Five voices. Five perspectives on the future of work. From Musk’s utopia to Hinton’s warning, by way of Amodei’s alert, Altman’s reorganization, and Li’s humanization.
The laboratory of the future is not a place with robots assembling boxes. It is a space for debate, for collective decision-making, for building institutions. Because in the end, the question about work is not a technical question. It is a question about what kind of society we want to be.
And as Hinton said, the answer is not in the laboratories. It is in all of us.
The Scene at the Summit, February 2026
The stage at the Cisco AI Summit in San Francisco had that air of solemnity that events take on when they know they are making history. And with good reason. On one side, Chuck Robbins, CEO of Cisco, moderated the conversation with the ease of someone who has spent decades in the industry. On the other, Sam Altman, the man who had put artificial intelligence in the pockets of five hundred million people, answered with that mix of technical precision and self-assurance that defines him.
Robbins’s question was direct: what is the total market for artificial intelligence? Altman smiled, as if the question struck him as both obvious and profoundly mistaken.
“People always talk about the total market demand for AI,” he replied. “To me it seems like something similar to the demand for electricity or energy. You can’t talk about it as one general thing. You can say how much demand there will be at different price levels. And in this case, you can talk about different price levels for different qualities: how intelligent it is, how fast it is. But if we keep making AI genuinely capable and really cheap, there will be a ton of demand at some price.”
The analogy was no accident. Altman had spent years thinking of AI as a new basic infrastructure, something as ubiquitous and necessary as the electricity lighting the room where he was speaking. But that afternoon in San Francisco, he was going to go much further. He was going to draw the map of an economic transformation that would make electricity, compared with what was coming, look like a minor invention.
Ten-person companies and the three forces behind them
The Ten-Person Company
The idea Altman has been refining in recent years is as simple in its statement as it is devastating in its implications: soon we will see ten-person companies valued at a billion dollars. And he doesn’t stop there. On the nearest horizon, between 2026 and 2028, we could witness the birth of the first “solopreneur” unicorn: a company created by a single person that reaches that same valuation.
To understand why this is possible, you have to understand three forces that Altman identifies as the engines of the new economy: cognitive automation, falling production costs, and the reconfiguration of organizations.
Cognitive automation is what happens when language models stop being mere assistants and become autonomous agents capable of carrying out complex tasks without constant supervision. Altman described it bluntly at a developer event held in January 2026: programming work, as we know it, is changing radically. Engineers will spend less time writing code and debugging errors, and more time deciding what to do with systems that already know how to write code on their own.
The fall in production costs is just as dizzying. Altman projects that by the end of 2027, systems at the level of the GPT-5.2 family could be roughly a hundred times cheaper to run. A hundred times. That means that what costs a hundred dollars today will cost one tomorrow. And when the cost of producing something approaches zero, the rules of the economic game change completely.
The reconfiguration of organizations is the natural consequence of the previous two forces. If one person with the right tools can do what an entire department used to do, the structure of companies has to be rethought from the foundations.
The End of SaaS and the Birth of Micro-software
There is a corollary to this vision that Altman has laid out clearly in his recent appearances: the software-as-a-service (SaaS) business model as we know it could be living its last days.
The reason is simple. If you can ask an artificial intelligence to generate the software you need at any given moment, for each specific task, why subscribe to a monolithic application that does many things you don’t need and nothing exactly the way you want it?
Altman imagines a future where much of the software we use will be written for a single person or a very small group. Each individual will have their own set of customized tools, generated on the fly by artificial intelligence agents that understand their specific needs. “We will all be continuously customizing our own tools,” he said at the January event.
This change has profound implications not only for the software industry but for the very nature of work. If every person can have their own team of developers working for them (even if they are silicon developers), the power to create value is decentralized in an unprecedented way.
What happens to programmers and to human attention
The Programmer and the New Meaning of Work
But what does this mean for flesh-and-blood programmers? Altman tackled this question with an honesty that many in his position would have avoided. The answer, according to him, is a paradox: the number of people working as software engineers could increase, not decrease.
The explanation is counterintuitive but sound. If the cost of producing software falls dramatically, demand for software soars. And even if each unit of software requires less human intervention, the total volume of human work needed to meet that demand can grow. It is what economists call the “productivity paradox” applied to software.
But there is something deeper. Altman believes the nature of the engineer’s work is going to change radically. “What it means to be an engineer is going to change a lot,” he said at the developer event. “The shape of the work and the amount of time you spend writing code or debugging code is going to change enormously.”
That change is not just quantitative but qualitative. Engineers of the future will spend less time wrestling with syntax and more time deciding which problems are worth solving. Value will no longer lie in execution, but in judgment.
Attention as the Supreme Resource
If software becomes cheap and abundant, what remains scarce? Altman has a clear answer: human attention.
At the January event, a developer asked him about the new challenge facing startups. Before, the hard part was building the product. Now, with tools like Cursor or Codex, anyone can build a product in a matter of days. What remains devilishly hard is getting someone to pay attention to that product.
“I’ve always thought the hardest part of starting a business isn’t building the product, but getting people to really care about it, use it, and connect with it,” Altman replied. “That hasn’t changed.”
Attention, that finite resource that humans have been trading for centuries without realizing it, becomes the most valuable currency of the new economy. And unlike software, attention cannot be generated with artificial intelligence. Only humans can give it, and in limited quantities.
Altman went even further: “In a world of extreme abundance, what will really be scarce is human attention and good ideas.” That sentence condenses his whole vision. AI can produce infinite variations of content, infinite technical solutions, infinite value propositions. But only humans can decide what is worth looking at, and only humans can have the ideas that really matter.
