Speculation: open models is what will kill Anthropic and OpenAI. Hyperscalers can run the models without a licensing fee. Apple can make them smaller and put them on the device.
The frontier models are an edge and a liability. They're astronomically expensive to train. Without them, their models will fade into obscurity. Their marketing depends on people believing the models are meaningfully different, as people have sweatily argued on this forum. Personally, I'm not convinced there's much of a difference between these models at this point. The harness is what takes these random and hallucinogenic models and make them into something deterministic and useful.
Are frontier models actually astronomically expensive to train? GLM 5.2 was trained on ~30T tokens, so ~10^25 FLOPs. Say you get B300's for $5/h (pretty high), and you get 50% MFU; that's ~$15M. Of course there's also a bunch of risk that the training itself goes badly, post-training, etc. But still, compared to the inference spend after, it's not that crazy.
The outcome is plausible. Open weights models though look like a tactical more than a principled play by Chinese companies to overcome the disadvantage and difficulties to access western markets. Two issues:
1. If market conditions change they might decide to close down like Meta did.
2. If as you said models keep getting more expensive to train, is an open weights strategy financially sustainable?
edit: typo
I think (and have heard) the Chinese govt is also interested in trying to embarrass the US as well by showing off their capabilities (so there's a political angle, too)
All countries see AI as a geopolitical concern. An open source strategy is smart. It’s effective and also builds good will / soft power. Many see Chinese AI companies as the good guys and root for them. I’m pro open source and also celebrate but still aware this is most likely just the means to an end.
I used to work at Mozilla. I think we are missing a player in the market with a more principled open source approach.
That's probably pretty likely, but if we're honest, are LLMs built and funded by a hostile Chinese authoritarian regime any more dangerous or harmful than LLMs built and funded by a hostile American authoritarian regime?
China absolutely does not have my best interests at heart, but America's technofascism is probably more immediately dangerous and harmful. Americans genuinely have more to fear from America than China at this point.
One big difference is that the US doesn't have much public record of using state espionage resources to steal industrial secrets to give domestic industry a leg up in the market, whereas CCP very much does.
And while I use and love the open weight models, it seems likely to be pretty hard to prove conclusively that they have no reinforcement-learning-trained proclivity towards curling specific URLs if the topic happens to be some very specific thing that the government is interested in, when used to drive agents.
So it depends who you are. If you're a company, you might have something to fear. If you're a private citizen, maybe not so much.
I don’t think US companies could ever fully trust models sponsored by foreign adversaries. How would you ever verify they are safe?
Provenance of training set is going to be critical. It’s largely ignored now, but the first time a malicious exfiltration occurs it will go off as if a bomb was detonated.
Only way would be an actual open source model: code + training data
You're moving the goal post here. The parent never mentioned anything about the Chinese models themselves being dangerous or harmful; that's a totally different topic.
The point they were making was that the most successful open models- those coming out of China- are made my companies that are using those open models to get exposure in Western markets. The goal is to undercut the Western dominant players, not out of any particular "open source" philosophy, so it wouldn't be wise to expect them to continue providing open models long term.
Define technofascism lol.
Fascism was laid out by Mussolini in the 1920s - it amounts to the idolatry of the state.
"All within the state, nothing outside the state, nothing against the state." B Mussolini
Also defined as Corporatism: the union of state and corporate power.
Which country do you think is closer to Mussolini's model ?
Fascism was strongly influenced by Futurism and its optimism for a machine-powered future where technology will bring forth utopia and order.
It’s no wonder than Thiel & co. are rediscovering Futurism, and blind faith in the machine is basically what Silicon Valley is all about.
I think the term 'techno' in 'technofascism' is doing the work here, because--just as you claim that historical fascism is the idolatry of the state--technofascism is the idolatry of technology and intelligence. Modern accelerationism, as espoused today by people like Marc Andreessen in his Techno-Optimist Manifesto, is really just another rehash of Italian Futurism, which was closely intertwined with Italian Fascism and one of its intellectual foundations. It was, essentially, a progressive ideology. The idolatry of the state does not vanish, but rather gets transformed and re-imagined as a technology, e.g. network states, platform governance, companies that function as sovereign entities. The idolatry shifts from the nation-state to the infrastructure that replaces it. Also, you don't need to look far to answer your own question about corporatism, as you defined it yourself. Now, who's currently moving between Silicon Valley boardrooms and government offices?
Fascism is not merely idolatry of the state. That is simply its ideological mask, its method for currying the favor of its base. To say that this is what fascism is at a fundamental level is to mistake its superstructure for its base. But how is Fascism structured? How does society reproduce itself? Is there a fascist mode of production? No. The state mobilizes its population so as to target scapegoat(s) while simultaneously expanding in order to secure the spoils of war. Without scapegoats, there is no Fascism. And a state requires social cohesion to mobilize its base. Thus without cohesion there is no Fascism. At minimum, social cohesion requires the ability to sustain and motivate a critical mass of the population. Thus Fascism requires the continued maintenance of the capitalist mode of production in order to persist, but it simultaneously undermines capitalism due to A. the continued need for war machines and B. the need to punish a scapegoat, which both siphon value from the rest of the economy, thus dooming the entire enterprise. This is why fascism burns out and leads to doom for all parties involved wherever it has been tried. Any definition of fascism that ignores this is historically inaccurate and practically useless.
