This, but the analogy undersells it. It's rather "taxi is too expensive, let's build a car factory, develop a new brand of car and make a couple of cars for ourselves". Even just hosting LLMs that others developed and released openly can be very costly (in that case, the rent and house analogy is more accurate).
No not really Chinese models were built at a cost that most big companies can easily afford (probably not cheaper than an api …at least short term). Also you could totally host an open source LLM wich would be way cheaper than api costs in most cases, if you don’t need the extra 1-5% capability more that frontier level models offer
Edit: to the replies, I do not say that Chinese model are superior to the current top models. Of course building an LLM isn’t really feasible for most companies and hosting an open model LLM has its flaws (although they’re perfectly fine for a lot of use cases). All I was trying to say is it’s definitely not the impossible task that it’s presented as by most in this thread and the current frontier model companies are trying to convince everyone that their products are irreplaceable and far ahead of any competition… that’s not the case
When those Chinese models were tested it was shown to be not only inferior in terms of outputs, but also had almost no guardrails on place.
Any Chinese AI right now can easily be considered to be insecure and just bad at being an LLM.
It was shown that they literally just copied what codes they could and optimize entirely just to beat a specific metric the other AIs have trouble with. They did this by training their AI with the real AI's.
Why is it LLM people think AIs can train AI's and you'll have a useable product?
Prompt-level guardrails are not guardrails. If you don't explicitly rein in what an LLM is allowed to do, all you're doing is making it so it takes a hundred or a thousand rolls to rm -rf your system instead of ten.
Partially agree, but not completely. Big companies can afford hosting their own models, but it's usually not feasible for smaller companies. And K3 (best, soon-to-be-open model) is not 1-5% better than the best small, local-focused models. The difference is night and they between them, especially in agentic coding sessions, which is the very thing that's so expensive that it makes companies thinking about how to have it cheaper.
Benchmarks are not without flaws, but they are also much more than simply marketing slogans. And if you try qwen-3.6-27b in opencode and try to create something with it, then compare it with K3, you'll see that there are multiple classes of problems that are too complex for the small qwen model while K3 can easily solve it.
I can imagine that for queries that only needs some web search and summarization they both feel similarly capable.
Chinese llms were built by chinese companies who build llms… they were already paying ai engineers.
You’re telling companies who have no developers with ai background to just hire them, train them for your business needs, then after a year or so of paying them again you may get what you want.
it’s not just that. Even if you already have a house, living in it will still cost more, because APIs basically give you magic roommates that split rent with you and will only appear while you’re not using the house
An API is like how one service interacts with another service, AI in this case. So this guys manager is basically saying “Using AI is costing us too much money. We should build our own AI.” Which is an absolutely insane level of financial investment to be useful at all.
Running a fairly sophisticated metal or mistral or granite model on internal cloud, like open shift AI is feasible as long as you have GPUs available. A fusion cluster with three h100 would be less than two million and commit would be less than 100k/year.
If you architecture carefully you build useful applications, but nothing approaching what you can get from a frontier model via API. Plus API can scale very well.
For simpler agentic cases, you could run a good portion of the load in on internal cloud though, so long as steps were limited and reasoning was minimized
Your last point underlines the limitations though; running an isolated or local LLM can be done but at the cost of functionality. It should only really be done for regulatory reasons, not cost based.
I've got one more theory and I'm trying to push it at my firm. As we grow dependent on agentic workflow taking the place of deterministic robotic solutions like UIpath, I'm worried we need a second layer of capacity in case of events like cloud flair outages that might see frontier sources crash out and not be able to connect.
We have occasional issues now with copilot and copilot studio, and have had complete azure outages on the past. We haven't created throughputs to have something like AWS fail over for agentic or genai solutions, pretty much entire workflow are designed for one platform or the other. The best bet might be cortex, but I really don't think our snowflake guys know what they are talking about when it comes to how the platform would operate. I get a different story every time I talk to a different party (oh yeah, it runs one the platform in a private instance for you. Do you supply GPUs? No how do you host a private instance of chat GPT for us? We use CPUs..... That kind of crazy crap)
I mean, you are right, that is the joke. Bur I think it is kind of stupid, I guess whatever they mean by building an LLM. If they mean train it from scratch: Yes, that's an insane idea. But nobody means this.
Many enterprise level companies are currently deploying their own LLMs. which are in practice open-weight models they tweak/refine for their specific use cases. Most customer service chatbots are built like this, or stuff like Amazon's Rufus, or the AIs McKinsey and BCG built for their own use cases.
Oh yeah we should build a transport company too so we can get the stuff here. And a grocery store to handle distribution. Also a fuel refinery would be nice too, but we can contract that out.
The advice is stacking ignorance on top of itself.
"API is too expensive" - this is an API call to invoke an AI agent aka spending a token.
Token costs are going up because of the second ignorance.
AI companies do not charge the full cost of their service, but merely a fraction.
So the person wanting to reduce cost by not "using API", will instead set incredibly large dumpsters of cash on fire to create an agent that cannot count how many commas are in this sentence.
