I spent 14 years in the nuclear industry.
My first lesson? A nuclear power plant can't explode like a bomb. (Blame The Simpsons.) Commercial reactor fuel is low-enriched uranium, nowhere near weapons-grade material. A reactor can't detonate like a weapon. That doesn't mean nothing can go wrong. Severe accidents can still happen. But the Hollywood mental model is wrong.
My second lesson was more important: the difference between a disaster and an engineering triumph is the harnessing.
What surprised me most about nuclear power was how sophisticated the engineering of harnessing actually is. The nuclear reactions in the core. Control rods absorbing neutrons to regulate reactivity. Soluble boron in the coolant. Water chemistry. Redundant cooling loops. Containment structures.
Layer upon layer of engineered systems, designed to take something incredibly powerful and make it reliably useful.
After fourteen years, I came to believe nuclear isn't "a bomb waiting to happen." It's an engineering triumph, and a culture of deep respect for power.
We didn't start there, though.
In the early days, researchers handled radioactive materials with almost casual disregard. In early criticality experiments, scientists manipulated plutonium assemblies by hand, sometimes using a screwdriver as a spacer. The Radium Girls licked their brushes to get a fine point while painting watch dials. People at bomb tests were told to watch the flash.
The human cost was real, and sometimes fatal.
The nuclear industry today is one of the most governed, most carefully engineered industries on Earth. Not because we feared the power, but because we learned to respect it.
I think we're in a similar moment with AI right now.
Most people still have the wrong mental model. A 2025 survey of U.S. adults (Searchlight Institute) found that 45% think tools like ChatGPT work by looking up answers in a database, like a sophisticated search engine. Only 28% described it as generating text by predicting what words come next based on learned patterns.
I'm not pointing fingers. I held my own wrong mental models about nuclear for years. But the gap matters. In nuclear, wrong assumptions and immature safety culture had severe consequences, including radiation sickness and, in some cases, death. In AI, the cost is different but real: systems that hallucinate confidently, data leaking where it shouldn't, business decisions made on "predictive text" mistaken for truth.
Large language models are genuinely powerful. They can often reason, synthesize, and solve problems in ways that still surprise me. And we're all still learning how to work with them, just like those early nuclear scientists were learning. The difference is we have a chance to build the harnessing systems before more hard lessons.
Whether or not AGI is close, the models we already have are extraordinarily capable. The bottleneck isn't their intelligence. It's the harnessing.
Think of a fresh graduate from a top university. Capable? Absolutely. Ready to run your company on day one? No.
They need context. They need to understand how the organization works, what the unwritten rules are, which problems matter and which are distractions. They need systems around them: mentorship, feedback loops, clear responsibilities. Systems to channel capability into real outcomes.
In nuclear, harnessing isn't about limiting power. It's about enabling it.
Control rods don't make a reactor weaker. They make it controllable. Containment systems don't reduce output. They make output sustainable.
The equivalent for AI isn't just "guardrails" or "safety filters." It's the harder work of building systems that make models reliably useful:
* Curating context so the model draws from verified information, not just plausible-sounding text
* Building evaluation so we catch hallucinations before they reach customers or boardrooms
* Designing tool use with permissions so models can act in the real world, but only within controlled boundaries
* Embedding human oversight into workflows as a structural requirement, not an afterthought
This is still relatively new territory. The Model Context Protocol (MCP), a standard for connecting models to tools and data, was introduced by Anthropic in late 2024, with broader industry adoption accelerating through 2025. We're still building the cooling cycles for AI.
Let the race for more capable models continue. That's a worthy pursuit. But there's a parallel track that deserves just as much attention: building the sophisticated systems that let us actually use what we already have.
Over those fourteen years, I learned that nuclear power isn't what I thought it was.
It's not a bomb waiting to happen. It's generations of hard-won knowledge about how to take something powerful and make it do extraordinary good.
AI can be the same.
The power is already here. The harnessing is the work.