The Jagged Frontier of AI is Coming For You, MBA Hopeful

Suppose you are an MBA candidate. Suppose you are an MBA candidate who aims high: T15, M7, maybe HSW. Now, suppose you are in search of ways to make your MBA application as strong and differentiated as humanly possible. If you are that kind of MBA candidate, you are likely considering how to use AI to do that.

Next, suppose you are a top MBA admissions expert, deeply embedded in the world of MBA admissions since 2009. Suppose you just came back from a week of meetings with fellow MBA admissions consultants and the admissions directors from all the top MBA programs. Now, suppose you kept hearing people conflate “use of AI” with “AI slop”. That expert is me, and what I kept hearing last week left me wanting to dig deeper.

Why Do Some MBA Candidates Use AI Better Than Others?

This is where you and I meet to answer exactly that.

By now, you’ve heard it all: AI is coming for your job; AI is coming for the jobs of MBA admissions consultants; AI can help you get into a top MBA; AI can get you dinged by a top MBA if you submit AI-slop; employers expect you to know how to use AI; the MBA is dead.

And underneath it all, sometimes but not often enough and not loud enough, is the same caveat: judgment now matters more than ever before.

What the heck do you do with all this, MBA hopeful?

Let me introduce you to 3 signals you should use to make sense of it all.

Signal 1: The Jagged Technological Frontier

In 2023, researchers from Harvard Business School, MIT Sloan, the Wharton School, and Boston Consulting Group ran a study on 758 BCG consultants. These were not junior analysts or interns. They were elite knowledge workers with degrees from Harvard, Yale, and Oxford, representing roughly 7% of BCG's entire individual-contributor consulting workforce.

They were given realistic consulting tasks: the kind of work they do every day, ranging from analysis and product ideation to strategy and written deliverables.

Half used AI. Half did not.

On many tasks, the AI users did better. For tasks involving creative ideation, open-ended business analysis, and written deliverables, in other words the kind of work with no single correct answer, the consultants using AI completed about 12% more tasks, finished about 25% faster, and produced work rated roughly 40% higher in quality. Pretty great, right?

Where Things Went Downhill

But the researchers also designed one task deliberately placed outside what they called the "jagged technological frontier." On that task, the consultants using AI were 19 percentage points less likely to get it right.

What was that task? Integrating spreadsheet data, layering in interpretations from qualitative interview information, and arriving at a nuanced business judgment where the answer is either correct or not. In other words: exactly what senior professionals get paid to do.

Where Things Got Even Worse

The AI-supported answers on the outside-the-frontier task were coherent. They sounded right. They read as authoritative. But correctness had deteriorated. The consultants using AI produced confident, well-structured, wrong answers. And they were less likely to catch all this BS than the consultants working without AI. Ouch.

The researchers called this phenomenon the jagged frontier because AI capability is not smooth or intuitive. It does not degrade gradually as tasks get harder. AI can perform brilliantly on sophisticated work and fail badly on something that looks, on the surface, easy. Heck, just last week, ChatGPT tried to convince me there is no recent adaptation of Wuthering Heights that starred Margot Robbie. But I digress (I’m famous for that).

Now, here is the part that should give every MBA candidate pause: "the location of the frontier of AI capability is not immediately obvious to knowledge workers, even though the tasks themselves are quite familiar."

You do not know where the edge is. Neither do I, always. Neither did 758 elite BCG consultants. Scary.

The researchers also flagged something specific about how this failure happens. Unlike previous generations of AI, large language models produce incorrect but plausible results in a way users find "believable." The output sounds confident and intelligent. And so smart people become overly reliant on it, potentially overlooking its limitations.

That is not a technology problem. That is a judgment problem.

The researchers were direct about the conclusions: "The effectiveness of AI in knowledge work will critically depend on human judgment." And: "Workers who skillfully navigate this frontier in their use of AI systems gain substantial quality and productivity benefits." The ones who don't, don't.

 Signal 2: McKinsey Enters the Room (and tells us where hiring is headed)

Last fall, McKinsey announced a 12% expansion in hiring in 2026. That is the headline, and it is good news.

But the more important signal is in how they described the workforce they are building.

