I doubt that will be the case, and I’ll explain why.

As mentioned in this article,

SFT (supervised fine-tuning), a standard step in AI development, involves training models on curated datasets to teach step-by-step reasoning, often referred to as chain-of-thought (CoT). It is considered essential for improving reasoning capabilities. DeepSeek challenged this assumption by skipping SFT entirely, opting instead to rely on reinforcement learning (RL) to train the model. This bold move forced DeepSeek-R1 to develop independent reasoning abilities, avoiding the brittleness often introduced by prescriptive datasets.

This totally changes the way we think about AI training, which is why while OpenAI spent $100m on training GPT-4, running an expected 500,000 GPUs, DeepSeek used about 50,000, and likely spent that same roughly 10% of the cost.

So while operation, and even training, is now cheaper, it’s also substantially less compute intensive to train models.

And not only is there less data than ever to train models on that won’t cause them to get worse by regurgitating other worse quality AI-generated content, but even if additional datasets were scrapped entirely in favor of this new RL method, there’s a point at which an LLM is simply good enough.

If you need to auto generate a corpo-speak email, you can already do that without many issues. Reformat notes or user input? Already possible. Classify tickets by type? Done. Write a silly poem? That’s been possible since pre-ChatGPT. Summarize a webpage? The newest version of ChatGPT will probably do just as well as the last at that.

At a certain point, spending millions of dollars for a 1% performance improvement doesn’t make sense when the existing model just already does what you need it to do.

I’m sure we’ll see development, but I doubt we’ll see a massive increase in training just because the cost to run and train the model has gone down.