Let’s talk about the latest drama in the AI world, because apparently, OpenAI has discovered the concept of irony—and it’s not sitting well with them.
In a twist that feels like it was ripped straight from a tech satire, OpenAI is now accusing DeepSeek, a Chinese AI startup, of stealing their data.
Yes, the same OpenAI that has been hoovering up data from the internet for years, often without explicit permission, is now clutching its pearls because someone else might have done the same thing to them.
According to reports from Bloomberg and the Financial Times, OpenAI and Microsoft are investigating whether DeepSeek’s R1 model—a large language model that’s been making waves for its efficiency and performance—was trained on OpenAI’s proprietary data.
The accusation? DeepSeek allegedly used OpenAI’s outputs to train its model without permission, potentially violating OpenAI’s terms of service.
But let’s pause for a second and take a deep breath. OpenAI, the company currently being sued by the New York Times for training on their articles without consent, is now upset that someone else might have done the same thing to them.
The irony is so thick you could cut it with a knife.
The Distillation Debate
At the heart of this controversy is a technique called “distillation,” which is a common practice in AI research. Essentially, distillation involves training a smaller model to mimic the behavior of a larger, more complex one.
It’s like a student learning from a teacher, but in this case, the student is an AI model asking millions of questions to the “parent” model to replicate its knowledge.
David Sacks, a venture capitalist and newly minted Trump administration AI czar, has been vocal about this. He claims there’s “substantial evidence” that DeepSeek used distillation to extract knowledge from OpenAI’s models. “I don’t think OpenAI is very happy about this,” Sacks told Fox News. Well, no kidding.
But here’s the kicker: distillation isn’t some shady, under-the-table practice. It’s a well-established method in AI research, pioneered by none other than Geoffrey Hinton, a giant in the field who recently won a Nobel Prize for his work.
Hinton’s research highlights how distillation can make large language models more efficient and accessible, especially for smaller organizations that can’t afford the computational firepower of giants like OpenAI.
OpenAI’s Double Standard
What makes this whole situation even more absurd is OpenAI’s own defense in the New York Times lawsuit. OpenAI argues that training on publicly available data is fair use and essential for creating generalist language models.
They claim that the sheer scale of data they use is what makes their models so powerful, and that no single source of data is critical to their success.
But if that’s the case, why are they so upset about DeepSeek potentially using their data? If OpenAI truly believes that scale is the key to success, then DeepSeek’s alleged use of their data shouldn’t be a big deal.
After all, according to OpenAI’s own logic, one source of data isn’t that important in the grand scheme of things.
The real issue here seems to be that DeepSeek has managed to create a model that rivals OpenAI’s without relying on the same “more data = better model” approach.
Instead, DeepSeek used reinforcement learning and other efficient strategies to achieve impressive results. In other words, they beat OpenAI at its own game—and OpenAI isn’t happy about it.
The Bigger Picture
This whole saga highlights the growing tensions in the AI industry as companies race to develop more advanced models.
It also raises important questions about data ownership, fair use, and the ethics of AI development. If OpenAI can train on data from the entire internet without explicit permission, why can’t others do the same to them?
At the end of the day, OpenAI’s outrage feels a bit like a pot calling the kettle black. They built their empire on the backs of data they collected from the web, often without consent.
Now that someone else might be doing the same thing to them, they’re crying foul. It’s a classic case of “do as I say, not as I do.”
So, while OpenAI may be furious, the rest of us can’t help but laugh at the irony. After all, if you’re going to play the game of data hoarding, you can’t be surprised when someone else plays it better.