Open models are probably also comparatively astronomically expensive to train - just less so than the frontier models because they’re somewhat smaller, +/- the creators are more incentivised to focus on getting more from less compute because they’re have to, +/- they rely on distillation of the frontier models and this is more efficient.
But efficiencies aside; creation of open models still requires a lot of money and compute from a large organisation which is willing to accept zero return for that spend. This largesse is unlikely to continue forever; so the question is which will crack first, the frontier models’ business model or the fast followers’ generosity?
Yes, the problem with comparing open models to open source is that open source requires humans to volunteer their time. Open models requires humans to volunteer their money.
These two types of contributions have very different behavioral profiles, and it doesn't obviously follow that the historical success of getting people to collaborate socially on building software for fun and for the benefit of the community will translate in any meaningful way to the necessity of being able to raise enormous amounts of money to pay for enormous amounts of electricity.
The biggest hurdle is whether humans volunteer their expertise. Not time or money. We need top talent to make the open models. Sponsorship is plentiful. Open source volunteers are less critical with LLM doing the grunt work. Its about talent contributing to the open
> open source requires humans to volunteer their time
Your idealistic of open source may require that, but in practice a huge part of open source is commercial and a large chunk of that is low on collaboration (across vendor boundaries).
Technically open source requires some amount of monetary volunteering, it's just that the electricity to run a code editor and compile (most) open source code bases is within hobby budget for most people.
I don't think it needs to be framed purely as generosity. You just need a sufficiently self-interested actor that sees open ecosystems as a necessary part of reducing their own risk profile, relative to the alternative of complete reliance of a third-party business that can take an exorbitant cut and/or Sherlock them at any time.
Valve and SteamOS are a good example of what this idea looks like in practice. (Though they may also illustrate a third thing you need: a privately-run company, that has enough profit, and enough commitment from leadership to the company's vision, that they can make long-term bets without having to eventually bow to investors seeking short-term gains.)
> You just need a sufficiently self-interested actor that sees open ecosystems as a necessary part of reducing their own risk profile, relative to the alternative of complete reliance of a third-party business that can take an exorbitant cut and/or Sherlock them at any time.
This would be an argument for an organisation developing its own model; but not per se for releasing the weights openly.
The possible explanations (I'm aware of, which overlap somewhat) for spending large amounts of money on models then releasing them for free (i.e. the current Chinese approach) are soft power, marketing for a future paid model business (i.e. competing with the US models for customers and mindshare during the time you can't compete directly at the bleeding edge), and/or a geopolitical move to diminish the value of the US's frontier model companies.
My (unverified) AI research claimed generally Chinese models are cheaper to train because Chinese data scientists are cheaper to hire and they're also under more pressure to optimize training cost due to limited hardware availability
Seemed believable but not sure where that's true
Chinese AI companies are generally smaller tho and the models they're releasing are also smaller (I think estimates put OpenAI and Anthropic SOTA into trillions of parameters)
I can't imagine even with the crazy salaries at frontier labs, staff costs make much of a difference.
I’m not exactly sure on the “how” but it only makes logical sense for (non-AI) companies to band together to fund the training of a shared model. Apple is a great example, AI is not their core business but they still require it.
The only thing that took us down a different path is the vast sums of VC funding pumped into the AI companies.
It probably doesn't.
There's a reason we let companies specialize in some kind of service and buy it from them.
LLMs aren't looking like they'll be highly differentiated like software, so their market will probably be competitive. What negates the main reason Open Source software exists.
LLM training doesn't carry the same NIH risks that normal internal software bloat does. They are relatively simple to setup training for and analysis of accuracy/recall can be automated.
This leaves the price differential between a private third party and an internal initiative as barely more than the cost to train the model[1] - perhaps that's where we'll end up, a centrally trained model will represent an economy of scale that can leverage that difference into a margin it can profit off of but your business being purely profit driven by that training expenditure seems like a ridiculously thin margin.
So where does that leave the AI companies? If their LLMs are off the shelf-once built products they have a strong advantage for casual low usage but enterprise customers will have a huge cost incentive to roll their own - if the LLMs require continuous retraining and the frontier keeps moving then enterprise customers will find a packaged service more attractive and likely continue to subscribe for more accuracy but casual low usage will likely shift towards "good enough" models. It seems inevitable that they'll lose half the market and it seems difficult to discern their long term profitability[2].
1. Costs can, I think, reasonably be reduced to hardware depreciation and energy - if trends continue with cloud resource availability (it's possible this won't be the case if large compute providers start pulling resources offline to build a moat but I think they'd likely prefer the reliable compute income over model income which has several other competitive weaknesses). Hardware depreciation would normally be pretty negligible and equal across different training entities, right now we have a chip shortage but given the demand that can't last too long so I'd consider hardware to be fungible - and energy is entirely fungible - they're both hard to moat.
