Skip to main content
Paul Welty, PhD AI, WORK, AND STAYING HUMAN

· artificial-intelligence · found

Article analysis: 3 AI competencies you need now for the future

Article analysis: 3 AI competencies you need now for the future

Master essential AI competencies to thrive in an evolving landscape and ensure your career remains irreplaceable in the age of artificial intelligence.

“The urgency comes from the pace of change. We no longer have time to retrain for new jobs before the old ones disappear. We must act now to become irreplaceable.”

3 AI competencies you need now for the future

Summary

Pascal Bornet, a recognized expert in artificial intelligence, presents a compelling argument for the necessity of becoming “irreplaceable” in the face of a rapidly advancing AI landscape, as described in his book, “Irreplaceable: The Art of Standing Out in the Age of Artificial Intelligence.” Bornet introduces the notion of “AI obesity,” drawing an analogy between our overreliance on quick AI-driven solutions and the consumption of fast food. He asserts that society is indulging in “fast creativity, fast connections, and fast decisions,” which leads to a complacency that risks job security and humanity itself. However, he emphasizes that AI, much like food, is neutral, and its impact depends on how it is utilized. To navigate these challenges and capitalize on AI’s potential, Bornet has developed a framework focusing on three core competencies: being AI-ready, human-ready, and change-ready. These competencies are crucial not only for mere survival but for thriving in an AI-augmented world. The rapid pace of AI-induced change leaves little time for retraining, underscoring the urgency Bornet stresses. “AI-Ready” involves more than familiarity with AI tools; it demands a transformative shift in work and life perspectives to adeptly engage in an AI-centric future.

Analysis

Pascal Bornet’s article presents a compelling and urgent case for developing AI competencies but lacks depth in some critical areas. His notion of “AI obesity” serves as a creative metaphor to describe our increasing dependency on convenient AI solutions, yet it risks oversimplifying the complexity of AI’s integration into daily tasks and business operations. The emphasis on urgency without a detailed roadmap can be seen as alarmist rather than instructive. While Bornet advocates for developing AI-ready, human-ready, and change-ready competencies, he does not provide comprehensive evidence or strategies for how individuals and organizations can effectively acquire these skills. From my focus on AI as an augmentation tool, the article does not discuss sufficiently how AI can complement and enhance human decision-making rather than merely replace existing roles. Furthermore, his framework lacks exploration of how AI can democratize access to education and resources, which aligns with my commitment to future-proofing through technology. Bornet’s argument would benefit from more specific examples of AI successfully augmenting human capabilities and fostering collaboration. Lastly, while the pace of change is acknowledged, there is a gap in discussing continuous learning and reskilling as critical components for adapting to AI-driven transformation, a cornerstone of my perspective on lifelong learning and adaptability.

The agent-shaped org chart

Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.

AI as staff, not software

Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.

Knowledge work was never work

Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.

The work of being available now

A book on AI, judgment, and staying human at work.

The practice of work in progress

Practical essays on how work actually gets done.

The file I almost made twice

A small operational footgun that runs everywhere — building a parallel system when the one you have is fine.

The actor doesn't get to be the verifier

The worker isn't lying. The worker is reporting what it thought it did, which is always one step removed from what the world actually shows. The fix isn't more self-honesty. The fix is a different pair of eyes.

Shopping is the last mile

Every meal planning app treats cooking as the hard problem and shopping as a logistics detail. They have it backwards. Cooking is mostly solved. Shopping is the last mile.

Article analysis: Unlocking autonomous agent capabilities with Microsoft Copilot studio

Unlock the potential of autonomous agents with Microsoft Copilot Studio, enhancing efficiency and innovation for businesses in the AI-driven landscape.

Article analysis: Gusto’s head of technology says hiring an army of specialists is the wrong approach to AI

Gusto's tech head argues for leveraging existing staff over hiring specialists to enhance AI development, emphasizing customer insights for better tools.

Article analysis: Anthropic CEO Dario Amodei pens a smart look at our AI future

Explore Dario Amodei's insightful analysis on AI's potential to revolutionize biology, neuroscience, and innovation in the near future.