer, mostly even a good one. OpenAi and bigG are strongly biased and refuse to go into anything remotely controversial and take strong don't sue me stance over advocating the user's responsibility.
Just a fun I had with Gemini 2.5 pro. I asked him to create a code for a toolchain that uses some ai at a point and it stubbornly used OpenAI api and refused to generate code using Gemini apis instead. Also it said it is model trained by google. Didn't knew its name and when asked to generate code for "youself" defaulted to openai.
Probably sending people to spend money at your competition is not the surefire way to market dominance.
It is very quick to abort conversations and if the safety stuff kicks in, it loses all context. But I will keep giving it a crack since everyone seems to think it’s great. Maybe I just need to learn the tricks.
Gemini 2.5 Pro might be one of the best for coding but for creative tasks like writing and sharing ideas, I vastly prefer GPT 4o and GPT 4.5 to an even larger extent.
Gemini 2.5 Pro's prose isn't quite as tight as GPT4.5s, but being able to have long form writing where your entire manuscript is in the context, along with all your source/background material, and it all gets used _well_ is pretty stellar. That lets Gemini update scenes in a really thoughtful, intelligent way, and frankly it's a better beta reader than ~85% of the people I've hired on Fiverr.
In the early interviews he was saying precisely that - that google has effective monopoly in ai and it will be extremely difficult to reach anything close to capacity they have.
If you search for Shockmaster, the AI Overview you get is as follows:
> Fred Alex Ottman, a retired American professional wrestler, is known for his WWF personas "Tugboat" and "Typhoon". He also wrestled as "Big Steel Man" and "Big Bubba" before joining the WWF in 1989. Ottman wrestled for the WWF from 1989–1993, where he was a key ally of Hulk Hogan. He later wrestled in World Championship Wrestling as "The Shockmaster", a character known for raising his fist and making a "toot-toot" sound.
Which is obviously false. The "toot-toot" was part of his gimmick as Tugboat, while the Shockmaster gimmick is known for its notoriously botched reveal.
Point being, Google is losing on the "telling one early 90s wrestling gimmick from another" AI front.
This post is claiming Google is winning on every AI front. Search summary is a front on which, as far as I can tell, no one is winning. But Google is one of the worst.
"They're also small, which makes them perfect for edge applications and phone integration."
- you can't locally install or onprem Gemini right, so why does small make it better for edge applications, essentially because small means light and fast, so it will respond quicker and with less latency? Requests are still going out over the network to Google though right?
[1]https://blog.google/products/google-cloud/ironwood-tpu-age-o...
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