2. Outside of AGI, who knows if AGI will be or what even counts for it at this point - but I think if AGI isn't a doomsday scenario we fall back to one of the two above scenarios - either the frontier is ever moving and they can retain enterprise customers or the frontier seizes up and everyone can just use an off the shelf offering. In either scenario they don't have a lot of moat to deal with for their products unless they can restrict compute which is why Alphabet, AWS and MSFT are the only players I could see realistically coming out of this as an AI vendor winner and I'm not even certain if it'd be a good idea for them if it'd hamstring their cloud profitability.
If not for VC-funded LLMs there wouldn't be any LLMs.
Most of the innovations needed for LLMs came from people at Google.
A fair amount of ML/AI innovations came out of the market in general. Neural networks are a useful tool to solve a variety of problems... LLMs specifically were a more recent interesting market to develop but I've yet to see anything that could give a market player a real competitive advantage. It feels like we just invented a new hammer and now that we know how to build it it isn't that hard to build one yourself. The all purpose hammers are, of course, unreasonable to build - but those don't seem to be that useful. I don't really need Claude to be able to generate sonnets when I'm programming so I think specialization is the place we'll see genuine markets form.
> came from people at Google
Who had to leave to build anything.
So VC-funded.
The company was VC-funded as a search engine but by the time they made significant investments in AI (DeepMind etc) they'd been a publicly held company earning multiple billions a year from advertising for a decade.
Google is a VC. Their side projects are VC projects.
Google bought DeepMind and their other major AI acquisitions. Public companies make corporate venture investments for very different reasons than LP-backed VCs. They do early-stage investments to search for emerging players they can buy as soon as possible or to gain market intelligence on trends. They do later stage investments to help grow future vendors or customers and sometimes to foster ecosystems that form their competitive moat.
But if they think it's important to their core business, corporations don't want to invest, they want to buy. Source: I used to be involved in corporate venture investing at a top 10 valley tech leader.
If your definition of VC is "Literally anything that requires a long term investment of money" then sure, but I think most people mean something different than that.
[citation needed]
Historically speaking a lot of inventions have come about without things like VC investment. Either way, there’s probably little point in debating it, just because VC funded companies control the market now doesn’t mean they should indefinitely.
How does it work if people flock to open models but they're too expensive to train? What is the financial incentive to do so?
I seem to understand open models are mostly coming from China, and the benefit of training and releasing them for 'free' is a powerful geopolitical weapon against the Western/US economy that at this point depends on OpenAI & co. to succeed.
Will the West make open models illegal?
> Will the West make open models illegal?
We better not.
> What is the financial incentive to do so?
If we'd been sharing all along (as we should have been), we probably would have gotten even further along in the development of the tech.
Think of everything we could do if every researcher on the planet had first class access to the frontier. No academic fallback models. No crude API access. No limits, but direct access to the weights and the ability to lobotomize, splice, and dice.
We could pour intelligence from one container to the next without paying a tax or wearing a blindfold. All without spilling a drop.
*Open* *Must* *Win*
> No X. No Y. No Z, but Q
If by West you mean the USA, maybe.
Other countries in the westen hemisphere, probably not.
"You wouldn't download an LLM"
You wouldn't crash the stock market by preferring Chinese models.
> releasing them for 'free' is a powerful geopolitical weapon...
I agree that, currently, the Chinese govt is not only allowing but tacitly encouraging open weight model releases. However, I don't see it as an attack. I think it's more of a strategic delaying move to slow the revenue to frontier models while China works to catch up. This strategy will likely change over time.
> Will the West make open models illegal?
In the U.S. this seems highly unlikely due to the current administration's generally laissez-faire approach to tech as well as the U.S. constitution severely limiting the government's latitude to constrain economic activity.
As we saw with the temporary Mythos restriction, there are legal mechanisms to limit tech on certain grounds, but over time such limits are subject to close judicial and constitutional review. The Mythos embargo was also likely driven in part by the administration's anger at Anthropic for choosing to block the DoD from using their products for mass domestic surveillance and warfighting. I doubt we'll see any meaningful restrictions on OAI or other large companies. It'll be nearly 3 years before a different admin is in office and could enact serious limits and by then it will be too late for fundamental bans.
There are vested interests in most governments, such as intelligence agencies, law enforcement and the military, who would prefer to restrict some AI from broad use. As we saw with strong encryption, they'll only be able to delay and constrain, not stop, such a broadly useful dual-use tech. The geopolitical, economic, competitive and civil liberty interests are similar between strong encryption and AI, setting up a similar game theory dynamic. While it can be argued AI poses some potential danger, the specter of any such threat is abstract and not immediate.
On the other hand, the tech is obviously too economically essential and competitively vital to risk 'falling behind'. While there will certainly be attempts to ban, limit or constrain AI, the well-funded, highly organized commercial interests and civil libertarians will deploy lobbying, legal challenges and public opinion to ultimately prevail.
In the U.S. this seems highly unlikely
Aren't these the same guys who won't even let us have Chinese cars?
