We are at a crossroads. It's no longer a choice between adopting AI or not - that's already been made. The question now is: which path will we take? That of machines that consume resources exponentially while the financial and environmental risks are transferred to the most vulnerable, or that of consciously building a technology that serves humanity without destroying the planet?<\/p>\n
The last 24 hours have brought a series of news items that, taken together, expose exactly this tension. On the one hand, physicist Mario Rasetti warns<\/a> that AI is developing at a \u201cdoubly exponential\u201d rate and consuming energy and water at unsustainable levels. On the other, companies like Meta and Microsoft are transferring the risks of this race<\/a> for smaller investors through sophisticated financial structures.<\/p>\n About that, the book Atlas of AI<\/em> documents the extractive trail<\/a> of this industry, and experts debate how to mitigate inequalities<\/a> in the job market.<\/p>\n It's not catastrophism. It's realism. And it's also an opportunity to choose the right path.<\/p>\n Mario Rasetti, world-renowned physicist and professor emeritus at the Politecnico di Torino, doesn't mince words. For him, AI represents \u201cperhaps the greatest cultural revolution in the entire history of the Homo sapiens<\/em>\u201c<\/strong> - an \u201canthropological transition\u201d.<\/p>\n But unlike other technological revolutions, this one is happening at \u201cdouble exponential\u201d speed. What does this mean in practice? That with each cycle, growth not only doubles, but accelerates at an ever faster pace. It's as if we were in a car whose accelerator is increasing its own power every second.<\/p>\n And here's the problem: the physical infrastructure does not keep up with this speed<\/strong>.<\/p>\n Rasetti specifically warns of two critical costs:<\/p>\n I've been working with companies and governments for years on developing AI strategies, and one question I always get is: \u201cDo you know how much it costs in environmental terms to run this model?\u201d<\/strong> The answer, more often than not, is silence.<\/p>\n Not because people are irresponsible, but because this information is deliberately made invisible. The cloud seems light, but as Kate Crawford documents<\/a> in Atlas of AI<\/em>, It is sustained by a brutal extractive chain.<\/p>\n Kate Crawford, an internationally recognized researcher, dismantles the myth of \u201cclean\u201d technology. Her book shows that AI relies on:<\/p>\n And then there are the social costs: privacy constantly mined<\/strong>, algorithms that reproduce historical prejudices<\/strong>, and a concentration of power<\/strong> in the hands of an ever smaller technological elite.<\/p>\n The point is not to abandon AI. It's about demanding that it be built in a sustainable and democratic way. In my work with companies, I always reinforce this: Responsible AI is not just an ethical issue - it's a question of long-term viability<\/strong>.<\/p>\n While environmental costs are being made invisible, financial risks are being transferred in increasingly sophisticated ways.<\/p>\n A New York Times report published on InfoMoney<\/a> exposes how Meta and Microsoft use complex financial structures to expand data centers without taking on debt directly on their balance sheets.<\/p>\n Meta, for example, has created a special-purpose vehicle called Beignet Investor LLC<\/strong>. Through it, it issued bonds that mature in 2049 (!), transferring the risk to private creditors such as Blue Owl Capital and Pimco.<\/p>\n The strategy is simple (and ingenious, from a financial point of view):<\/p>\n Who takes the loss?<\/strong> Smaller companies, suppliers and investors who have taken on long-term infrastructure.<\/p>\n As Shivaram Rajgopal, professor at Columbia Business School, said: \u201cRisk is like a tube of toothpaste. You squeeze it here, it comes out somewhere else.\u201d<\/em><\/p>\n It reminds me of the accounting structures that preceded the dotcom bubble and the 2008 crisis. The difference is that now the asset in question is not just mortgages or shares in internet companies, but the AI computing infrastructure itself.<\/p>\n Experts compare these arrangements to outdated methods that add opacity in financing<\/strong>. And when there is opacity, there is a systemic risk.<\/p>\n If you are an entrepreneur, executive or investor, pay attention: the concentration of risks in smaller players can create a domino effect<\/strong>.<\/p>\n Imagine a Brazilian startup that has developed a promising AI solution and needs computing power. It can:<\/p>\n While the giants have the flexibility to navigate these options, startups and medium-sized companies are exposed<\/strong>.<\/p>\n In my mentoring work with executives and companies, I always reinforce this: It's not enough to have a good AI idea. You need to understand the cost and risk structure of the infrastructure that supports it<\/strong>.<\/p>\n Rasetti uses a powerful metaphor: we are at a crossroads. And like every crossroads, we have to choose a path.<\/p>\n Continuing the exponential race, ignoring environmental and social costs, transferring risks to the most vulnerable. It's the path of growth at any cost.<\/p>\n Consequence:<\/strong> Environmental collapse, systemic financial crises, brutal widening of inequalities.<\/p>\n Imposing moratoriums, restricting development, treating AI as an existential threat. It's the path of fear.