<a href="https://archive.is/kmOqm" rel="nofollow">https://archive.is/kmOqm</a>
Every larger company I talk to these days has an active project on moving away from OpenAI and Anthropic to open models. And they’re actively shifting, as the article says, so the threat is far from theoretical.<p>Unless they both dramatically slash prices then they’re in big trouble. Neither of them can afford to do that and both desperately need to convince the street that the opposite will happen if they want any hope at a successful IPO.<p>However the cold reality for both is that there is zero moat to a model anymore. It’s a pure commodity. Those selling compute and access to open models are gearing up to wipe the floor with Open AI and Anthropic.
This is exactly true, I get annoyed by Claude one day and switch to something else, and the only thing that's ever keeping me tied towards Claude is the ability to search my old chats easily.<p>But Claude also makes it really hard to do that, so what am I even really paying for? Time to extract all my data, put it into a sqlite with FTS5 and make sure I never rely on the overly-opinionated, low-thinking PMs from these giant orgs again.<p>Of course, that "easy" step has lots of partial solutions like CTK (Conversation Toolkit) or MyChatArchive and I haven't found the perfect one yet, ideally it'd be something that dumped everything into Obsidian or an Obsidian-alike, but surely somebody is working on that? I'd pay $5/month for somebody to solve that problem for me, as long as I still owned the data...
Maybe open AI and Anthropic could just license their models to run on your own hardware. So a fixed cost instead of per token pricing or subscription with limits
I knew that there was no real moat from the very start, I mean, these things were close enough from the very start, how could it not result in a race to the bottom, especially as you can't really prevent distillation reliably?
I support and use open models as much as possible, but I'm not totally convinced that OAI or Anthropic have no moat, even as open models catch up to the frontier. Serving and inference are still hard problems when you're talking about a 2 trillion parameter model. Fine-tuning, if that remains a realistic need for businesses, is also a difficult infra problem at that scale. In the most bearish case, where there is no competitive advantage to using their models, big labs still have an advantage in this area.<p>Maybe there is some threshold where the price/quality math for your standard business tips in favor of smaller models and self-hosting the entire stack. I'd certainly love that.
Also people are people and they will get emotionally attached to claude :)
True.<p>The problem is serving is a skill readily mastered by the hyperscalers. That's their MO.<p>All they need is weights to serve. And the open models provide that.<p>OpenAI is relatively well placed in that they have inference chips they've designed and they own compute.
A race to the bottom is where you lower standards, wages, or regulations to cut costs and attract business. What's actually happening is the opposite: a race to the top. Every model is trying to get better. Simultaneously they also happen to be getting more cost effective, but it's sort of a coincidence. Companies still require very good models, but they are not picking the "absolute best at any cost" anymore, because it turns out "any cost" isn't worth it.
They have good friends in the big ballroom to not allow you to use something cheaper and be locked in on them for your own safety
If companies are really doing this, then we're saying they have no problems spending tens of millions to get somewhat decent TPS and then having their employees complain they are timesliced and getting lots of timeouts because their org has 500 employees?
At least accord to the MIT study last year, most employees are using their own AI subscriptions to do work.<p>Keep in mind that a vanishingly small number of workers are SWE's churning millions of tokens daily.
This is all spitballing, but I'd wager it's a third the type of workloads, a third hedging against your business depending on a single external provider, and a third trust.<p>Not everybody is coding or doing work that lends itself to burning tokens for warmth. Reuters for example seems to be more interested in using it for research, editing and formatting citations and the like. There's only so much of that work that needs doing, it doesn't always need to be real-time, and they probably don't see it scaling exponentially. They also need to be very aware and in control of their model's biases, or they risk it compromising their work output.<p>It's widely expected that all of the major providers will need to - and surely <i>want</i> to - drastically raise prices to justify the ludicrous amount of capital they're burning. Multiple companies have already talked about how their AI costs have exploded, and from what I understand that scale of enterprise is paying API rates. I would be disappointed if big business wasn't having a think about what that liability could look like. It's one thing to be reliant on a relatively "stable" vendor like Microsoft for Windows and Office, another to get AWS sticker shock, and then this is promising to be an order of magnitude worse.<p>Then just plain trust. What if ChatGPT starts recommending your competitors products, or the USA bars export of Anthropic's latest model (again, but for real this time), or they stop serving a model your business now depends on, and so on... That's a lot of risk to leave outside of your control.
