Artificial intelligence systems like ChatGPT could soon run out of what keeps making them smarter — the tens of trillions of words people have written and shared online.
A new study released Thursday by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade – sometime between 2026 and 2032.
Comparing it to a “literal gold rush” that depletes finite natural resources, Tamay Besiroglu, an author of the study, said the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing.
In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models – for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets.
In the longer term, there won’t be enough new blogs, news articles and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private — such as emails or text messages — or relying on less-reliable “synthetic data” spit out by the chatbots themselves.
The primary problem with LLMs as they stand today isn’t a lack of time to train them. It’s is that they have a really primitive structure. They’re like only part of what’s in your head.
What an LLM can do, what generative AIs are doing, is basically take a very large amount of data, where each instance of data is annotated with something, and find commonalities between between them, which lets them learn to associate the annotation with characteristics of that data. And they can synthesize data based on those annotations.
That’s quite useful for a number of things. It lets us do things that we couldn’t do before. We can do really potent voice and image synthesis, upscale images, stuff like that.
But it’s really simple and mechanical.
But what’s going to happen here isn’t that we take that approach and then throw more data or more hardware at it and it suddenly becomes a human.
And there’s a lot of stuff that that doesn’t do.
It doesn’t permit for learning to perform tasks, say. It can’t develop higher-level concepts of things than associations with the annotations. You can give Stable Diffusion 1000 years of continuous running, and it will never start sending you messages about living inside your computer.
So the existing things that people are calling “generative AI” – those aren’t going to be a human because they just don’t have the infrastructure to do it. Some of the things they do might be part of something human-like, but they’re missing a lot of parts.