alt Hacker NewsCorporate America is getting hooked on open-source AI
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Open source does not apply to AI and we should discourage anyone from using that term. All the models are opaque and proprietary. You cannot go into any source code and fix bugs, or add features, or study it to learn more. It's not the same thing at all as open source software.
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
> 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.
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.
I'd love to! For real coding though, SOTA models barely get the job done. It wasn't until Opus 4.5 that you could really get decent results.
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.
Spent most of today reworking a rack and rig of gpus for all of our internal ai work… our big server is 8 rtx 6000 pro and 3 psu, I definitely feel I made a mistake not upgrading our wall power to 240v but so far we have multiple 30amp 120v and with 3 PSU uninterrupted power we have been very stable. My big upgrade will be moving to epyc motherboard from threadripper so we get gen5x8 with bifurcation instead of what we are stuck with today gen5x4 due to bios limitations . What has been so encouraging though is first deepseek v4 flash at 200+ t/s for single user and much more in aggregate- now on qwen 3.8 flash next for image support and eyes on glm5.3 flash for some testing … next is realistically considering co location and quiet a sizable loan to scale this to real hardware instead of miner rip vibes
> AT&T turned to artificial intelligence models from Anthropic and OpenAI in recent years to help with customer service, call transcription and coding. [...]
> 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.
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?
Call transcription is trivial to do with open models. I can run Whisper or Parakeet on a low-spec laptop.
"Customer service" could mean a lot of things, but it sounds feasible for open models too.
"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.
From later in the story:
> 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.
Gemma 4 is great, but really, Llama, in 2026?
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.
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.
They couldn't pick a more sinister headline for such an awesome technological development.
The financials are incredible. If you spend one engineer's salary on hardware, you get a system running local AI that can multiply the efforts of an entire (small) team of engineers. It's a very "you can't afford not to" situation. Even considering the hardware prices today.
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.
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.
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.
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.
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.
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
I definitely see a future race of who can accelerate open models and their usage locally with realistic desktop hardware.
I fully await my ai in a usb box. The models are plateauing and some clever company is secretly working on this already I’m sure of it.
Imagine baking in a model weight set in ROM with an analog computer doing what otherwise takes way too much power in digital form.
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.
It's possible that LLMs will be a commodity in the future. Just like airline industry, AI will be tremendously important for society, but AI companies will not be making lots of money. It will be Nvidia, Micron, Dell and others shovel makers making money.
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
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 risk.
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?"
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".
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.
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.
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.
This is not a viable strategy. You're effectively paying for hardware and/or cloud. Why do this when OpenAI and Anthropic are both significantly subsidizing costs to win the market?
Talk to “AI” executives at large firms and 95% of them are clueless sales types that crawled their way to the top. Then again, it is basically a repeat of IBM, Microsoft, Oracle, etc. Same dumb executives making decision to not get fired and enjoy their place at corp.
The article clearly distinguishes between open source and open weights models, and the headline and parts of the article state that it's open source models that are having a moment. But it doesn't list any examples. The only model families explicitly named are open weights only.
Could someone clarify whether there are actual open source models that are competitive with the likes of Gemma (mentioned in the article), or is the headline just wrong?
Anecdata:
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).
It's just Linux all over again. Except nowadays open source isn't a "cancer", so it will happen faster.
Been wishing my company was doing this. We are locked in with chatgpt for chat and claude for coding. I have a 36gb m4 max laptop but I'm not allowed to run anything on it even though it would be totally free. Absolute pain.
Maybe the bubble doesn’t come for all of us maybe it comes for Anthropic and OpenAI.
I think what they're really getting hooked on is the lowest cost provider.
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 think using open-source AI is no longer about API cost but about company survival.
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?
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.
A lot of corporate use doesn’t require SOTA models. Things like summaries or writing comments in Slack are light work. For those reasons, local AI makes a lot of sense for some companies.
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
The amount of downtime the big AI companies are showing doesn't help. Do you want your company's sales and customer service to shut down every time OpenAI or Anthropic goes down?
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.
How is the NYT's copyright lawsuit against OpenAI going and why have you abandoned your start witness Suchir Balaji?
Have you been brought into line? Open source AI also violates copyrights.
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.
Gotta love this article's use of both the terms OpenAI and open A.I. which are pronounce the same but mean very different things
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?
It makes me skeptical that the flagship companies are sustainable. Every company is going to maximize “fuel efficiency” to save time and money.
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.
Contrary to what is mentioned in the thread there are no seats anymore, it is all raw token usage nowadays.
Google can make themselves the heroes of the AI story by releasing a 120B dense Gemma model.
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.
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
The race is still on
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.
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.
Just use OpenRouter
Copilot has enterprise controls. Its not open source. This is the easiest integration and large companies are eating it up as the path of least friction, using API cost calls. I call BS.
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Using models that are created as a psy-op by foreign countries will end badly.
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.
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.
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.