Teaching AI to Spend Its Intelligence Well

The frontier models keep getting stronger. The harder question is whether we know how to guide them.

I keep noticing the same thing in my client work.

I want more from AI.

More speed. More capability. More precision. More help turning a clear set of instructions into something useful. That is the whole point. I want to be able to describe a problem, give the tool the right context, and have it produce something pragmatic, functional, and efficient.

What I do not want is waste.

That is the distinction I am trying to understand.

AI can handle an enormous amount of complexity. It can make decisions, evaluate paths, generate options, write code, build documentation, summarize requirements, and produce working artifacts at a speed that still feels strange to me. Some of that work may require a lot of hidden processing. I understand that.

But I keep asking myself: did it need all of that?

Did it need the extra framework?

Did it need the expanded terminology?

Did it need the ten-step process when three steps would have solved the problem?

Did it need to burn all those cycles to get to a simple answer?

Some people would say it does not matter. If the result works, who cares how much complexity the machine created underneath the surface?

I understand that argument. I am not sure I fully agree with it.

Because waste is still waste, even when the resource feels abundant.

There is a principle in systems design called Gall’s Law. The simple version is this: a complex system that works is usually found to have evolved from a simple system that worked.

That feels important right now.

In organizations, we already know this. We have processes, procedures, checklists, reviews, approvals, and operating rhythms because the work has to survive contact with people. The simplest example is flying a plane. Pilots use checklists every time, regardless of how experienced they are, because the cost of missing something is too high.

The checklist is not there because the pilot is incapable.

It is there because the system matters.

That is what I keep thinking about with AI.

It is fun to release a frontier model on a problem and see what it creates. Sometimes the result is remarkable. It can produce a product spec, a technical architecture, a workflow, a data model, a requirements document, and an implementation plan before I have even fully settled the problem in my own mind.

But in a controlled environment, especially one where humans still have to understand the work, that is not always the best path.

Most of us learned to work in tasks, features, requirements, and projects. Break the problem down. Define the next piece. Build it. Test it. Learn from it. That way of working exists for a reason. Systems are hard to describe all at once. You usually do not understand the whole thing until you have built enough of the parts to see what the system is trying to become.

AI changes that, but it does not eliminate it.

I can hand the tool a large requirement and tell it to go build. It will try. It may generate the code, write the documentation, name the edge cases, and make a long list of decisions along the way.

Some of that may be useful.

Some of it may be impressive.

But without a feedback loop, I do not know if it is good.

That is where the waste shows up.

The waste is not only tokens. It is rework. It is misunderstood requirements. It is complexity introduced before the problem has been understood. It is ten decisions made in a row when the first one needed a human check. It is asking the model to run far ahead, then realizing we have to bring it all the way back because the direction was slightly off.

That is not the machine’s fault.

It is a guidance problem.

We are learning how to use a level of capability we did not grow up with. The frontier models are growing in power faster than our habits are changing. They can consume more context, reason across more material, and produce more complete answers than we know how to ask for well.

In some scientific contexts, that may be exactly what we want. If a model is analyzing DNA patterns or searching through a massive possibility space, maybe the right move is to let it run in ways no human could fully direct step by step. There are problems where the whole point is to release the model into complexity and see what it finds.

But most of the work I am doing is not that.

Most of the work is still connected to people, teams, clients, systems, and decisions someone has to understand. A product manager needs to know what is being built. An engineer needs to know why it is being built that way. A client needs to know what they are approving. A user needs the thing to work without caring how sophisticated the system is underneath it.

That means the human interface still matters.

The process still matters.

The feedback loop still matters.

AI can generate complexity faster than humans can absorb it. Again, that is not a criticism. It is a reality of the work. The machine can keep going. The team cannot. The client cannot. The person who has to own the result cannot.

So the human job is not disappearing. It is becoming more specific.

We have to ask better questions. We have to challenge assumptions. We have to decide when to let the model run and when to slow it down. We have to know when the problem has been framed well enough to deserve more intelligence, more tokens, and more complexity.

That last part matters to me.

Tokens may become cheap. Maybe someday they’ll feel unlimited. Maybe the cost drops so far that no one thinks about them anymore. That may be where this goes.

I still think we should treat them with care.

Not because scarcity is the only reason to be careful. Because attention matters. Energy matters. Time matters. The people who have to understand the output matter. The work itself matters.

I recognize some of this because I have done it myself. I have used too many words. I have over explained. I have defended a simple point by surrounding it with more language than it needed. There is something familiar in watching AI do the same thing at scale, with better grammar and more confidence.

It has made me more aware of my own words.

Be greedy with them.

Not withholding. Not vague. Just careful.

Do not spend ten words when three will do. Do not create a name for something that already has one. Do not call something governance when what you mean is, someone needs to check the work before it goes out.

Maybe the same thing applies to tokens.

Use the complexity when the problem requires it. Let the model work deeply when the question has been framed well enough to deserve that depth. Give it room to make decisions when the feedback loop is clear.

But do not confuse motion with progress.

Do not ask it to run ten miles down a path no one has checked.

Do not burn tokens because they are available.

Do not create complexity because the machine can.

There is a kind of respect in that. Respect for the model. Respect for the energy behind it. Respect for the people who have to understand the result. Respect for the work itself.

The future may give us more intelligence than we know what to do with.

I still think we should learn how to spend it well.