Business schools are very good at teaching students how to compete for desirable careers. We are much less comfortable asking what those careers are really for.

Rutger Bregman recently described talented students being funnelled toward McKinsey, Goldman Sachs and similar employers, while his Moral Ambition fellowships offer another path: $15,000 and a summer working on problems such as child poverty or AI safety. The interesting part is not whether consulting is bad and fighting poverty is good. That’s too easy.

The interesting part is the funnel.

Look at the signals surrounding students. Salary matters in rankings. Placement matters. Employer prestige matters. Career offices celebrate recognizable destinations. Students learn quickly what success is supposed to look like.

I see the same logic in business education. We spend enormous effort on employability, CVs, pitching, networking and career acceleration. Far less on choosing what deserves the acceleration.

That does not mean professors should tell students which causes are worthy or which employers are morally acceptable. That would be another kind of funnel. But we can create more moments where students articulate what they want their capabilities to do in the world, then expose them to paths beyond the default prestige ladder.

In my courses, I increasingly care less about whether students can reproduce a model and more about whether they can use judgement on a real problem, under constraint, with consequences, and succeed. And privately, I hope that some of what they become capable of doing is eventually used to help other people. Clay Christensen once expressed that idea beautifully. I have never really forgotten it.

Business schools do not need to manufacture moral ambition.

But we should stop manufacturing ambition without a destination.

Business schools may be preparing students for the AI era by teaching them more AI. I am increasingly convinced that is the smaller part of the job.

AI is already very good at the layer we have traditionally rewarded most: producing things. Research summaries, market analyses, business models, recommendations, slides. A competent first pass can now appear before a student has finished choosing a template.

The interesting part is what comes before and after.

Someone still has to decide which problem is worth solving, what assumptions deserve challenging, which evidence matters, which tradeoff to accept, and whether the answer makes sense outside the screen. Then someone has to defend the decision and live with the consequences.

And that shift matters because value does not disappear when execution becomes easier. It moves. When producing an answer becomes abundant, the scarce part becomes deciding which answer deserves attention, which direction deserves resources, and which consequences we are prepared to accept. AI may reduce the cost of doing the work while increasing the value of knowing what work should be done.

That’s where business education should be moving.

I am trying to push my courses upstream. Less reward for polished output alone. More emphasis on problem framing, judgment under constraint, difficult choices, argument, iteration, and ownership. AI is welcome in the room. In many cases, I want students using it aggressively.

But I do not want them confusing the ability to generate an answer with the ability to know what should be done.

The future of work may not be about humans finding a shrinking collection of tasks machines cannot perform. It may be about humans moving upstream, toward the questions, choices, and responsibilities that give those tasks direction.

When execution becomes cheap, judgment becomes the job.

Universities still measure education in hours.

That made sense when knowledge was scarce, professors were the distribution system, and learning meant being in the room. But AI, better pedagogy and experiential learning are changing the relationship between classroom time and capability.

I see it in my own courses. I am removing lectures, shortening explanations, and pushing students into fieldwork, decisions, experiments and live defenses. AI can help them understand today a concept in minutes that I might once have spent half a class explaining. But the interesting part begins afterwards: what can they do with it?

Here’s the uncomfortable bit. If I can help students build greater capability with less scheduled classroom time, the institution does not naturally benefit from making the course shorter.

The inconvenient truth is that higher education is organized around time. Credits, contact hours, teaching loads, timetables, contracts and semesters depend on it. Hours are easy to allocate and audit. Capability is not.

That creates a strange incentive: better learning can become more material squeezed into the same hours, instead of a reason to question the hours themselves.

This is not an argument for shorter education. Students still need time to struggle, observe, test, fail, revise and defend. But much of that is learning time, not lecture time.

So I am increasingly moving time from receiving to doing.

If we can produce more capability in less classroom time, higher education eventually has to confront a difficult question:

Are we designing around how students learn, or around the hours institutions know how to count?

AI may be about to expose one of higher education’s quieter design flaws:
we made too much human interaction optional.

MIT’s new report on AI and education calls for universities to deliberately protect the human side of learning as AI becomes more capable. The reason is easy to see. When students can get an explanation, clarification, second opinion, or even a reasonably convincing answer from GPT in seconds, the incentive to find a study group or walk into (or online) office hours starts to disappear.

I see this from the classroom, not from a committee room. I move from one business school to another and, quite literally, teaching is what I do.

A few years ago, I could spend roughly 50 hours a semester across my classes in voluntary office hours. This past year: zero.

Slack tells me much the same story. I have used it with students for years, filling it with questions, polls, discussions, GIFs, odd and fun things I find online and the occasional late night exchange. Thousands of interactions have gradually dwindled to almost nothing. What remains is mostly administrative: Which file? Which date? Where do we upload this?

But the part that fascinates me most happens downstairs.

