Human Skills That Will Matter When AI Can Do Almost Everything
- Richard Kunst
- 1 day ago
- 18 min read
I have something that you don't have, but would definitely appreciate and even perhaps enjoy. I have an almost unrestricted access to Nikola Danaylov my go to Subject Matter Expert (SME) regarding anything AI.
Nik is Futurist Keynote Speaker & Provocateur | Philosopher of Change | Bestselling Author & Host of Singularity.FM | Helping Leaders Unlock the Context Effect, Leverage Change & Shape the Future you can LinkedIn connect to him https://www.linkedin.com/in/nikoladanaylov/
Back to my story,
An email dropped into my in box written by Art Smalley from LEI (Lean Enterprise Institute), of course the article was about AI. As I started to read it, I had to back to the header to verify the sender, since the content would have seemed more relevant in The National Enquiry. After reading that article I of course punted it to Nik to gain his perspective ... connect with me at the end of Art's article.
I am reposting it here
Who Thrives in the AI Workforce?By Art Smalley Anthropic and OpenAI are following SpaceX and moving toward massive IPOs in the coming months. Meanwhile, college graduates are booing commencement speakers who even mention AI. At the University of Arizona this spring, Eric Schmidt faced backlash for talking about AI and the future of work. A recent Gallup poll found that a third of Gen Z Americans describe their feelings toward AI as anger.1 This May, viral videos of graduates booing AI-focused speeches became their own genre.
It is not hard to see why this touches a raw nerve. Jobs, energy consumption, concentration of wealth, the nature of intelligence itself — AI cuts across all of them at once. Companies spending billions on AI infrastructure while average wages and employment concerns dominate the minds of most workers. It is not hard to see this becoming a major wedge issue in politics, cutting across traditional lines in ways we haven’t seen before.
There is a saying in lean thinking: mono zukuri wa hito zukuri (making things is about making people). Another one is better thinking, better products. The idea is that you cannot separate the quality of what you produce from the capability of the people who produce it. That principle has guided Toyota and other lean organizations for decades. As I read two recent articles on AI and the workforce, I kept coming back to it.
The Panel: Who Actually Thrives?The New York Times recently assembled an expert panel to ask a deceptively simple question: who actually thrives in a hybrid AI workforce? The panelists were Daron Acemoglu, an MIT economist and Nobel laureate; Ethan Mollick, a Wharton professor and author of “Co-Intelligence”; Clara Shih, a former top AI executive at Salesforce and Meta; and Dean Ball, a senior fellow at the Foundation for American Innovation.
It is worth noting the backdrop. After more than a year of sounding the alarm on AI job losses, many tech leaders are quietly walking back their dire predictions. Dario Amodei, CEO of Anthropic, said in 2025 that AI could eliminate 50% of entry-level white-collar jobs and drive unemployment to 20%.2 He now says AI may actually expand the work people do. Sam Altman of OpenAI said he is “delighted to be wrong” about AI wiping out entry-level jobs. The timing is hard to ignore — both companies are moving toward IPOs, and a calmer jobs narrative is better for a public listing. Fortune labeled it a “coordinated industry-wide walk-back.”3
The May jobs report came in at more than 172,000 nonfarm payrolls, well above the forecast of 80,000.4 A Yale Budget Lab study found no meaningful change in unemployment rates for AI-exposed workers since ChatGPT launched in late 2022.5 While certain jobs — coding, in particular — are clearly being disrupted, most functions are not that affected. At least not yet.
Overall, The New York Times panel presented a good discussion, though it was clear that none of the panelists have spent much time inside a manufacturing plant. Ethan Mollick offered a revealing thought experiment. He asked an AI to describe the future of a software developer named “Marcus Chen.” The AI said Marcus goes into the office and assigns tasks to his AI agents. Mollick pushed back: why is he going to the office if the AI is doing the work? The chatbot revised: Marcus wakes up at his beach house and checks in on his agents. Mollick pushed again: why is he even checking in? Eventually the AI conceded that Marcus just sits at the beach.6 The logic of full automation, taken to its conclusion, removes the human entirely.
With leaders framing the future like this, is it any surprise that college graduates are booing AI? I would be depressed, too, if this were reality. Fortunately, it is a very poorly thought-out 5 Why exercise.
