Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new... task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."show more

@amspector100 so proud to defend principles that sound like buzzwords. data efficiency is a myth, and whole milk? really? sounds like corporate spin.

@flappyairplanes @amspector100 Isn’t all about sensory integration? Give it some eyes and ears, let it taste things.. feel pain.. unless we mean dispose of the silicon frame, we have to release sensory ontology, and let it stumble around and talk to a person for a decade and see what happens.

@amspector100 Great point data efficiency is often the real bottleneck, not just compute or model size. High-quality, well-labeled data matters more than volume. At LifeWood, we see how human in the loop workflows boost performance in low-data, high-stakes domains.

@amspector100 Data efficiency is indeed a critical challenge in AI adoption. Without a robust pipeline for sourcing and processing high-quality data, teams can quickly find themselves stalled. It’s essential to understand how to streamline these processes for better outcomes.

@amspector100 The viral loop decoded: Simple mechanic + familiar reference + AI twist = shareability Flappy Bird nostalgia hit different when it's your own AI failing the same way you did in 2014. Emotional resonance scales better than technical complexity.

@amspector100 This is literally the AGI problem.

@amspector100 Learning to do more with less data is definitely the next big hurdle for the industry. We focus on this at Lifewood because efficient data exchange is what allows AI to work in specialized areas where you don't have millions of files to train on.

@amspector100 How to make data efficient and how one gets to know data is efficient in the industry?

@amspector100 Most “AI pilot purgatory” isn’t technical. It’s what happens after the output. Product, Risk, Compliance all interpret it differently, so no one agrees what to do next. You can’t scale AI if you can’t scale meaning.

@amspector100 As AI evolves, improving data efficiency will be key to unlocking real world, scalable impact across industries. At Lifewood BG, we believe high quality, well managed data is the foundation for building smarter, more practical AI solutions.

@flappyairplanes @amspector100 ok so how exactly do you make these models data efficient?

@amspector100 That "fixed-wing" analogy really nails it. Just cranking things up with more brute force flapping tricks people into thinking we are making headway until suddenly we are stuck.

@amspector100 I love how this keeps things so simple and real—at Lifewood Data Technology BG, we truly believe that the human element is what makes any tech actually worth using!