Concentration of power and the hiring slowdown at OpenAI
The Risk of Concentration
Altman is not a naive optimist. In his appearances, both at the January event and at the Cisco AI Summit, he has shown a recurring concern: artificial intelligence could concentrate power and wealth in a few hands instead of democratizing them.
“AI could have a very strong ‘deflationary’ effect,” he said. “But it could also reverse direction and concentrate power and wealth in the hands of a few. If these tools are only in the hands of a few people or a few companies, what they will do is amplify inequalities, not reduce them.”
This concern is not new for Altman. He has been talking for years about the need for public policies that ensure a broad distribution of AI’s benefits. But in 2026, with the models already deployed and the transformations under way, the urgency of those policies has become more pressing.
The solution, according to him, is not to slow the technology down, but to design institutions that guarantee its benefits reach everyone. And that is where universal basic income comes in, an idea Altman has defended for years and that in 2026 is beginning to sound less like a utopia and more like a practical necessity.
OpenAI and the Hiring Slowdown
Perhaps the most concrete example of how Altman applies these ideas to his own company came in January 2026, when he announced that OpenAI plans to drastically slow its pace of hiring.
The statement was as honest as it was unsettling: “We are planning to dramatically slow down how quickly we grow because we think we’ll be able to do much more with fewer people.”
Altman explained that the right approach is not to hire aggressively and then, once you realize AI can do many of those functions, have to lay people off in “very uncomfortable” conversations. Better to hire more slowly from the start, assuming that productivity per employee will grow thanks to the very tools they are building.
This move by OpenAI is a window onto the future of many companies. If the world’s most important artificial intelligence maker decides it needs fewer humans because its machines are good enough, what will every other company do when they get access to those same machines?
Which professions hold up, and the image of the farmer
The Career You Shouldn’t Study
While Altman sketches these futures at tech forums, he also offers practical advice to those trying to navigate the present. In an interview with Tucker Carlson, he warned that professions tied to customer support, both by phone and in IT, will be the first to disappear.
The data back him up. A global Salesforce survey reveals that 32% of customer service cases are already handled by artificial intelligence, and that figure is projected to reach 55% by 2027.
But Altman doesn’t limit himself to pointing out what is disappearing. He also points toward what remains. Professions that require deep human interaction, such as medicine, show greater resistance to automation. Not because AI can’t diagnose diseases (it already can, and in many cases better than humans), but because care, presence, the ability to hold another human being’s gaze in a moment of vulnerability, are things technology still doesn’t know how to replicate.
The Farmer and the CEO
There is an image Altman has used in several recent interviews that condenses a good part of his philosophy. When asked what he will do when artificial intelligence can do his job better than he can, his answer is surprisingly down-to-earth: he wants to be a farmer.
“I have a farm where I live part of the time, and I love it,” he said in an interview with Mathias Döpfner, CEO of Axel Springer. Before ChatGPT took off, he had more time to drive tractors and pick things. And even though his schedule is now taken up by meetings and strategic decisions, the farm remains his image of the future.
There is something deeply revealing in this answer. Altman, who spends his days building the most advanced technology on the planet, dreams of a future where he can devote himself to the most primitive activity: working the land. He doesn’t do it out of necessity (his investments in startups such as Stripe, Reddit, and Helion have given him a personal fortune that exceeds $1 billion), but by choice. Because farming, like the work of the future that Musk imagines, is something done for pleasure, not out of obligation.
“We humans, human society, have so much energy as protagonists that we really don’t care that machines are smarter than us,” Altman reflected. “They already are.”
That is perhaps his deepest idea. The intelligence of machines is not the problem. The problem, and also the solution, is what we humans do with ours. How we decide to use it to take care of one another, to pay attention to one another, to cultivate our farms and our relationships.
Robbins’s Question
Let us return to the stage at the Cisco AI Summit. Chuck Robbins, at the end of the conversation, put one last question to Altman, one that summed up the unease of many in the audience: how does this change the way we run our companies, the way we live our lives?
Altman took a few seconds to answer. When he did, his voice had a different tone, less executive, more human.
“I think this will be how we do things,” he said. “How companies work, how scientific discovery happens, how we use most of the software in our personal lives. And if we can have it at a reasonable price, it seems like a very good bet.”
He did not speak of mass unemployment or radical transformations. He spoke of the everyday, of how we will do things. Perhaps because he knows that great transformations do not arrive with a roar, but by seeping into the crevices of daily life. One day, we simply start doing things differently. And only when we look back do we realize that the world is no longer what it was.
Altman came down off the stage to applause. Outside, in the streets of San Francisco, life went on. People were going to their jobs, their schools, their homes. They carried in their pockets, without knowing it, the technology he had helped create. And in a few years, perhaps, some of them would be those solopreneurs, owners of billion-dollar companies built from their bedrooms. Others would simply have learned to do their work in a different way. And others, the least fortunate, would look for a place in a world that no longer needed them.
That is the paradox Altman leaves hanging in the air. The same technology that enables ten-person companies valued at a billion dollars is the one that leaves out those who cannot find their place in the new landscape. And the question that no one, not even Altman, can yet answer is: how many will be in the first group and how many in the second?
In the next article in the Laboratorio del Futuro series: we delve into the vision of Fei-Fei Li, the Stanford researcher who reminds us that intelligence is not only language, but also space, context, and human dignity. We call it “Fei-Fei Li and Spatial Intelligence: Why the Future of Work Is Also Physical”.
