Future of Work on Carles Abarca

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.

The Chart That Predicts Which Jobs AI Will Kill (And They're Not the Ones You Think)

Mon, 09 Mar 2026 00:00:00 +0000

Look at this chart carefully. It’s not an analysis of what AI has destroyed. It’s an X-ray of what it’s about to destroy.

Source: Anthropic — Labor market impacts of AI (March 2026)

The blue area is what AI can do today. The red area is what AI is doing today. The difference between them isn’t a safety margin. It’s a tsunami that hasn’t hit shore yet.

The Study: 2 Million Conversations with Claude
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Anthropic just published Labor market impacts of AI: A new measure and early evidence, and it’s the most rigorous analysis I’ve seen on AI’s real employment impact.

What did they do? They crossed three data sources:

The O*NET database, cataloging tasks across ~800 US occupations.
Real Claude usage data — 2 million conversations analyzed via the Anthropic Economic Index.
Theoretical estimates from Eloundou et al. (2023) on which tasks an LLM can make at least twice as fast.

The result is a new metric: observed exposure — not what AI could theoretically do, but what it’s actually doing in professional settings. And the most revealing finding isn’t the absolute numbers — it’s the gap between the two.

The 10 Most Exposed Jobs
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The ranking won’t surprise anyone who’s been paying attention, but the numbers are brutal:

Computer Programmers — 75% coverage. Three out of four tasks a programmer does, Claude already handles.
Customer Service Representatives. First-party API traffic shows massive automation.
Data Entry Keyers — 67%. Reading documents and entering data. The perfect automation use case.

The list continues: actuaries, financial analysts, technical writers. Office jobs. White-collar work. People with college degrees.

On the other end, 30% of workers have zero exposure. Cooks, motorcycle mechanics, lifeguards, bartenders. Jobs where hands, bodies, and physical context are irreplaceable.

Ironic, isn’t it? Decades telling us automation was coming for manual labor. It’s coming for the desks.

The Demographic Surprise
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This is where the study shatters the dominant narrative.

The workers most exposed to AI are:

16 percentage points more likely to be female
11 points more likely to be white
Nearly twice as likely to be Asian
Earn 47% more than unexposed workers
17.4% hold graduate degrees (vs. 4.5% in the unexposed group)

This isn’t the displaced factory worker narrative. These are lawyers, analysts, programmers, university professors. The professional class that thought it was untouchable.

When I say this will reshape social structure, I’m not exaggerating.

The Gap IS the Prediction
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Go back to the chart. Look at the categories:

Computer & Math: 94% theoretical capability, 33% actual use
Legal: ~85% theoretical, less than 15% observed
Education: ~70% theoretical, less than 15% observed
Office & Admin: 90% theoretical, a fraction of actual use

That distance between blue and red isn’t comfort. It’s latency.

It’s the time companies take to adopt, regulators to adapt, workflows to reconfigure. But the technology is already there. The model already knows how. The ecosystem just needs to catch up.

And every month, the red area grows. Anthropic says it explicitly: “As capabilities advance, adoption spreads, and deployment deepens, the red area will grow to cover the blue.”

This isn’t speculative prediction. It’s an empirical observation with trajectory.

What Changes with AI Agents
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Here’s the factor the study doesn’t fully measure — because it didn’t exist at this scale when they collected the data.

The study analyzes LLM usage — conversations with Claude. Chat interactions. A human asks, the AI answers. It’s the augmentation model: AI helps you, you execute.

But AI agents are something else entirely. They don’t answer — they act. They execute task chains autonomously. They navigate systems, make intermediate decisions, complete entire workflows without human intervention.

What we’re building at Tecnológico de Monterrey with AgenTECs is exactly this. Not a chatbot that helps you draft an email. An agent that manages the entire process: reads context, drafts, sends, follows up, escalates if there’s no response.

When agents arrive at enterprise scale — and they’re already arriving — the red area in the chart will expand explosively. Because you no longer need a human interacting with AI task by task. The agent covers the entire role.