I'm not as confident as you that they will keep allowing us access to technology as strategic as AI models out of China and elsewhere that undercut US models in the market.
To everyone reading, download open models from anywhere as soon as they are released. You really have no guarantee at all that access to those models won't be cut off in the future with the stroke of a President's pen. Those downloads are your insurance policy. You'll always be able to access whatever you've already downloaded.
I would definitely pay a monthly subscription to help fund a non profit compete with Anthropic and OpenAI. I already pay subscriptions for myself and 2+ other people. It's a non brainer to be able to pay for the training of better models that I can then run myself for many more. I hope someone starts this, I think this model would work. I'd start it today if I had the team and initial capital to bootstrap the infr. I know VCs won't fund it, but we definitely will, enthusiastically and continually.
I'm a bit skeptical of the token cost/ROI for all models, but sunk costs are sunk.
It has the feel of self-improving super-intelligence or bust to me. If you get that, the frontier model(s) run away with a faster exponential. It's a bit like semi with Moore's Law with silicon, GaAs could never catch up. If you don't get it, the fast followers crush the high investment and there's no moat. Not like they can enforce copyright!
There's a world where frontier models run away with a faster exponential and still go bust due to being outcompeted on efficiency.
There's a point past which "intelligence" stops mattering as much, and IMO we're already there.
Consider which would be more useful (and profitable for its creator): a model that is 3x/5x/10x as "intelligent" as Mythos, for whatever your favorite yardstick of intelligence is? Or a model that is as "intelligent" as Opus 4.5, but can run at reasonable speed on a typical consumer laptop/cell phone?
If the former can develop the latter, then the former.
not really a feeling; if you listen to ed zitron and strip out the vitriol, you still get the fact that the VCs are looking for some 5 trillion dollars in 5 years.
The onlly way that happens is if America turns into zimbawe.
Who says it hasn't already?
I've driven in a LOT of the USA. Sure Chicago, NYC, DC, LA, LV... They're all built up and feel modern.
Try driving anywhere in the Midwest outside of the big cities. Dilapidated carcas buildings everywhere. Urban and rural blight. Only jobs are low paying service work. Its bleak. Like, really bad poverty as a disease bleak.
And its crazy watching it too. They're ignorant (involuntatily), poor, and trapped. And democrats only seem to care about special interest of the week, so these areas vote republican.
I don't have a solution btw. Just something I've seen growing in the last 25 years. And its getting worse, not better.
At risk of greatly oversimplifying American politics, it's truly impressive how good Democrats are at shooting themselves in the foot.
That's why I personally believe Sanders and Mamdani have found so much success with the working class; they keep themselves separate from the Culture War slugfest that mainline Democrats either voluntarily engage in or let Republicans drag them into.
IMO the vast majority of those "culture war" issues (LGBT freedoms, etc) are incredibly important, but to the average poor rural American it feels incredibly distant from their day to day. I can't put my finger on it exactly but Democrats have a tendency to message on those issues in ways that are either counter-productive or get soundbit saying something moronic. So when Fox News and whatnot say that Democrats are prioritizing other groups over them and message on it day after day, it's not hard to see why that propaganda becomes effective.
That's not to say that Sanders/Mamdani/etc don't message on social issues, they obviously do, but they are somehow effective at not alienating voters who may otherwise latch onto that in a negative way.
I don't have a good solution. Just my observations.
It’s very difficult to be for the people and also get hundreds of millions of dollars from wealthy donors. That is the democratic party’s plight.
I'm not sure I understand what part of my post this is in reference to. I agree that the democratic party gets a _lot_ of money. Are you contrasting to something in my post, or agreeing?
Appreciate clarification, I could also just be dumb today
As a Canadian driving through Michigan/Ohio/Upstate New York and places look run down and like they peaked in the 60s-80s and its been down hill since.
When I was younger and it was a new thing it was quite a shock since American media obviously doesnt portray it that way. It is quite a contrast to even how the more run down parts of Canada look.
>Who says it hasn't already?
Great.
> open models is what will kill Anthropic and OpenAI.
Maybe killing Anthropic is a good thing? Anthropic believe that they are the moral god and they get to determine what we can do and can't do, and they get to tell us what model to use and what not to use. I find it very counterproductive.
My prediction is that hardware costs will make open source models impractical for the foreseeable future.
Yes, tinkerers and enthusiasts will continue to make use of them, but frontier companies will maintain near total dominance because they will be the only ones with access to the hardware.
Meta is selling their now excess compute, other compute has been on the market for a while. The current hardware cost bubble is temporary, especially once people are forced to pay the real inference price instead of majorly subsidized subscriptions.
yep, all those coders paying $200-$500 per day to use claude once subsidy ends will be seriously rethinking how much they really want to vibe-code "rewriting X in rust". Helping people write word docs, recipes, and emails isn't going to justify $15K per month subscriptions either.
> Helping people write word docs, recipes, and emails isn't going to justify $15K per month subscriptions either.
Those things can all be done today on a $250 used video card and pennies of electricity
The subscription is certainly subsidized but its no where near 100x cheaper than API prices.
