"Human In The Loop" Doesn't Mean What They're Selling You
Joe Procopio called out the FOMO. Here's what the real opportunity actually is.
"Human In The Loop" Doesn't Mean What They're Selling You
Joe Procopio published something worth reading this morning.
His title: "Human In The Loop Is The New 'The Guy Who Knows AI Will Replace You.'"
His argument: the phrase "human in the loop" is being weaponized as AI FOMO — the same way "the guy who knows AI will replace you" was two years ago. The people selling this narrative have a stake in the platforms they're promoting. You should be skeptical.
He's right.
But the frame stops short of the most important part. And that gap is exactly where the real career opportunity lives right now.
The FOMO Machine Never Changes. Just The Vocabulary.
Every major technology shift produces the same cast of characters.
First come the builders — the people who actually understand what the technology does and what it's for. Then come the educators, the consultants, the platform vendors. And right behind them come the FOMO merchants.
The pitch never changes. It just updates the words.
"Social media is going to transform your industry. You need to become a social media expert."
"The cloud is going to replace your infrastructure. You need to get certified."
"The guy who knows AI is going to take your job."
Now: "You need to become the human in the loop."
Each pitch has a nugget of truth buried inside it. The transitions are real. The urgency is manufactured. And the people selling courses, certifications, and platform subscriptions are the ones who benefit most when you buy the fear.
Procopio cites Stanford's actual definition of Human-in-the-Loop AI Design from 2019. An intelligent system designed to augment the human, serving as a tool to be wielded through human interaction. AI as tool. The expert wields it.
Not a new mystical skill set. Not a prompting superpower. A tool — like spreadsheets. Like Hubspot. Like Salesforce.
And here's the part that lands hard: the first thing the AI-native revolution did was wipe out the need for the superusers that the previous generation of SaaS had created. Tech sets you up to knock you down. It's what it does.
If you make someone else's UX and UI the totality of your job, your job becomes redundant the moment the platform changes. That warning is real. That's Joe's point and it's worth heeding.
But there's a version of this he isn't talking about.
Two Completely Different Professionals Can Call Themselves "Human In The Loop"
Person A took a prompting course. They know which model to use for which task. They became proficient at a specific AI platform and now offer services based on that proficiency. Their value is entirely tied to the platform.
Joe is warning you about Person A. He's right about Person A.
Person B spent 20 years running the workflow. They know where the data is dirty. They know where the edge cases live. They know where the AI will produce a confident, well-formatted, completely wrong answer — because they've seen the actual wrong answer in the real world and know what it costs.
Person B doesn't need a course. They need one additional layer.
Stanford's definition is describing Person B. The subject matter expert who picks up AI as a tool. Not the tool operator. The expert who knows when the tool is off.
That person is almost certainly you.
The question isn't whether you need to become the human in the loop. The question is whether you're naming it that way.
Why Most Professionals Miss This Completely
Here's the pattern that plays out constantly.
The professional with 20 years of domain depth responds to the "AI will change everything" narrative in one of two ways. Either they dismiss it — "this is overhyped, I'll wait" — or they overcorrect — "I need to become an AI expert."
Both responses miss the same thing.
Dismissing it means you're not building the one layer that will make your existing expertise exponentially more valuable. Overcorrecting means you're spending time and money on tool proficiency that will depreciate as fast as the platforms change.
The move that actually works is neither of those.
Companies are spending serious money on AI right now. The average enterprise is running $85,000 per month in AI investment, up 36% in a single year. Token costs fall roughly 10x per year, so as AI gets cheaper, companies buy more of it. The spend accelerates even as the per-unit cost drops.
What they're discovering — slowly and expensively — is that they have plenty of tools and not enough judgment.
The AI doesn't know the data is dirty. It doesn't know this edge case blows up in production every third quarter when the seasonal adjustment runs wrong. It doesn't know the C-suite will reject this output not because it's wrong but because it conflicts with a strategic decision made 18 months ago that nobody wrote down.
The person who knows all of that isn't a prompt engineer. It's the professional who spent two decades building institutional knowledge that the AI is now trying to replicate — and can't, because that knowledge lives in pattern recognition, not training data.