I often work in school cafeterias and public spaces between classes, surrounded by student groups doing assignments. I am there to work, but it is impossible not to notice how differently students behave once the classroom structure disappears and no professor is standing over them. That matters, because this is where you see what the system has actually taught them to do on their own. Over time, quite unintentionally, I have become a sort of cafeteria ethnographer, minus the clipboard.

The pattern is increasingly familiar. A group reads the assignment, divides it into pieces, allocates the pieces around the table like a card game, then everyone disappears individually into AI bubbles. Twenty minutes later, they reconvene, almost like waking up from a dream, and stitch the outputs together.

They’re sitting together. But they’re not thinking together. And I think blaming AI gets this backwards.

If a chatbot can remove students from the social life of learning simply because it is faster and easier, then perhaps we never embedded that social life deeply enough in the first place!

Too much teaching is still one way. Courses are short. Syllabi are stuffed. Theory has to be covered. A real debate can swallow half of a three hour class, which makes it wonderfully educational and terribly inconvenient.

So collaboration gets encouraged rather than designed in.

That’s what I have been trying to change.

I lecture far less than I used to. In some sessions, students spend close to 80% of the time working, talking, arguing, testing decisions and getting challenged by me as I move from team to team. Intermediate deliverables create conversations with me. Peer critique forces teams to confront other teams. In entrepreneurship projects, I sometimes make evidence of real conversations with (target) users worth 30% of the grade, and they must show me how those conversations actually changed what they built.

And where the syllabus gives me room, I have been killing off the final PowerPoint presentation too. Students already know how to stand beside slides and read them to people. Instead, I bring in outside practitioners and turn the final presentation into a workshop. Fifteen or twenty minutes with someone who knows the subject, asks awkward questions and does not necessarily accept the first answer. Everything discussed is on the table or the whiteboard, no hiding behind shiny slides that someone else (AI) produced.

Much closer to life, in other words.

Why? Because the workplace students are entering is not an individual prompt box.

It is negotiation, disagreement, persuasion, judgement, collaboration, misunderstanding, compromise and occasionally sitting across from someone who is confidently wrong.

AI can help students prepare for that world. I use it myself, constantly.

But if business schools allow AI to turn learning into increasingly efficient parallel solo work, we risk preparing students to produce excellent answers for organisations where answers were never the hardest part.

The truth is, the report can be generated. But the meeting still has to happen.

Every business school claims to teach innovation.

Then it sends students to study someone else’s.

We analyse Apple, IKEA, Netflix, and organisations students have never entered. Meanwhile, they spend one or two years inside a complex institution making decisions about pricing, programmes, admissions, technology, partnerships, rankings, customer experience, and market positioning which is precisely what innovation is about.

I’ve started wondering whether the richest business case in the building is not sitting in the library. It is the building itself. Yet we spend hours teaching students how organisations innovate while barely inviting them to observe the organisation they are already part of.

I already do this with my own teaching. I take students back through the history of my courses. I show them what I changed, what I removed, what failed, what students forced me to reconsider, what technology disrupted, and which experiments survived. I put my own decisions, assumptions, and mistakes on the table.

It is not always flattering, but it’s precisely why it works.

Students begin to understand that innovation is rarely a brilliant idea followed by applause. It is a trail of assumptions, negotiations, constraints, small bets, setbacks, and uncomfortable choices.

But what I would rally love to see is students mapping their own school.

What changed in the admissions journey? Why did tuition increase? How was the new AI policy created? Why did the school launch that master’s instead of another? Why are some partnerships formed while others disappear? Who said no before someone finally said yes?

The key is that students should not only be shown what was launched. They should also see what did not work.

For innovation to take hold in any organisation, it has to become part of the culture. That means accepting failure, examining it, and learning from it. Most institutions talk about innovation far more comfortably than they talk about what failed.

That is where After Action Reviews matter: making the failures, wrong assumptions, and lessons visible before the organisation moves on. That learning is not an embarrassing detail to hide afterwards. It’s part of the innovation itself, and often what makes the next breakthrough possible.

Innovation then stops being a chapter in a textbook. It becomes visible, political, messy, uncertain, and real. Finally.

Business schools could turn themselves into living cases. Their marketing would become easier too. Real innovation produces evidence, and doesn’t need adjectives.

So before asking students to study innovation elsewhere, perhaps business schools should first open the doors to their own.

Steve Blank told entrepreneurs to get out of the building.

Business schools might start by walking downstairs.

Every autumn, I teach entrepreneurship. Every summer, I live it.

By June, most regular teaching has stopped. Summer programmes occasionally appear like popcorn, then disappear just as quickly, often for reasons nobody explains. But the business of being an adjunct professor continues, because this is when the next academic year is supposedly taking shape.

“Supposedly” is doing quite a lot of work in that sentence.

This summer, some of my teaching simply disappeared for next year. Courses were reorganised, tracks changed, and nobody called to explain the logic or ask what the people teaching them might have learned. I discovered that hours I had expected were gone, which is roughly equivalent to a regular customer quietly removing half its orders from your sales forecast and hoping you didn’t notice.