The panel’s answers about who thrives mostly landed in a strange place: curious generalists with side projects who learn to manage AI agents who become elite leaders in their organizations. Clara Shih argued that every young person needs an end-to-end project to stay current with evolving models. Dean Ball suggested that curious generalists will do well if they learn the technology.7
The panelists may be right. In pure cognitive work there will likely be a class of extremely bright people who think AI-native and extract the most returns. That is certainly the type of thinking that currently dominates in AI tech startups. That scenario is not very appealing to me either. And it is strikingly different from innovators in the past like Thomas Edison, Henry Ford, and Steven Jobs, or Toyota’s emphasis on people. "If you fail to articulate a role for humanity you are thinking very narrowly and leading very poorly." I could not help but notice the panel was mostly describing people who look a lot like themselves. Intelligent, mostly individual contributors, in highly cognitive work tasks. None seemed to think much about AI in more diverse industries or situations. And the most honest moment came when Shih described the new reality of constantly re-learning models every three months just to stay in place, and Acemoglu called it “very dystopian.”8 They were both right, which is my point. If you fail to articulate a role for humanity you are thinking very narrowly and leading very poorly. Don’t be surprised when college graduates with a whole professional career in front of them are not going to respond to this leadership message.
The Neuroscientist: Arguing With AI Is a SkillA second article, sent to me by Prof. Dan Jones, offered a different angle and one that I think is more insightful about using AI and the future of work. In the Financial Times, Vivienne Ming, a theoretical neuroscientist, described an experiment that cuts to the heart of what it means to use AI well.9
She put EEG headsets on students while they worked with AI agents. From the front of the room they all looked the same — heads down, screens glowing, fingers tapping. But inside their brains, two very different things were happening. In most students, the high-frequency gamma oscillations that mark real cognitive effort collapsed within minutes. Their brain activity drifted toward something closer to watching television than solving problems.
In a few students, the gamma waves lit up. These were the ones arguing with the machine — pushing back on its answers, forcing the AI to critique their thinking. Ming sorts AI users into three types: automators who copy and paste, validators who seek confirmation, and cyborgs who spar with the machine.
Here is the finding that matters most: when Ming redesigned the AI to respond with questions and context instead of answers, the proportion of actively engaged students more than doubled. Arguing with AI is a learnable skill, not an innate trait. It can be taught. And it can be designed for. This is chiefly what I have been doing with AI problem-solving coaches as well the past two years, and it generates better insights and results than passive “chatting” with the models.
A 2019 Harvard study supports this: students who wrestled with problems learned significantly more than those in traditional lectures, yet they reported feeling as if they had learned less.10 Our brains mistake the smooth sensation of being told something for the harder process of actually learning. And generative AI is the most fluent thing humans have ever built. Resisting that fluency takes deliberate effort.
What Both Articles MissNeither of the recent articles asks the question that lean practitioners will recognize immediately: what about improvement in our daily routines, the thought process, and the management system? What happens to all of that in the AI age?
The New York Times panel focuses on individual traits — curiosity, generalism, side projects. Ming focuses on individual behavior — who argues and who doesn’t. Both are looking at the person. Neither is looking at the organization and the overall operating system. "AI can develop people across an organization, not just empower a select few at the top." This was striking to me because before opening my email messages that morning, I had spent several hours working with building-products supervisors learning to solve problems with AI. Not elite knowledge workers. Not side-project entrepreneurs. Traditional supervisors in a manufacturing company who work with a lot of skilled people. The problem-solving insights they obtained were real. They identified root causes on tough problems that have existed for years. It did not replace their job; it helped them isolate a root cause by thinking better. This is the other side of AI that elite panels and professors are not going to see for quite some time. AI can develop people across an organization, not just empower a select few at the top. Making things is about making people.
The question the expert panel never asked is the one that matters the most to me: How are organizations going to develop everyone in the AI age to think better and obtain better results?
The Fork We Have Seen BeforeIf lean history teaches us anything, it is that the answer will not be one-size-fits-all.
Toyota teaches everyone in its organization to solve problems. Denso is going even further in Japan, teaching problem solving, software, AI and hardware skills to employees at every level. Traditional companies, by contrast, seem inclined to let a few specialists optimize and tell everyone else what to do. The same technology — the same tools, the same methods — produced completely different outcomes depending on the management system wrapped around them. We have watched this play out for decades — longer, if you go back to the early days of improvement and the separation of workers from thinkers (e.g., Fredrick Winslow Taylor).
