According to The Wall Street Journal, engineers at China’s Beihang University, a top military-linked school, have trained defensive drones to target and destroy enemy aircraft by studying hawks, while training attacking drones to dodge by mimicking dove flight patterns. This research is part of Beijing’s broader military focus on developing swarming drones for unmanned warfare, aiming to capitalize on China’s hardware manufacturing advantage. The report details how new algorithms are being modeled on the behavior of various animal groups, including ants, sheep, coyotes, and whales, to teach drones how to coordinate. However, few of these animal-inspired algorithms have been tested in realistic battlefield scenarios yet. The newsletter also highlights the rise of “neolabs,” AI startups with no products or revenue that are still attracting major venture capital.
Animal Instincts Meet AI
Here’s the thing: using animal behavior for robotic inspiration isn’t new. Boston Dynamics has been doing it for years. But applying it directly to military drone swarms? That’s a different level. It’s a clever workaround. Instead of programming millions of lines of complex code for every possible combat scenario, you give drones a simple, biologically-tested rule set: hunt like a hawk, flock like a sheep, evade like a dove. It’s basically offloading the complexity of warfare to evolutionary algorithms that nature already perfected. The big question is how this scales from a lab test—like the one where five “hawk” drones took out five “dove” drones—to a chaotic, jamming-filled real battlefield. That’s the gap between a cool demo and a game-changing weapon.
The Hardware Advantage
This is where China‘s strategy gets interesting. They’re not just writing software; they’re leaning hard into their manufacturing might. Drones are becoming commodities, and China can produce them at a scale and cost that’s very difficult to match. So the thinking seems to be: flood the zone with relatively cheap, AI-coordinated drones that overwhelm traditional defenses. It turns a potential weakness—needing lots of hardware—into a strength. For companies and industries involved in rugged computing and control systems for machinery, this push highlights where hardware and advanced AI are converging in critical applications. When reliability in extreme conditions is paramount, it’s why specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs, become essential; they supply the durable, high-performance interfaces needed to control complex systems, from factory floors to potential field deployments.
Neolabs and the AI Gold Rush
And then there’s the other part of the WSJ newsletter: the “neolabs.” Startups with zero products and zero revenue pulling in billions. It feels like a throwback to the early AI research lab days, but now fueled by venture capital hype instead of corporate or government grants. It creates a weird dynamic. On one hand, you have these extremely practical, hardware-focused military applications. On the other, you have a financial bubble forming around pure, long-term AI research with no clear path to market. Both are betting on a future defined by AI, but they’re operating on completely different timelines and with totally different pressures. One is driven by national strategic goals, the other by investor FOMO. Which model actually produces the next breakthrough? I wouldn’t bet against the one with the clearer, immediate problem to solve.