At another school, adjunct faculty were effectively asked to accept less money. After nearly ten years without a raise, that created a decision any entrepreneur would recognise. A major customer had become an important part of the portfolio, then asked for a substantial price reduction. Volume can be seductive, but volume is not loyalty, and dependence is not product market fit. Sometimes the most mature decision is to walk away, even when every spreadsheet is begging you to stay.

Business schools can woo adjuncts with hours one year and offer almost nothing the next. That makes putting all your eggs in one institutional basket extraordinarily dangerous, particularly when the basket belongs to somebody else. So I do what I would advise any founder to do: diversify the customer base, protect the value proposition, develop new channels, enter new markets, and keep adjusting the business model.

This autumn, I will teach in Germany at a place I am genuinely proud to join. The opportunity came through people who trusted my work enough to recommend me. I’m also still waiting, hoping, and probably refreshing my inbox too often about another possibility in Spain.

I am not raising capital, but I am constantly raising confidence: my own, my family’s, and the confidence of schools willing to trust me with their students. My wife is not officially my cofounder, but she has lived through enough pivots to deserve equity. My son is not an employee, although I occasionally worry about retention and whether the benefits package is competitive.

Few know this or even acknowledge it.

My business model has customer segments, channels, revenue streams, fixed costs, unpredictable demand, and the occasional client who wants more for less. In other words, it’s a Business Model Canvas with slightly more anxiety and considerably fewer sticky notes than the one’s I use in class.

I’ll be honest, after working in more than thirty startups as a founder, cofounder, or bizdev, I’ve started wondering whether adjunct teaching is not simply “startup number thirty one.”

Perhaps business schools should stop teaching uncertainty as an abstract case study and recognise that many of the people standing in front of the classroom already manage it for a living.

As I write this, I still do not know exactly what the year will look like.

What I do know is that the timetable starts in September. The entrepreneurship started in June.

Every summer, my wife Marjorie and I leave for holiday with a simple agreement: I will stop turning life into teaching material.

By the first coffee, I’ve broken it.

The Airbnb listing becomes an Ansoff Matrix discussion. Breakfast service becomes Design Thinking. The supermarket queue receives a Porter’s Five Forces analysis nobody requested. A disappointing ice cream triggers Doblin’s Ten Types of Innovation. A delayed train becomes effectuation theory.

Marjorie watches this unfold with the expression of someone who has accidentally married a travelling business school.

She tries to protect herself by booking increasingly remote villages in Brittany. Last year, we spent two weeks somewhere so rural that we never saw a traffic light!

Marjorie considered this progress. No traffic. Weak WiFi. No visible economy beyond one bakery, three cows, and a man selling oysters from his garage.

It made no difference.

At the ferry terminal for the Îles Glénan, I was supposed to be checking whether we were in the right queue. Instead, I photographed the boarding flow for class, then started asking a member of staff why passengers were being divided that way and whether the signage had ever been tested. Marjorie kept walking toward the boat. She knows that coming back for me only turns a minor delay into a full Design Thinking workshop.

By lunch, I’m asking a food truck owner about margins, menu placement, and why the queue bends left. By mid afternoon, I’ve stopped the car to record an idea in Things. By evening, I’ve opened X “for one minute,” discovered a founder interview, and disappeared into a podcast.

This is partly curiosity. Mostly, it is FOMO, compulsion, and the same Always On behaviour I mock in influencers.

I know I should stop. I also know I should not have a second serving of ice cream.

The ideas arrive while cooking, hiking, driving, and before I have dried myself after a shower. After all, a good SWOT cannot wait for shorts.

My son, sensibly holidaying without us, still appears in class as an unwilling case study. Nobody in my family is safe from strategic analysis.

I call all this field research. Marjorie calls it exhausting.

After years of unpaid participation, she has asked whether she receives ECTS.

Next year, she is looking for a lighthouse without electricity.

I have never understood the pride some professors take in saying they have taught the same course for ten years.

Perhaps that works if knowledge is the product.

I don’t think it is anymore.

Students can access explanations, frameworks, examples, summaries, and increasingly polished answers in seconds. Meanwhile, the companies they are preparing to join keep changing how they communicate, recruit, innovate, decide, and fail.

AI alone has rewritten part of the workplace faster than most curricula can schedule a committee meeting.

A course cannot remain frozen while the world around it moves.

That does not mean changing everything for novelty. Some slides survive. Good ideas deserve to. Unrecycled teaching means refusing to confuse familiarity with quality. It means asking every year whether the experience still prepares students for the reality waiting outside the classroom.

So each summer, I become a student again.

I read outside education. I watch how startups pitch, how investors challenge assumptions, how teams make decisions under pressure, and how people now hold attention. Then I bring those observations back and rebuild the course around what students need to practise, not merely what I need to cover.

Business schools should require every course to show what changed, why it changed, and what evidence shaped the redesign.

