🇺🇸 Obsolete - The last stage of human work

“Hope everyone enjoys their last year of meaningful work!”

— Chad Hurley, YouTube co-founder, February 26, 2026

John Danaher, in his book Automation and Utopia: Human Flourishing in a World without Work (2019), describes this moment as the transition from the Anthropocene to the Robocene: the historical shift in which relevant productive activity ceases to revolve around human effort and comes to rest primarily on autonomous systems.

This is not a distant prediction. It is a process already underway, accelerating visibly in 2026. I invite you to reflect on how to remain relevant before the board finishes reshuffling — and to avoid becoming obsolete this very year.

The inevitability of the shift to the Robocene

Danaher builds his argument at the intersection of law, technology, and ethics. His central thesis is clear: the automation of work — understood as any activity performed primarily in exchange for economic compensation — is both possible and desirable. Possible because technology has already reached the point where it can replicate, and in many cases surpass, the routine, cognitive, and even creative components of work. And desirable because work, as we organize it today, generates more structural suffering than fulfillment for the majority of people.

Danaher devotes several pages to explaining why “you should hate your job” not as personal advice, but as a systemic diagnosis: precarity, lack of autonomy, the colonization of time, domination, and inequality. Even if you enjoy what you do, the system surrounding present-day employment turns it, on average, into a poor investment of life.

But Danaher does not stop at critique. He also acknowledges the real risks automation brings when it extends beyond work itself. In his book and related articles, he identifies five concrete threats to human flourishing, arising from the way automated technologies reconfigure our relationship with the world, personal achievement, and moral agency. These are not vague speculations but analyses grounded in current trends in AI, robotics, and algorithms, expanded in publications such as “The Rise of the Robots and the Crisis of Moral Patiency” (2019) and “Automation, Work and the Achievement Gap” (2021, co-authored with Sven Nyholm). Let us look at each:

1. It blocks the sense of achievement and excellence: Automation creates “achievement gaps” by taking over tasks that once allowed humans to demonstrate skill, overcome challenges, and contribute causally to valuable outcomes. AI can replace or assist in creative or cognitive processes, diminishing the attribution of success to human effort. Danaher argues that genuine achievement requires difficulty, competence, and a direct causal connection; when machines dominate those elements, work loses its potential to generate personal meaning and relegates us to secondary roles that fail to satisfy that deep need for excellence.

2. It makes the world more opaque: Machine learning algorithms operate with an internal complexity that escapes human understanding, fostering a kind of “techno-superstition” in which we accept results without comprehending their mechanisms — almost as if we believed in magic. In articles such as “The Threat of Algocracy,” Danaher explains how this erodes our capacity to reason about and predict the world, breeding blind dependence and reducing transparency in everyday decisions, from social media recommendations to judicial and financial systems.

3. It fragments our attention: Digital interfaces are designed to capture and monopolize mental focus: notifications, infinite feeds, addictive algorithms that interrupt deep thought. This does not merely distract at work; it fragments life in general, preventing the sustained concentration required by activities like reading, reflection, or genuine interaction. Over time, it undermines mental well-being and creative productivity.

4. It undermines autonomy: Automation predicts human behavior and guides it subtly — recommendation systems, workplace surveillance, algorithms that shape preferences. Danaher analyzes how these technologies erode self-determination: machines do not only assist; they configure decisions, turning us into passive subjects of algorithmic control that prioritizes efficiency or corporate profits over individual freedom.

5. It turns us into moral patients rather than active agents: This is perhaps the deepest threat, explored in “The Rise of the Robots and the Crisis of Moral Patiency” (2019). Danaher distinguishes between moral agents — beings capable of ethical reasoning, assuming responsibility, and acting to influence the world — and moral patients — beings worthy of moral consideration but passive, receiving actions without the capacity to respond actively. Automation triggers a “crisis of moral patiency” by transferring ethical decisions to robots or AI, diminishing our will and ability to exercise agency. If algorithms make decisions in employment, politics, or leisure, humans become passive recipients of well-being (or ill-being), losing the practice of moral responsibility. This threatens the very foundations of liberal societies, where active agency is central to democracy and personal ethics.

The large-scale destruction of human work is not, then, an accident or an avoidable risk. It is the logical outcome of decades of technological progress. And that process is already happening in real time.

But between the ending Anthropocene and whatever future utopia (or dystopia) may come, there exists a window of transition. That window is now. And in that window, the most meaningful work that remains is, in Mark Cuban’s words, guiding implementation.

The last meaningful stage: the era of implementation

Cuban argues that we are not entering an era of AI creation, but an era of implementation. Millions of small and medium-sized businesses — 33 million in the United States alone and hundreds of millions worldwide — need people capable of translating complex AI tools into practical solutions that address real business problems.

That is the key connection. Large-scale automation in major organizations is already producing significant layoffs of technical roles that have become redundant. That available talent pool could redistribute and find fertile ground in SMBs that never had access to elite technical teams and can now, suddenly, count on one or two professionals to deliver a productivity leap of years in a matter of months.

From the perspective of someone who has spent nearly 20 years building and maintaining systems — from physical infrastructure to autonomous agent architectures — this pattern is already evident from the inside. Large companies cut what AI does better and cheaper.

The role that emerges in this last stage is not that of the traditional developer who only writes code — that profile is being commoditized rapidly. In its place arises the AI Strategist: a hybrid professional who does not compete with AI in speed or volume, but understands where to apply it to generate real value. This person combines technical depth with business understanding, communication skills, negotiation ability, and human judgment. They map concrete pain points, connect intelligent tools with actual processes, and translate all of that into measurable impact: better margins, less wasted time, improved service, smarter decisions. Their differentiator is no longer just knowing how to build technology, but knowing how to implement the right technology, in the right place, to solve concrete problems for real people and organizations.

If you are an engineer, student, or professional who feels the ground shifting underfoot, this is the moment to orient yourself toward that role. It is the moment to think hard — in the strongest philosophical sense — about how all these new technologies can be combined to build real solutions that transform the current, inefficient processes of organizations of every kind.

This is a historic moment of convergence that levels the playing field: those who historically lacked deep technical knowledge can now leverage artificial intelligence to tear down once and for all a barrier that seemed insurmountable.

On the other side, software engineers — whose comparative technical advantage shrinks steadily in the face of this wave of new competitors — will need to adapt quickly to the new reality. Perhaps this is a particularly momentous time to deepen one of the principles of software craftsmanship: not just collaboration with the client, but productive partnerships.

The transition period toward the inevitable future — where much of human work will be performed by machines — will necessarily be preceded by a period of intense implementation. Those who are able to understand, translate, and define the architecture of the processes that will replace existing value streams will be the crucial links. Perhaps the protagonists of the last stage of occupational transcendence for our species.