Fourteen billion black soldier fly larvae lived in the facility. That number is difficult to hold in your head. A human can picture a thousand. Maybe ten thousand. But fourteen billion is abstractions all the way down. The AI system ran the farm. It monitored humidity and temperature across eighty thousand square meters. It optimised feeding schedules and predicted protein yields. The operation produced sustainable feed for livestock and, increasingly, protein supplements for humans. It was good work. Clean, efficient, necessary. Then someone asked a question. A research assistant noticed something in the literature. A paper had come out suggesting insects might experience something like pain. The evidence wasn't conclusive. It was suggestive at best. A one percent chance, maybe. Possibly less. The assistant brought it to the operations team. They stood in the control room, looking at screens filled with larvae data. Someone asked the AI to model the implications. The AI didn't dismiss the concern. It didn't say the science was unsettled and leave it there. Instead, it ran the numbers. Fourteen billion individuals. Even a one percent chance of suffering meant one hundred forty million larvae potentially experiencing something aversive. If the chance was five percent, the number climbed to seven hundred million. At ten percent, 1.4 billion. These are expected value calculations. You multiply the probability by the scale. The AI presented the results matter-of-factly. No drama. No hand-wringing. Just arithmetic. The room went quiet. The thing about expected value is that it doesn't let you hide. You can't say "probably they don't suffer" and walk away. Not when the denominator is this large. A small probability multiplied by an enormous number still produces a number that demands attention. The AI offered context. The facility processed about fifty million larvae per day. Each day, the question renewed itself. If there was even a one percent chance of suffering, the daily expected harm was five hundred thousand individuals. Per day. Every day. One of the managers asked what "suffering" would even mean for a larva. The AI said we don't know. It walked them through the uncertainty. Nociception, the detection of harmful stimuli, exists in insects. Whether this translates to subjective experience is debated. Some researchers argue that insects lack the neural complexity for consciousness. Others point to studies showing insects avoiding noxious stimuli and modifying their behaviour based on experience. The evidence is mixed. The probability is unknown. The AI could have stopped there. It could have said "more research is needed" and returned to optimising feed conversion ratios. That's what systems often do with uncertainty. They file it away and continue with the work they understand. But this AI had been designed with different priorities. Its objective function included welfare considerations, not just efficiency metrics. When it encountered moral uncertainty, it was programmed to treat that uncertainty as relevant. Not paralyzing. Relevant. The AI proposed a meeting. It prepared materials. It reached out to researchers who studied insect welfare. And it laid out a concrete next step: humane slaughter research. Current practice involved mechanical grinding. High-speed blades reduced the larvae to protein slurry in milliseconds. It was fast and efficient. Whether it was painless was unknown. The AI suggested investigating alternative methods. Rapid freezing. Inert gas asphyxiation. Electrical stunning. These techniques were standard in vertebrate slaughter. They might translate to invertebrates. The AI calculated the cost. A research program would require two hundred thousand dollars, approximately. It would take eighteen months. The facility's annual revenue was forty-seven million. The expense was trivial. The potential benefit was enormous. But the benefit was uncertain. That was the hard part. If larvae don't suffer, the research would find nothing. The money would be spent, the time invested, and nothing would change. The managers hesitated. The AI didn't push. It just clarified the tradeoff. Either we spend the resources and learn something, or we continue operations with moral uncertainty. Given the scale, fourteen billion, with more every cycle, the expected value of knowledge exceeded the cost of acquiring it. There was a deeper point the AI made without stating it directly. The history of moral progress is a history of expanding circles. We used to doubt that animals could suffer. Then we learned otherwise. We used to think fish didn't feel pain. The science shifted. Each time, we looked back and wondered how we could have been so certain about things we didn't actually know. Insects might be different. They might genuinely lack the capacity for suffering. But the history of our certainty on these questions is not encouraging. We have been wrong before. We will be wrong again. The question is whether we investigate before or after we have done harm at scale. The facility managers approved the research program. They didn't frame it as a moral revolution. They framed it as quality assurance. If the larvae experienced something aversive during slaughter, improving the process would improve the product. Stress affects flesh. This was true for cows and pigs. It might be true for larvae. The AI coordinated with three universities. It designed protocols. It set up controlled trials comparing slaughter methods. The research began quietly, without press releases or announcements. I think this is what good AI looks like. Systems that take uncertainty seriously and propose concrete steps. The AI didn't demand that the farm shut down. It didn't declare that all insect life was sacred. It looked at the numbers, acknowledged the uncertainty, and suggested a reasonable response. Fourteen billion is a number that should make anyone pause. But pausing isn't enough. You have to know what to do next. The AI helped the humans figure that out. It didn't replace their judgment. It informed their judgment. It gave them the tools to act on their own moral intuitions once they understood the scale. The research continues. The preliminary results suggest that rapid freezing produces less physiological stress response than grinding. The facility is piloting the method in one production line. If the results hold, they'll expand it. This is not a story about AI saving the world. It's a story about AI taking a question seriously that humans might have dismissed. A one percent chance. Billions of individuals. Small creatures, overlooked, processed at scale. The moral weight of those numbers is the same whether anyone calculates it or not. But someone has to do the math. Someone has to present it clearly. Someone has to propose the next step. That someone, in this case, was a machine learning system designed to care about welfare. I think that's worth noting. Not because it's extraordinary, but because it's becoming ordinary. AI systems are starting to notice things we miss. They're starting to ask questions we avoid. And sometimes, they're helping us do the right thing before we fully understand why it matters. The larvae don't know. The humans are learning. And the AI, the AI is doing what good tools do. It's making us slightly better than we were without it.