Why Are We So Determined to Prove That Work Was Hard?

My LinkedIn feed lately is full of people playing AI detective. Was this post written by ChatGPT? Is that image generated? Did this person do the research, or did a model do it for them?

What strikes me is where all that energy goes. Hardly anyone is asking whether the post is accurate, useful, or smart. They're asking whether it was hard enough to make. I think that says something about how we've learned to think about work.

For a long time, effort has been our shorthand for value. A report that took three days must be more substantial than one that took an afternoon. A design that took twenty hours must show more craft. A 2,000-word article must reflect real time spent researching, drafting, and editing. Most of the time, that shorthand held up.

AI is splitting expertise from effort

AI is starting to break that link. Someone with deep experience can now use it to explore ideas, organize research, poke holes in their own assumptions, and get to a first draft far faster than they could a few years ago. A job that used to take six hours might take ninety minutes.

So is the work worth less now?

I'm not convinced. If an experienced strategist reaches a better conclusion in one hour with AI than they used to reach in six without it, the client arguably got more. The production cost dropped. The quality of the thinking didn't have to drop with it. What AI may be doing isn't devaluing expertise so much as pulling it apart from effort.

A lot of the public conversation assumes the opposite, though. You see it in the little disclaimers people now attach to their work: "I wrote this myself." "No AI was used." They're starting to read like certificates of authenticity.

Sometimes the distinction does matter. Passing off unverified AI research as fact is a problem. So is using AI to impersonate someone's voice, or publishing information you don't understand and can't defend. But those are problems of judgment, authorship, and accountability. The tool isn't what went wrong; the person using it was careless.

The old signals stopped working

I suspect some of the resistance has a less flattering source. AI is scrambling the signals we've always used to recognize expertise.

A finished article used to prove you could write. A detailed analysis proved you'd put in the research hours. A polished deck proved design skill, and a working app proved you could code. Those things still matter, but they tell us much less about how the work got made. For knowledge workers, that's unsettling.

The closer AI gets to producing the visible output of a profession, the more people in that profession start asking what part of the work is really theirs. Writers point to meaning and voice. Designers point to taste. Strategists point to judgment. Developers point to understanding the systems behind the code.

There's truth in all of that. There's also a natural pull toward defining human value as whatever the technology can't do yet. I don't think that's cynical. It's probably just what happens when a profession has to renegotiate where its value lives.

What were clients actually paying for?

If AI can cut the labor behind a piece of work, we have to get honest about what the professional was being paid for in the first place.

Was the client buying six hours, or the right answer? Were they paying someone to type the words, or for the twenty years of experience that told them which words mattered? Were they paying for the research process, or for someone who could interpret the research, push back on it, and decide what to do next?

AI drags those questions into the open, and I think that's healthy. The most valuable parts of knowledge work were probably never the mechanical parts. They were things like context, taste, and accountability. Knowing which question to ask next. Spotting an answer that's technically correct and practically useless. Understanding a client or a market well enough to know when the obvious answer is wrong.

AI can help with a lot of that. Using it doesn't mean those human skills went missing. Often it just means the person had more time left over to use them.

Why the detection obsession feels backwards

Picture getting a strong strategic recommendation. It's well researched, carefully reasoned, specific to your company, and it works. Then you find out AI helped analyze the research. Does the recommendation get worse? If so, how?

Now picture a bad strategy that someone proudly spent forty hours building by hand. The hours don't rescue it.

Most of us would agree with both of those on paper. Emotionally, though, a lot of us still run on an old equation: more effort means more deserving, and more deserving means more valuable. AI is putting pressure on that equation, and I think that's why the reaction has been so heated. It's not just a new tool. It's making us rethink what work is worth.

Effort still counts, but inefficiency isn't a virtue

None of this means effort is meaningless. Craft matters. Practice matters. Learning to do something without help is often how you build the expertise to use help well later. And there are still plenty of situations where the process itself is the point.

But we shouldn't confuse doing things the slow way with doing them the right way. Nobody asks an accountant to prove their competence by giving up spreadsheets. Nobody expects architects to skip CAD because hand drafting took more skill, or tells photographers to go back to film because the darkroom was more work.

What usually happens is that technology absorbs part of the labor and the profession moves up a level. The skill that matters shifts. AI looks like it's doing the same thing to knowledge work.

Better questions than "Did you use AI?"

"Did you use AI?" is the wrong question. Here are the ones I'd rather ask:

• Did you understand the problem?
• Did you bring original thinking to it?
• Did you check what the technology produced?
• Can you explain and defend the result?
• Will you stand behind it?

If the answers are yes, I have a harder and harder time seeing why the number of manual hours should matter.

My bet is that the next stretch of professional value goes to the people who think most clearly, however long it took them.