Open weights != self hosted
I'm not sure why someone hasn't developed a company offering services that distributes AI across all idle or under-utilized VM's and PC's for enterprises in order to serve open sourced models. Outside of the electricity bill, there's no additional expenditure and you get the AI.<p>We've all seen the office spaces where there's 200 empty computers on a floor. Combined, it's something like 500 cores at ~3 Ghz each and around 3 TB of RAM. The networking is already there and software like exo already exists.
There are most definitely is a moat - but it works both ways. The railguards in the models create moats keeping customers out. And the cost to build a modern agentic model is in the 10 figure range and growing. This is an expensive arms race that is going to create moats.
But most commodities are the same way. It’s super expensive to drill for oil. I need oil and I’m in no position to mine my own because of the massive capital investment. But it doesn’t stop it from being a pure commodity.<p>I couldn’t care less which company drilled for the oil… it’s all the same to me. Models are increasingly no different.<p>OpenAI and Anthropic are a gas station saying “buy our gas for 10x the price!” When the world is looking at them saying it’s just gas, we’ll take the cheaper brand. We’ve tested your gas and it’s really no better than the stuff that’s 1/10th the price.<p>Thats why their present business plan is screwed.
I argue there's no difference. At least OpenAI/Anthropic can be considered premium like Octane 93 while OSS ones are 87.<p>I agree 90% of the world can work with 87 gas, but there's always niche/luxury market where 93 can make small difference.<p>(edit: typo)
Yes… and as the article says folks are still leaving some work to the big labs. But the big money is to be made at scale and those use cases don’t require OpenAI or Anthropic.<p>The crazy setup here is that even with that fraction of the pie these companies might be worth say $100 billion optimistically, which would be amazing in normal times. Problem is it’s a train wreck for their investors and the associated debt bubble if they can’t sustain a valuation of 1-2 trillion and the present setup does not put them on a course to that trajectory.
Of course. But that market won’t produce a $800 billion company, unless AI becomes ludicrously widespread — energy is used every day by virtually every person on the planet, and of course has plenty of mass consumption & “luxury” customers too.
So the business challenge is to balance sizable investments and relatively small marginal costs. Not so different from other digital goods.
Fair assessment. The challenge for OpenAI and Anthropic is that they need sizeable margins to pay for the massive costs incurred. Market forces are driving things in the opposite direction and fast.<p>When your competition has a tiny cost base compared to yours and lacks the bonkers future capital commits you made then that’s a terrible position to be in… hence their conundrum.
With other digital goods the distribution and operating costs have been essentially free. No business worried that much about the cost of running Microsoft Office on the PCs they already distributed to their employees. They were only concerned about the licensing costs. And Microsoft didn't worry about the cost of printing CDs or the costs of serving Office online. It wasn't zero, but again negligible compared to the cost of development and the licensing costs.<p>For LLMs the costs of training and inference are a very significant part of the overall costs.
Except OpenAI and Anthropic has brought in a lot of money, that with this trajectory will make it some of the worst investments in ”software” ever (if it’s true enterprise clients are actively moving away, I know we are but for other reasons).
The key difference is that the models upgrade multiple times a year. It is an inherently different than a commodity market
But they're all converging on capability. Do I care if it's a 72% or 74% on SWEBench? Practically, probably not. And if I'm not paying per token locally, then if it takes a tiny bit longer to get to the result, I don't care.