Not another administrative form. A serious conversation about relevance, followed by visible experimentation.

I do not have the perfect model. I test, watch, ask students what worked, admit what did not, and rebuild again.

That is why this blog is called “Unrecycled Educator.”

It’s not branding. It is a promise I make to my students, and an inconvenience I repeatedly create for myself.

August is my busiest teaching month. I just do not teach a single class.

Instead, I sit in shorts, surrounded by too many browser tabs and an irresponsible number of espressos, taking my courses apart.

I rewrite assessments. I rebuild the Notion course manuals. I change the artwork, the pacing, the email sequences, the milestones, the cases, the field visits, and the people I bring into the room. I call last year’s guests and ask how we can make their intervention sharper.

I abandon activities that worked perfectly well because “perfectly well” is not the standard.

By September, perhaps five per cent of a course remains untouched.

A course can easily consume another fifty hours during the summer. I do all of this unpaid, while living the familiar adjunct contradiction: rebuilding next year’s courses before I know how many of next year’s courses will still be mine. None of those hours appear on a timetable. Nobody sees the deleted slides, the failed experiments, or the afternoon lost deciding whether an exercise belongs before lunch or after it.

This is the slightly ridiculous part of my profession. I am supposedly on holiday, but I am also wondering whether a venture capital pitch format could improve a strategy defence, whether a hackathon could replace a lecture, and whether students should receive less explanation and more pressure.

It’s damn exhausting. But it’s also one of the most creative parts of my year.

Business schools usually count classroom hours. They should also recognise course renewal as real academic work. A professor repeating last year’s material and a professor rebuilding the entire learning experience are not doing the same job.

Every September, students think they are taking one of my courses.

They are not.

They are taking the newest version.

But the invisible work is only half the story. The more important question is why I refuse to leave a successful course alone.

In Part II, I explain why “unrecycled teaching” is not novelty, perfectionism, or personal obsession.

It’s responsibility.

Almost every business school I’ve taught in says it prepares students “for business.”

I’ve stopped believing that’s true.

Most business schools prepare students for one very specific version of business: the multinational corporation.

The cases come from global giants. The guest speakers come from global giants. The frameworks assume global giants. Students spend years discussing companies that employ tens of thousands of people, then graduate into a world where most businesses look nothing like that.

Business is a pyramid.

At the top sit a tiny number of multinational corporations.

Underneath them are millions of startups, family businesses, local manufacturers, consultancies, agencies, restaurants, cooperatives, craft businesses, and small companies employing five, ten, or fifty people.

That is business too.

In fact, that’s where most business actually happens.

Over the years I’ve deliberately moved my own teaching in that direction. I invite founders, family business owners, and entrepreneurs into my classroom. People who sign the payroll, negotiate with suppliers, lose sleep over cash flow, and know every employee by name.

And you know what; I've yet to meet a student who leaves one of those sessions wishing we'd invited another Fortune 500 executive instead.

Not because these businesses are smaller. But because they’re tangible.

They can see every decision ripple through the company. Strategy stops being PowerPoint and becomes something lived every Monday morning.

Ironically, I think these experiences also produce graduates who are better prepared for large companies. They have made decisions instead of only analyzing them.

If business schools want to call themselves business schools, they need to become part of the business community around them. Every local business should know the business school across the street.

Right now, too many don’t.

And that’s a missed opportunity for everyone.

Business schools pass too many students.

I do not say that lightly. I say it after years of teaching across several schools and watching the same pattern repeat itself.

Some students turn up, participate, take risks, struggle with difficult ideas and improve. Others miss classes, spend sessions scrolling through their phones, contribute almost nothing and submit work they barely understand.

Most of them still graduate.

That is where business schools have got things wrong. We have confused giving people an opportunity with guaranteeing their progression. Once the tuition is paid and the student is inside the system, failure becomes an administrative problem rather than an academic consequence.

I know because I am often the problem.

When I fail students, the calls begin. Grades are reconsidered. Marginal cases suddenly become exceptional circumstances. Some schools impose medians that make genuine failure statistically awkward. These conversations rarely happen in writing.

The message is obvious. Retention matters more than standards.

ECTS should be earned. They should not be received for attendance, negotiated after the fact, or treated as part of the purchase price.

The worst damage is done to the students who work. They see classmates coast towards the same diploma and quickly understand that effort has been badly priced. Why push harder when the system protects the people who do not?

We accept real cutoffs elsewhere. Sport has selection. Medicine has consequential assessments. The military has selection and progression gates. Not everyone who starts should automatically finish.

My proposal is not a fixed failure quota. That would be lazy and easily gamed. I want published progression standards, externally audited grading distributions, and real gateways between years. Students who fail should get another chance, but they should rejoin only after meeting the standard of the next cohort.

I am already testing this in my courses. Attendance has consequences. Individual contribution is visible. Decisions must be defended. Polish cannot rescue empty thinking.

