Stop Crying About AI and Jobs

Wed, 15 Apr 2026 00:00:00 +0000

Stop Crying About AI and Jobs
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The public conversation about artificial intelligence has filled up with a strange mix of fascination, fear, and theatrical panic. Every week brings a new headline claiming that AI is about to destroy human work, empty offices, make entire professions obsolete, and push millions of people into irrelevance. The problem is that when you actually look at the data with even a minimum of rigor, the real story looks very different.

No, AI is not wiping out employment on a massive scale. What it is doing, and this part is genuinely profound, is transforming tasks, redesigning workflows, and changing the skills the market will demand over the coming years.

And that difference matters enormously.

The Wrong Narrative
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There is a reason the catastrophic narrative spreads so easily: it works extremely well as content. “AI is going to take your job” generates immediate attention. It activates fear, outrage, and anxiety. It is easy to consume and even easier to share.

But a good narrative does not always describe reality well.

The most serious studies emerging in 2025 and 2026 point to a fairly consistent pattern. AI is not operating primarily as a force of brute job elimination, but as a technology that reorganizes the content of work. It automates parts, accelerates others, raises the expected standard in many roles, and creates new demand in layers that either did not exist before or were still marginal.

The right question, therefore, is not whether AI will make all employment disappear. The right question is different: which parts of human work become automatable, which ones gain value, and which new capabilities become decisive?

What the Data Actually Says
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The data currently on the table does not support the caricature of total labor collapse.

An MIT study published in April 2026 points precisely in this direction: AI transforms tasks far more than it destroys entire jobs. This fits with an idea labor economists have understood for a long time: most jobs are not made up of a single task, but of combinations of activities. When a technology automates one part of the work, the role does not automatically disappear. In many cases, it gets redefined.

Other signals point in the same direction and should not be ignored.

LinkedIn has highlighted the creation of 1.3 million new jobs linked to AI. We are not talking only about data scientists or prompt engineers. We are talking about specialized recruiting, technology integration, operations, governance, cybersecurity, sales enablement, training, product, automation, and entirely new service layers.

On top of that, infrastructure expansion is creating a major pull effect. More than 600,000 new roles tied to data center infrastructure are expected, from construction and operations to energy, cooling, maintenance, networking, and specialized support. When the digital economy scales, not only do the models grow. The entire system that makes them possible grows with them.

BCG has also been clear: AI will reshape more jobs than it eliminates. In other words, the dominant effect will be redesign, not pure extinction.

That does not mean there will be no disruption. There will be, and a lot of it. What it means is that the disruption looks less like the mass disappearance of work and more like an accelerated reassignment of value.

The Blind Spot: Your Job Title May Survive, Your Skills May Not
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This is the nuance many people still do not want to face.

The biggest impact of AI will not necessarily fall on the title of your job. It will fall on the actual content of what you do every day and on the skills you need in order to remain valuable.

It is estimated that 70% of the skills associated with many roles will change before 2030. That number should be more unsettling, and more motivating, than any apocalyptic prediction about job destruction.

Because it implies something simple: your role may still exist, but you may no longer be able to do it in the same way.

An analyst will still be an analyst, but with tools that drastically compress analysis time. An executive will still be an executive, but will no longer be able to make decisions without sound judgment about automation, models, data, risk, and augmented productivity. A teacher will still be a teacher, but will need to redesign the learning experience in an environment where knowledge is abundant and judgment becomes the scarce asset. A doctor will still be a doctor, but will work in a context where AI can assist with documentation, diagnostic support, and clinical prioritization.

The continuity of a job title does not guarantee the continuity of professional value.

And that is where the real conversation begins.

What Teams Are Actually Experiencing
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Those of us who have spent years working in technology keep seeing the same thing again and again: teams that integrate AI well do not necessarily reduce headcount as an automatic reflex. What they do, above all, is increase their execution capacity.

They deliver more. They iterate faster. They test more hypotheses. They reduce friction. They reallocate time from the mechanical to the strategic.

That completely changes the competitive standard.