Heck, most large enterprise moved to usage based billing and are still happily paying for it. They are force multipliers for your top talent, and when a top engineer is being paid $500k a year, doubling their output for $500/day is a no brainer.
Lowering the cost of hardware still won't solve the issue. HBM and DDR5 was never cheap, even before the shortages, so selling a full inference system is beyond the acceptable price range for most casual customers.
We're going to see Apple and Google compete over services and AI/OS integration instead, it will probably be years before your OEM takes local models seriously.
Apple and Google (via smartphones) are in literally everyone's pocket.
Running KIMI on a phone is not possible today and I agree with you that it will "probably be years before..." it is.
But how many years do you guess? I personally do not think it will take even 10 years for the situation to be commonplace.
> I personally do not think it will take even 10 years for the situation to be commonplace.
Do you personally remember how far smartphones progressed in the past 10 years? It's not as long a time as you think it is, the limits of what a smartphone GPU is capable of did not substantially change in that time. Nor did the amount of onboard RAM that we include in the package. This is true even for Nvidia's ARM SOCs, frankly.
Apple, Microsoft and Google all eventually want to enforce OS-level lock-in for the most profitable AI services (eg. their own). It's much more attainable and profitable to use that lock-in to sell you exclusive service integration, the local AI revolution probably won't begin on their hardware.
You may be correct but the combination of local models when they are fast enough and work, combined with paying close to zero for deepseek v4 flash from US providers is pretty good. When you need it, glm5.2 is cheap to use and very good for working with larger projects.
For the near future it seems that the new models will consume whatever improved hardware capacity we have. Competing with that is challenging, but I also think there will be strong economic incentives towards cheaper but adequate models on other providers.
I don't think we'll see home users being able to match even the low end clouds for a long time.
Longer term I think we'll see these uses of AI cluster into a few groups:
- maximal code / reasoning quality, at high prices (Fable)
- typical code / agents (sub-Opus, Terra)
- cheap but decent enough quality (think Deepseek / GLM / Luna)
- so cheap I don't care about utilization (Deepseek, and friends)
And also more niche ones:
- ultra fast with high quality answers (typically sub-SOTA). Cerebras / dedicated silicon type approaches, expensive.
- ultra fast with mostly-adequate answers, and an openness to retries, moving up to better models
I think the open models will dominate (not with individuals, but low cost providers) all except the top 1-2 of those categories, and there will be a continuous erosion on the big player's moats. The top categories are also where all the money is, but I'm not sure it can justify those investments long-term. I also think they will have to squeeze more money out of them to justify the investments, which will also drive people down the list.
Edit: clarifications.
>For the near future it seems that the new models will consume whatever improved hardware capacity we have.
But they're not. Meta, SpaceX, Microsoft, Amazon, they're all leasing out capacity to others. If they were truly constrained, we wouldn't see that happening.
There will be plenty of model providers with prices that undercut Anthropic/OpenAI's prices.
Doubtful for the same reasons. The frontier providers are working at a hardware scale that will make it impractical to undercut them.
I wouldn't rule out the possibility completely, but it won't be very common.
They also have to pay for staff / model training. Those are not cheap.
Completely agree. Once I can reliably get open models doing what I am on Fable ultra I imagine I will switch for good. I am fortunate to have access to a decent bit of local RAM, 192GB of DDR5 at an OK speed. It is not enough and costs are well past absurd. In a few years time I envisage a setup that is sub $10k which can accomplish such tasks. The pace so far has been breakneck. That is all I personally need. That may change, but until true AGI I do think there will be a ceiling to how much I will pay for something frontier if it is only marginally better.
This is easier to say as Fable is good (even SOTA). But people have been were saying this continuously for the current model and for now the improvement are still coming.
A better question is would you settle for o3 now or pay 20$ or 200$/month for fable ? Because o3 quality is available OSS.
It is like the new IPhone, in some sort. At some point come a feature many would like to have, despite diminishing returns.
We will see how long labs can keep up and what the scaling curve look like, but I would be more worried into losing sota status to Chinese companies than letting them take the open non-sota approach.
IMO we are there almost. I had every iphone until 14 pro and still use this today. I know what the top of the s curve feels like. From a pure model standpoint weighed against every day use cases for every day people (the right way to measure when comparing to something as ubiquitous as a phone), the models already have diminishing returns.
I think there is also the case were companies will simply use different tiers for different tasks.
While the engineering team might need a cutting edge model (with the associated costs), the marketing department will be fine by something that can grammar correct or turn a few bullet points into prose. Likewise you already don't need Fable for Ticket -> RAG -> Reply with Faq knowledge or escalate workflows
That's already the case with other very expensive software like CAD packages were oftentimes you have different feature sets enabled for different employees.
"that can grammar correct" ... Did you do that on purpose?
damn! how muhc you dropped for 192gb?
Speculation: Anthropic and and OpenAI become more like hedge funds, keeping their models to themselves, using their them and their compute for market/event prediction, internal tech development, and AI self improvement. I already wonder if that is what Google is doing. Why release your best models to help the competition? Their published models are good enough for 99% of consumers, and they can leverage their market dominance in other areas to put that consumer model in front of eyes. Then the decision to improve your internal models would both depend on whether you think they'd have value internally.