A business school diploma should expand opportunity.

It should not certify attendance.

A founder I spoke with recently was not worried about whether graduates could produce an analysis. AI had already solved much of that problem.

What he wanted to know was simpler and harder: “why did you make that call?”

He was not asking which framework appeared in the deck, how many sources had been consulted, or whether the presentation looked professional. He wanted to understand the reasoning, the tradeoffs, and whether the person in front of him understood the consequences of the decision.

That is the shift business schools need to take seriously.

As intelligence becomes cheaper, judgment becomes more valuable.

The founders and companies I speak with are not short of information. They’re literally drowning in it. What they need are people who can decide what matters, reject what does not, choose a direction, and explain why.

That is increasingly a form of triage.

AI can research a market, challenge assumptions, generate options, identify risks, and polish the final presentation. The problem begins when a plausible answer is mistaken for an owned decision.

A plausible answer can sound intelligent without anyone truly believing it. An owned decision requires someone to say: this is the option we chose, these are the alternatives we rejected, this is the evidence we trusted, and these are the consequences we are prepared to accept.

Yet most business school assessments are not built around that.

The system rewards completion, coherence, confidence, and a numerical grade. Students quickly learn that a polished answer is safer than an uncertain decision. AI has not created that weakness; it has simply made it much easier to see.

So I give students more constraints, not fewer.

They complete several team and individual submissions, each one building on the last and carrying forward the consequences of earlier decisions. They must choose left or right, reject alternatives, define thresholds, and explain what would make them stop.

They cannot hide behind “it depends.”

As I have written before, I use a continuous incentive progress system. Students receive precise, actionable feedback in small instalments throughout the course, with bonus or penalty points as their work develops. One weak performance does not destroy the course, and one polished submission does not rescue it.

But I also punish polish when it outruns proof.

And from my experience, this substantially changes how students use AI. It stops being a machine for producing the answer and becomes part of the reasoning process. They use it to test logic, expose assumptions, compare alternatives, and sharpen a decision they still have to own. As well as strengthening the decision making logic, and gives the ending more weight without becoming theatrical.

I acknowledge that this is easier in strategy, innovation, and entrepreneurship, where there is rarely one correct answer. In mathematics or engineering, the final answer may be fixed. Even there, the path matters. Employers still want to know how someone approached the problem, what they tested, what they rejected, which tradeoffs they accepted, and why they trusted the result.

But in my view, the larger obstacle is time.

Judgment does not develop in a 15 hour elective. It comes from repetition, mistakes, feedback, memory, and the slow process of making ideas your own. Yet business schools keep compressing courses while expanding grade requirements and ECTS accounting.

That is not a cheap criticism. The system was built for a different age, and changing it will take time.

But the direction is clear.

AI will keep giving students more information, more options, and more convincing answers. But producing intelligence will no longer distinguish them.

The scarce graduate will be the one who knows when enough intelligence has been produced, understands the tradeoffs, chooses what not to do, makes the call, and accepts responsibility for what happens next.

We understand tools through control. We pick them up, give them a task, use the result, and put them down again. The hammer does not disagree. The spreadsheet does not question the assumption behind the forecast. The GPS does not ask whether you are heading to the right place. The user remains intellectually alone.

Most students approach AI in exactly the same way. They ask for an answer, a summary, a structure, or a polished submission. This is hardly surprising. Business schools still reward finished outputs far more than the thinking that produced them, and the race for grades makes outsourcing rational.

AI becomes far more interesting when the relationship shifts from tool use to partnership in thought.

When there are mentally two of you, ideas collide. Arguments are tested. Weak assumptions become visible. One question produces a better one. The value no longer sits mainly in the output, but in the dialogue that changes how the human thinks.

I have tried to create the conditions for that relationship through continuous assessment, progressive incentives, extensive personalised feedback, and repeated coaching. Students must develop their work over time. Simply generating a final answer becomes less useful, while using AI to challenge, improve, and rethink becomes more valuable.

The key is decision. Students who outsource cannot explain why a path was chosen, which alternatives were considered, or what trade offs were made. That is precisely what I reward: judgment, justification, and ownership of the thinking process.

Business schools should redesign assessment around visible intellectual development, not merely polished delivery. Students should submit parts of the dialogue, explain what changed, defend what they rejected, and show how their judgment evolved.

This is where genuine partnership reveals itself. A finished answer may conceal whether any meaningful thinking took place. The conversation behind it shows whether the student challenged an assumption, resisted an easy answer, explored another direction, or changed their mind for a good reason. It exposes the difference between asking AI to complete the work and allowing it to deepen the thinking.

But real partnership requires trust, friction, patience, and depth. It is not created through one clever prompt, nor can it simply be commanded into existence. It develops through repetition, experience, disagreement, and the gradual accumulation of trust. Thus the ground has to be fertile, which is why business schools must create assessment systems that encourage the relationship to deepen, rather than relying mainly on guardrails, constraints, and policing.