I don't think most motorists would care if OpenAI's gas stations just released 106-Octane "Intersteller" gas, unless their cars specifically require it.
Yes, but changing models, even across providers, takes about two seconds and one line of code.<p>It’s literally the least stickiest thing in the history of tech. Which is a big problem for these companies.
Even commodity markets recognize different grades of product. The oil market separately prices different grades of oil, different refined products. All that really matters is that when you go to the market to buy, you can say, "I need X amount of this grade of this product" and that is what you will get. If AI models can be sold that way, you basically have a commodity market.
OK but even if F1 teams are a very expensive arms race it doesn't prevent me to bike to shop cheaply. You eed to have a moat around what people <i>need</i>.
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I know what happens in many big companies and not a single one is moving away from Anthropic/OpenAI/SpaceXAI.<p>> Unless they both dramatically slash prices then they’re in big trouble<p>False, they have already done so many times.<p>> Neither of them can afford to do that and both desperately need to convince the street that the opposite will happen if they want any hope at a successful IPO.<p>False, margins are higher and I can have a formal bet that prices will go lower.<p>> However the cold reality for both is that there is zero moat to a model anymore<p>False, LLMs are not fungible and there exists a natural moat. I like the behaviour of Fable, not the behaviour of Opus - the fact that many people speak about this is evidence.
I don’t know what your sources are, but I work on AI at a Fortune 100 and open models now make up >90% of our internal token spend. Used to be 100% closed before this summer.
Not sure who you’re talking to but the NYTimes reporting clearly refutes your statements that nobody is doing it with clear facts. It’s been a tidal shift in attitudes over these last few months and the messaging back to OpenAI and Anthropic has been clear. Slash your prices by an order of magnitude or you’re done for most use cases.<p>We’re heading into corporate budget season for 2027 when all this is coming under a huge microscope in boardroom after boardroom across the country at a terrible time for companies trying to IPO.
They kind of are fungible, up to a certain level of task. And much like most software developers don't need to exercise deep comp sci skills, most sw engineering doesn't have tasks that require the best models.
> like the behaviour of Fable, not the behaviour of Opus - the fact that many people speak about this is evidence.<p>I'm wondering how much of that is the harness vs the model. Overall, the 'feel' of a model seems to be largely due to the harness than the model itself.
> I can have a formal bet that prices can go lower<p>Is this a typo? Have you actually made a formal bet on a prediction market or something to put your money where your mouth is, or are you just saying that you <i>could</i>? There's a lot of things I could plausibly make bets on, but that doesn't mean that they're likely to happen.
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Its not that complicated, people use Cursor and that comes with the option to use other models but also Grok. Grok Bot is not used within enterprises.
> lower-middle class<p>Classism is the new racism.
BS - Grok 4.6 is a legit model.
I swear, Qwen 3.8 27B @ Q8 is smarter than Sonnet 5 most of the time. Why wouldn’t corporate America self host at this point, especially with better options like Deepseek Flash and GLM 5.3 flash that’s a middle ground between Sonnet and Opus
@q4 is definitely smarter than sonnet from what I’ve seen so far. It’s even caught problems in code made by fable, when using it as a code reviewer.
Agreed. And conversely, American models can also just as easily be secretly influenced for bad things, or be more tightly controlled by the government, to corporate America's own detriment.
Post-IPO I'd trust the American models far far less than the Chinese models.<p>The most insidious advertising in the world is about to be surfaced as people use LLMs to look for product recommendations.
America's own demise will be made in America, stamped by American laws
> Why wouldn’t corporate America self host at this point<p>Because they've been trained to think "cloud-first" for a decade?
is this actually the case? I haven't kept up with the small models<p>but if there's roughly Sonnet 4.6 level capable open small models, then I'd be impressed
> Some U.S. firms remain reluctant to use Chinese A.I. models because of concerns over regulation and data privacy. AT&T researches Chinese models but is not using them, Mr. Markus said. Instead, it is working with popular alternatives made by American companies such as the Gemma A.I. model from Google and the Llama A.I. models from Meta.<p>This makes sense since corporations require legal certainty, and using an open model from an American company (probably) provides them some level of indemnity, and also someone to sue.
