Corporate America is getting hooked on open-source AI (nytimes.com)

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.

I couldn’t care less which company drilled for the oil… it’s all the same to me. Models are increasingly no different.

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.

Thats why their present business plan is screwed.

Source? The proliferation of labs building competent models would seem to suggest the opposite.

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.

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 could? There's a lot of things I could plausibly make bets on, but that doesn't mean that they're likely to happen.

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.

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.

It's widely expected that all of the major providers will need to - and surely want 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.

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.

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.

Keep in mind that a vanishingly small number of workers are SWE's churning millions of tokens daily.

I think there will be separate and huge markets for models, hardware and compute. That will maximize competition and innovation.

Why? Because even Blind Freddy can see the huge usefulness and power of these (and future non-LLM) models and no-one in their right mind is interested in becoming OpenAI's or Anthropic's bitch. Those companies have tickets on themselves.

Given the recent behavior of tech companies and the US administration, no one trusts either anymore.

I’d try:

1 exporting my data (I imagine it’s common outside of GDPR?)

2 asking Claude to convert it to an easily digestible format :)

Don't rely on chat history. Have it write and maintain summary files that you can import into different sessions, at least for anything important.

Think about it: even if they distill the shit out of frontier models, the model still gotta learn, right?

If anything, as you can see from the K2 Horizon release, aggressive (self-proclaimed) reliance on distillation does not result in a model that has remotely any frontier capability. Try asking K2 Horizon to write iambic pentameter for instance, or even give it the car wash prompt. I tried both these on the Q8 quant for the 7B model and the results were depressing.

Chinese AI labs do well because they have the best AI people coming out of a huge talent pool.

One way or another they're getting the same results as proprietary labs, with a fraction of the hardware for a fraction of the cost. OpenAI can't keep raising funding rounds of $100billions to subsidize their compute costs and get results by brute forcing parameter count. And if they're having trouble keeping up with Chinese labs' efficiency, maybe they should stop worrying about distilling and instead hire some of the smart people responsible.

They’re burning cash like there’s no tomorrow. They desperately need to show that they have a real business and not just a giant burning pile of cash doing academically interesting things. If they could simply sell models that are 95% as good at 1/10th the price they’d do that. They’re losing the enterprise sector because they’ve not done that.

1. Competitors to OpenAI and Anthropic are good because of distillation.

2. OpenAI and Anthropic will come up with some methods for preventing distillation in the future.

Both of these are dubious imo. For RLVR tasks like coding in particular, you definitely don’t need continuous distillation to improve, otherwise OpenAI and Anthropic themselves would not be able to improve because there is no better model to distill from.

You're giving him too much credit if you think he figured anything on his own - this is the guy who thought 'metaverse' was a good idea.

All it takes is one incident, and all your company's internal data will start showing up in public users' chats. You can rely on a contract to prevent this, or you can guarantee it by using a locally hosted model you fully control.

When combined with the cost savings and good enough performance mentioned in the article, this can become a huge selling point.

OpenAI and Anthropic are fighting to win a race (build the biggest baddest model) that has no prize. The prize is mass adoption at scale at the best price, which is why companies are rapidly shifting to open model. They don’t need to pay 10x for a model that’s provides no practical additional benefit.

> As for intelligence, the frontier models from OpenAI and Anthropic are still superior, and they have at least a 3-6 month head start. Distilled models are closing the gap on some metrics, but they still can't compete. That's why they cost so much less.

I would argue that the reason they cost so little is because anyone can run open models and offer them as a service, so there's actual competition and the price is closer to cost. i.e. if the open models were just as intelligent as frontier models but cost the same to run as they do right now, the price wouldn't be higher (unless demand went up so high that marginal cost to provide more of the service went up, due to scarcity of hardware and or electricicy).

On the other hand, if what you're saying is the frontier labs have some pricing power due to their models being better, and that is the reason they are able to charge more than the companies providing open models as a service, then I would agree.

One of the basic questions/concerns here though is that it's not like the AI places are getting the GPUs for 10x less. It's true they have some economies of scale, but they also have some waste, and frankly in this particular case it's not clear they get that much gain over what a lot of businesses could achieve. The biggest traditional gain for central providers is that a lot of typical computing usage is burst-y, and in turn local kit might be underutilized. But with LLMs heavy users tend to use them all the time assuming their tokens allow it (and in the case of local hardware there's nothing stopping you, quite the contrary), they can use it directly interactively or leave them to go overnight on something too.

So it's reasonable to suspect that the reason subscriptions are only a fraction of the cost is that we're in a bubble seeing these companies losing money in an attempt to gain some sort of durable advantage. Just as every previous time, there is the chance that the music stops at some point, and they need to crank up pricing or pull other schemes to actually make money. Of course, it can be a good deal in the mean time, you basically get to suck down investor money for nothing, but it's also not unreasonable to at least be consider fallbacks. Even beyond questions of control and risk etc. I know at least a few places that are now genuinely considering questions like "what happens if a datacenter we depend on gets droned" that would have never had an iota of thought devoted to them even 5 years ago.

