The Management Problem — When Execution Becomes Infinite and Judgment Becomes Everything

The Management Problem — When Execution Becomes Infinite and Judgment Becomes Everything

Business school teaches execution. MBAs learn frameworks for optimization, process improvement, resource allocation. All of that assumes the constraint is doing the work. When the constraint shifts to deciding the work, those frameworks lose their leverage. Knowing how to execute efficiently matters less when execution itself is nearly free.

The skill that matters is the one we've always treated as unteachable: the ability to look at incomplete information, conflicting signals, and uncertain futures — and make a call. Not the safest call. Not the most defensible call. The right call.

If that sounds vague, that's because it is. Judgment doesn't decompose into bullet points. It doesn't scale through training programs. It develops through repetition, failure, and feedback loops that take years to mature. Which means the people and organizations with the best judgment today have an asymmetric advantage in a world where everything else can be automated.

What happens next

The transition is going to be messy. Companies will over-automate, under-automate, and automate the wrong things. There will be high-profile failures. AI agents will make expensive mistakes because someone didn't define the guardrails correctly. Humanoid robots will break things, hurt people, and get pulled from production lines while engineers figure out what went wrong.

None of that changes the direction. Execution is becoming abundant. Judgment is becoming the bottleneck. The sooner you treat that as the default state of the world, the better positioned you'll be when everyone else figures it out.

The abundance trifecta isn't just about cheaper energy, smarter AI, and capable robots. It's about what happens when all three converge and the constraints we've spent centuries optimizing around simply vanish.

We're not ready for that world yet. But it's coming anyway.

Unscarcity

Every week, we break down the three forces reshaping the global economy: robots getting cheaper than labour, AI getting cheaper than thought, and energy getting cheaper than ever. No hype. No hedge. Just the signals that matter. If someone forwarded this to you, now's the time.

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ROBOTS Figure's robots are working at BMW's South Carolina plant. Tesla is targeting Optimus production costs between $20,000 and $25,000 per unit. Unitree is shipping humanoids for $16,000. A year of minimum wage labor in the U.S. costs around $15,000 before taxes, benefits, or overhead. The cost curve has crossed. Humanoids are now cheaper than humans for an expanding set of tasks, and the performance gap is closing fast.

AI NVIDIA's Blackwell chips deliver five times the energy efficiency of the previous generation for AI inference. That's not incremental. When the cost of running AI tasks drops by 80%, entirely new use cases become viable. Real-time video analysis. Continuous monitoring. Autonomous decision-making at the edge. The constraint was never whether AI could do the task — it was whether you could afford to run it a million times a day. That constraint just broke.

ENERGY Octopus Energy just committed $1 billion to California clean technology projects. When a European energy giant starts writing nine-figure checks into U.S. renewables, that's a signal. The economics have flipped. Solar and batteries aren't the future anymore. They're the present, and the capital is moving accordingly.

Austin's robotaxis crash four times more often than human drivers.

That's not proof the technology doesn't work. It's proof we're in the messy middle of the learning curve. Autonomous vehicles are doing millions of miles of real-world driving, collecting data, iterating on edge cases, and improving faster than any human driver ever could. The crashes make headlines. The exponential improvement curve doesn't.

By 2028, that number will be inverted. And by 2030, letting humans drive will seem as reckless as letting humans do surgery without anesthesia seemed in 1900.

The gap between what AI can do and what businesses are using it for is widening, not closing. Every company says they're adopting AI. Most of them are using it for email summaries and meeting transcripts. The ones rethinking workflows from first principles are quietly building an execution advantage that will be impossible to close in two years.

Watch for the companies that stop talking about AI adoption and start talking about orchestration systems. Those are the ones that figured it out.

If management becomes the scarcest skill in an AI-abundant world, what happens to the millions of people currently training to do the work that AI is about to handle?

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