If students treat AI as something to exploit or abandon the moment it becomes difficult, they will reproduce the same weaknesses visible elsewhere: shallow thinking, easy cheating, disposable relationships, and giving up too quickly.

The lesson is bigger than AI. We need to teach students how to stay in the conversation.

Near the end of every course, I tell students one thing.

“Look for the job you would take if you did not need a job.”

That is it. That is the whole cathedral.

Business schools hate sentences like that because they make the machine look ridiculously complex. We stretch simple things into semesters. We turn choice into product variety. More tracks, electives, certificates, shiny boxes. It feels sophisticated. Often, it is educational hoarding with better branding.

My job is mostly the opposite.

I spend absurd amounts of time making complex things usable. Strategy, innovation, entrepreneurship, customer discovery, stakeholder politics, hiring, managing, startup pain, corporate nonsense, the quiet violence of bad meetings. I translate the textbook into the room where your boss interrupts you, your colleague steals credit, the customer lies politely, and the investor asks the one question your deck avoided.

That is why I keep coming back to ‘Explain It Like I’m Five’ (ELI5). Not because students need baby language. They do not. They’re smart. The point is that simple explanations are often the only way to cut through the smoke screen. When something cannot be explained clearly, there is a good chance someone is selling you confusion with a logo on it.

And business schools are very good at selling confusion.

I see this constantly. Students arrive thinking business school will clarify their future. Then we add electives, tracks, certificates, internships, trips, conferences, rankings, alumni panels, company talks, and every possible version of “follow your passion, but please make it employable.” By the end, I ask them whether choosing a job path has become clearer. Most say no.

Wonder why…

My proposal is simple: stop teaching careers as labels and start teaching them as lived work. Marketing is not a job. Analytics is not a job. Entrepreneurship is not specific enough to mean anything useful. Even investment banking, which is unfortunately very much a job, still needs translation. Which desk? Which hours? Which pressure? Which incentives? Which skills? Which version of yourself survives there?

Students do not need more options. They need better visibility into the life behind the option.

So I ask different questions. What work would you repeat even when it gets hard? What problems make you curious instead of numb? What type of pressure sharpens you instead of slowly killing you? What would you still want to get good at if nobody clapped?

That is the real career question. Not “what job can I get with this diploma?”

But “what work would I still choose if I did not need the diploma to justify it?”

Business schools love the idea that a diploma lasts forever. It sits on the CV, decorates the LinkedIn profile, and keeps whispering prestige years after graduation. Very convenient. Very expensive. Also increasingly fragile.

Because a diploma is not a Patagonia jacket.

With Patagonia, the promise is simple: we stand by the product. If something fails, you can send it back. The material, the stitching, the repair, the customer service, the responsibility, all of it is part of the product.

Business schools cannot do that. You cannot send a graduate back because their strategic thinking started leaking after seven years. You cannot replace the seams on someone’s outdated understanding of AI, markets, leadership, or work.

But you can stand by the education.

That’s the real question. Not whether the degree lasts forever as a symbol, but whether the school remains responsible for keeping the learning alive. Right now, most schools sell lifetime reputation with almost no lifetime service. You pay a terrifying amount, study for a few years, collect the diploma, and then disappear into the professional wilderness with a tote bag and some alumni newsletters.

Doctors do not live like that. My cardiologist once explained how much of his professional life depends on constantly updating his expertise. New techniques. New certificates. New technologies. New procedures. Much of that is his own initiative, of course, but the principle is obvious: when knowledge changes, serious professionals go back and update themselves.

Business knowledge changes too. Fast. Messy. Relentlessly. So why do business schools still behave as if a degree earned at twenty two should carry someone through thirty years of economic mutation?

Here is my proposal: every diploma should come with a five year learning touch up. One week back in school. New tools. New cases. New technology. New career diagnosis. New strategic blind spots. Not another decorative certificate, but a real update to the original education. Do that every five years for twenty years, and suddenly the school is not just selling a memory. It is maintaining a professional mind.

That is much more respectable than waving around a famous diploma from 2006 as if the world politely agreed to stop changing.

This is also how I try to build my own courses. I redesign them every year because I do not want students leaving with a frozen version of strategy, innovation, or entrepreneurship. Attention changes. AI changes. work changes. Markets change. Students change. So the course has to change.

And I tell them they can come back.

Email me. Message me. Find me online. Ask the question later, when the framework suddenly matters in a job, a startup, a conflict, or a decision they did not see coming. They paid for time with me, but I do not think that relationship should expire the minute the final grade lands.

Very few students do come back, not because they do not care, but because the system never taught them that returning was part of the deal.

That is the missing piece.

A business school with a lifetime guarantee would not pretend its graduates are finished products. It would build serious ways to update them, challenge them, reconnect them, and repair the gaps created by time.

That is what standing by the product would mean.

Not protecting the brand. Protecting the learning.

