The easy answer is yes.
We like AI when it writes faster, summarizes meetings, reduces costs, automates boring tasks, and makes the spreadsheet look a little less terrifying. Executives like AI because they can see the efficiency. Founders like AI because they can build faster. Marketers like AI because they can produce more. Consultants like AI because they can package intelligence, automation, and transformation into something that sounds inevitable.
But maybe we are asking the wrong people.
Because the more interesting question is not whether mature executives like AI. Of course many of them do. They are not entering the job market. They are managing it. They are not applying for junior roles. They are redesigning them. They are not wondering whether their degree still has value. They are wondering how much productivity they can extract from the tools they have just adopted.
The real question is different.
Do young people like AI?
Do the people who are about to enter the marketplace see AI as a ladder, or as a wall? Do they see it as a tool, or as a threat? Do they feel invited into the future, or replaced before they even started?
The hard truth
Recent graduation ceremonies in the US gave us a small but very loud answer. Students booed pro-AI commencement speakers. Not because they hate technology. Not because they are anti-progress. Not because they want to go back to typewriters, fax machines, and paper CVs. They booed because they felt that the people on stage were not reading the room.
Imagine the scene.
You are graduating after years of studying. You probably carry debt. You are entering a job market that already feels unstable. You have been told your whole life that education is the path to opportunity. Then someone walks on stage, already successful, already wealthy, already protected, and tells you that AI is transforming everything, that the industry has changed, that what you learned may already be obsolete.
That is not inspiration. That is not leadership. That sounds like a warning delivered by someone who will not pay the price.
And this is where the AI conversation breaks.
For executives, AI often sounds like efficiency. For young people, AI often sounds like a disappearance. For companies, it is a productivity story. For graduates, it is an access story.
Who gets access to the first job? Who gets trained? Who gets mentored? Who gets the messy, repetitive, junior-level work that used to be the entrance ticket into a profession?
Because here is the uncomfortable truth. A lot of the work AI is “saving” is the same work young professionals used to learn from. The first drafts. The research. The summaries. The reports. The analysis. The coordination. The ugly first version that teaches you how the beautiful final version is made.
We call it inefficiency. They may call it the beginning of a career.
My own example
I remember this from the very beginning of my own career. As a newbie accountant, one of my first tasks was archiving. Yes, archiving. Putting invoices into folders, by date and by category. It was not glamorous. It was not strategic. It was not something anyone would describe today as high-value work.
But it taught me everything.
For months, before I was allowed to create an accounting entry, I had to touch the documents. Read them. Classify them. Understand what they were. I learned why the issue date mattered, why the stamp date mattered, why the booking date mattered, and how a simple document could affect a transaction, a report, a tax obligation, or a business decision.
That job probably does not exist anymore. Or if it does, it is disappearing fast.
And maybe that is fine. Maybe no one should spend months filing invoices in folders. But then we need to answer a serious question: how does the new junior learn the lesson hidden inside that boring task? How do they learn the nature of the document, the importance of the dates, the logic behind the entry, the impact of the transaction?
Because when we remove the task, we may also remove the learning path.
So when we say AI will remove repetitive work, we need to ask what else it removes with it. Does it remove boredom, or does it remove apprenticeship? Does it remove waste, or does it remove entry-level opportunity? Does it make people more productive, or does it make companies less willing to invest in people who are not productive yet?
The booing
This is why the booing matters.
It is not just a cultural moment. It is a market signal.
The next generation is not automatically excited about AI just because they are young. They may use it. They may understand it. They may be faster with it than most executives. But use is not the same as trust.
You can use something and still resent it. You can depend on something and still fear it. You can be fluent in a technology and still believe it is being deployed against you.
That is what many AI evangelists miss.
The resistance
The resistance is not always about the tool. It is about the deal.
Young people are asking a very simple question: what future are you inviting us into?
If the answer is “a future where you must compete with machines before anyone has given you a chance to become good,” then we should expect more resistance. If the answer is “a future where your first job disappears but your rent does not,” then we should expect anger. If the answer is “a future where executives get efficiency and young workers get uncertainty,” then we should not be surprised when the applause turns into boos.
So, do we really like AI?
Maybe the honest answer is this: we like AI when we benefit from it. We fear AI when it is done to us.
And the generation entering the marketplace tomorrow is looking at AI not as a magical assistant, but as a negotiation they were never invited to join.
That is what we should expect next.
Not a simple rejection of AI. Not a romantic return to the old world. But a demand for a different social contract around work, education, training, and opportunity.
Young people will use AI. Of course they will. But they will not automatically worship it.
They will ask who owns it, who profits from it, who is protected by it, and who is made disposable because of it.
And if companies keep presenting AI only as a cost-cutting machine, they should not expect applause from the people who fear they are the cost being cut.
So what can we do about it?
The first answer is that companies must stop presenting AI only through the language of efficiency. Efficiency is not wrong. Cost cutting is not evil by default. Productivity matters. But when the entire AI narrative is built around doing more with fewer people, young people hear the message very clearly: fewer people means us.
If leaders want AI to be embraced, they need to change the promise. Not “AI will replace junior work,” but “AI will help junior people learn faster.” Not “AI will reduce the need for entry-level roles,” but “AI will create better entry-level roles.” Not “AI will remove the boring work,” but “AI will turn boring work into guided learning, feedback, and acceleration.”
That means redesigning the first years of work, not deleting them.
Companies need AI apprenticeship models. They need to show graduates how AI can become a coach, a reviewer, a research assistant, a simulation environment, a sparring partner. They need to prove that AI will not close the door to the profession but open it faster. A junior marketer should not fear that AI writes the first draft. They should be taught how to brief it, challenge it, improve it, fact-check it, and turn it into strategy. A junior analyst should not fear that AI summarizes the report. They should learn how to question the summary, interpret the assumptions, and explain the business meaning behind it.
Education also needs to change. Universities cannot pretend AI is just a cheating problem. That is too small a frame. AI is a literacy problem, a career problem, and a power problem. Students need to learn not only how to use AI tools, but how AI changes work, value, expertise, authorship, and decision-making. They need to understand where AI helps, where it fails, where it hallucinates, where it hides bias, and where human judgment becomes even more important.
And young people need something even more basic: honesty.
They do not need another keynote telling them that the future is exciting. They need leaders to admit that the transition will be uncomfortable. Some jobs will change. Some tasks will disappear. Some career paths will be rewritten. But they also need to hear what will be protected, what will be created, and how they will be included.
Because the problem is not that young people do not like technology.
The problem is that they do not trust the people deploying it.
And trust will not be built with demos, slogans, or futuristic speeches. It will be built with choices. Hiring choices. Training choices. Education choices. Product choices. Policy choices. Management choices.
If AI is used only to extract productivity, it will be resisted.
If AI is used to expand capability, it has a chance to be embraced.
That is the real challenge ahead.
Not whether AI is powerful. It is.
Not whether young people will use it. They will.
The question is whether they will believe that AI is being built with them, or against them.
Theodore has 20 years of experience running successful and profitable software products. In his free time, he coaches and consults startups. His career includes managerial posts for companies in the UK and abroad, and he has significant skills in intrapreneurship and entrepreneurship.
