Look, I know we’re all tired of “revolutionary breakthrough” claims in AI (I write about them daily, trust me), but this one made me do a double-take. A new study is claiming that instead of the massive datasets we’ve been obsessing over, you might only need 78 carefully chosen training examples to build superior autonomous agents. Yeah, seventy-eight. Not 78,000 or 78 million—just 78. The research challenges one of our core assumptions about AI development: more data equals better performance. We’ve been in this escalating arms race of dataset sizes, with companies bragging about training on billions of web pages and trillions of tokens. But these researchers are saying “hold up, what if we’re doing this completely backwards?” Here’s what’s wild about their approach—they’re focusing on the quality and strategic selection of training examples rather than throwing everything at the wall. Think of it like this: instead of reading every book ever written to become a great writer, you carefully study 78 masterpieces and really understand what makes them work. (Obviously the analogy breaks down because AI training is way more complex, but you get the idea.) The implications here are honestly staggering. If this holds up under scrutiny, we’re looking at a fundamental shift in how we think about AI development. Smaller companies and researchers who can’t afford to scrape the entire internet suddenly have a path to building competitive agents. The environmental impact drops dramatically (no more burning through data centers to process petabytes). And development cycles could shrink from months to weeks or even days. Now, before we all lose our minds with excitement—and I’m trying really hard not to here—this is still early-stage research. The devil is always in the details with these studies. What specific tasks were they testing? How does this scale to different domains? What’s the catch that makes this “too good to be true”? (Because there’s always a catch.) But even if this only works for certain types of autonomous agents or specific problem domains, it’s a massive development. We’re potentially looking at democratization of AI agent development in a way we haven’t seen before. Instead of needing Google-scale resources, you might be able to build something genuinely useful with a laptop and really smart data curation. The broader trend here is fascinating too—we’re seeing efficiency breakthroughs across the board in AI right now. Better architectures, smarter training methods, and now potentially revolutionary approaches to data requirements. It’s like the field is maturing past the “throw more compute at it” phase and into the “work smarter, not harder” era. This is exactly the kind of research that could reshape the competitive landscape practically overnight. If you can build competitive agents with 78 examples instead of 78 million, suddenly every startup, research lab, and curious developer becomes a potential player in the autonomous agent space. Read more from THE DECODER Want more than just the daily AI chaos roundup? I write deeper dives and hot takes on my Substack (because apparently I have Thoughts about where this is all heading): https://substack.com/@limitededitionjonathan