The current U.S. regime is also replacing some amount of that legal certainty with regime fealty. Picking Chinese options over American ones probably runs a risk of upsetting their leader. I've got to imagine American companies are weighing this factor in their decisions.
That's certainly one factor, yes. But even before getting to that part I think the bigger issues for Big Corp legal teams is mostly around the legal ambiguity of the models themselves. What representations are made about the training data? What jurisdiction governs the license? If somebody alleges that the model infringes their IP, what rights does AT&T have?<p>Counterparty risk is a lot more straight-forward to evaluate when dealing entirely within the US, with US companies.
To me, the legal concerns seem blown out of proportion. If you use open weight models (of dubious origin) to generate code, you can still verify them with code review and tests and other methods, used to verify human output, right? That is still a great win, maybe not as much as having AI write all the code, but that would be a reasonable point in control vs quantity spectrum for most solid and well made products.<p>But if you let LLMs talk to people (customers, for example) directly, then yes, you need an LLM provider that you can hold responsible.
Haven't american companies outsourced manufacturing to China for a long time now? I think they have enough experience with dealing with China.
My knowledge is a few years outdated by now, but I remember digging into this and realizing that most of the chinese open-source libs were license-washing software. E.g. PaddleOCR is licensed under Apache 2.0, a very permissive license, however its models were often-times built on/fine-tunes of less permissively licensed foundation models such as Microsoft's LayoutXLM (Creative Commons Attribution Non Commercial Share Alike 4.0). (Which in my laymans understanding is also a kind of viral license in that changes need to be shared back under a similar license?)<p>The link is annoying enough to find that I can imagine "Mea Culpa" being an effective enough strategy for businesses moving into the ML/AI field, changing their tune after they get caught, but matured their own software to stand on its own feet.
Regime fealty has always been there in US. The current admin is just more corrupt and throughly incompetent at hiding it.
Exactly. See TikTok trouble as example and quite honestly, try a local open source LLM and ask it to use profanity, paint nudes - the LLM doesn’t answer the question of it is from OpenAI or Google.<p>The thing is that needs more attention is reverse engineered a LLM which is highly fascinating. I tried it, but it seems I am not there yet to put it mildly. It requires serious effort.<p>I am just speculating but can LLMs be sleepers? You write software and it seeds traces here and there under certain conditions that pose a serious security risk.<p>Or a kill switch?<p>I don’t know. I distrust Chinese LLMs but even more due to training data.<p>It is after all not a Western model. Different biases and the might be subtle but nevertheless substantial.<p>In short: no open source LLM may be usable without additional Finetuning for certain valid use cases.<p>The real value is versioning and autonomy as well as lot more stable answering despite model rot.<p>Also testing and the supporting systems are easier to maintain.<p>It is mainly an infrastructure challenge.
Read the license again.
> AT&T turned to artificial intelligence models from Anthropic and OpenAI in recent years to help with customer service, call transcription and coding. [...]<p>> By May, open models accounted for 20 percent of AT&T’s A.I. use. That has since risen to 40 percent and may jump to 60 percent in the coming months, Mr. Markus said in an interview.<p>This is missing a crucial detail. We know they "help with customer service, call transcription and coding", but which of those have been upgrade to open models?<p>Call transcription is <i>trivial</i> to do with open models. I can run Whisper or Parakeet on a low-spec laptop.<p>"Customer service" could mean a lot of things, but it sounds feasible for open models too.<p>"Coding" - they might go to open models for that, but I expect the costs involved in paying for closed models for software developers within AT&T are a fraction of the costs involved in transcribing all of their calls or handling aspects of custom service for millions of customers.<p>From later in the story:<p>> AT&T researches Chinese models but is not using them, Mr. Markus said. Instead, it is working with popular alternatives made by American companies such as the Gemma A.I. model from Google and the Llama A.I. models from Meta.<p>Gemma 4 is great, but really, Llama, in 2026?
