When my parents were young, the message was simple. Do not have too many kids. By the 1980s, they were told, the world would be out of food. The oceans would be empty, the fields barren, and billions would starve.
It didn’t happen.
Not because of enlightened environmental policy or a coordinated global rescue plan. Scarcity meant higher prices. Higher prices meant profit. Profit meant more land under cultivation, more seeds developed, more fertilizer produced, more ships built, and more grain moved wherever it could be sold or used as political leverage. Capitalism turned scarcity into action because there was money to be made. Fertility rates fell because cities and industrial jobs changed family economics, not because a UN pamphlet said so. The system adapted chaotically, imperfectly, creating new problems along the way, but it adapted fast enough to outrun the doomsday clock.
Fast forward to 2025. DeepSeek releases a small, efficient AI model, and the hot takes fly. “This will kill Nvidia. Nobody will need giant GPUs anymore.” The stock dips on fears that small models will replace big ones. Meanwhile, another meme makes the rounds, “Don’t learn to program. AI will do it all.”
Same flawed logic as the famine forecasts. Straight-line projections in a complex, adaptive system.
Cheaper AI means lower costs. Lower costs mean more users. More users mean more use cases, and more use cases mean more aggregate demand for compute. Capitalism loves efficiency because efficiency breeds new markets. Nvidia won’t sell fewer chips in that world. They’ll sell more, to more buyers, in more configurations.
The idea that AI will kill programming jobs is just the latest in a long line of bad predictions. High-level languages were supposed to do that. So were compilers. So were frameworks, IDEs, and low-code tools. Each one lowered the cost of creation, and when the cost of creation goes down, the number of things worth creating goes up. That expansion creates more work, not less. AI will follow the same pattern.
The speed is different this time, admittedly. AI capabilities are advancing faster than previous technologies, and the potential scope is broader. But markets adapt faster when the stakes are higher, and the stakes have never been higher. The same forces that drove rapid agricultural innovation in the face of predicted famine will drive even faster adaptation in the face of AI disruption.
I’ve seen this panic up close. My middle child, who has strong math skills and is a thoughtful problem solver, is planning to earn a Master’s in Computer Engineering. He asked if that was a mistake. I told him no. Hot takes at this scale are almost always wrong. The system adapts in ways first-order forecasts miss, and the people who understand the tools are the ones who thrive when it does.
Doom sells better than nuance. “AI will end all jobs” gets more clicks than “jobs will change in unpredictable ways.” Hot takes spread because they’re clean and simple. But complexity is where the truth lives, and where the opportunity hides.
In the 1960s, the refrain was “Don’t have kids, the world will starve.” Today, it’s “Don’t learn to code, AI will do it all.” Both ignore the same truth, when there’s money to be made, markets adapt, and the winners are the ones who adapt with them.