A client receives two deliverables…
Both solve the problem they were hired to solve. Both are accurate and useful, and they lead to the same business outcomes. The client is happy with the work and sees no meaningful difference in the results.
Then they learn that one deliverable took 20 hours to create while the other took 20 minutes. Now the questions start rolling in:
- Was AI involved?
- Should the faster deliverable cost less?
- Is the person who completed it somehow less skilled because they found a way to work more efficiently?
What’s interesting is that most of us have completely different reactions to AI depending on which side of the transaction we’re sitting on. We love using AI to save ourselves time, but many become uncomfortable when they’re the customer and discover AI was used to create something they purchased.
I recently ran a LinkedIn poll asking a simple question: If the outcome is great, do we really care how it was made?
The responses reinforced something I’ve been thinking about for a while. The biggest objections people have to AI often have little to do with quality.
The time vs. value fallacy
I think part of the discomfort comes from the fact that we’ve spent decades connecting value to effort.
- Long hours feel valuable.
- Fast work feels suspicious.
- Struggle feels like expertise.
The harder something appears to be, the easier it is to justify its price.
The story is about a ship engine that stopped working. After multiple attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.
His invoice was $10,000.
The owners were furious and demanded an itemized bill.
The response:
- Hammer tap: $2
- Knowing where to tap: $9,998
The debate whether this is a true story or just a fun tale that people like me use to validate value-based pricing. Whether the story is true almost doesn’t matter. The lesson does.
People aren’t paying for the tap. They’re paying for the expertise behind it.
That’s what makes AI such an interesting topic. It forces us to confront a question many of us have avoided for years:
- Are we paying for expertise or for visible effort?
Those aren’t always the same thing.
The objections that actually matter
To be clear, not all objections to AI are bad ones. I certainly haven’t had an issue sharing my opinion.
In fact, I think some of the strongest arguments against AI have very little to do with how quickly something was created.
These are all legitimate concerns, and what’s interesting is that none of these concerns has much to do with how long it took to create the deliverable.
They’re questions of trust.
- Can the output be trusted?
- Can the recommendation be defended?
- Can someone confidently stand behind the work if it’s questioned six months from now?
Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.
That’s why I’ve always found the quality debate to be the least interesting part of the conversation. The more important question isn’t whether AI was involved. It’s whether the outcome is trustworthy enough for someone to put their name behind it.
The outcome test
The more I think about AI, the less interested I become in whether it was used.
Instead, I find myself asking a different set of questions.
- Was the outcome accurate?
- Was it useful?
- Was it better than the alternative?
- Would you be willing to stand behind it with your name, reputation, and credentials on the line?
If the answer to all of these is yes, should we really care how it was produced?
I suspect this is where many people become uncomfortable because it shifts the conversation away from tools and back toward results.
Ironically, this is also where humans become more important, not less.
The future isn’t machines versus humans (I know, “The Terminator” and “I, Robot” movies will never be the same). It’s humans using AI versus humans who don’t. The premium won’t come from refusing to use AI. It will come from judgment, taste, decision-making, communication, and accountability.
AI can accelerate execution, but humans still decide what should be built, what should be published, and what risks are acceptable. More importantly, humans are still the ones responsible for the outcome.
The people who lose to AI won’t be the ones using it. They’ll be the ones still evaluating effort while everyone else is measuring outcomes
This post first appeared on the author’s website and is republished here with permission.
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