They couldn't pick a more sinister headline for such an awesome technological development.
Ah, so you're one of those hippies hooked on free software too, yeah? What's that your smoking there? Emacs, huh? what's your OS? Linux? I <i>knew it</i>.<p>Corporal, put him away.
Its almost as if NYT has had a hawkish agenda for the past few ...generations.
I'd love to! For real coding though, SOTA models <i>barely</i> get the job done. It wasn't until Opus 4.5 that you could really get decent results.<p>I'm sure this will change (and I can't wait for it!) but as of today, open models might be fine for summarizing and writing docs, but you need SOTA to work on code if you want to be competitive.
Every time I check in on this I hear a more recent model is the one where they started doing good work. I'm excited to here that Astra is where it got capable enough to work on code next year
I do think there are now open weight models that are on par with (or beating) Opus 4.5 by now (e.g. Kimi K3, GLM5.3). But yeah obviously the frontier closed source models seem to have pulled away once again, so open weight seems to be a few months behind right now (which might be too long to wait for a lot of people!).
You're not corporate America (and trust me, I mostly mean that as a plus).<p>I also work in software, and while I vaguely disagree that open models can't be used (they <i>absolutely</i> fit into productive niches here, and holy hell are the last generation [ex laguna s1, kimi k3, glm 5.3, etc] actually decent) - I will agree that SOTA are a better fit for software development, especially when used in conjunction with an already very expensive employee who's driving them.<p>But for "Corporate America"... no. You absolutely don't need SOTA. They're doing things like transcription, summarization, customer interaction, minor technical tasks like form creation in existing tools, report generation (ex - powerpoint, pdf, docs, etc) and other general "white collar tasks". Think about roles in business that are in the 60-85k compensation range.<p>It's mostly busy work that keeps existing processes flowing and the business on the rails. Important, but not research/novel.<p>And cheap ai... is a wonderful fit for a lot of this. No one wants to replace an employee making 80k with a less reliable AI that costs 45k a year in tokens (SOTA). But they're absolutely willing to drop 2-3k/year on AI (~100/month - right in the open model cost range) for that employee if they can get a 10% bump in productivity or happiness.
"real coding" carries a lot of the weight in that comment.<p>Seems like if you ask 5 different people what "real coding" means you might get 5 different answers.<p>Not everyone is building the next framework or compiler.<p>Self-hosted Qwen 3.8 @Q4 on my RTX 3090 can produce beautiful functional CRUD pages and apps all day long. And that is 90% of the "real coding" being done in corporate settings.<p>The quote in the article about Mazda vs. Maserati captures this. Many might want the Maserati and drool over its specs and capabilities, but balk at the cost and how often are they really going to run it up to full performance limits on their daily commute to their cubicle?
Yeah, I totally agree. I'm sure these open models are more than good enough for non-dev work. I'm also sure they'll be good enough for dev work soon enough (and some people are saying the latest already are). My point was that given the difficulty of writing great code and dealing with large systems, SOTA just recently emerged as a viable option. I expect open models to catch up soon.
Good luck to the poor shleps trying to make a living performing "white collar tasks" I guess, right? They can all go be poets or painters...
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Anecdata:<p>I use opensource models at work because my work is too cheap to spring for a $20/mo account for me. Since HuggingFace models can be run on my laptop now (still very slow though), nothing is leaving the 'secure environment' and so I can actually get work done (instead of the 'old' version of coding and writing - google).
My long term guess: A&OAI will move away from being interference providers to just training models and then licencing the models for local use
It's just Linux all over again. Except nowadays open source isn't a "cancer", so it will happen faster.