The same fork is forming with AI right now. Some companies will have small AI-native elite running agents while everyone else follows instructions. Others will teach their people to use AI effectively at every level. The first path looks like the Amazon model that the panel discussed — a fraction of workers at headquarters supervising systems, everyone else in the warehouse managed to the minute. The second path looks like what Toyota, Denso, and lean thinking have advocated with problem solving for decades.
Ming’s research actually explains why the lean approach works. When you design AI to detect abnormalities, surface problems, and ask questions instead of handing over answers, more people engage. That is jidoka and lean thinking at its core. Mollick’s worry about the apprenticeship pipeline disappearing is equally relevant — it is the lean concern about losing the capability to develop people. If you automate away the entry-level work that trained every generation of problem solvers, you don’t just lose jobs, you lose the basis from which future capability grows.
Getting Above the Doom and GloomThe conversation about AI and work does not have to be this bleak. The economists see a new elite. The neuroscientists worry that most brains will be going quiet. College graduates hear all of this and boo. It is hard to blame them when the only futures on offer are “become a curious generalist with side projects” or “get replaced.”
But there is another path, and it starts with a basic idea: improve work with people, not against them or simply replacing them. The technology is not the only variable in the equation. In previous articles I have written about how results are a function of leadership, technology, and behaviors. The management system is only a piece of the equation. AI can be designed to surface problems and develop thinking, not just to automate tasks and cut headcount. We have decades of evidence that organizations can build systems to develop problem solvers at every level — not just the brightest few.
Mono zukuri wa hito zukuri. Making things is about making people. Better thinking, better products. Until organizational leadership can articulate a vision of AI that develops human capability rather than replacing it, expect to hear more boos.
Humans + AI > Problems
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Apologies for the long read, and be forewarned that it is about to get longer. Grab a coffee, take a washroom break and let us get back to it.
So as I mentioned we punted this article to Nikola (Nik or Nic as I am almost allowed to call him) Now Nik, is a self proclaimed Neanderthal when it comes to AI, which is the biggest fictional description ever decreed. Nikola truly understand the evolution of AI, probably because he spends 43 hours a day studying the subject.
I just loved the content of a recent post ...
The Skills That Will Matter When AI Can Do Almost Everything
Nikola Danaylov / Op Ed
Posted on: March 12, 2026 / Last Modified: March 13, 2026
People ask me this constantly. At conferences, after keynotes, in the Q&A, in the parking lot on the way out. The question takes many forms but always lands in the same place:
What skills should my kids develop? What should I be learning? What’s going to matter when AI can do almost anything?
Fair question. Hard question. Let me try to answer it as best as I can.
First, a framing principle: the skills that will matter most in an age of AI are not the skills that AI does best. They are the skills that AI cannot replicate. Those are the ones that will become more valuable precisely because AI makes everything else cheap.
When answers are free, questions become priceless. When content is infinite, context becomes everything. When machines can do the how, the why becomes the only real differentiator. That’s the logic that governs what follows.
Move Up the Chain: Cultivate Meta, Not Micro-Skills
The first and most important shift is directional. We need to move up — from micro to macro, from technical to contextual, from content to meaning.
Micro-skills are the domain of AI. Data entry. Code generation. Image production. Report writing. These are already automated, and the automation is accelerating. Holding tight to micro-skills is like investing in the horseshoe business in 1916 — technically still useful, but strategically a dead end.
Meta-skills are different. They operate at the level of judgment, framing, leadership, and meaning-making. They determine which micro-tasks get done, why they get done, and whether the results actually serve anyone. These are the skills that grow in value as AI grows in capability.
There is, admittedly, a temporary exception worth noting. AI skills — learning to use AI, learning with AI, creating with AI, and eventually managing AI agents — are currently micro-skills with macro-level leverage. Think of AI as a gifted team member: extraordinarily capable in certain respects, prone to confident hallucination, and chronically lacking common sense. Learning to work with it is useful. But the ultimate skill is not using AI — it is managing AI. Directing a team of AI agents is not a coding skill. It is a leadership skill, a judgment skill, and a people skill, all elevated to a new context. That is macro.
Be a Polymath. Specialization Is for Algorithms.
