by DR. Ricardo Petrissans | May 10, 2026 | The protagonists of IA, Theology and Technology
Geoffrey Hinton’s warnings about AI are not warnings against intelligence itself. They are warnings about abundance without governance: abundant synthetic labor, abundant persuasion, abundant automation, and abundant power in systems few people fully understand.
The Paradox of Success
The better AI becomes, the more society wants to use it everywhere. But success creates dependency. When systems write, advise, code, design, persuade, and decide, the cost of error rises with adoption.
Abundance and Displacement
AI can produce more text, more software, more analysis, more images, and more automation. Yet abundance in output can mean scarcity in employment, attention, trust, and institutional control.
The Alignment Problem in Public Life
Hinton’s concern extends beyond technical alignment. The social question is whether institutions can adapt fast enough to govern tools that evolve faster than law, education, and labor markets.
Conclusion
The price of abundance is responsibility. If AI multiplies capability, society must multiply oversight, resilience, and humility at the same time.
by DR. Ricardo Petrissans | May 3, 2026 | Fight Against Technological Monopolies, The protagonists of IA
Fei-Fei Li’s work reminds us that intelligence is not only linguistic. Humans understand the world through bodies, space, vision, movement, and context. If artificial intelligence remains trapped in text, it remains incomplete.
From Words to Worlds
Large language models changed how machines handle symbols. But the physical world is not a document. It has depth, occlusion, force, risk, and embodied action. Spatial intelligence is the bridge from conversation to reality.
The Importance of Vision
Li’s contributions to computer vision helped make visual recognition a central part of AI. Seeing is not passive. It is a way of structuring attention, identifying relationships, and preparing action.
Robots and Embodied AI
The next frontier is intelligence that can operate in kitchens, hospitals, warehouses, streets, and laboratories. That requires models that understand not only what objects are, but where they are, how they move, and what humans intend around them.
Conclusion
Beyond language lies the intelligence of space. Fei-Fei Li’s perspective helps correct the illusion that thinking is only text. Real intelligence must eventually meet the physical world.
by DR. Ricardo Petrissans | Apr 26, 2026 | Fight Against Technological Monopolies
Sam Altman’s idea of a ten-person company generating a billion dollars in revenue captures the radical compression of business made possible by AI. It is a vision of extreme leverage: tiny teams orchestrating software, agents, infrastructure, and distribution at global scale.
The New Company Shape
Traditional growth required departments: sales, support, operations, finance, engineering, and management. AI agents can absorb parts of those functions, allowing a small team to coordinate systems that once required hundreds of people.
Leverage and Fragility
A ten-person billion-dollar company sounds efficient, but it also concentrates dependency. A few founders, a stack of models, cloud providers, payment systems, and distribution platforms become the entire organism.
The Labor Question
If companies can scale revenue without scaling employment, the social contract changes. Economic value may grow while job creation weakens. That is a profound challenge for policy and education.
Conclusion
Altman’s scenario is plausible because AI multiplies leverage. It is unsettling because modern societies still rely on employment as the main bridge between productivity and livelihood.
by DR. Ricardo Petrissans | Apr 19, 2026 | The protagonists of IA
Elon Musk’s image of work as a garden rather than an obligation belongs to a larger debate about abundance, automation, and meaning. If machines produce more of what humans need, what becomes of work as identity, discipline, and social structure?
The Post-Scarcity Question
Musk often speaks from a horizon where AI and robotics make many forms of labor optional. In that future, work becomes less about survival and more about preference, craft, status, or purpose.
The Garden Metaphor
A garden requires care, rhythm, and attention. It is productive, but not in the same way as a factory. To imagine work as a garden is to imagine human activity as cultivation rather than coercion.
The Hidden Problem
The transition is not automatic. If automation removes wages before society builds new institutions, freedom from work can become exclusion from income. The garden can become a privilege for those who own the machines.
Conclusion
Musk’s metaphor is powerful because it points to a possible future of voluntary creativity. It is incomplete because it does not solve distribution, dignity, or the social role of work.
by DR. Ricardo Petrissans | Apr 12, 2026 | The protagonists of IA
Dario Amodei’s warnings about artificial intelligence point toward a profound transformation of white-collar work. The threat is not always dramatic replacement. Often it is quieter: compression of tasks, shrinking of teams, and automation of the cognitive middle layer.
The New Target of Automation
For decades, automation was associated with factories, logistics, and repetitive manual work. Generative AI changes the center of gravity. Reports, summaries, coding, legal drafts, analysis, customer support, and management routines can now be partially automated.
The Silent Tsunami
The change may arrive without a single dramatic announcement. A company may keep the same products while needing fewer analysts. A department may produce more output with fewer juniors. A manager may rely on AI drafts before asking a team. The labor market shifts under familiar job titles.
Productivity and Anxiety
AI may raise productivity, but productivity does not automatically translate into secure employment. If the same work can be done by smaller teams, the gains may flow to firms and shareholders before workers.
Conclusion
Amodei’s warning is useful because it refuses easy optimism. White-collar work is not immune to automation; it is becoming one of its main laboratories.
