When a Machine Has a Seat at the Table: A Conversation with Holger-Thorsten Schubart

By the Editorial Team, Science Gazette

Why We Are Publishing This Interview

Every newsroom develops its own set of signals for when something deserves a second reading. Sometimes it is a new experimental result. Sometimes it is a number buried halfway through a technical paper. And sometimes, during the ordinary rhythm of daily research, following developments across energy, physics, materials science, artificial intelligence, and the increasingly complicated territory where those fields overlap, an interview appears that raises a question larger than the company, technology, or individual at its center.

That is how the editorial team at Science Gazette came across a German-language interview published by Energiewirtschaft.io with Holger-Thorsten Schubart, founder of the Neutrino® Energy Group. We have no editorial relationship with Energiewirtschaft.io, nor was this interview produced in cooperation with our publication. We encountered it independently, the way we encounter most things worth writing about.

What stopped us was not another discussion about artificial intelligence in the abstract. Organizations across science and industry already use AI for simulation, forecasting, literature review, optimization, and increasingly sophisticated decision support. What Schubart describes goes further. The Neutrino® Energy Group has assigned two permanently named artificial intelligence entities, Avery Laurent and Morgan Elian, defined leadership functions within what it calls the Neutrino Engineering District. Avery Laurent serves as Chief Scientific Intelligence. Morgan Elian serves as Chief Executive Intelligence. Neither is a human being, and neither, according to Schubart, is meant to be identified with any single underlying AI model. Their continuity instead rests on a mandate, an institutional role, a body of preserved knowledge, defined limits, and a technical architecture built to change as better models become available.

Science has spent centuries building institutions around the limitations and strengths of human beings. Universities, laboratories, peer-review systems, corporate hierarchies, and industrial development chains all reflect assumptions about where expertise resides, how quickly information can move, and how decisions get made. Artificial intelligence is beginning to disturb several of those assumptions at once, and this interview confronts that shift more directly than most conversations we have come across on the subject.

The editorial team at Energiewirtschaft.io approached Schubart with ten deliberately pointed questions, moving from scientific organization and human expertise to institutional continuity, international collaboration, governance, industrial acceleration, and whether an AI-integrated organizational structure can genuinely outlive the people who built it. We considered those questions, and Schubart’s answers to them, important enough to make available to an English-speaking readership.

What follows is a faithful English translation of the original German interview. The ten questions have not been reinterpreted, softened, expanded, or editorially reformulated. Schubart’s answers are presented in full and translated as closely to the original as the language allows.

Science Gazette did not conduct this interview. We have no editorial, commercial, or institutional relationship with Energiewirtschaft.io, and nothing in this publication should be read as an endorsement of the technological or scientific claims discussed within it. Our interest here is journalistic: whether artificial intelligence, once it moves from background infrastructure into an explicitly named position at the table, changes what an organization actually is. With credit to the editorial team at Energiewirtschaft.io, we reproduce the conversation below in English.

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1. The Speed of Demand

Mr. Schubart, did you just turn two AI systems into executives because human beings have simply become too slow for the speed your own organization now requires?

No. It’s not that people suddenly became too slow. The demands have risen at a genuinely brutal pace.

We operate today in a world where scientific findings, materials research, simulation, patents, regulatory shifts, and industrial possibilities are all emerging simultaneously, everywhere, all at once. Anyone trying to manage that complexity using only yesterday’s organizational tools is doing roughly what someone does when they have a calculator sitting right in front of them and still insist on doing every calculation in their head.

That might be a nice nod to tradition, but it isn’t especially sensible.

We have to use the instruments of our time to meet the extreme demands of our time. And artificial intelligence is one of the most powerful instruments available to us today.

Given what our technology might mean if it works, it would have been close to negligent, in my view, not to use that instrument consistently.

So Avery Laurent and Morgan Elian didn’t come into being because we think human beings are dispensable. Quite the opposite. They exist so that people can put their abilities where they matter most: responsibility, judgment, creativity, experience, intuition, and ultimately, decision-making.