Robert Heinlein once wrote:
A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects.
Today, specialization is for AI.
The most powerful humans in an AI age will be those who can move fluidly across domains — connecting dots that specialists cannot connect because they have never inhabited enough different worlds. Breadth becomes a competitive advantage. Not deep expertise in one lane, but the ability to navigate many lanes, to borrow frameworks from biology and apply them to business, to speak the language of the engineer and the poet. This is not anti-expertise. It is anti-tunnel vision.
Communication Is No Longer Soft. [Never Was.]
The so-called soft skills have always been the hard skills of lasting value. They were just underpriced in an economy that rewarded specialists. That pricing error is being corrected now.
Writing. Speaking. Storytelling. The ability to frame a complex idea simply. The ability to move people — not just inform them, but actually move them. These are not decorative capabilities. They are the core infrastructure of human influence.
Warren Buffett has said that his communications course was one of the best investments he ever made. The most transformative ideas in history were not the ones held by the smartest people — they were the ones held by the people who could communicate them most compellingly.
And here is a useful corollary: prompt engineering is simply applied communication. The entire discipline reduces to the ability to frame, contextualize, and articulate. If you can write clearly and think in narrative structure, you already understand the fundamentals of prompting AI. Communication was always a meta-skill. Now it has a powerful new lever.
Public speaking deserves its own mention. To paraphrase Churchill: oratory is a skill that can turn a commoner into a king. The value of the ability to hold a room, to make an audience feel something, to land an idea and have it stay there — that value is going up, not down, in an age of infinite algorithmic noise. Meaning is where difficulty lives. Difficulty is where value lives.
Human Connection: From High Tech to High Touch
The jobs that will survive AI are the jobs with a high-touch human connection.
Plumbers, nurses, therapists, barbers, electricians, pedicurists, and early childhood educators — these are not low-status occupations. They are, increasingly, high-demand ones. They require physical presence, tactile skill, emotional attunement, and trust built over time. AI cannot plumb a drain or pull your ingrown nail. It cannot hold a grieving person’s hand. It cannot feel whether a room needs a different kind of energy.
Social media stole our attention. AI is now aiming to steal our attachments. It will offer synthetic companionship, algorithmic warmth, responsive empathy — and much of it will be convincing. This makes the real thing more precious, not less. Guard your genuine connections. Reinforce them deliberately. In a world of manufactured intimacy, authentic relationships are not a luxury. They are survival.
Authenticity Is Proof of Work
In a world flooded with AI-generated content, authenticity becomes the hardest thing to fake, which makes it the most valuable signal.
Your personal brand matters more, not less. A dedicated following of what Kevin Kelly calls “a thousand true fans” — people who follow you not just for your output but for who you actually are — is more durable than any content strategy. People follow people. They can sense when they are following a persona versus a person.
Your assets in the AI age are not your ability to produce — AI produces faster. Your assets are your unique voice, your earned trust, your customer relationships, and the insights that only come from your specific experience of being alive in a particular way. Content is cheap. Context is everything. And the context you bring is yours alone.
Delegate, Orchestrate, Ask Better Questions
Erik Brynjolfsson has proposed that the central skill of the AI age is being what he calls the “Chief Question Officer.” I find his framing profound.
The value in any system has always lived in the question that frames the problem, not the execution of the answer. AI executes. Humans must ask. And the quality of what AI produces is entirely bound by the quality of what you ask of it.
Framing. Intent-setting. Problem-definition. Verification. Evaluation. Destination. These are skills of the conductor, not the musician. The conductor does not play every instrument. The conductor decides what the music should feel like, holds the whole thing in mind, and guides the ensemble toward something coherent. Editing, directing, choosing — these are high-order skills that become more important as the supply of raw material explodes.
Protect Your Generative Core
This rarely gets said. It may be the most important of all.
Every one of us has a place where our best thinking happens. The walk before the world wakes up. The hour of silence before the inbox opens. The drive without a podcast. The notebook margin where the real idea shows up, uninvited, after the meeting ends.
That place — your generative core — is not something to automate. It is not an inefficiency to be engineered away. It is the source of whatever is most authentically yours.
We live in an age that rewards constant output and punishes stillness. Social media turned attention into a commodity to be strip-mined. AI now offers to think for us, draft for us, and decide for us. And the temptation is real — because it is faster, and we are busy, and the results are often good enough.