In a Forbes article, Bob Sternfels, McKinsey's global managing partner, put it this way: "When people ask me how many people McKinsey employs, my answer is 60,000: 40,000 humans and 20,000 agents."

Twenty thousand agents. And let me spell it out clearly: That is not a future projection, that is now.

Sternfels named what he believes AI cannot do: set the right level of aspiration, exercise judgment, and make discontinuous leaps in thinking. Then he went even further.

McKinsey Already Tests for Judgment in Interviews

McKinsey is now testing for exactly those things. Candidates are given a case study and asked to work through it using Lilli, the firm's internal AI platform, refining their conclusions in real time. This is not a prompting test; it is a judgment test.

Marie Christine Padberg, McKinsey's global talent attraction co-leader, said it plainly: "We're interested in how candidates use the outcomes, not just how they generate them. Prompting can be trained. Judgment about what to do with the results and how to build on them is harder to teach."

Read that again. Read it in the context of the BCG study. The consultants who failed the outside-the-frontier task did not fail because they couldn't prompt. They failed because they couldn't evaluate and make the right call. McKinsey has drawn the exact same conclusion and built it into their hiring process.

By the way, this is why I am pushing all my MBA candidates this year to think carefully about this new reality, whether their target is management consulting or not. McKinsey is the leading signal, not an isolated one. If you are going into any knowledge-intensive field, some version of this test is already in place or it is coming.

The puck has moved.

Signal 3: Without a Semantic Layer, None of This Works

The third signal comes from a slightly different direction.

On a recent episode of the “All In” podcast (don’t judge me; I suppress many sighs when I listen to these guys but I want to know where the minds of the top tech bros are at), Salesforce's Mark Benioff made an argument. That argument cuts to the heart of why so many candidates are using AI poorly without knowing it.

His point was this: without a semantic layer, AI cannot function well. Or in his own words: "AI is very probabilistic. That is, it can kind of figure things out, but it needs to be grounded in real data and it needs to have that semantic layer. That means it needs to be locked down into the truth, into a single source of truth, or it just cannot work well."

Let that sink in (and not in an Elon Musk way, please): You need a single source of truth. Without it, the model is operating in a vacuum.

Think about what that means for an MBA candidate sitting down with an AI tool, typing things about themselves, and asking it to help build their MBA story. The output is coherent. It may even be impressive on the surface. But the problem runs deeper than that.

The candidate has no frame of reference to evaluate what comes back. They haven’t read thousands of MBA applications and neither have Claude and ChatGPT. They have never sat in an admissions committee room. They do not know what a compelling narrative arc feels like from the other side of the table. They cannot tell whether what the AI produced is actually strong or just sounds that way. And because it sounds coherent, it registers as correct.

That is the jagged frontier, friends, playing out in real time, in the most personal and high-stakes context imaginable for an MBA hopeful. The output is convincing. The candidate cannot evaluate the merit of it. And the cost of being wrong lands in “Deny” territory.

And don't even get me started on the cases where the candidate actually had something real — a strong point of view, a specific moment, a detail that would have stopped an AdCom reader cold — and the AI smoothed it into oblivion. That is its own problem, and I write about it here.

Who Are You Before You Open the AI Prompt Window?

Jack Clark, a co-founder of Anthropic, said something in a recent interview that stopped me mid-motion (like literally; I was out for a walk in my neighborhood and I stopped in order to rewind the podcast and capture the reference verbatim.)

He was talking about his own children, and his No. 1 worry about all of this: that if you discover yourself in partnership with an AI system, you become uniquely vulnerable not just to its failures, but to its personality. 

This is how he put it, in his own words:

“My bet is that in the future, there will be two types of people. There will be people who have cocreated their personality through a back-and-forth with an A.I., and some of that will just be weird. They will seem a little different from regular people. There will maybe be problems that creep in because of that. And there will be people who have worked on understanding themselves outside the bubble of technology and then bring that in as context with their interactions. I think that latter type of person will do better. But ensuring that people do that is actually going to be hard.”

What Does ALL This Have to Do With Your MBA Application?

Quite simply, a whole lot.

MBA admissions is a prime example of work that sits outside the frontier. Let me be very clear about why.