Think about the Legal category: 85% theoretical capability, <15% current use. What happens when an agent can review contracts, identify risk clauses, generate executive summaries, and prepare response drafts — all without a lawyer touching the keyboard? The 85% becomes the new floor, not the ceiling.

What to Do (Which Is Not Panic)
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I’ve been saying the same thing for years: this isn’t about fear. It’s about preparation.

When I wrote “El fin del desarrollador” on LinkedIn, the reaction was predictable: “exaggerated,” “developers will always be needed,” “AI can’t do X.” The same arguments I heard about TECgpt when we launched it and people said professors would never use it. Today we have over 60,000 active users.

The metaphor I use is the orchestra conductor. The value is no longer in playing the violin — it’s in knowing what music to perform, who plays what, and when to change the score. Future professionals don’t execute tasks — they orchestrate systems that execute them.

Specifically:

Massive upskilling, now. Not “intro to AI” courses — real training on production tools.
Redefine roles, don’t eliminate them. A lawyer who masters AI agents is worth more, not less. But a lawyer who only knows manual contract review has an expiration date.
Measure exposure in your organization. Use Anthropic’s framework. Identify which tasks in each role an LLM can already perform. Design the transition before it’s imposed on you.
Create new roles that don’t exist yet: AI orchestrators, agent prompt engineers, autonomous systems supervisors.

The Bottom Line
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The BLS projects that the most exposed occupations under this metric will grow less through 2034. For every 10 points of observed coverage, the growth projection drops 0.6 percentage points. This isn’t casual correlation — labor market analysts are seeing the same thing.

And yet, Anthropic also finds that there’s no systematic increase in unemployment in the most exposed professions. Yet.

That’s the window. We’re in the moment between seeing the lightning and hearing the thunder. The bolt already struck. The question isn’t whether the sound will arrive, but whether you’ll be ready when it does.

Those who read this chart as “AI hasn’t affected employment much yet” are confusing latency with safety. Those who read it as “a structural labor market shift is coming and we need to act now”… they’re the ones who’ll still be conducting the orchestra.

The End of the Developer: The Future of Software Development with AI Agents

Tue, 20 May 2025 00:00:00 +0000

The End of the Junior Developer: The Future of Software Development with AI Agents

In a world where artificial intelligence advances at breakneck speed, an uncomfortable truth looms on the horizon of the technology industry: the junior developer role may be on the path to extinction. This is not science fiction but an emerging reality that is already transforming how we build software.

The Silent Revolution of AI Agents
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Today, AI is no longer just an assistant that completes code. AI agents are evolving into autonomous entities capable of perceiving the development environment, making complex decisions, and executing complete programming tasks with minimal human oversight. We are no longer talking about simple tools, but digital collaborators that are reconfiguring the entire development chain.

From Assistant to Autonomous Agent
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AI-based code assistants like GitHub Copilot or Codeium have already transformed developer productivity. However, what is coming is far more disruptive: specialized agents working in concert to manage the entire development lifecycle.

What does this mean? While today a junior developer can still ask an AI to generate boilerplate code or explain complex systems, tomorrow a technical architect will be able to instruct a complete team of agents to:

Develop complex code based on high-level requirements
Perform exhaustive testing and bug resolution
Optimize performance without manual intervention
Manage deployments and update documentation

The Prediction That Is Already Happening
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Mark Zuckerberg stated it without ambiguity: “By 2025, AI will be capable of functioning as a mid-level engineer, writing code and potentially replacing software developers.” We are not talking about a distant future, but a reality that is already emerging.

According to Gartner, by 2027 generative AI will require 80% of the engineering workforce to upskill, creating new roles and eliminating others. The question is no longer whether it will happen, but when it will reach the tipping point that transforms the entire ecosystem.

Orchestration: The New Paradigm
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The key concept here is AI agent orchestration: a process by which multiple specialized agents work together within a unified system. Each agent focuses on a specific task — UI design, backend development, testing, security — while a central entity (human or AI) conducts the symphony.