<\/p>\n Consequence:<\/strong> Loss of real opportunities to solve complex problems, competitive backwardness, concentration of power in the countries that didn't brake.<\/p>\n This is Rasetti's proposal - and the one that makes the most sense. Deep understanding of technology<\/strong>, This means establishing clear ethical and environmental limits, democratizing access, and building responsible governance.<\/p>\n Consequence:<\/strong> Sustainable, inclusive AI that serves humanity without destroying the planet.<\/p>\n I choose the third way. And I believe that most people, when they understand the real options, choose it too.<\/p>\n Rasetti makes a point of reaffirming something we often forget in the hype: intelligent machines will never have feelings or consciousness<\/strong>.<\/p>\n AI represents<\/strong> reality, but not understands<\/strong>. It's like Plato's allegory of the cave: we see shadows projected on the wall, but not reality itself.<\/p>\n And more:<\/p>\n Rasetti's conclusion is comforting and challenging at the same time: \u201cWe human beings are much more powerful than machines.\u201d<\/em><\/p>\n But that also means that the responsibility lies with us.<\/strong><\/p>\n In my work with companies, one of the main transformations I seek is precisely this change in mentality: from \u201cAI will solve everything\u201d to \u201cAI is a powerful tool that needs strategic and ethical human guidance\u201d.<\/p>\n An article published in JOTA<\/a> addresses exactly this tension: AI transforms the labor market by creating new jobs and making others obsolete, but the pace is much faster than our ability to adapt<\/strong>.<\/p>\n The main concern is not whether AI will create or destroy jobs - it will do both. The question is: who will have access to the necessary retraining?<\/strong><\/p>\n The article highlights three proven ways to mitigate inequalities:<\/p>\n 1. Digital skills training<\/strong><\/p>\n Successful examples in Kenya show that proper training generates real income growth. It's not theory - it's empirical evidence.<\/p>\n 2. Reskilling within companies<\/strong><\/p>\n Companies that adopt AI and reorganize functions (instead of just cutting jobs) generate significant salary gains for those who are retrained. And watch out: socio-emotional skills are increasingly valued<\/strong>.<\/p>\n AI can process data, but it can't negotiate conflicts, lead diverse teams, or understand the cultural nuances of a market.<\/p>\n 3. AI-facilitated inclusion<\/strong><\/p>\n AI tools for creating CVs have increased the chances of less qualified candidates being hired by 8%. Technology can democratize access - when applied well.<\/p>\n The risk:<\/strong> Screening algorithms can reproduce historical prejudices. If the training data reflects past discrimination, AI amplifies that discrimination.<\/p>\n That's why I always reinforce it: Responsible AI requires constant auditing, diversity in development teams, and transparency in criteria<\/strong>.<\/p>\n The article raises a crucial point: it will be necessary to rethink social security in order to accommodate the gig economy<\/em> and flexible working.<\/p>\n The traditional model of formal employment with a formal contract is being complemented (and in some cases replaced) by more fluid arrangements. This requires integrated public policies<\/strong> that protect workers without stifling innovation.<\/p>\n It's a complex debate, but one that can't be postponed.<\/p>\n The news of the last 24 hours has also brought seemingly minor cases, but which reveal important trends.<\/p>\n Researchers from the USA and South Korea have developed OmniPredict<\/a>, an AI that predicts pedestrian behavior in real time with 67% accuracy - 10% more than previous models.<\/p>\n The system analyzes 16 video frames (half a second) to predict the action 30 frames ahead (about a second). It doesn't sound like much, but is enough for a car to plan maneuvers in advance<\/strong>, This makes traffic run more smoothly and reduces sudden stops.<\/p>\n The innovation lies in the use of zero-shot learning<\/em> and language models to \u201creason\u201d about human intentions. Instead of just reacting, the vehicle anticipates.<\/p>\n Limitations:<\/strong> Difficulties with strong shadows and differentiating between pedestrians and cyclists in certain conditions.<\/p>\n This perfectly illustrates what Rasetti says: AI represents, but it doesn't understand. It identifies patterns of behavior, but it doesn't understand why<\/em> someone acts in a certain way.<\/p>\nThe Fastest Revolution in History and Its Invisible Cost<\/h2>\n
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The Unsustainable Lightness of the Cloud<\/h3>\n
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How Giants Transfer Risk (And Why It Matters)<\/h2>\n
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What Does This Mean for the Innovation Ecosystem?<\/h3>\n
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The Crossroads: Three Possible Paths<\/h2>\n
Path 1: Accelerate Without Limits<\/h3>\n
Path 2: Radical braking<\/h3>\n
Path 3: Turning AI from Practice into Science<\/h3>\n
AI Is Not Creative, It Has No Conscience - And That's Fundamental<\/h2>\n
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The Impact on the World of Work: Between Opportunity and Inequality<\/h2>\n
Three Strategies That Work<\/h3>\n
Rethinking Social Security<\/h3>\n
Curious Cases That Reveal Deep Trends<\/h2>\n
Autonomous Cars That \u201cRead the Minds\u201d of Pedestrians<\/h3>\n
Fake AI Videos and the Crisis of Confidence<\/h3>\n