IMO this will be a blip. There’s a lot of talk in the wake of all the Uber handwringing about token spend. Legacy enterprises want to look innovative to Wall Street without spooking them, so it’s easy to hop on the narrative and “show” that they’re innovating in a cost responsible manner.<p>This feels reminiscent of the big push to RAG a few years ago. And, more broadly the skunkworks projects that big companies tout in the press before they end up killing, when the operational overhead becomes too much for their liking.<p>Ultimately, the narrative is good for the consumer and the enterprise. It’ll mean OpenAI and anthropic will have to keep prices low. But ultimately, in the course of the next 10 years, I don’t see enterprises wanting to do this themselves. It’ll just be simpler (and eventually safer in their eyes) to send traffic to the big labs.
I had a conversation with a large group of friends and we independently came to the conclusion that Openai/Claude does not deliver more than a open source model. It takes about the same and the quality is about the same, and this does not mean it is good
My hot take is that open models don't really save you money and introduce more router complexity and security risk (because you're now sending your company data through more less trustworthy providers). Look at cost per task not cost per token and the pareto curve is largely owned by closed models.<p>Just use Fable 5.1/Opus max for the hardest problems, GPT Sol high as your workhorse, and maybe terra for async batch stuff you don't really care about. Gemini 3.8 High also looks pretty good and is quite fast if you're already a GCP shop. You can basically benefit from open models without using them because they force the frontier models to be cheaper.
I don't think the "don't really save you money" hot take holds water in every case.<p>Coding, maybe.<p>But for operationalized/repeatable tasks it definitely does.<p>For example I have a workflow that I was running in April that effectively would cost $30k in token spend for each full run.<p>However now, with GLM 5.3-flash, we've brought the cost down to $7k-9k with our evals showing we've had no loss in recall, precision etc..
Maybe the bubble doesn’t come for all of us maybe it comes for Anthropic and OpenAI.
I think using open-source AI is no longer about API cost but about company survival.<p>Take Anthropic for an example. Anthropic has successfully destroyed customer trust, at least for me. DHH in a recent interview mentioned that Claude refused to translate an article about immigration. Not summarize. Not editorialize. Translate! I think this reveals an unacceptable level of paternalism: Anthropic fundamentally believes that it possesses a moral authority superior to the people actually paying for the API. If such basic and mechanical translation is already too sensitive to touch, the goalposts have moved from safety into outright censorship. What prevents them from quietly deciding tomorrow that your proprietary business logic, financial data, or legal documents cross their invisible moral line?<p>Let alone how Anthropic treats Cursor and Figma - not that they are wrong as companies are free to compete legally, but nonetheless it shows that companies can't outsource their intelligence to a potential competitor.
I get what you're saying and it's concerning how much power these big labs have amassed and how little transparency there is in what they do with it...<p>But I doubt this a major factor in the trend. I just don't think it's something most corporate users run into. My understanding is these guardrails are negotiable for enterprise customers anyway.<p>And, not for nothing, but if I owned a human-powered translation company I would've refused to translate it too.
I like the Claude constitution overall - I hope it becomes something representatives vote on and amend, to avoid the centralized corporate censorship you describe. In the meantime, I am fine with it abstaining from doing DHH’s bidding, especially because there are so many AI alternatives.
Ah, yes, I'm sure the article that moral paragon DHH wished to translate was not at all harmful, and that this was a good-faith effort on his part /s<p>While I agree that Claude can be overly paternalistic at times, how <i>should</i> it respond to a request to translate, say, bomb-making instructions? It's reasonable to me that it might refuse this.
At our small company we are hooked on individual subs. But yeah a larger dev shop can't really pull that off and I get how they'd be dying by the token cost.
I think what they're really getting hooked on is the lowest cost provider.<p>Which makes the Muse 1.3 launch this week particularly interesting, although to get the low cost version you do need to agree to share data with Meta.