The BCG study's outside-the-frontier task required integrating data from multiple sources, interpreting qualitative information, and making a nuanced judgment under ambiguity. That is a precise description of what a strong MBA application demands.

Your application requires you to have a strong understanding of yourself outside the bubble of technology. And it requires that you hold your full story in working memory. To know not just what you have done, but what it means, in the context of where you want to go, in the context of what AdComs value in both the evaluation and selection stages of the admissions process (which happen to be distinct).

It requires reading between the lines of your experience to find the signal. It requires knowing when a story that sounds impressive is actually not the most powerful one to tell. One of my recent MIT admits wrote an 800-word story about that.

It requires catching the contradictions, the gaps, the things you said that don't quite land the way you think they do, and the ones you glossed over that had the power to move the needle, of your admissions odds, that is.

An AI tool has none of that context. And most of the time, neither do you.

This is not fear-mongering. It’s the reality.

And as the BCG study showed, the outputs it produces without that context will sound coherent. They will read well, with neat grammar, and smooth paragraphs of equal length. That part drives me absolutely crazy because no good writer writes that way. They will feel like a strong MBA application. And yet they may be confidently wrong in ways you are not equipped to catch.

That is not a knock on AI. It is not a knock on you, either. That is where the frontier is.

I see this in my work every day. Candidates come to me with materials they are proud of, produced with AI assistance, that have the shape and tone of a strong MBA essay but have lost the thing that makes an essay matter: the specific, idiosyncratic texture of this person's actual experience.

The New Goalpost: Epistemic Specificity

I first introduced the term “epistemic specificity” at my MBA Admissions Predictions 2026 live event earlier this year (you can see the full predictions and judge for yourself how they're holding up.)

Five months since those predictions, I'm doubling down on this one. And clearly some of the biggest names in AI are thinking about the exact same thing.

The Best MBA Admissions Consultants Are Thinking Partners

What my work has always been about is helping my candidates think. They all use AI by the way and I encourage them to. As we already established, their future employer will want them to know how to use the tools.

But I’m the human in the loop and make sure there is a semantic layer, interrogating the outputs, pushing back on what sounds right but isn't quite right, and making the calls that require knowing both the candidate and the admissions process at a level no model does.

That is Benioff’s semantic layer. That is the human in the loop the BCG researchers were pointing to. That is what McKinsey is now testing for before they make a hire.

And in a way, I am helping them strengthen their own judgement. As one of my MBB candidates (now a happy HBS admit who turned Wharton down) once told me, “You’ve changed the way I think about writing.”

What About MBA Admissions AI Coaches?

By the way, I’ve tested multiple MBA Admissions AI Coaches. And the same failure mode the BCG researchers have documented keeps playing out in front of me: coherent, confident advice and writing that misses the point in ways that mattered. I’ve written about that here.

A Word About MBA Admissions Consultants

I want to say something carefully here, because I have genuine respect for my colleagues in this field.

The conflation of AI use with AI slop is understandable. What many admissions professionals are seeing in applications and drafts is often exactly that: text that is technically competent, tonally smooth, and completely hollow. The reaction is reasonable.

But it is a narrow read of what AI actually is or does.

I have used AI as a thinking partner in ways that produce something far from slop. I have seen plenty of MBA candidates do it. The distinction is not about using the tool. It is about what you bring to it. Strong editorial instincts, deep subject knowledge, and a real point of view can (and do) produce something different. The output reads differently. The problem is that not everyone reviewing applications has developed the vocabulary yet to tell the difference.

This matters for candidates, because some of the most intellectually honest work they can do may get caught in a dragnet informed by fear of AI and misinformation about AI detection.

A Liability: When Your MBA Admissions Consultant Is Not Proficient With AI

Consultants who have not worked closely with these tools, who have not tested them enough to understand the range of what they can produce, are operating with a limited ability to advise you. They may catch the obvious cases, the AI slop. What they may miss is more subtle: the difference between AI used well and AI used poorly, when neither looks like obvious slop. And in their fear of AI, they may instill fear in candidates that prevents them from using AI in ways that are actually helpful.

