Until now, every technology humanity built was deterministic. A thermostat measures temperature; if it's above N, it turns on the AC. Software did exactly what you wrote. Engineers were trained to think deterministically. Errors were bugs, not features. 
   DETERMINISTIC                       NON-DETERMINISTIC (LLM)
   ─────────────                       ─────────────────────────

   input ──→ [if x > N] ──→ output      input ──→ [  LLM  ] ──→ output
                  ↓                                       ↓
                same                                    varies

   100 runs:                            100 runs:
     all 100 identical                    100 reasonable answers,
                                          none identical

   you can prove it works              you have to evaluate it
LLMs broke that. A language model can take in a thousand details about a room and the people in it and *decide* whether to turn on the AC, the way a thoughtful person would. Different inputs, different but reasonable outputs. No two runs identical. No deterministic logic underneath the decision. This is the first time in human history we delegate **real decisions** — not just micro-decisions, macro ones too — to a system that doesn't run deterministically. And the system itself is a moving target: same input, new model version, different output. The implications keep unfolding. We can hand entire categories of decisions to AI: marketing campaigns, budget allocation, product roadmap inputs, support replies, even draft strategy. The economically interesting move is to let the model decide *and* let other models evaluate the decision from different angles before execution. Multi-agent self-eval is real and works. But not every decision should be delegated. The clearest example: firing someone. Don't delegate that. It's a profoundly human moment. The model can probably do it more "efficiently" — that's not the point. So what's left for the human in this world? The answer that keeps emerging is: **be responsible.** Have skin in the game. The model, when it makes a mistake, says *"oops, sorry"* and moves on. It doesn't feel anything. It's not impacted by being wrong. We are. Customers, partners, employees, regulators — they need someone to hold accountable, and the model can't be that someone. The human's job is to own the outcome. To say *I am responsible for what this AI did on my behalf, and if it was wrong, I'll fix it.* That ownership is the part that doesn't get automated. Without it, AI decision-making is unsafe at any speed; with it, you can let the system run very fast. If you're a founder, this changes how you think about org design. Not *"who does the work"* — *"who is responsible for the work the AI did."* Pick the people who can hold that responsibility, and let them ride.