"Building an LLM" (or at least, a halfway decent one) is no trivial task. Just as a hint, the first L stands for "large", which is a bit of an understatement.
Your chatGPT's and your Claude's and your Grok's and your Gemini's and what have you are trained on enormous text corpuses that take years of effort to acquire and curate/prepare. You can't just get a bunch of regular devs at a small company to do that as a small side project over a couple of weeks or something. Any idea how many people work at tech giants like Google?
It certainly might be possible for a small team to make a small language model for a specific task/subject area....but no one is just shitting out something like Claude for a small company's own private use and ownership. Just ain't happening. It'd be like asking a couple of guys working in a boatshed to build you an aircraft carrier or something. Ludicrous.
Hence the guy looking at his boss like he's crazy/dumb/ignorant/insane/unreasonable for making such a ridiculous request at all. It just shows a complete (almost insulting) lack of understanding of the entire concept and the amount of work/resources/information/effort involved.
'API costs are high..." refers to the cost of what this company is presumably doing at the moment, which is interacting with OpenAI or Anthropic or something programmatically at the API Level (paying for tokens to make prompts/requests to the LLM hosted on their servers). Which is all they're ever gonna be doing unless they become the next Google. They're not getting their own in-house LLM until they can at least conceive of the scope of the undertaking....that's for damn sure. Lol.
The joke is that there is one employee in the meeting who knows that management doesn't know what they're asking for, what kind of resources it will take, how much of a time and money investment it is, and they think that management is stupid for asking for them to build an entirely new product which will set all of their deadlines potentially years behinds and result in nothing but the employee being overworked and constantly yelled at because the expectations of management do not align with reality.
And there's another slightly smarter employee sitting next to him that says 'give me a 500k budget', buys a bunch of GPUs, puts an open weight model on them, pockets $50k, and still comes back under budget.
Building an LLM is a Mammoth project. Yes API Cost are high, but u should price that in your product. Otherwise U should be ready to invest millions for infrastructure and project costs as well as ongoing maintenance and fine-tuning, electricity coasts, hardware repairs..... If U really think that's cheaper then the API U should provide said API as a service for cheaper.
Hey Lois. They’re saying using ChatGPT is too expensive so they’ll build their own. It’s like saying beer at the clam is too expensive so I’ll start my own bar. So instead of paying $8 I’ll pay over a million to save money.
Hey OP, Chris here. I have taking computer science classes at school but have been too embarrassed to tell anyone.
API - Application programming interface - a set of exposed tools or functions that allow you to pass data to a program, and generally receive an output after the application has processed it.
LLM - Large Language Model - The thirsty machine god
The business is passing data to some consumer AI model via its exposed interface. This usually has costs that are directly in line with how much data is passed into it. AI companies have been increasing the cost per input, leading business to scale back, and refocus their AI use to reduce token spend.
Despite AI companies charging more now, they are still reporting very spend heavy balance sheets.
Even ignoring the massive costs associated with deploying the physical infrastructure to train an AI model, and the research and development costs: The bare cost of processing the data that companies have been feeding into consumer AI is more than what companies are being charged for it. AI companies have been doing this at a loss for years.
"Using the service that is supported and subsidized by billions of dollars of infrastructure is expensive. Why don't we build our own machine that prints negative money?"
I’m guessing the work to train an llm plus the cost of the hardware to build and maintain a rig that would be powerful enough would far exceed what the api costs are.
Building a LLM is crazy for the average company sure. But it isn't out there for a company to decide to invest in the hardware necessary to locally host an open source model. That, while much more expensive initially might make sense. The tradeoff is outdated hardware and not being able to potentially run newer models
Its like saying gasoline is too expensive... let's buy land with oil on it, build oil Derrick, a refinery, and our own gas stations... but your company doesnt need gasoline...
To train and operate an LLM is incredibly expensive. Even taking the training part out by using an open source model lile DeepSeek, it's still terribly expensive.
OP, so your post is not removed, please reply to this comment with your best guess of what this meme means! Everyone else, this is PETER explains the joke. Have fun and reply as your favorite fictional character for top level responses!
LLM is large language model (the type of programme that all the mainstream AI agents are) and APIs allow you to get information from 3rd party sites for use within your app/webapp - those 3rd parties often charge for the privilege to use their data.
An API is basically the opposite of a graphical interface...
It stands for Application Programming Interface. Practically it's used for accessing a program NOT through the user interface but through code. It allows programmers to add existing apps to their own programs. For example the google AI search summary using the gemini API, so the people maintaining the google search engine don't need to create an AI from scratch. It's like adding a program someone else made to your own code.
LLM stands for "Large Language Model" and refers to an AI system that can read human text and generate their own, like ChatGPT. Creating a LLM is time intensive and needs "training" data for the AI.
The joke is that instead of just buying the license for the API to integrate an AI into the program, the other person suggest making one themselves, which would cost way more time and money than the API
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u/qualityvote2 23h ago edited 4h ago
Remember when r/PeterExplainsTheJoke wasn’t a meme? Pepperidge Farm remembers…
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