That is not a criticism of intentions. It is a structural gap the whole profession is navigating in real time. The advisors best positioned to help candidates right now are the ones who understand AI from the inside, not just as a risk to flag and a possible detection liability.

So Yes, AI Is Coming for Some MBA Admissions Consultants

Just not the ones doing the actual value-added work.

The ones at risk are those who add little more than line editing, template peddling, or attempts at reverse-engineering their own admissions outcomes.

The ones even more at risk are those running factories. Dangling their judgment in front of candidates to justify the price tag but not getting in the weeds with them every single day for weeks before deadlines. The ones outsourcing your essay work to junior editors with zero knowledge of how decisions in MBA admissions are actually made.

As for me? Right now, I am working with dozens of MBA candidates in MBA Momentum, for free, because I want judgment and discernment to be widely accessible.

What Judgment Actually Is (And How You Can Build Yours)

As I was finishing this article, I started to realize we need to address what judgment is and where it comes from. So here we go.

First of all, judgment is not a soft skill. It is not emotional intelligence, it is not wisdom that only arrives with age (and thank God for that!), and it is not something you can read about in a self-help book and acquire.

Here’s a useful working definition: judgment is the capacity to decide, the ability to hold a point of view, and the guts to take responsibility for making a call you will live with for years. That’s a lot, isn’t it?

But that definition matters because it draws a pretty clear line. Knowledge and analysis are, increasingly, commodities. In a way, this is what the HBS, Wharton, and BCG study shows. Access to information is free. The analytical work that used to take a junior associate a week now takes minutes. That is not going to reverse anytime soon (make that ever). But when knowledge and analysis are free, judgment becomes more valuable, not less. It is the thing that cannot be commoditized, because it cannot be separated from the person exercising it.

But where does it come from, how does it develop? How can you grow it in yourself? First things first. There’s no way you can do it alone. There’s no way to acquire it by reading and you cannot cram your way into better judgment by watching YouTube videos either.

Judgment develops in circumstances where the cost of being wrong is real (I’m looking at you r/MBA!).

It requires you to be in situations where you cannot fake your way through, and where people push back and hold each other to a high standard. In other words, you need real stakes, real friction, and yes, real accountability.

The Redefined Answer to "Is the MBA Still Worth It" Just Entered the Room

Perhaps paradoxically, one place to develop judgment is an MBA classroom.

Not because of the curriculum but because of the conditions. Think back to the three criteria: the cost of being wrong is real, you cannot fake your way through (at least not all the time), and people push back and hold each other to a high standard. A well-designed MBA program has all three simultaneously, and that’s by design.

Take HBS, for example. The case method puts you on record, in a room of ninety peers who are tracking your reasoning in real time. The professor is not looking for the right answer. They are looking for how you defend a position under pressure, how you recalibrate when someone punches a hole in your argument, and whether you have the intellectual honesty to change your mind publicly or the backbone to hold your ground when you shouldn't. Your reputation in that room is forming from day one, and it follows you directly into recruiting and into how the famous “HBS network” will show up for you for years down the line. Those are real stakes.

The people pushing back are not junior colleagues being polite. They are future MBB partners, founders, and CFOs who have no incentive to let weak thinking slide, because they are being evaluated too. You cannot charm your way through a study group of people who are as sharp as you are and have read the same case.

And the standard is set by the cohort, which is its own kind of pressure.

The MBA is dead. Long live the MBA.

So why do some candidates use AI better than others?

I had no idea this article would end up being so long! But here we are at the end and I want you to have a clear answer to the question that launched this quest. Here’s the answer:

The reason some MBA candidates use AI better is not because they have access to better tools. And it’s definitely not because they have learned better prompting.

They simply did the work: they knew who they were, what they had built, and what they were actually trying to do next and why. Because they had developed, through real stakes and real friction, the judgment to know the difference between output that sounds right and output that is right. And often also because there was someone to push them to do it and help them evaluate the output.

That is the answer. It was always the answer, by the way, even before AI.

Your MBA application is one of the most judgment-intensive processes of your professional life. The AdCom reading your application is not on the hunt to detect whether you used AI or not. They are asking whether there is something in your reasoning, your epistemic specificity, your point of view, that could not have been generated without you.

Build that first.

Onwards and upwards,

Petia