AI does not instantly replace the competent professional. But it does expose, with great clarity, the people who decide to stay still while the environment moves forward. Not because the machine is magical, but because a team that learns to work with AI can, in certain contexts, produce twice as much in half the time.

And when that happens, the problem stops being “AI versus humans.” The problem becomes “humans who evolve versus humans who refuse to evolve.”

The Collapse of Barriers to Creation
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There is another angle that I think matters enormously, and that too often gets buried under all the fear: AI is lowering barriers to create, build, launch, and scale.

Today, one person with judgment, initiative, and the right tools can do things that only a few years ago required much larger teams. Prototyping, analysis, writing, design, automation, research, material preparation, product launches, idea validation, and process operations are now within reach of far more people.

That does not reduce the importance of talent. It multiplies it.

And this is not theory. There are already cases that illustrate the shift extremely well. Peter Steinberger, for example, has shown just how far an entrepreneur with technical judgment and an intense layer of AI agents can operate at a speed that once seemed reserved for full teams. Another widely cited example in the recent conversation around AI-powered solopreneurs is Maor Shlomo with Base44, a project run with an extremely lean structure and supported by AI-assisted development, which scaled rapidly and became a powerful signal of what changes when the cost of building software collapses.

These are examples that help clarify the direction of change: AI is radically expanding the opportunities available to talented individuals with focus and execution capacity.

As LinkedIn’s CEO has put it, AI is lowering barriers to create and build. And that is an enormously powerful signal. Because where some people only see substitution, others are already seeing expansion of capability.

The Real Risk Is Not AI
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The real risk is not that AI exists.

The real risk is responding to this transition with denial, cynicism, or paralysis.

It is confusing comfort with safety. It is assuming the future will respect inertia. It is believing it will be enough to keep doing what you have always done while the rest of the market reconfigures its capabilities.

Resistance to change always disguises itself as prudence. But in deep technological cycles, it often is not prudence. It is delay.

And delay, when the environment accelerates, becomes expensive.

So What Should We Actually Do?
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There is no need to dramatize. There is a need to act.

At least across five fronts:

1. Learn to work with AI, not just talk about AI
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There are too many people expressing opinions about artificial intelligence without having integrated the technology into their real workflows. Useful literacy is not about being able to define an LLM. It is about knowing when to use it, for what, with which limits, and with what judgment.

2. Strengthen judgment
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When intelligence becomes abundant, judgment becomes more valuable. The ability to interpret, decide, prioritize, contextualize, and assume responsibility will matter more and more.

3. Redesign processes
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AI does not produce deep results when it is bolted on as a superficial accessory. Real impact comes when processes, roles, metrics, and forms of collaboration are redesigned.

4. Commit to continuous learning
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This is not a technology you learn once and then finish with. Competitive advantage will come from sustained adaptability.

5. Replace fear with discipline
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Anxiety does not build capability. Disciplined experimentation does.

An Uncomfortable but Useful Conclusion
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AI is not automatically going to take your job.

What can leave you behind is the decision not to evolve while everything around you changes.

That is why I believe it is time to abandon technological victimhood and start speaking more honestly about what is really happening. AI is not destroying the value of human work. It is redefining which human work creates value.

And in that transition, the future will not reward the loudest complainers. It will reward the people who learn faster, combine technology with judgment more effectively, and have the courage to redesign themselves before they are forced to do so.

The question is not whether AI is going to change work.

The question is whether you are going to change with it.

Skills on Carles Abarca

Stop Crying About AI and Jobs

Stop Crying About AI and Jobs #

The Wrong Narrative #

What the Data Actually Says #

The Blind Spot: Your Job Title May Survive, Your Skills May Not #

What Teams Are Actually Experiencing #

The Collapse of Barriers to Creation #

The Real Risk Is Not AI #

So What Should We Actually Do? #

1. Learn to work with AI, not just talk about AI #

2. Strengthen judgment #

3. Redesign processes #

4. Commit to continuous learning #

5. Replace fear with discipline #

An Uncomfortable but Useful Conclusion #