The Architect as Orchestra Conductor
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In this new paradigm, the technical architect becomes the true protagonist. Their role evolves from solution designer to strategic director of an AI agent team, defining:

The system vision and requirements
Technical and business constraints
Architecture and quality standards
Resolution of complex problems that require human judgment

This transformation is already happening. According to ServiceNow/Pearson research, by 2027 18.7% of technical architect tasks will be at least partially augmented by AI. Architects will focus less on guiding code implementation and more on directing and supervising the autonomous work of agents.

The Experience Crisis
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Here the fundamental dilemma arises: if AI agents can handle the tasks traditionally assigned to junior developers, how will new professionals acquire experience?

One of the greatest concerns is precisely how junior developers can grow into mid-level and senior roles if AI handles most of the routine coding. Traditionally, developers have learned by doing — writing, debugging, and refactoring real-world code. Without that hands-on experience, there is a risk that developers will not fully understand the complexities of software development.

A Future Without Traditional Juniors
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My thesis is that the developer role, as we know it, will disappear. In its place, we will see:

Prompt and orchestration engineers: Professionals specialized in directing and extracting maximum value from AI agents.
Verification and review specialists: Experts in evaluating AI-generated code, identifying edge cases, and testing its reliability.
High-level system designers: Professionals focused on architecture and system design, where higher-level thinking remains primarily human.

Companies are already hiring fewer junior engineers due to AI-driven productivity improvements. This trend will only accelerate as AI agents mature.

Adapt or Fall Behind
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For current professionals, the message is clear: the developer career is evolving, not disappearing. The future belongs not to those who resist AI nor to those who depend on it exclusively, but to those who learn to work symbiotically with these tools.

The most successful developers will be those who:

Master prompt engineering to guide AI agents
Develop sharp evaluation and verification skills
Focus on areas where human creativity and systems thinking are irreplaceable
Deeply understand orchestration and collaboration between multiple AI agents

Are We Ready for This Change?
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If my thesis is correct, we face a radical transformation in how we educate future developers and structure technical teams. Universities, bootcamps, and companies will need to completely rethink their training and hiring programs.

The question is not whether AI agents will revolutionize software development — they already are — but how quickly we will adapt as an industry to a world where humans design and direct, while AI agents build and implement.

Software Developers Are the Blacksmiths of the Last Century

Tue, 20 Feb 2024 00:00:00 +0000

Software developers are the blacksmiths of the last century. As we’re living on the edge of technology change, AI has clearly come across every line of business; it is in the code. The traditional role of a developer is about to change significantly, making room for a future where “artificial developers” will be the protagonists.

In such a scenario, the focus comes upon process engineers transformed to be the architects of a new era. Their role transforms itself from the conventional boundaries: orchestrating AI to build complex systems with precision and innovation.

Such a shift in paradigm promises to redefine efficiency – minimizing human error and shortening the cycle of development. This will push traditional coding into the background while, at the same time, it gives way to an increase in demand for strategic, design-focused, and analytic skills – all of which imply the importance of adaptability in the digital age.

The future isn’t about an end of developers, it’s about transcending traditional roles to unlock unprecedented innovation and efficiency. Today’s developers will soon be like the blacksmiths of the past, who crafted essential tools by hand. The developers of the future will instruct AI to develop essential tools on an industrial scale and at a speed never before seen in the software industry.

Ten Professions at Risk of Extinction

Thu, 15 Feb 2024 00:00:00 +0000

Ten professions that could be significantly affected by the advancement of AI and automation:

Data entry clerks — The arrival of automation and AI systems capable of processing data at high speed could significantly reduce the need for manual data entry roles.
Telemarketers — The rise of AI chatbots and voice assistants could soon eclipse the need for human telemarketers, as these technologies improve at handling sales calls and customer inquiries.
Accountants and bookkeepers — Modern AI and machine learning algorithms are reaching a level of sophistication where they can effortlessly handle financial transactions and audits, potentially streamlining the numerous tasks traditionally performed by accountants and bookkeepers.
Proofreaders — Advanced AI in language processing could take over proofreading tasks, identifying grammatical and stylistic errors, sometimes even more effectively than humans, which could diminish the demand for human proofreaders.
Retail cashiers — The introduction of AI-powered retail technologies and automated payment systems poses a significant challenge to the need for cashiers in retail environments.
Travel agents — AI-powered platforms offering personalized travel advice and bookings could effectively replace traditional travel agencies.
Assembly line workers — The long integration of automation and robotics in manufacturing could be taken a step further with AI, potentially reducing the need for human workers on assembly lines dramatically.
Customer service representatives — The increasing use of AI chatbots and virtual assistants across various industries to handle customer service inquiries could make some human customer service roles obsolete.
Translators — Although the nuanced nature of language translation still benefits from human expertise, AI translation tools are rapidly improving and could soon handle simpler translation tasks with minimal human intervention.
Paralegals and legal assistants — AI and machine learning technologies are being employed to automate document review and legal research, tasks that have traditionally been the domain of paralegals and legal assistants.

The Double-Edged Sword of Generative AI: The Future of Work on the Brink

Wed, 14 Feb 2024 00:00:00 +0000

With the dawn of the Generative AI era on the horizon, the overall professional scenery as we know it has gone to the verge of massive alteration. It is amazing that AI is able to automate and even generate content on its own, yet it brings fears of job security for humans in the future.

The most concerned about this change are professions where routine work and data processing are a prerequisite. Administrative roles, data entry clerks, and parts of customer service are most subject to the influence of artificial intelligence systems. These technologies can analyze and process information at speeds that humans could not reach. They can be invaluable for efficiency but at the same time could potentially render current roles redundant.

The creative sector is certainly not immune either. If AI is now able to produce written content, art, and even music, creative professionals of the future may very well find themselves up against machines able to produce similar outputs in a fraction of the time. But human-created content has an edge — the unique human touch, emotional depth, and cultural understanding that it provides.

That is not the alarm bell — it is the wake-up call. The secret to surviving in this new AI-governed landscape lies in irreplaceable human skills: creativity, empathy, strategic thinking, and emotional intelligence. The professionals who are flexible enough and decide to work with AI are the ones who will not just survive but thrive.

The future of work is not about humans versus AI but about coming together to synergize with these technologies in a way that makes the world more efficient, more creative, and more empathetic. Let us deal with it upfront. Upskill and re-skill ourselves, because what is indispensable in the age of AI is the individual.

The Future of Work: AI-Driven Professions on the Rise

Tue, 30 Jan 2024 00:00:00 +0000

The job landscape is changing fast, in unimaginable ways as we inch closer to the dawn of an AI-centric era. Within the next three years, some AI-related professions are set to dominate this landscape and change our perspective on work, skills, and education.

The Most Popular AI Professions
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AI Ethicists — the individuals steering AI technologies in their ethically-correct development and application.
Machine Learning Engineers — professionals creating self-learning algorithms, as well as developing, adjusting, and optimizing neural networks.
AI Data Analysts — understanding complex datasets to make AI systems better.
Conversational AI Designers — building advanced chatbots and virtual assistants.
AI Integration Specialists — embedding AI technologies into your existing tech stack without friction.

Bridging the Skills Gap
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A change in educational offerings is being driven by increasing the demand in such roles. Top universities in collaboration with online platforms are rolling out specialized degrees, diplomas, and master’s programs in AI, machine learning, data science as well as ethics in technology. The programs are not only designed to impart technical knowledge but critical thinking ability, ethical considerations as well as creative problem-solving capabilities required for the emerging professional fields.

With this new digital era at hand, learning on-the-go is the order of the day and adaptability comes along. Anyone could jump into the world of AI, whether being a well-oiled professional or taking those baby steps in your career.

Future of Work 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 #

The Chart That Predicts Which Jobs AI Will Kill (And They're Not the Ones You Think)

The Study: 2 Million Conversations with Claude #

The 10 Most Exposed Jobs #

The Demographic Surprise #

The Gap IS the Prediction #

What Changes with AI Agents #

What to Do (Which Is Not Panic) #

The Bottom Line #