The case for Human Centered AI
It is uncertain how we will learn and work in the future, but most people today will agree that change is everywhere. Whatever your profession, level of seniority, or career stage, Generative AI is likely to be a topic of interest or concern for you. The recent AI moment is often compared with Johannes Gutenberg’s invention of the printing press. I believe this is an apt analogy—and a profound one. In his 1962 book The Gutenberg Galaxy, Marshall McLuhan wrote: “We are today as far into the electric age as the Elizabethans had advanced into the typographical and mechanical age. And we are experiencing the same confusions and indecisions which they had felt when living simultaneously in two contrasted forms of society and experience.” Only 60 years later, we are about to transition into a new form of society, experience, and work—what some call the AI age. And once again, rapid change is causing confusion and indecision. There may be quick wins, but there are no easy answers. Drawing on my own experience with digital technology as a physicist, enterprise software professional, entrepreneur, and humanist, let me try to explain why I believe Human Centered AI is the way forward—because it helps us truly understand what is happening and how to thrive, both as a business and as a professional, in the face of change.
In physics, you make progress not by looking at what is changing, but at what remains constant. By contrast, the technology industry is obsessed with change. If you have worked in IT for more than three decades like I have, you will have heard these mantras of our profession: change is the only constant, the software industry does not honour tradition, disrupt or get disrupted, adapt or die, only the paranoid survive. Throughout my career in the IT sector, I found all of these principles to be true—except that change is not the only constant. To notice this, you simply need to look up and turn your attention away from tools, metrics, and the media.
First and foremost, you will notice that people are all around. Or, as Jaron Lanier observed: “Without people, computers are just space heaters making patterns.” My point is that behind every AI model there is a data scientist; behind every AI business, an investor; behind every creative piece of content used for model training, a human creator; behind every usage metric, a human user. And all of them remain fairly constant in terms of their needs, hopes, beliefs, capabilities, and vulnerabilities—the human condition, or anthropological constants, if you will. When I left physics and entered the software business back in 1994, I truly did not know what to expect beyond learning new programming languages. It did not take long before I fell in love with my new profession—not because of the technology, but because of the human touch. I remember telling my friends who were still at university that the software business is not about technology; it is all about people. That has not changed in the thirty years since.
Now look back at all the technology we have—but from above. Like Brian Arthur, you might ask yourself what technology actually is. During the peak of the mobile revolution, and as product owner of SAP EMR (a first-of-its-kind iPad-based mobile solution for physicians and nurses), I read his book The Nature of Technology. In his view, technology is not just tools or gadgets—it is a combination of other, already existing technologies, shaped by human needs and natural laws. What is interesting is that the nature of technology (like the human condition and the nature of business) does not change. As Arvind Narayanan and Sayash Kapoor propose, we should look at AI as a normal technology. It builds on all innovations since the invention of the first computer. There is no magic—just maths. However, what is special about Generative AI is that it is a multi-purpose technology, a trait it inherits from human language. In the words of David Deutsch, a philosopher and pioneer in quantum computing, it is another beginning of infinity.
So where does this leave us with the future of work in the face of technological breakthroughs? In order to truly understand what is happening, I believe it is important not to be mesmerised by the change at hand (or in the news) because of what Daniel Kahnemann calls the What You See Is All There Is bias (WYSIATI). In November 2023, the world saw ChatGPT and, since then, a number of very impressive applications of Generative AI technology. As impressive and promising as all of this is, it is by no means all there is. Worse, it prevents us from seeing what AI actually is. In light of the discussion above, I suggest we accept the definition given by Jaron Lanier: AI is an exchange of value between people. Once you take this perspective, you start seeing the system of systems and the interconnectedness of people, business, and technology. From here, it is easy to accept that there are humanists worrying about the impact on society, business leaders focused on staying competitive, and technologists pushing the limits. In my experience, great leaders—like great companies—combine all of these faculties. Henning Kagermann (SAP), Steve Jobs (Apple), Marc Benioff (Salesforce.com), and Satya Nadella (Microsoft) come to mind. I was fortunate to work for some of them (SAP, Microsoft), partner with (APPLE), or compete against (Salesforce). It strikes me that all of these companies are doing extremely well to this day, despite all the technology changes since I started working. My hunch is that this is because all of them were guided by a timeless, compelling vision shared by employees and customers. When thinking about the future of work, I believe Human Centred AI (or HAI for short) can be such a compelling shared vision. As Simon Johnson and Daron Acemoglu point out, there is no fixed trajectory—neither for technology nor for the organisation of work. Rather, there are choices made by individual businesses as well as by society. To inform such choices, technology, business, and the humanities need to come together. I first learned about HAI when reading The Worlds I See by Fei-Fei Li. She started as a physicist, then turned to computer vision and ultimately AI, with stops at Princeton, Stanford University, and Google. With ImageNet, she enabled the breakthrough of deep learning and everything that followed. When her mother became seriously ill, she began spending a great deal of time in hospitals—appreciating the humanity of doctors and nurses, and at the same time seeing countless opportunities to make their jobs easier (and patients safer) with the help of AI technology. This became her new North Star guiding her research work, ultimately leading to the creation of the Stanford Human-Centered AI Institute. At its core, it is about interdisciplinary collaboration—bringing together experts from technology and the humanities, guided by the belief that AI must be developed with human dignity, fairness, and social benefit in mind.
