Technically Present, Structurally Demoted

Anthropic recently published results from 81,000 interviews asking people what they most wanted from AI. The responses were classified into nine categories and ranked. Professional excellence came first, at nearly 19 percent. Creative expression came last, at 5.6.

The quote they chose to illustrate the top answer is from a healthcare worker: “So much of my cognitive labor was spent on documentation... Since implementing AI, the pressure of documentation has been lifted. I have more patience with nurses, more time to explain things to family members.”

That’s a great outcome. A person freed from paperwork to be more present with other people is a person whose life got better. If you asked me to design an AI system that produced that result, I would, and I’d be proud of the work. But read the wish again. The magic wand doesn’t conjure something new. It removes something in the way.

It’s worth noting what came last: creative expression, at 5.6 percent. The obvious reading is that people don’t value it. But that’s not what the survey measured. It measured what people want AI to do for them. And there’s a version of that ranking that means something almost opposite — that people instinctively protect creative expression from automation, the way you’d keep a handwritten letter even if a printer would be faster. The low number might not reflect indifference. It might reflect a boundary.

Around the same time the survey was published, Syracuse University announced it was closing or pausing roughly 20 percent of its academic programs. Ceramics, sculpture, painting, jewelry and metalsmithing — gone as standalone majors. Classics, German, Italian — gone. In all, 93 of 460 programs, with the humanities and fine arts representing the largest share.

The university’s dean wrote that “sunsetting a major does not mean closing a program or abandoning an intellectual tradition — it means sustaining that tradition in the form that best serves our students today.” Ceramics will still exist as a concentration within a broader BFA — the way you might take an elective in poetry while pursuing a degree in marketing.

A professor in African American Studies, whose program survived but was told to “re-envision” itself, put it differently: “Humanities used to be central to the curriculum, but now it is central in a service type of way.”

Service. The tradition persists, but only in support of something the market values more.

These two data points aren’t causally connected. A survey of AI aspirations and a university restructuring in upstate New York have different origins, different stakeholders, different logics. They may not even point in the same direction. If the survey reflects an instinct to protect creative expression from automation, then people still sense, at some level, that this is territory worth keeping human. But institutions don’t run on instinct. They run on enrollment numbers and market demand. And the gap between what individuals intuit and what institutions do is where the damage happens.

Syracuse isn’t responding to a population that stopped caring about art. It’s responding to a system of incentives — tuition revenue, employer expectations, enrollment trends — that doesn’t have a mechanism for valuing what can’t be measured, and the logic is defensible at every step. Eighty percent of students are enrolled in a third of the majors. No one is making an argument against art. They’re making arguments for efficiency, alignment, stewardship, demand — and art is simply what’s left over after those arguments are settled.

This is how a culture reclassifies what matters. Not through opposition or direct hostility, but through prioritization. Creative expression doesn’t get eliminated. It gets absorbed — into professional development, into personal branding, and the soft skills section of a résumé. It survives, technically present, structurally demoted. And it happens regardless of whether individuals still value it — because individuals don’t set the curriculum.

If this were only a cultural loss, it would be worth mourning and easy to dismiss. But I think it’s something worse. I think it’s a strategic error — one we’re making at exactly the wrong moment.

How do you know when you’re interpreting a phrase in a Beethoven piano sonata correctly? The score gives you the notes, the dynamics, the tempo markings. But the score can’t tell you how long to hold the silence before the recapitulation or whether the sforzando in a given bar should elicit fury or grief. Those decisions belong to the performer.

Every performance is a commitment made without certainty. You study the score, you study the period, you internalize what your teachers passed down, and then you sit at the instrument and use your best judgment, knowing the decision might be wrong and there’s no way to verify it.

Artists and philosophers wade in this kind of uncertainty every day. Historically, engineers mostly don’t. You write a function. It passes the test or it doesn’t. Correctness is verifiable. Ambiguity is a bug.

Until now.

I build orchestration systems — the layers that decide which model handles a request, how to evaluate whether a response actually answered the question, when to escalate from a simple pattern match to something more capable. I also participate in workshops, teaching other engineers how to work with these tools.

Here’s what I see in those rooms: the uncertainty is often unbearable for people trained to eliminate it.

A large language model is not a database. You can’t query it and expect a deterministic result. You can’t unit test a conversation. The same prompt produces different outputs. The system’s confidence and its correctness are unrelated. Evaluating whether a response is good — not just syntactically valid but actually useful, actually truthful, actually appropriate for the context — is an act of interpretation, not verification. It’s closer to deciding how to phrase a passage in a Beethoven Sonata than it is to debugging a SQL query.

The engineers who thrive in this space are the ones who can commit to a judgment without proof, hold competing interpretations simultaneously, and make decisions that are defensible without being provable. They are comfortable operating in the gap between what the system produced and what the situation required, and they can tolerate that gap long enough to make something useful out of it instead of retreating to a framework that promises false certainty.

Those are not engineering skills. Those are the skills trained by philosophy, literary criticism, music, rhetoric — by the practice of engaging with material that doesn’t have a right answer and learning to express something meaningful anyway. They’re the skills you develop when you spend years interpreting texts, performing scores, or writing a novel. They’re the skills Syracuse is cutting.

I build AI systems for a living, and I play piano. These are not separate competencies. The practice of examining a score and making an interpretive commitment I can’t verify is the same practice I bring to evaluating whether an AI agent’s response is trustworthy. The ability to hold ambiguity without resolving it prematurely is the ability that separates engineers who build useful AI systems from engineers who build impressive demos.

People may still sense this. That survey ranking — creative expression last — might be evidence that most people already know, intuitively, that this isn’t work to hand to a machine. But the institutions that train people to do the work aren’t listening to that instinct. The university that cuts its ceramics program to expand information science is not making a trade between art and technology. It’s undermining the technology by eliminating the cognitive training that the technology demands.

No one is doing this on purpose, and every individual decision is rational. The student who chooses information science over sculpture is making a reasonable bet. The dean who consolidates fine arts programs is responding to real enrollment data. The survey respondent who doesn’t ask AI for help with creative expression may be the most rational of all because they know it’s not something to outsource.

But the aggregate effect is a system that’s optimizing away the very capacity it requires — a culture training people to build tools they are increasingly unable to think clearly about. Not because they lack intelligence, but because they were never asked to practice the specific discipline of operating without a verifiable answer. The individual instinct to protect creative expression doesn’t matter if the institutions that cultivate it have already moved on.

There is a moment at the beginning of every piano performance where the audience gradually quiets, and the pianist thinks about the score one last moment before touching the keys. The notes are fixed, but the meaning isn’t. You have to decide what this phrase means to you, right now, and commit to it in front of an audience, knowing that the commitment might be wrong and that the wrongness will be audible.

That is not a metaphor for what AI engineering requires. It is a description of it. And we are dismantling the institutions that teach people how to do it — not because no one values it, but because the systems that make institutional decisions have no metric to account for its value. The market is not wrong about what people want. It is wrong about what the work requires, and it cannot hear the difference.