This may be a question that each of us in knowledge or creative work is asking ourselves in the wake of AI. And if not, maybe we should.

The point of hard problems

When OpenAI announced that one of its internal models had proposed a solution to the Navier–Stokes Millennium Prize Problem, it sounded like a big deal. Out of curiosity, I read reactions from some of the smartest people I follow on X. And they were, unsurprisingly, primarily expressions of awe.

But a few days later, twenty-five Fields Medalists wrote a public letter about what they called a severe misalignment between AI companies and mathematicians. Their view is that the point of hard problems is not only the solution, but how we arrive at it. Problems, they said, help train students, organize inquiry, generate new concepts, and connect one generation of mathematicians to another.

I would not have thought about mathematics this way. But I can relate to it in my own line of work.

The point of planning

As an engineering manager, I witness, participate in, and lead many product-planning cycles. There is planning of various kinds, conducted at different intervals, but it produces similar artifacts: product requirements, designs, technical plans, estimates, roadmaps.

Having been at the same company for about a decade now, I have seen the planning process change a few times. Through all that change, I have come to believe that the primary benefit of planning is not the outputs at the end but the thinking and collaboration required to create them. Sure, someone looks at the outputs, so they are not waste, but they quickly become stale. What remains durable is what happened to produce them.

One way to see this is to imagine deleting an artifact after the fact. It can often be recreated relatively easily because the good things that came before it—not just individually, but organizationally—are still there: the conversations it spawned, the relationships it formed, and the clarity it brought. That thinking and effort make the result easier to trust, explain, and stand behind. They also create a sense of ownership and accountability because people know what the decision was, why it was made, and that they had a hand in making it. So, as with mathematical proofs, if AI generates planning artifacts without the underlying human systems that made those artifacts worthy of trust, I’m not sure that is a good outcome either.

Optimizing the time to solve problems

It's hard to argue the position that we need to reduce the time it takes to solve problems, whether it's solving math problems, or building products.

But two common views on it, well-reasoned and well-intentioned, seem wrong to me.

The first treats reducing the time to create artifacts as the goal. The time required to create an artifact includes the thinking that needs to happen before someone creates it. But because that thinking time cannot be measured as easily, there is an urge to rush into the doing: just have AI spin up the artifact. Faster generation of artifacts does not necessarily produce faster understanding. If anything, rushing through the messy process of deciding what to build and why leads to shallower understanding.

The second optimizes for eliminating artifacts altogether and skipping to the building part. This risks going one step further and misses the point of the role artifacts served in the first place. Without intentionality, both approaches can remove thinking and collaboration from the process in favor of building fast.

In both of those failure modes, we mistake artifacts for the goal when they are the means to achieve it. The goals are clarity about what we want to do and alignment so that we move in lockstep.

This is true not only of documents but also of working software. Software is an artifact too, and as seasoned engineers say, code is a liability, not an asset. Building software is not the goal. Unlocking customer value and making money are.

Mistaking means for goals comes partly from reductionist thinking: the instinct to break things into smaller pieces, eliminate what we cannot measure, and optimize what remains.

I see the same pattern in how we think about food. Once we reduce food to calories, cooking and cleaning can look like overhead, and eating together can seem wasteful. The whole ceremony of cooking and eating can seem inefficient. An instant drink or packaged meal may deliver the calories and nutrition more efficiently while replacing the broader system that gives eating some of its social and personal value.

Means and outcomes

The central question is what the point of an activity is. But the difficulty in answering it comes from the fact that an activity does not have a fixed meaning. Its meaning differs from person to person, and even the same person can be inconsistent about it for various reasons.

An activity that may look like an outcome to an individual may be a means to the institution. An activity that someone does for pleasure may look like a means to achieve a different outcome for someone else.

I love spending time alone reading. That is when my creative juices flow. I then write to shape those ideas. I run and bike because I love them, not particularly because of the health benefits. The experience itself, including the effort, solitude, opportunity to reflect, and perceived growth, is what I'm doing it for.

Paperwork is different. I hate it, and I would happily use almost any tool that gave me the result without wasting my time.

So as I think about all this, activities, it seems, can be viewed from three different angles:

• What we get. The outcomes, which can be divided into what we can readily see or measure and what we cannot. A clean room, a healthy body, a working piece of software, and a proof fall into the former. Relationships, expertise, skills, growth, and meaning fall into the latter.
• What we experience. The pleasure, absorption, companionship, frustration, dignity, mastery, purpose, or belonging contained in doing the activity.
• What we become. The judgment, character, and confidence that develop through repeated practice—and the identity, pride, and self-belief we form around them.