I actually think they’re hooked on models they can fine tune.<p>You can’t further train the closed models. The open models can be fine tuned for your company. Big companies fine tune models on all the internal systems and documentation, not just through .md files (you’d blow up the context trying it that way) but actual fine tuning of open weights models. A low tier but open weights model actually beats frontier models when you do this for a specific task.<p>I think the frontier providers need to have a way to isolate instances (bedrock style?) and allow fine tuning to compete. Big companies are absolutely fine tuning models right now and getting better results than even the best frontier models for their use cases.
What IDE/extensions do you use for open-source LLMs? I tried VSCode with ollama and lm studio, and the experience is very subpar to the built-in copilot. It's not very usable.
The innovative edge markup already faded and the race is to the bottom, more features, more reach, less cost. It's going to be extremely hard to recoup those giant investments. No, the bubble won't pop, it already popped and morphed at the speed of AI that we didn't even notice, money just realigned, llms keep pushing the frontier, and peripherals are gaining momentum<p>The race is still on
The thing is, for big companies (or even small ones owned by PE, which is MOST of them), it's not just about cost. The big thing is <i>risk</i>.<p>In my experience as a tech diligence assessor for PE firms for the last 7 years, investors really, really don't like companies being beholded to single entities that they don't control. Anthropic and OpenAI have demonstrated that they are not trustworthy, or predicatable, or finanically safe, or even capable of hitting three fucking nines. Investors know they need companies to be on the AI train, but they really don't like vendor lockin to the big AI companies. Every diligence I get asked "how easily can they change models?"<p>I think when open models reach 80% or 90% capability (or maybe even less!) a whole lot of companies are going to say "almost as good with way less risk is a better deal".
I'm not seeing it. Corporate America needs someone they can sue if anything goes sideways with AI given the rate of change and legal ambiguities. It took years/decades for actual, real, open source to be widely adopted in corporations for the same reasons.
Corporate America has been using open source for decades, and it wasn’t anywhere as slow as you portray. This argument simply doesn’t hold water.<p>Besides, in their present rather dire financial state there isn’t much to sue these companies for anyway cash wise. NYTimes is suing on IP grounds.
Easy prediction: LLMs will get shrunk down further and further until GenAI is just something that ships on a chip as part of your hardware. In the future it will seem quaint that we needed a network connection to talk to our LLM.<p>Adoption of open-source models to my mind is a similar step in that direction. In all cases, the goal is to become untethered from a mercurial vendor.
We're gonna start baking in models like TTS with thousands of voices available in any language as a chip on device. They just need to hit 99% accuracy and then it's a done deal.
Like taalas.com (very recently acquired by AMD), or cerebras.ai (whole wafer is a chip)? As you said, I also think that is one of the main direction many companies (and academia) is moving to.
Yeah, I'm looking forward to this actually.<p><a href="https://chatjimmy.ai/" rel="nofollow">https://chatjimmy.ai/</a> blew my mind at how fast etched model weights can be.<p>For on-device LLMs, there's a point of diminishing returns, meaning you don't need to have the latest frontier model for most operations.
for every small GenAI model there will be larger model or cluster of models which are smarter than small model
How is the NYT's copyright lawsuit against OpenAI going and why have you abandoned your start witness Suchir Balaji?<p>Have you been brought into line? Open source AI also violates copyrights.
Oh no! I'll make sure to tell the Chinese companies about the copyright risks.
Most open source fans are also hostile to copyrights existence and are openly IP abolitionists. As such, they collectively respond with "good."<p>This is actual communism, and the fact that Bernie Sanders and every other member of the DSA isn't actively fighting for open source and is often fighting against all AI shows how fake their purported movements are and have always been.
If IP were entirely abolished tomorrow with no other change to our economic system, you would still not have universal Healthcare (maybe drug prices would be lower, though), Elon Musk could still donate however much he wants to get his preferred politicians elected, fossil fuels would still be used in amounts that destroy the world, etc. Very importantly, AI would still be used to try to manipulate and control the public.<p>The fact they prioritize other fights more than OSS, and have a rather dim view of AI, is hardly proof that they are fake.