While I hope that many will intuitively agree with the principles of HAI, I can also see an equal number of practitioners asking how it relates to them or their business—and, overall, how it can potentially be operationalised. In his book Superagency, Reid Hoffman writes
“With the advances in machine learning that we began to see in the early 2010s, new frontiers suddenly emerged, unmapped and in some ways quite unfathomable. Now, it's a little bit like we're inhabiting the world before Copernicus again, the world before Magellan, and trying to figure out the best way to proceed”
In a very real way, practitioners, decision-makers, and workers are confronted with opportunity and uncertainty, necessity and ambiguity. There are no textbooks or best practices to follow yet. To help them, Hoffman introduces the idea of a techno-humanist compass—one that enables progress while avoiding solutionism (with AI as the hammer, everything becomes a nail) and problemism (thinking about AI only in terms of what could potentially go wrong). I love this metaphor because it emphasises human agency. It is us navigating and deliberating—not suffering and being driven. Just as a compass needs a fairly constant magnetic field to work (Earth’s magnetic field flips, on average, only every 0.5 million years), the techno-humanist compass needs constants beyond change to work. So how can it be operationalised? I am afraid a techno-humanist compass is not something you can buy, download, or copy. Rather, it is something you must develop for yourself and for your organisation. In my view, such a compass is not a device or an algorithm—nor even text or knowledge. It is, instead, an intuition (or culture) formed by an ever-increasing understanding of (and appreciation for) technology and the humanities.
To bring this to life, let me reflect on my own profession and speculate about its future. A few days after ChatGPT launched, a product manager on my team showed me how he created a spec document with a simple prompt and quipped that product managers probably would no longer be needed. Indeed, there are voices saying that AI-first companies no longer need product managers. All that is needed are salespeople connecting with customers and a few experts turning knobs to fine-tune AI models. I believe this is wrong—not out of nostalgia, but because this is not how things (or humans) work. At its core, product management is about vision, empathy, creativity, and innovation. By definition, none of these skills can be automated. I used to tell new hires that there is no textbook for becoming a product manager. The only way to get there is learning by doing: practising, failing, and growing. According to Steve Jobs, it is not the customer’s job to know what they want, and “Our job is to figure out what they're going to want before they do.” Put differently: there is no data or algorithm that can automate innovation. Product managers must be able to put themselves in the customer’s shoes; they must be wedded to the problems, not to the solutions (or the technology) at hand. Of course, AI can help product managers tremendously to become more effective and efficient. AI agents that support market research, requirements analysis, product documentation, testing, and so on will change the profession by automating tasks that can be automated. But without the human product manager in the loop, any company will produce, at best, mediocre products.
What about science? I am a physicist by education and at heart. Will we see artificial physicists in the future? The answer is no, for the same reasons described above. In The Beginning of Infinity, David Deutsch writes
Without the likes of Albert Einstein, Werner Heisenberg, Erwin Schrödinger, Paul Dirac, and Niels Bohr, there would have been no paradigm shift and no modern physics. Any AI would have fitted the data to classical physics—like many physicists at the time. Nevertheless, AI is a truly wonderful tool that helps physicists push the limits and explore the world even further. It may even become an indispensable research tool for fields such as molecular biology. But this is no different from astrophysicists who are lost without telescopes. Without a human scientist in the loop, nobody discovers anything.
However, there is yet another way to look at the role of AI. In a recent article in Die ZEIT, Lambert Tobias Koch, president of the German University Association, argued that AI is not the evil—but a catalyst for much-needed change. The way higher education often works today (students following tight schedules, chasing points, and gaming the system using AI) is far removed from the original goals of universities. So what if lecturers and students met in AI-supported discussion labs, focusing on dialogue instead of monologue? Here, AI would not be a replacement for human interaction, but an enabler. I like this example because it shows that change is necessary for progress. In operations research, there is a method called simulated annealing, which helps to find a global minimum of a given objective function. Its name is derived from a method in materials science used to eliminate defects in crystals. Careful, iterative heating and cooling of the material allows the system to find its perfect (defect-free) configuration. Following this analogy, one can think of professional practices (such as education) that are stuck in a moment. People know it can be done better, but do not know how. Here, AI can indeed be a catalyst and an enabler to get moving again—creating new formats, professions, and tasks for workers.
To put it in a nutshell: HAI is a paradigm shift. Similar to what Copernicus did with the sun, HAI puts humans at the centre—not out of nostalgia, but in recognition of reality. There is no technology without vision, and no vision without humans who share it. Instead of dreading the race to the bottom of automation, we are invited to reinvent our professions and shape the future of work for the benefit of businesses, workers, and learners. If I were asked for advice by young people starting their careers in the age of AI, I would refer them to Steve Jobs, who gave a timeless answer to this question back in 2005.
“Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do”
Stay Hungry. Stay Foolish. Stay Human.