>On electricity, this is a surprising cost center depending on location. A system with just one 5090 can easily pull 1kW, and to achieve usable performance for a workplace is going to require dozens of machines. This can represent an extra $10-20k in electricity in cheap places.

I don't think that's "surprising" at all, everyone knows about power use. And this seems like it gets heavily into what you're defining as "usable" and is also more useful to define in terms of cost-per-employee vs total. Obviously a bigger business will have a higher line number total even if the cost per employee is identical, but simultaneously can be expected to be making more revenue to pay for it.

If we're defining an average of a dedicated 5090 pulling 1 kW for every single employee (presumably some people wouldn't use it all the time, but others would then pull the compute for other work), running 24/7 (to cover people running stuff when they're away), then that'd be 8760 kWh per year. At my not particularly cheap New England location that'd be about $1900 per employee per year at the generalized residential rate (~$0.22/kWh), or $156 per month. That doesn't seem radical if it really does boost productivity. However, there is a lot of room to go lower. I'd expect a business to run backup anyway, and these days there are a lot of incentives to do that at least partially with batteries. That also opens up rate shifting as another way to pay back the cost. If we change to time of day pricing, that's 8 hours of peak pricing with the rest off-peak. 8 kWh of battery can now be had for a few thousand. And the off-peak rate is only ~$0.14/kWh, cutting the cost per year by about $700 to $1200 per employee per year. Solar power is also usually far more valuable to use yourself then sell back to the grid, and also continues to plummet in price.

None of this is to say that it makes sense for every place at all, but it's close enough to the the line that the math is at least worth exploring, or could at least lower the cost enough to be worth it given other things. It really comes down to how much extra value the company (or individual) expects to come out of it per month.

>In California or Europe this could be $30-60k per year.

Dunno about Europe, but at the kinda prices I see for California I'm really surprised more places aren't trying to move a lot of usage to battery+renewable.

>The only real moat that local LLMs have right now is privacy.

I don't think resiliency and control are things that can be taken for granted anymore, particularly on the global scale. War and terrorism is getting worse again. International relations are getting nastier, and governments have the power to just order places cut off. If LLMs aren't particularly valuable to a business, then why an expensive subscription? But if they are particularly valuable, then insurance is something leadership should be contemplating.

I'll grant they are superior at least right now. But also, they are too expensive.

We ($work) are finding that it is best to build engineering discipline around AI usage (who would've thought!) and use the cheaper models like Cursor Composer.

Using Opus we can blow through an entire month budget in an afternoon, so while more powerful, it is no longer practical except for rare very complex tasks.

OpenAI and Anthropic rent their compute from AWS & friends. When we say large enterprises are moving to open weight models it means they are cutting out the middleman and renting the compute directly from AWS instead of giving OpenAI and Anthropic a margin.

> they have at least a 3-6 month head start

This is a moat of nothing. Our company still hasn’t gotten access to Fable so switching to open weight models would mean getting access to similar quality models. In some orgs they are still on 2025 models.

Depreciation is designed to _encourage_ purchasing of useful local tools, by incrementally matching fractions of the cost of the tool to the revenue it generates over its useful life. The fact that a graphics card might have a book value of $0 after five years of depreciation is a feature, not a bug.

Since the invention of corporation tax it has also had the benefit of offsetting tax over the same period, instead of just one big offset in the first year.

The important part is if they break the contract or raise their prices you can always move to a different provider. You get lower cost and lower risk at the same time which is extremely rare in business. That's just not possible for closed models where your only options are the official branded API or Azure/Bedrock.

It's the same with the subscriptions. Local models can't compete because they're simply giving too much value for money. They're effectively subsidised by Big AI. Again something that won't last.

Having trained on your own chips, that is the impressive part.

The most insidious advertising in the world is about to be surfaced as people use LLMs to look for product recommendations.

https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

Counterparty risk is a lot more straight-forward to evaluate when dealing entirely within the US, with US companies.

Why play ball with a hostile government when you can host your own frontier models?

Seems like if you ask 5 different people what "real coding" means you might get 5 different answers.

Not everyone is building the next framework or compiler.

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.

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?

But in terms of reliability - uptime, product, legal - they are in a different league.

There were exactly 0 instances waking up to a product decision at AWS completely breaking your product or workflows.

On the other hand, nobody trusts the big AI companies not to pull shenanigans or dramatically raise prices... or even be in business in five years.

I think ultimately the folks with the purses won’t care enough about a for it to be taken seriously, even if it’s an engineering bottleneck.

B definitely has scope but still smaller than I’d expect. When I was an intern at yelp, I was migrating us off internal credit card management to Braintree/stripe. No one would have imagined outsourcing that in early 2000s. There’s a long tail of stuff that you can’t sent to a 3rd party; but for most use cases it’ll suffice.

For c, true; but this is expensive for everyone, including the Chinese model companies that are trying to undercut Open Ai / anthropic. In the nth degree, i think the field will bring the cost down to the place where it’s manageable (see the existence of the cheap Chinese models). The real question is the $$ spent on pushing the research forward at scale.