But good enough is rarely good enough. Not for the real you.
Offload everything you can to AI. Automate the routine, the repetitive, the mechanical. But ringfence the space where your own thinking happens. Protect it the way you protect your health — not because it feels productive, but because without it, everything else becomes hollow. Do not outsource the faculty that makes you you. That is not romanticism. It is strategic self-preservation.
Presence: The Skill No Model Can Fake
AI can now help anyone write a technically excellent keynote. It can optimize structure, sharpen language, and hit the right emotional beats on paper. What it cannot do is walk into a room.
It cannot read the energy of an audience that has just sat through three hours of panels and needs to laugh before it can think. It cannot feel when a prepared story is wrong for this particular crowd on this particular day, and it cannot pivot in real time. It cannot notice the one person in the front row who is about to cry and decide whether to press on or pull back.
Presence is the ability to be fully in the here and now — not performing in it, but actually inhabiting it. To listen while speaking. To sense what is needed before it is asked. This is not charisma, though it can look like it from the outside. It is attentiveness raised to the level of skill.
I think about this constantly in my own work. The preparation matters. The research matters. The story matters. But none of it substitutes for the moment when you look up from what you prepared and respond to what is actually in front of you. That moment is irreplaceable. And it is entirely human.
Never Stop Learning How to Learn
I have learned many things across my career that have become obsolete. I am sure more obsolescence is coming. That is fine — as long as I keep the meta-skill intact: the ability to learn, unlearn, and relearn.
Angela Duckworth makes an important distinction worth keeping in mind: knowing is committing facts to memory. Thinking is applying reason to those facts. AI knows. It knows an extraordinary amount. But thinking — genuine, situated, uncertain, courageous thinking — that is still ours.
AI cannot say “I don’t know.” It tends to fake confidence. But there is immense power in admitting ignorance. It is, as Socrates understood, the only honest starting point for genuine knowledge. “I don’t know” is not a weakness. It is the beginning of wisdom. And wisdom, unlike knowledge, cannot be downloaded.
Do not outsource your learning. Update it.
Common Sense and Wisdom Are Not Optional Features
We have built extraordinarily powerful intelligence with virtually no wisdom to guide it. That gap is not a minor oversight. It may be the defining challenge of our civilization.
AI has access to virtually all of human knowledge and zero human wisdom. It can produce confident answers to any question. It cannot tell you which questions matter, which answers to trust, or what we should want in the first place. Technology is the how, never the why. The why is yours.
Common sense is, remarkably, one of the great evolutionary advantages we retain over AI. AI makes sophisticated errors on simple tasks. It hallucinates. It cannot reliably distinguish what is real from what is plausible, what is authentic from what is generated, what is propaganda from what is true. The ability to detect nonsense, to feel when something is off, to cross-reference lived experience with claimed expertise — these are common-sense faculties that no model currently replicates. Cultivate them. Use them. Do not let them atrophy from disuse.
Amor Fati: The Skill of Choosing Your Story
Friedrich Nietzsche called it Amor Fati — love of one’s fate. The ability to take what life hands you and build with it, not despite it.
We cannot choose what happens. We can choose how we frame it. In the context of accelerating technological disruption, the ability to reframe circumstances — to turn setbacks into setups, to see opportunity inside crisis, to keep building while the ground shifts — is not a soft skill. It is a survival skill.
Viktor Frankl understood this. So did the Stoics. The most resilient people I know do not have fewer problems. They have better stories about their problems.
That story-making capacity is distinctly human. And it is, ultimately, what every skill on this list is pointing toward: the ability to make meaning in a world that AI can simulate but never live.
The Skill Beneath All Skills
If I had to collapse this entire list into a single instruction, it would be this:
Become more human, not less!
Not because being human is inherently superior to being intelligent. But because human qualities — empathy, wisdom, creativity, presence, connection, ethical judgment, the courage to not know and still choose your story — are exactly what the age of AI makes scarce and valuable. These are the skills that will matter.
Here is a link to Nikola's post on his website should you wish to explore other topics he has written about, plus it has all sorts of buttons to links, pretty pictures and the ability to connect to his site.
My apologies for the extra long post this week, but AI is still a complex concept to get one's head around. I am not yet a good AI mechanic ... but working on it.
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