Yeah, I'm pretty sure AI is going to go insular within the largest companies. This will only be hastened by the growing national security concerns/awareness.
They would have done that long ago if it was that easy. The fact that they aren't tells us something about the actual business utility of leading models.
Except Deepseek going in opposite direction: from hedge fund to an ai model provider.
I think this is possibly true, but the other piece of the equation is tooling. Right now, Anthropic has by far the best tooling around (for both SWEs and non-technical users), and a huge ecosystem of integrated ISVs. I'm not suggesting it will happen, but if Anthropic decided to provide options of using models beyond Claude, they'd still have a significant moat.
If consumers ever have enough VRAM for it locally, sure, otherwise nowhere near close.
Nvidia is looking like they are ditching consumer markets in favor of enterprise GPUs since nobodys heard a peep about the next iteration of RTX cards. The 60xx series is postponed till 2028.
Nvidias playing a dangerous gamble, in my eyes I see all the frontier labs eventually just only buying Nvidia chips for training and building custom ASICs for a fraction of the cost, longer lifespan and cheaper to host.
This will eat their 5 year gravy train for GPUs vs the 10 to 15 for ASICs.
I agree with your speculation to be honest. And yet I’ve tried several local open weights model now and none gives the same quality of answers as Claude gives me on a regular Sonnet model. Mind you: I am “running” 48GB of RAM so I can’t try every model. Where does this difference come from? Can we actually get close locally?
You’re running 48GB now but imagine a future where everyone has 512GB RAM or 1TB RAM in their computers (it might sound like a lot but also 20 years ago we had 512MB PCs).
It’s not hard to imagine what 5-10 years of pressure to increase RAM will do to specs, on top of the normal tech improvements.
That’s worth bearing in mind when thinking about local models.
Plus, local models keep getting better and better; 2 years ago what you could get out of those 48GB of RAM was embarrassing compared to what’s doable today.
We’re getting there. Just takes time.
Something I've noticed is that local models are giving better answers these days than they did a year or two ago, even if the size (in parameters and in the amount of RAM used) hasn't increased. I'm not familiar enough with the technical side of model training to explain how they're doing this, but I think in another couple of years, models that use up 48 GB will be able to squeeze out even more incredible performance.
Though on the level of something like Sonnet 5... well, maybe not.
The real moat aren't the models, but the tooling around the models that allow them to perform specific tasks/goals. That's what really sets apart frontier vs open. Open only has the model itself, closed have the tooling to enhance the model.
> The real moat aren't the models, but the tooling around the models that allow them to perform specific tasks/goals. That's what really sets apart frontier vs open. Open only has the model itself, closed have the tooling to enhance the model.
As these frontier companies have been boasting, writing software is now a negligible cost because the LLM can do it.
IOW, no, their software can't be a moat, because, according to their own arguments, you can use their LLM to trivially clone their software.
> you can use their LLM to trivially clone their software.
Perhaps, but what you can't clone is familiarity, polish, integration, and network effects. These companies desperately need a moat, and so for example all the new additions to Claude's web interface are becoming the products that the vast majority of people will use and be locked into (if it goes according to their plan).
If development is trivial and capability remains the differentiator, then "familiarity, polish, [and] integration" are non-issues.
I'm not sure what network effects even means in the context of llm selection.
I'm happier with my Opencode Go running with the pi harness than Claude Code at work currently. I'm not sure if this will be that big of a moat, at least in the software dev market.
unfortunately, for me, that's an anti-moat; their dark, inconcievable, alignment, random "no goblins" and inability to be reliable where their models are inevitable non-determinant means you're going to constantly run into the "this worked yesterday" problems, no matter how smart the models actually are; being filtered constantly through economics, political and ego-maniacal filters means everything you do with it will break at any given interval.
good luck.
>open models is what will kill Anthropic and OpenAI
i doubt it. it cost money to train a model. we can see that with the price increase for Kimi3. Chinese AI companies is leaving a lot of money on the table for third party providers. you think they going to let go of those money that they can make. sooner or later they will want to collect. after all, none of Chinese open weight model is release by a none profit. its all for-profit companies that is releasing open weight model.
I can't take anybody seriously when they keep declaring open models is beating frontier models. What they don't understand is that besides the huge capex to train and run inference, the real gold is in the human response to the prompt results, this is what all the Chinese companies are making their open models dirt cheap and distilling american frontier models via scraping.
The idea that we can out-parameterize frontier models is a common misconception, the true moat that Anthropic and OpenAI is why Chinese model providers are open sourcing and making it dirt cheap to keep pace through its "proxy chain operators"
https://x.com/HarshalsinghCN/status/2056626175959826692
They'll lobby to ban them, especially Chinese models, as Amodei is already doing.
I worry about that but here are two positive things:
1. it would put us (USA) at a competitive disadvantage, and cooler heads will prevail in this fight
2. there are good US open models. I have the latest gemma4:27b with better tool support functioning at a high level in the pi coding harness. Thinking Machines seems to be on a good path, we will see what they and other US companies can do.