There's no reason that one's feelings about AI can't supersede their feelings about open source. We all live with a complex tapestry of values.
While I don’t disagree that they should be fighting for open source AI, Sanders is hardly “fighting against all AI”.<p><a href="https://jacobin.com/2026/07/ai-nationalization-sanders-libertarians-property" rel="nofollow">https://jacobin.com/2026/07/ai-nationalization-sanders-liber...</a>
Who cares about power user fans? They don't own the copyright.<p>Open source authors have always been protective of their copyright. There are numerous examples when drivers have been copied between BSD/Linux (I forget which direction) which led to huge flame wars.<p>The whole point of the GPL is that it uses copyright <i>and copyright assignment</i> to the FSF to protect what it calls software freedom.<p>BSD authors are very upset if the attribution clause isn't observed. And so on.<p>It is communism to exploit poor open source authors? I have to read Marx again.
Wanting to replace monopolies with competitive markets is the strangest definition of "actual communism" I have ever heard.
Anti-capitalism does not automatically qualify as communism. Notably, if IP were to be abolished, it doesn't belong to anyone. The expectation that open anything includes some sort of DRM-like content gating is counterproductive.
> Most open source fans are also hostile to copyrights existence and are openly IP abolitionists.<p>Open source licenses are only enforceable <i>because</i> of copyright law. How are you going to enforce GPL3 when you have no legal authority to say what people are allowed to do with your code?
Some of the most insidious parts of AI infrastructure includes the embedding model. Corporations have already spent an outstanding amount of time and money creating embedding vectors that are closed source and not reproducible. This means that all their data is locked into whatever embedding model they chose initially.<p>I highly recommend utilizing an open sourced embedding model instead of paying for a closed source one. It's vastly more reasonable to run an open sourced embedding model as a first step. They're much, much smaller and, due to the overhead of network latency, and running it locally has almost the same speed as through an API even on slow computers.<p>I would even go so far as to say that closed source embedding models have a high risk of data hostage. If a team doesn't have access to the embedding model, the embeddings become useless. A corporation like OpenAI could, say, hike the prices to that model by 1000x and everyone would have to pay up or forfeit any utility of the data.<p>I envision a future where open source embedding models are shipped with relevant technologies and implemented by currently under-utilized chips like NPU's. A startup developing cheap microprocessors that can run them is an idea I would pay cash for. Or perhaps they will be bundled with security tokens.<p>While it might be impractical for all corporate teams to run language models, it is very realistic for everyone to operate an open sourced embedding model, at least in their private cloud. Better yet, utilize transfer learning on an open sourced one to train your own, that way the embedding vector is more secure against competitors and trade secrets.
I have recently come to the conclusion that thinking for 2 seconds and using a cheap model with a slightly more detailed prompt works just as well as zero-shotting an idea with a fancy model. I work in science, and instead of asking the model “write a topic extraction algorithm”, I just say “hey look at this matrix factorization script I found in a repo, now make it use plotly and duckdb”. Have others come to the same conclusion here?<p>It makes me skeptical that the flagship companies are sustainable. Every company is going to maximize “fuel efficiency” to save time and money.<p>Then again, maybe the cheaper models have more markup for them, in which case they are probably happy w this arrangement. I’d be curious to know how the money making varies by model.
Just use OpenRouter
Google can make themselves the heroes of the AI story by releasing a 120B dense Gemma model.
how much of performance comes from inference time tricks like scaling, topn ect .
maybe models providers are also in position to run their models vs running os models by a generic providerc
Meanwhile the rest of the world tries to un-hook itself from corporate America. Too many problems coming from the USA lately - it is not worth it to help sustain this anylonger. Canadians have realised this - others are realising this as well right now. Mr. Trump "no more forever wars", starting another forever war.
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