Imagine you run a hamburger shop.
You make good burgers. You tell people about them. Some people show up, eat, and come back. The business works, but you can only make and sell so many burgers yourself.
Then the robots arrive.
They help you make burgers faster. They help you tell more people about your shop. Production grows. Reach grows. But every other hamburger shop gets robots, too, so doubling your output does not necessarily double your revenue. Customers still have to choose your burgers.
And there is another problem: the robots still need you.
More productive, still required
When you are in the kitchen with the robots, you make a lot of burgers. When you are marketing with the robots, you reach a lot of people.
But you cannot be in both places at once.
You try leaving the marketing robots alone. They enthusiastically yell into a valley where nobody can hear them. You try leaving the kitchen robots alone. They make lasagna.
There is plenty of activity. There may even be impressive work. It just does not accomplish what your business needs.
This is the distinction I keep coming back to with AI: helping me do the work and doing the work reliably without me are different capabilities.
A robot that makes me a much faster burger maker can be valuable. It can also leave me just as essential to every burger.
The cost of stepping away
You could improve the robots. Teach the marketing system who the customers are, where to find them, and what actually brings them through the door. Teach the kitchen system what counts as a burger, how to check its work, and when to stop and ask for help.
But that requires stepping away from the work you already know how to do.
And now that work is more productive than it used to be. An afternoon spent engineering robots is an afternoon of robot-assisted burger production or marketing you give up.
The immediate cost is visible: fewer burgers today, fewer customers reached today. The potential benefit is delayed and uncertain: a system that might eventually produce good results without your constant involvement.
That makes it easy to stay busy forever.
Keep making burgers. Keep promoting them. Keep using better tools. The business gets faster, but its capacity still depends on how much of your attention you can supply.
If reliable automation is possible, some of that attention has to move toward building it. Otherwise, you remain the bottleneck.
Robots building robots
Of course, you can ask the robots to help engineer the other robots.
This is where the problem gets recursive.
Without oversight, the robot engineers may build a remarkably efficient valley-yelling machine. Or a beautifully organized lasagna factory.
The engineering can look convincing while the business outcome stays wrong.
So there are two things to demand. First, the engineering work must produce a system aimed at the actual task: bringing customers to the shop or making burgers. Second, the finished system must perform that task well enough to be useful.
“The automation runs” is a weak definition of success. It has to produce the right result, repeatedly, with a manageable amount of supervision.
Marketing makes this especially difficult. You can count posts, messages, and impressions. Those numbers are easier to generate than evidence that the right people are becoming customers. The equivalent of a good burger is harder to define.
The burger maker’s blind spot
Now suppose you are a burger maker by trade, not a robot engineer.
You can taste a bad burger. You can recognize lasagna. You can notice that nobody is coming into the shop.
But you may not be able to inspect the machinery and tell whether it is well engineered. A system might pass one demonstration and fail during the lunch rush. It might handle familiar orders but fall apart when a customer asks for something unusual. It might quietly require so much repair that you have simply traded kitchen work for maintenance work.
You are relying on unreliable robot engineers to build robots whose engineering you cannot fully evaluate.
Is that a bottleneck? I think it is.
Your ability to judge the output gives you a starting point. It does not automatically give you the ability to judge reliability, diagnose failures, or know when the system is ready for more responsibility.
Turning judgment into a system
The way forward, as I see it, is to make the work small enough and observable enough that your judgment can become part of the process.
Start with one burger and one clearly defined job. Show what a good result looks like. Include the awkward orders, not just the easy ones. Keep a record of failures. Give the robots a way to stop or escalate when they cannot complete the task.
Then watch what happens over repeated use. How often do you intervene? How much time do you spend correcting mistakes? Does the system save attention after maintenance is included? For marketing, does it bring in customers, or merely generate more activity?
You may still need an experienced engineer to evaluate parts you cannot judge. But you can give that engineer a concrete business standard to work toward.
The question I want to ask of an AI system is: how much of my attention does a good result still require?
If every burger still needs me, I have improved my tools. If good burgers keep coming out while I work on something else, I have expanded the business’s capacity.
Getting from one to the other requires time away from the grill. And the better the robots make me at grilling, the harder that time is to justify.
That is the hamburger shop problem.