The Domain Translator Is The Real Human In The Loop
I've been calling this the Domain Translator framework.
Not because it's a catchier phrase. Because it describes the actual function more precisely than "human in the loop" does.
A Domain Translator does three things that no AI can do on its own.
First: they know what the right problem is. Before the model runs, before the prompt is written, before anyone looks at an output — the Domain Translator knows whether the team is even asking the right question. Twenty years of running the workflow gives you a pattern library that no LLM trained on generic internet text can access.
Second: they can evaluate whether the output is actually right. Not "does this look plausible" — anyone can check surface plausibility. The real evaluation is whether this output would hold up in the real environment, with real data, against real operational constraints. That requires domain depth that can't be downloaded from a course.
Third: they can translate the result into action. This is where most AI implementations silently fail. The model produces a correct output. Nobody acts on it. Because it's framed wrong, because it conflicts with existing org politics, because nobody has the credibility to push it through. The Domain Translator bridges that gap. That's not a technical skill. That's the organizational muscle that comes from two decades of getting things done inside complex systems.
These aren't AI skills. They aren't prompting skills. They're leadership skills — with technical fluency layered on top of deep domain expertise.
And here's the critical point: the technical fluency is the layer you can develop in months. The domain expertise is the 20-year investment you already made.
What This Means If You're In A W-2 Role Right Now
The conversation that determines who stays and who goes in the next reorg isn't about AI tool proficiency. It's about this:
Who in this organization knows where AI goes wrong — and can fix it before it costs us something?
Who can take what the model produces and convert it into a decision the business will actually act on?
Who understands the workflow well enough to aim the tool correctly before it runs, not just evaluate the output after?
That's not a technical conversation. It's a capability conversation. And the professionals building that positioning now are the ones who will be on the right side of the next restructuring — not because they resisted AI, and not because they became the most enthusiastic platform users, but because they figured out how to make their domain expertise the irreplaceable component in an AI-augmented workflow.
The shift is subtle but it's everything. You're not positioning yourself as someone who uses AI well. You're positioning yourself as someone whose judgment is what makes the AI useful.
What This Means If You're Considering A Fractional Path
The fractional opportunity here is even cleaner.
Four to five companies at $50,000 to $75,000 each. $200,000 to $400,000 annually. Twenty to thirty hours a week. You bring 20 years of domain expertise plus AI fluency to problems that are too specific for Google, too small for McKinsey, and too valuable for the companies facing them to leave unsolved.
That practice isn't built on AI skills. It's built on domain depth that most professionals have been accumulating for two decades and dramatically undervaluing because they don't know how to price it in the current market.
The AI layer changes the math in one specific way: it multiplies your output. The Domain Translator with AI fluency can do in two days what used to take two weeks. That changes the economics of fractional work entirely — and it changes what you can credibly charge.
But the AI is the multiplier. The domain expertise is the value. That sequence matters.
The Warning Worth Heeding — And The One That's Missing
Joe Procopio's warning deserves to land. If someone is selling you a course, a certification, or a subscription premised on the idea that learning their specific system will make you irreplaceable — that's the FOMO machine at work. The value was never in the tool proficiency. It was always in the domain depth that makes the tool useful.
The companies that won the mobile transition didn't win because they hired the best iPhone developers. They won because they had leaders who understood what the mobile-first shift meant for their specific business — and could move the organization accordingly.
Same dynamic. Faster timeline.
Token costs falling 10x per year means the gap between early movers and late movers compounds faster than any previous platform shift. The window isn't years. It's months.
But here's the warning that's missing from the conversation:
Dismissing AI FOMO is not the same as having a strategy.
Seeing through the hype is the easy part. The harder question is: what are you doing with the 20 years you already have? Are you layering on the one additional capability that turns existing expertise into a $200K–$400K practice or an untouchable W-2 position? Or are you waiting for the landscape to clarify?
The professionals who win this transition won't be the most skeptical. And they won't be the most credulous.
They'll be the ones who recognized that they already were the human in the loop — and got strategic about what to do with that.
Ready To Position What You Already Have?
Written by
Bill Heilmann