If AI is ultimately capable of doing everything we as humans are capable of doing, does this increase or decrease the meaning of the things we choose to do?

It increases the meaning, because we choose to do it despite AI and robots capable of doing the same thing for no cost. It increases it, because we will do whatever we will still be doing out of inner inspiration and deep purpose.

The fear that AI will create a meaning crisis would mean that AI can ‘take away our meaning’, but meaning is not the result of outer validation or economic value. Meaning arrives when we unite what our heart is longing for – our inner calling or ‘purpose’ – and start acting on it with clear intention. Meaning is when we do something for the sake of doing it, not for the sake of accomplishing it.

If technology allows us to accomplish without doing, it creates a vacuum. This vacuum is neutral. It is neither good nor bad, it simply exposes us. It reveals whether we were living an authentic life from inner purpose – or one that was shaped by external forces. How we will react to this vacuum is going to be everything. Either you numb this vacuum with endless stimulation. Or you move beyond it. Moving beyond the vacuum means starting to live. It means rediscovering action as a creative expression of love. We will start creating for the sake of inspiration, exploring for the sake of curiosity, caring for the sake of love. We will stop doing something because the world demands it, and we will start doing things because something in us demands it from us.

Technology that is capable of doing what we are capable of doing is not the threat, the threat is technology designed to replace living: technology that is designed to capture our attention at any time, to manipulate our reward systems, technology designed to fill any silent moment of our life. And this technology is not coming, but already in our hands. And perhaps the real crisis is that many humans do not yet know what they would be doing without necessity forcing them and technology distracting them.

Yesterday I read a book chapter where the author, Thomas Gonschior, interviewed neuroscientist Gerald Hüther. Pre-AI he diagnosed what he called the “machine age mindset”. He said that decades of efficiency optimization treated employee as objects, suppressed intuition, punished initiative, and produced a workforce that functions as prescribed but creates nothing novel. Companies then began complaining that “the spirit of innovation is gone”, oblivious that they had created systems that killed it.

Hüther’s argument is more structural than sentimental: when you treat humans like machines, they lose the capacities that make them human.

While reading the chapter, I realized that the entire “AI transition” debate might be build on a fantasy. Everyone is saying once AI is handling the boring routine work, humans will be “freed” for higher-order thinking – creativity, intuition, judgment, inspiration, vision, etc.

But Hüther exposed this fantasy before today’s AI was even launched. What he said is that the machine age didn’t just automate tasks but also the people operating the machines. Fifty years of KPI regimes, efficiency mandates, and management-by-fear produced exactly what was incentivized in the beginning: a workforce that waits for instructions, avoids-risks, and counts the hours until closing time. The human capacities that AI cannot replicate – the ones every CEO and HR department now claims to value – were systematically destroyed by the very management philosophies those CEOs inherited.

I call this Human Debt: the accumulated deficit in creativity, intuition, courage, intrinsic motivation which were created by decades of efficiency-first management. Like technical debt, it was invisible as long as the old system kept running. AI is now the transformation that will make it visible.

Consequentially, this makes the companies most celebrated for their operational excellency – tight processes, lean operations, disciplined execution cultures – carry the highest amount of Human Debt. They optimized for decades for the exact qualities and capabilities which AI now commoditizes, while destroying the capacities that AI cannot replicate.

At the same time, this is not an easy training problem. You can not re-build intuition in a 6-week “re-skilling” program, you cannot “restore” courage through a change management seminar. Hüther talked about Enthusiasm which he refered to as a “spark that jumps”; it will be hard to “legislate” sparks in organizations that spent decades extinguishing them.

This means – ironically – that companies that will win the AI transition are not the ones deploying the most AI but the ones that, against every incentive of the machine age and the new AI era, somehow preserved and preserve their people’s capacity to be human.

If you are CEO of a company ask yourself honestly: if you free your people from routine and delegated tasks, can they actually create? If the answer is no, your AI “transformation” will bring efficiency gains but undifferentiated from anyone else you compete with. The organizations that combine AI automation with genuine human agency (not the PowerPoint version) will win.

At the same time, Human Debt is an yet invisible and unpriced liability on every balance sheet. Companies with high operational discipline + low innovation culture are short a put they don’t know they sold. We are still early, but post-AI-deployment we will see organizations with productivity gains plateauing within 12-18 months, because the “freed” humans have nothing genuinely creative or inventive to contribute. This plateau will be the Human Debt surfacing.

For companies that will now face decisions over AI-driven restructurings, the most important assessment must be on Human Debt. Organizations risk laying off people who are genuinely creative but AI-slow, while promoting and keeping those who operate the AI like machines. Those with high Human Debt will see AI ROI pleateau faster than they will expect. Those who will outcompete will be organizations who have a culture of genuine autonomy, intrinsic motivation, and a lived tolerance for failure (very rare).

For anyone interested: the source is a German book called “Auf den Spuren der Intuition” from Thomas Gonschior – which itself is based on his documentary series aired on BR.

Monoculture farming collapsed entire food supplies. I do believe we risk doing the same thing to corporate cognition.

Today I read a thesis drawing on Gödel’s ‘Incompleteness’ Theorems to argue that AI, by accelerating the construction of purely logical systems, will expose the rigidity and fragility of our cognitive and organizational structures.

It is a loose but quite interesting analogy because it instinct instinctively points at something real that AI adoption ignores entirely.

When every organization offloads cognition to similar AI systems, which are trained on the same data, optimized for the same benchmarks and metrics, what you get is a cognitive monoculture at organizational (and societal) scale.

Let’s call it algorithmic monocropping.

Agriculture learned that with Irish potatoes or American bananas that if a system optimized only for one output, it becomes catastrophically fragile as soon a single point of failure arises.

Corporate (and societal) AI adoption is repeating this mistake – not because AI is dangerous, but because uniform AI adoption as we observe today will by definition eliminate the cognitive variance that previously made organizations resilient.

Individual atrophy is bad enough (offloading thinking makes you worse at thinking). But individuals work in organizations or run countries, and this is where collective atrophy becomes a serious problem. A company whose judgement layer will rely entirely on a ChatGPT 6 or Claude Opus 5 model, will share the same points of failure as every other company doing the same thing.

The biggest advantage over the next decade will not accrue to individuals that adopt AI the fastest, but ironically to those who maintain their cognitive sovereignty – and to organizations who preserve their cognitive “biodiversity” alongside it. If you are capable to reason independently when models converge to the same average answer – you have a competitive advantage.

The scariest is that monocultures don’t fail gradually but ALL AT ONCE.