Theres more than just chatgpt and American data center/llm companies. Theres openAI, google and meta (american), mistral (French), alibaba and deepseek (china). Many more smaller companies that either make their own models or further finetune specialized models from the big ones. Its global competition, all of them occasionally releasing open weights models of different sizes for you to run your own on home consumer computer hardware. Dont like big models that were trained on stolen copyright infringed information? Use ones trained completely on open public domain information.
Your phone can run a 1-4b model, your laptop 4-8b, your desktop with a GPU 12-32b. No data is sent to servers when you self-host. This is also relevant for companies that data kept in house.
Like it or not machine learning models are here to stay. You can self host open weights models trained on completely public domain knowledge already. It actually does provide useful functions to home users beyond being a chatbot. People have used llms to make music, generate images/video, see images for details including document scanning, boilerplate basic code logic, check for semantic mistakes that regular spell check wont pick up on.
Models around 24-32b range in high quant are reasonably capable of basic information processing task and generally accurate domain knowledge. You can’t treat it like a fact source because theres always a small statistical chance of it being wrong but its OK starting point for researching like Wikipedia.
My local colleges are researching multimodal llms recognizing the subtle patterns in billions of cancer cell photos to possibly help doctors better screen patients.
The problem is that theres too much energy being spent training them. It takes a lot of energy in compute power to cool a model and refine it. Its important for researchers to find more efficent ways to make them, Deepseek did this, they found a way to cook their models with way less energy and compute which is part of why that was exciting. Hopefully this energy can also come more from renewable instead of burning fuel.
Most of these companies offer direct web/api access to their own cloud supercomputer datacenter, and All cloud services have some scaling with operation cost. The more users connect and use computer, the better hardware, processing power, and data connection needed to process all the users. Probably the smaller fine tuners like deephermes that just take a pre-cooked bigger model and sell the cloud access at a profit with minimal operating cost do best with the scaling. They are also way way cheaper than big model access cost probably for similar reasons.
OpenAI, meta, and google are very expensive compared to competition and probably operate at a loss. Its important to note that immediate profit is only one factor. Many big well financed companies will happily eat the L on operating cost and electrical usage as long as they feel they can solidify their presence in the market in the coming decades. Control, (social) power, lasting influence, data collection. These are some of the other valuable currencies corporations and governments recognize that they will exchange monetary currency for.
I assume you mean in a tech progression kind of way. A better comparison is that its being treated closer to the invention of transistors and computers. Before we could only do information processing with the cold hard certainty of logical bit calculations. We got by quite a while just cooking fancy logical programs to process inputs and outputs. Data communication, vector graphics and digital audio, cryptography, the internet, just about everything today is thanks to the humble transistor and logical gate, and the clever brains that assemble them into functioning tools.
Machine learning models are based on neuron brain structures and biological activation trigger pattern encoding layers. We have found both a way to train trillions of transtistors simulate the basic information pattern organizing systems living beings use, and a point in time which its technialy possible to have the compute available needed to do so. The perception was discovered in the 1950s. It took almost a century for computers and ML to catch up to the point of putting theory to practice. We couldn’t create artificial computer brain structures and integrate them into consumer hardware 10 years ago, the only player then was google with their billion dollar datacenter and alphago/deepmind.