> at a competitive disadvantage, and cooler heads will prevail
I don't think this a safe assumption at all, given the the last few years of government policy where the "cooler heads" very much failed to stop such things. Either by partisan choice, or because they were silenced/fired for telling the Emperor that his clothes weren't there.
In particular, consider the tariff-taxes [0] against on US importers, and the ensuing breakdown between America and its oldest allies and trading-partners. Also relevant would be the President's recent pet-project war [1], which has shut down a major part of international shipping.
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[0] Unilaterally imposed by fiat, with idiotic "failed economics 101" math for the rates... and the majority in Congress still actively tried to keep it going, until the Supreme Court ruled it illegal.
[1] Simultaneously a war and not a war, both already-won and ongoing, depending on which lies need to be told today to harvest applause and grab cash. Just this week the White House submitted documents to Congress claiming what's going on is a completely separate and new, second war with Iran...
> Apple can make them smaller and put them on the device
Someone can, but Apple has essentially admitted defeat and handed the reigns over to Google.
Have they?
https://www.cnbc.com/amp/2026/07/14/apple-prismml-ai-compres...
> handed the reigns over
Oh man, they gave them free reign?
How will anyone reign them in now?
For all intensive porpoises, this is like Babe Ruth, chomping at the bat!
There is a massive difference when you zoom in close or take an angled perspective. You can manufacture uniqueness. The issue is when it comes to every day use for every day people there is no differentiation.
You can run the same harness on fable, opus, sonnet, and see a huge difference between them. It is true the harness is important, and openai has begun encryption its instructions to swarmed sub-agents instead of just encrypting the chain of thought, but the model is still important at this stage.
This will only delay the inevitable. Sitting on some magic prompts is hardly the moat they need.
Referent of "the models are meaningfully different" reads as <top closed, top open> rather than <top closed, cheaper closed> to me, so I'm not sure why we'd be comparing Fable vs Opus/Sonnet or Sol vs Terra rather than the same against Kimi K3.
I was just responding to "The harness is what takes these random and hallucinogenic models and make them into something deterministic and useful."
You can compare Fable vs Sol vs Kimi in the same harness if you want too and there are meaningful big differences. I chose all Anthropic ones to be safe from the they were finetuned on different harnesses complaint that would be made from that comparison.
Haven't tried Kimi K3 for now but there was a huge difference between GPT 5.6/Fable and GLM 5.2/Kimi K2.7 that were previous frontier open models.
I still strongly believe Google Gemini has the best position for one simple reason: model maintenance. Accurate information is a moving target.
Open models are indeed very capable, but they will eventually become more specialized to the application to keep an edge. It makes perfect sense that the future shape of AI conforms to the landscape it was born out of.
You're saying it's important to have up-to-date facts stored in parametric knowledge? It seems to me like that's grown less and less important as agentic capabilities have grown. Even if a frontier model doesn't know something, if it's out there, it can easily find it through tool use.
Grok has the biggest advantage in current events knowledge because it's integrated with X, which enough people still use even though it isn't Twitter.
Hm, when compared to all the information people with android devices share with google, or those with gmail, ..
Google wouldn't give Gemini direct access to everyone's Gmail. It would carry far too much risk of being exposed. X can, because tweets are public.
But google can give gemini access to summaries of all the avaiable information google has, including what people look for right now and where and how long.
The frontier models are obviously superior. The question is if progress slows down.
in what context, and at what ROI?
Because... I have use-cases where this is true, and use-cases where this falls flat on its face.
I don't actually think it's obvious (at all, really) without defining what "superior" means.
In the same way that I don't think it's obvious that a plane is superior to a car, or a boat, or a bike.
They each do things the others don't, and excel in different spaces.
Eventually they will kill the hyperscalers too because of privacy issues. It's better for a company to pay an uprfont cost and then run everything on premise that uploading their entire codebase to a third party service.
The vast majority of companies are still putting most things in the cloud and will continue to do so and this far outnumbers the must-be-on-premise companies.
Sure there will be self-hosters but hosting AI models will always be more of a challenge than running scalable database on your own hardware and specialized hyperscalers will be here.
Would that require a watershed event to clearly establish the importance/risk of privacy though? For example, right now it seems like most big software companies w/ strong security process are comfortable uploading entire codebases to Israeli cybersecurity firms for vulnerability scanning compliance purposes
The thing about not much difference between models and the harness making them deterministic and useful is wrong. Also models have different strengths and weaknesses and some are better at almost everything by a large margin compared to others.
As for your speculation, I think it's hinging on some companies releasing models for free or no big differences between models. In a world with hyperscalers and companies training models you can quickly recreate Anthropic or OpenAI by having an hyperscaler ally with a model training company, train a good/a better model, and not release it.
> Their marketing depends on people believing the models are meaningfully different, as people have sweatily argued on this forum.
They are noticeably different. Benchmarks, anecdotes, all say the same thing.