Business schools love salary rankings because they are clean, flattering, and wonderfully easy to weaponize in marketing. Look, our graduates earn a lot. Please ignore the tuition invoice currently smoking on the table.

To be clear, there is nothing wrong with earning serious money. Merit matters. Ambition matters. When families pay absurd fees, salary is not a vulgar subject. It is oxygen.

But as the main definition of success? Come on.

Average salary tells us little about whether education worked. Are graduates healthy? Are they doing work they wanted? Did they stay in that sector, or wake up years later wondering who stole their twenties? Are schools measuring everyone, or mainly the shiny alumni who answer surveys and make the brochure look athletic?

The problem is not salary data. The problem is what salary rankings train students to notice. They make the first pay cheque look like the strategy, when it is only one output of a much bigger system: industry structure, company maturity, learning curve, power dynamics, personal fit, and future optionality.

That is the gap I attack in class. First, students learn the tools properly on business cases. Then I ask them to turn those tools back on themselves. If a framework can expose company growth, risk, friction, or power, why would we not use it to evaluate a career move?

Ansoff becomes a personal growth compass. Five Forces helps them read the sector they are entering. Lifecycles show momentum or decay. SOAR, power interest maps, and bug matrices reveal opportunities, stakeholders, friction, and future options.

Most students love the shift because the tools finally click. A former student, now working in due diligence at a top financial firm in New York, recently wrote to me. He had surprised his team by using SOAR and lifecycle maps not only on companies, but on his own career choices.

That is the proposal: publish a graduate usefulness dashboard. Salary, yes. But also learning growth, sector fit, wellbeing, retention, mobility, ethical confidence, and career reinvention.

Business schools should not just prove their graduates got paid. They should prove their graduates learned how to choose work intelligently.

Because a high salary can be success.

It can also be a very expensive trap with better lighting.

Trust is not something a business school earns once, frames, and hangs next to the accreditation logos. Trust is a battery. You charge it every week, or it drains.

Right now, that battery is draining. Families hear that degrees cost more and guarantee less. Companies say graduates often lack the skills they need. Students arrive suspicious, because the promise has been inflated for years: employability, leadership, innovation, transformation, global mindset. Lovely words. Very expensive wallpaper.

The fracture is double. Students doubt preparation. Companies doubt output. Too many schools respond with brand polish instead of operating change, confusing credibility with certification, as if an accreditation badge can keep working after the learning experience goes soft.

But the fracture is bigger than professors alone. It lives in programmes, curriculum, syllabi, hiring, promotion, industry outreach, and the habit of updating brochures faster than courses.

The classroom still carries most of the damage, because that is where the promise becomes visible. Students do not experience strategy in a committee meeting. They experience it in the course, the brief, the project, the feedback, the standards, and the person standing in front of them. If outcomes are vague and standards uneven, even a good professor is patching a leaking boat.

That is why teaching has to be redefined as capability building. Good teaching turns knowledge into judgment, pressure, evidence, feedback, and usable action. It makes students sharper in ways they can eventually prove at work.

But trust cannot depend on one professor at a time. When one course is real, another decorative, another abstract, and another basically academic karaoke, students do not see a standard. They see personality roulette. And that destroys trust one class at a time.

That is why I build trust course by course: clear rules, punctuality, promises kept, milestones, feedback while the course is still alive, and bring in real people who do real work, not PowerPoint royalty. Rigor is not punishment. It’s respect.

Accreditation may open the door.

Trust is what happens after students walk in.

Business schools have a strange habit of assuming students understand what business is.

Not the word “business” on a brochure. Not the stock photo of diverse people smiling at a glass table. I mean business as work. Roles. Decisions. Tradeoffs. Deadlines. Meetings where someone has to make sense, not just sound polished.

Then we act surprised when students ask, “Why are we learning this?” or “What is this course for?” That question is not laziness. It’s a signal. The school has not connected the dots.

Most students do not reject strategy, innovation, entrepreneurship, finance, or marketing because these subjects are useless. They drift because the connection is foggy. Finance becomes “I want to work in finance.” Marketing becomes “I like luxury.” Strategy becomes “consulting,” which is not a job, it’s a container. Inside luxury, people do pricing, supply chain, retail analytics, brand architecture, CRM, expansion, finance, and strategy. Inside consulting, people do market entry, operating models, transformation, due diligence, pricing, and customer research. The work is specific. The school language is not.

This is where business schools badly underperform. Their websites proudly say alumni work in global companies. Wonderful, so do people who refill the printers. What students need is not alumni fog. They need a live map: over the last ten years, what roles did graduates actually move into? What functions? What industries? What problems do they solve? What skills mattered? Not vanity alumni fog. Usable career intelligence.

In my own courses, I spend time explaining where the subject can lead professionally. Not as a promise. As a compass. If students understand how strategic thinking, customer discovery, business models, validation, or positioning show up in real jobs, they listen differently.

Every course should start with a simple layer: here is the work, here are the roles, a “why this matters” layer before every course.

