Flapping Airplanes on the future of AI: ‘We want to try really radically different things’ Russell Brandom 6:00 AM PST · February 16, 2026 There’s been a bunch of exciting research-focused AI labs popping up in recent months , and Flapping Airplanes is one of the most interesting. Propelled by its young and curious founders, Flapping Airplanes is focused on finding less data-hungry ways to train AI. It’s a potential game-changer for the economics and capabilities of AI models — and with $180 million in seed funding, they’ll have plenty of runway to figure it out.
Last week, I spoke with the lab’s three co-founders — brothers Ben and Asher Spector, and Aidan Smith — about why this is an exciting moment to start a new AI lab and why they keep coming back to ideas about the human brain.
I want to start by asking, why now? Labs like OpenAI and DeepMind have spent so much on scaling their models. I’m sure the competition seems daunting. Why did this feel like a good moment to launch a foundation model company?
Ben: There’s just so much to do. So, the advances that we’ve gotten over the last five to ten years have been spectacular. We love the tools. We use them every day. But the question is, is this the whole universe of things that needs to happen? And we thought about it very carefully and our answer was no, there’s a lot more to do. In our case, we thought that the data efficiency problem was sort of really the key thing to go look at. The current frontier models are trained on the sum totality of human knowledge, and humans can obviously make do with an awful lot less. So there’s a big gap there, and it’s worth understanding.
What we’re doing is really a concentrated bet on three things. It’s a bet that this data efficiency problem is the important thing to be doing. Like, this is really a direction that is new and different and you can make progress on it. It’s a bet that this will be very commercially valuable and that will make the world a better place if we can do it. And it’s also a bet that’s sort of the right kind of team to do it is a creative and even in some ways inexperienced team that can go look at these problems again from the ground up.
Aidan: Yeah, absolutely. We don’t really see ourselves as competing with the other labs, because we think that we’re looking at just a very different set of problems. If you look at the human mind, it learns in an incredibly different way from transformers. And that’s not to say better, just very different. So we see these different trade offs. LLMs have an incredible ability to memorize, and draw on this great breadth of knowledge, but they can’t really pick up new skills very fast. It takes just rivers and rivers of data to adapt. And when you look inside the brain, you see that the algorithms that it uses are just fundamentally so different from gradient descent and some of the techniques that people use to train AI today. So that’s why we’re building a new guard of researchers to kind of address these problems and really think differently about the AI space.
Asher : This question is just so scientifically interesting: why are the systems that we have built that are intelligent also so different from what humans do? Where does this difference come from? How can we use knowledge of that difference to make better systems? But at the same time, I also think it’s actually very commercially viable and very good for the world. Lots of regimes that are really important are also highly data constrained, like robotics or scientific discovery. Even in enterprise applications, a model that’s a million times more data efficient is probably a million times easier to put into the economy. So for us, it was very exciting to take a fresh perspective on these approaches, and think, if we really had a model that’s vastly more data efficient, what could we do with it?
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Aidan: The way I look at the brain is as an existence proof. We see it as evidence that there are other algorithms out there. There’s not just one orthodoxy. And the brain has some crazy constraints. When you look at the underlying hardware, there’s some crazy stuff. It takes a millisecond to fire an action potential. In that time, your computer can do just so so many operations. And so realistically, there’s probably an approach that’s actually much better than the brain out there, and also very different than the transformer. So we’re very inspired by some of the things that the brain does, but we don’t see ourselves being tied down by it.
Ben: Just to add on to that. it’s very much in our name: Flapping Airplanes. Think of the current systems as big, Boeing 787s. We’re not trying to build birds. That’s a step too far. We’re trying to build some kind of a flapping airplane. My perspective from computer systems is that the constraints of the brain and silicon are sufficiently different from each other that we should not expect these systems to end up looking the same. When the substrate is so different and you have genuinely very different trade-offs about the cost of compute, the cost of locality and moving data, you actually expect these systems to look a little bit different. But just because they will look somewhat different does not mean that we should not take inspiration from the brain and try to use the parts that we think are interesting to improve our own systems.
It does feel like there’s now more freedom for labs to focus on research, as opposed to, just developing products. It feels like a big difference for this generation of labs. You have some that are very research focused, and others that are sort of “research focused for now.” What does that conversation look like within flapping airplanes?
Asher: I wish I could give you a timeline. I wish I could say, in three years, we’re going to have solved the research problem. This is how we’re going to commercialize. I can’t. We don’t know the answers. We’re looking for truth. That said, I do think we have commercial backgrounds. I spent a bunch of time developing technology for companies that made those companies a reasonable amount of money. Ben has incubated a bunch of startups that have commercial backgrounds, and we actually are excited to commercialize. We think it’s good for the world to take the value you’ve created and put it in the hands of people who can use it. So I don’t think we’re opposed to it. We just need to start by doing research, because if we start by signing big enterprise contracts, we’re going to get distracted, and we won’t do the research that’s valuable.
Aidan: Yeah, we want to try really, really radically different things, and sometimes radically even things are just worse than the paradigm. We’re exploring a set of different trade offs. It’s our hope that they will be different in the long run.
Ben: Companies are at their best when they’re really focused on doing something well, right? Big companies can afford to do many, many different things at once. When you’re a startup, you really have to pick what is the most valuable thing you can do, and do that all the way. And we are creating the most value when we are all in on solving fundamental problems for the time being.
I’m actually optimistic that reasonably soon, we might have made enough progress that we can then go start to touch grass in the real world. And you learn a lot by getting feedback from the real world. The amazing thing about the world is, it teaches you things constantly, right? It’s this tremendous vat of truth that you get to look into whenever you want. I think the main thing that I think has been enabled by the recent change in the economics and financing of these structures is the ability to let companies really focus on what they’re good at for longer periods of time. I think that focus, the thing that I’m most excited about, that will let us do really differentiated work.
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