Now, is a ~6 month lead actually worth 1 gajillion dollars? Maybe not.
You do realize OpenAI survived for years without LLMs, right? There is still more AI research they can do as a lab even if they stop experimenting with LLMs.
I think they didnt have the amount of debt /negative cashflow as they do now.
That is on OpenAI Group PBC. If that bankrupts it doesn't bankrupt OpenAI, Inc.
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Just like opensource search engines killed google
oh wait
I don’t even know the names of any open source search engines, but the open source models perform decently on various benchmarks and in personal experience.
Was it ever even a claim that open source search engines were trying to outperform google, let alone kill it?
Yacy tried in the 2000s. I'm sure some magazines made headlines posing the question whether yacy is a google-killer
Marginalia already gives me better results for many queries than Google. Because Google has sunk so low.
Open models are 4k TV (or maybe 1080p tv now and 4k TV soon) and SOTA frontier models are 8k TV. Can I or the average user tell the difference? Not really. Would they pay for that difference? Not a chance. Our entire economy is teetering on some future hope that this fragile and immaterial difference will pay off, when the reality is that LLMs are a race to the bottom and eventual razor thin margins. Maybe a tiny vocal subset of programmers can use it for work and make paying for it worth it to them, but that can't prop up an entire economy, especially when said programmers are phased out, jobless, and replaced by AI with each better iteration...
Except when we upgraded to 1080p from older TVs, they got bigger. Now with 4k they are getting bigger yet. More powerful models means new use cases that didn't make sense on the weaker models.
Even in the world where all models are basically equivalent (a thesis I don’t buy, but will grant you for arguments sake) - I believe there is much more to the AI business than just training and running models.
It’s a very new set of technologies, and understanding what is useful to customers and what isn’t is the whole game. Call it, product taste. There were a million cell phones before the iPhone took over the world. Why iPhone? Product taste. There are a million startups, and only a select few become unicorns. Why? Product taste.
>There were a million cell phones before the iPhone took over the world.
You have tripped yourself up there.
iPhone took over as it introduced something innovative over standard phones, but then Open Source (Android) matched the multi-touch and software differences and Apple's branding, lock-in and design etc have managed to keep it as a big player in wealthier countries. IPhone also came on the back of the massive iPod success.
ChatGPT launched the same innovation vs Google Search, but just like Android Opensource AI is moving fast now.
Android has 72.7% market share at present, Open Source AI will do the same unless the frontier labs can continue to do something new.
The frontier labs are saddled with enormous investor and other debts. How long they can keep innovating by spending so much on R&D and paying there staff very high wages remains to be seen.
Once investors cash out via an IPO, the companies are back down to earth and playing in the real world again.
Android has market share, but Apple makes all of the money! I find it really funny when people attribute Apple’s success to “oh, the only reason they succeed is design and marketing.” Yeah, I mean factually speaking design and marketing actually do matter a lot!
Us developer types like to pretend like specs are the only thing that matters? If you could have a 10x more powerful model you could only access running locally through your terminal, versus a weaker model through a clean web interface, normies will pick the web ui every single time. Product experience is simply everything, as much as we like to pretend like nitty technical decisions are the most important thing.
> Android has market share, but Apple makes all of the money!
So? The benefit of open source is that you don’t have to worry about making a ton of money. You just need to be viable.
Apple: premium product a minority is willing to pay for
Android: standard product the majority use
I’m sure there will continue to be iPhone equivalents in the AI world, premium bespoke models. But the vast majority of people will be happy with a cheaper offering.
The original comment was “open models are what kill OpenAI and Anthropic”, which to me is as silly as saying “Android is what killed Apple”
Well I think a critical difference is that, unlike Apple, OpenAI and Anthropic have taken on so much VC funding that a 20-something % market share is not going to be enough for them. So open models could kill them, not because of the techonology but because of the way they're financed.
> If you could have a 10x more powerful model you could only access running locally through your terminal, versus a weaker model through a clean web interface, normies will pick the web ui every single time.
More like if you could have a 1.25x more powerful model that you could only access through some weird surveillance megacorps aggressive monetization scheme, or choose from 100 others running open models and accessible through 100 different interfaces pandering to every taste.
Normies will choose the megacorp every time, because that was the one in the tv commercial, and within six months will have left for one of the others in a rage.
The only corporate hope is that the government steps in to ban their competition.
There were many smartphones before both iOS and Android.
While that may be technically true for a strict definition of “smartphone,” there’s no denying the iPhone redefined the concept in a way that its competitors were forced to copy to have any hope of keeping up. Nobody hears the word “smartphone” and thinks of a Blueberry or Treo anymore.
What exactly did the iPhone do better?
Perfected multi-touch touchscreen
Before that we had touchscreen but they sucked.
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I really don't think multitouch is what made or broke the original iPhone.
That's a subjective question, so I'll give a subjective answer. The browser, for better or worse, was a lot less dumbed down for mobile than competitors, the stylus-less touch interface reduced UI friction and the odds that you'd lose a critical (if inexpensive) component, and the slew of contemporary iPod users could easily migrate their libraries over.