Through this lens, the value of AI is easy to see when it optimizes something tangible and quantifiable, or when it removes drudgery, such as the administrative work many of us would happily give away. It becomes difficult when removing the labor also removes part of the experience or meaning. In other words, it's easy to see and agree on the value of AI when all we care about is the tangible part of what we get. But not so much when it's entangled with what we experience and what we become.

Despite being married to a biking enthusiast, my wife sometimes wonders why anyone would bike up a mountain when one could drive to the top and enjoy the same view. From her perspective, the summit is the destination. For me, climbing is why I do it. I may not even stop to enjoy the view at the top.

I make the same mistake. She can spend hours talking on the phone, and I sometimes wonder what “better” thing she might have done with that time. But that is because I misunderstand the point of small talk and gossip. I have to remind myself that the value is in the act of conversation.

In both cases, the person observing from outside mistakes the purpose of the activity.

When differences are allowed and when they are not

Our different preferences and worldviews do not seem to be problems in and of themselves. We do not need to fully understand what motivates each other, or why. We only need to recognize that those motivations matter and make room for each other’s choices.

But differences of opinion around something as influential as AI play out differently, particularly within institutions.

AI is not merely a tool that individuals use however they choose. It is also a tool that organizations allocate to individuals and govern through policies. They can use it to redefine work for everyone. Work that people relate to as a craft, a source of judgment, a collaboration, or part of their identity can be reclassified from above as output-producing factory. While leaders care about delivering results, people on the ground also care about deriving meaning and satisfying other psychological needs through their work.

Then there is also the difference in how people see the same work. Some do it for what they get out of it. Others do it for what they experience and become, or for some mix of all three.

When designers are encouraged to generate code, product managers to generate designs, and engineers to review and ship all of it faster, the boundaries between disciplines appear to dissolve. That democratization is liberating. But not always in the most intuitive way. Rushing to collapse those boundaries with only a superficial understanding of another function can move overhead and unintended consequences downstream. Agent-generated code may look complete to its author and look like technical debt to the engineer accountable for operating it.

Each discipline sees nuances that are difficult to perceive from outside because those nuances were acquired through practice. The so-called taste and judgment of an expert are often the accumulated experience of years of thinking, effort, failure, and attention.

So the choices are not between giving specialists the exclusive authority to decide who contributes to their domain and breaking open the floodgates. The choice is how to strike the right balance: how to widen access without giving up quality, craft, judgment, or sanity. And doing that requires intentionality.

Both sides are true

The hard part about this debate is that both sides are right because they are optimizing for different things. The mathematicians’ word for that is apt in that sense: misalignment.

AI has undoubtedly lowered barriers to writing, design, programming, and pretty much all other kinds of knowledge or creative work. For someone who could not previously do those things, that feels like freedom.

The experts can also be right that a shiny output may still be mostly fluff. At the same time, they may also be protecting identity, authority, or status. Human motives are rarely separable.

Work offers people mastery, community, dignity, and a story about who they are. At the same time, personal and emotional reasons cannot by themselves justify preserving an inefficient system or denying useful capabilities to others.

That is why the debate is less about what AI can or cannot do and more about what we should and should not do with it, and how we adapt to it, because it is a force that is inevitably going to change how we work across every possible domain of knowledge or creative work.

What's the point when output becomes cheap?

I do not think anybody knows what matters when outputs become cheap or intelligence becomes abundant. It's all conjectures at this point, and it will take time to answer that in the broader sense. But it is something each of us can ask ourselves now.

We do not seem to be well equipped for this transition. It is happening faster than any individual or system can make sense of it. That is why Dario Amodei’s phrase “pace the frontier” feels like a good way to put it. Pacing gives people and institutions time to think, decide what they want the technology to mean, and adapt with some deliberation.

Sources and inspirations

1. In Defense of Strategy, Packy McCormick’s essay on the relationship between strategy and execution.
2. A Severe Misalignment of AI in Mathematics, the declaration published by Terence Tao and signed initially by twenty-five Fields Medalists.
3. OpenAI’s account of its proposed Navier–Stokes solution. This draft uses “proposed solution” because the announcement was recent and independent evaluation was still developing at the time of writing.
4. The Mathematicians Rebel Against AI, Marginal Revolution’s commentary on the mathematicians’ declaration and the debate around adapting to AI.
5. We Must Pace the Frontier, Dario Amodei’s proposal for balancing continued frontier-AI development with the time and safeguards needed to manage its risks.
6. Dario Amodei’s original post introducing “pace the frontier”.
7. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, Theodore M. Porter’s history of how quantification became a technology of objectivity and trust in science, business, and government.
8. Measurement in Science, the Stanford Encyclopedia of Philosophy’s overview of measurement as a way of representing aspects of concrete systems in abstract terms.