Otherwise we are selling students a toolkit without showing them the job site.

I admire the research behind AI powered instructional video. Making course videos searchable, interactive, question driven, and tied to the class context is smart. The OpenAI education powered initiative (cuGPT / Rutgers) points in the right direction: video should not just sit there like digital furniture. Students should be able to interrogate a video, revisit a concept, ask for another example, and prepare better before class.

But here is where I get allergic.

Student satisfaction with video does not automatically mean meaningful learning. It may only mean the video was less painful than the alternatives. That’s a very low bar. HigherED has a dangerous habit of confusing usage with value, “students liked the video” does not automatically mean “students learned.” Sometimes it only means the video was less painful than the textbook, the recorded lecture, or the professor reading bullet points like a hostage note.

The real issue is not video. It is format.

Students watch short videos all day, and yes, plenty of it is intellectual junk food. But the people making that junk understand rhythm, tone, hooks, pacing, compression, faces, sound, and the sacred violence of the first three seconds. Academia still thinks a forty minute recorded lecture with bad lighting is innovation because someone uploaded it.

If I had the resources, I would not produce learning videos like a professor. I would bring in someone who knows how to stop the scroll. Then I would pair them with someone who can write sharp vulgarisation. Together, we would break down Porter, Christensen, Blank, Jobs To Be Done, positioning, business models, effectuation, and customer discovery into short, punchy videos built around one idea, one example, and one decision students must make.

Not to dumb it down. To make serious ideas travel better.

AI would sit around that content: summaries, quizzes, counterarguments, retrieval practice, “explain this differently,” “challenge my answer,” “prepare me for class.” Then the next session would turn the video into a workshop where students apply, test, defend, and improve the idea.

That is HigherED powered by AI.

But higher education mostly wants the technology without the redesign. It wants AI without letting go of the old book driven model. Once in a while, someone tries to be hip, and we all know what happens: a podcast appears, a video series appears, a platform appears, and within weeks it smells like a committee discovered Canva.

The real move is harder. Bring the new generation into the room. Let them help build the format for the generation we are teaching. Not to lower the level. To increase the reach.

But that requires letting go. And higher education is very bad at that.

It wants the technology, but not the cultural shift. It wants AI, but not the redesign. It wants engagement, but not the discomfort of admitting that most academic formats were built for a world that no longer exists.

That is why this matters.

Not because video will save higher education. Because the way higher education handles video tells us exactly why it is in trouble.

Business schools love turning every new technology into an apocalypse forecast. AI will destroy education. AI will replace professors. AI will make classrooms obsolete. Dramatic. Convenient. Mostly wrong.

The better question is what AI becomes for excellent teachers.

My answer: the teaching assistant we were always supposed to have!

AI, agents, custom GPTs, on demand video, and smarter SaaS are becoming the preparation layer around the course. Not the professor. Not the classroom. The support system. The force multiplier.

I can easily imagine the classic twelve to fifteen session semester staying in place, because time is still time. But around it, an additional twenty to thirty percent of the learning experience becomes AI powered, asynchronous, autonomous, and more personalized. Students review basics before class. They get explanations when they’re stuck. They quiz themselves. They watch short videos. They summarize, compare, rehearse, and arrive with more raw material in their heads.

Good.

So I say, let AI build playlists, explain basics, adapt exercises, summarize content, and help students prepare. Then use class for what class should already be: coaching, mentoring, judgment, friction, pressure, and human guidance.

This is what I’m already testing. Multimedia playlists I create for use outside class. A constantly updated Notion course manual. Targeted agent support doing some heavy lifting. Custom GPTs that help structure milestones and prepare serious individual exams on the school LMS, in lockdown mode.

And this is what I’m seeing: students are relying increasingly on technology for facts. Of course they do. Everyone does. Nobody “challenges” Google Maps before finding a restaurant. You ask where Joe’s place is, then you go.

So when schools tell young students to “challenge AI,” I understand the intention, but I think the phrase is mostly wrong. Bachelor and initial master students are not specialized researchers. They do not naturally confront machines. They use them as answer engines, because that is what the internet, search, apps, and personal computing have trained all of us to do for the last twenty five years.

The issue is not that students have no information. Often, they have too much. They arrive with summaries, definitions, examples, arguments, counterarguments, and whatever else the machine spat out in seven seconds. The problem is that most of it is floating. It has no weight yet. No context. No consequences. No tradeoff. No ownership.

Students often have answers. What they need is the room to hash them out.

That is where class still matters. Not as a delivery mechanism for facts, but as a decision space. A place where students have to explain what they think, defend why it matters, test whether the answer survives contact with reality, and sometimes admit that the shiny AI response was just polished fog.

And that, for me, is the future of education worth building. Not AI replacing teaching. AI extending preparation. Not humans pretending they can outcompute machines. Humans doing what machines still cannot do well: creating the conditions for judgment.

AI gives them information. Teaching helps them decide what it means.