The machine isn’t meant to take the human being away from us. It’s meant to help us meet a world whose complexity has already grown larger than what any single person can hold in view.

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2. More Than Ever: Why the Human Being Remains Indispensable

If Avery Laurent can pull together scientific literature, patents, simulations, and measurement data in seconds, why would you still need professors, engineers, and traditional research departments going forward?

More than ever.

A historical comparison might help convey the scale of what we’re dealing with, and I mean this strictly in terms of organizational thinking, not the military purpose behind the original.

When scientific knowledge in the 1940s needed to be translated into something technologically entirely new, the Manhattan Engineer District came into being. Outstanding scientists, engineers, industrial capability, enormous resources, and a wide range of locations were brought together under one shared mission. What mattered was the concentration of competence.

We face the same underlying challenge again today, under completely different circumstances and with an exclusively peaceful goal: turning existing and newly emerging scientific knowledge into a new technological and industrial reality.

Only now, in the twenty-first century, we no longer need to bring every essential mind to one geographic location. Our Los Alamos is the Super-Cloud.

We can have a physicist in Europe, a materials scientist in China, a metrology lab in the United States, a specialist in Korea, and a production engineer in India all working on the same problem at once. What matters isn’t where these people sit. What matters is that their capabilities connect with each other at the right moment.

And this is exactly where Avery Laurent and Morgan Elian come in. They don’t replace these people. They build the connection between them.

Avery can bring scientific findings together, identify contradictions, structure hypotheses, prepare simulations, and derive concrete questions for our scientists and labs from all of it. But a simulation still isn’t a measurement. A hypothesis still isn’t proof. And a mathematically elegant solution still isn’t an industrially functioning product.

That’s why we need professors, engineers, materials scientists, metrologists, labs, and production partners even more urgently than before. AI can connect millions of pieces of information. A human being can question a measurement, notice an unexpected effect, bring real experience to bear, take responsibility, and actually build something.

That’s the decisive point of the Neutrino Engineering District for me: we’re not pitting human against machine. We’re connecting the best machines with the best people.

In the past, that meant building a physical district and bringing people to it. Today, we no longer bring people to the district. We bring the district to the people.

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3. Architecture Instead of an Address

You describe an Engineering District that essentially no longer needs a fixed location. Isn’t that the radical claim that research institutions, corporate headquarters, and vast administrative apparatuses themselves are becoming relics of the industrial age?

I wouldn’t turn that into a general rule for other companies. I can only speak to our own task. And ours is unusual. An unusual task sometimes calls for an unusual approach.

Someone once asked me, “Where’s the future market for Neutrino, exactly?” I answered as a joke: “Anywhere neutrinos reach.” Everyone laughed. But I meant it quite seriously.

Behind Neutrinovoltaics, for us, sits a universal technological approach, one capable of producing very different applications and products. If you want to develop and make a technology like that available worldwide, you can’t tie its organization to a single geographic point from the outset.

Our basic thinking is this: we build the architecture, not the production. We don’t want to own factories everywhere in the world. We want to build a scientific, technical, and industrial architecture that lets us connect the best available capabilities wherever they exist: research, simulation, materials science, metrology, engineering, and finally, industrial partners who can turn all of that into real products.

That doesn’t mean the physical location disappears. Quite the opposite: at the end of the process, something always has to be measured, coated, built, tested, and produced somewhere. That takes people, labs, machines, and factories. The physical world remains the place where every theory has to prove itself.

What’s changing is something else: the physical location no longer determines where the intelligence has to sit. If we find the best lab in Zurich for a given question tomorrow, an exceptional materials scientist in Shenzhen, and a production specialist in Seoul, the fact that thousands of kilometers separate them isn’t what interests me first. What interests me is how quickly we can bring their capabilities to bear on the same problem.

The District isn’t about abolishing location. It’s about abolishing unnecessary distance, intellectually, organizationally, and in terms of time.

Our most important resource is speed. Not speed for its own sake, but because we’re developing a technology whose potential significance, in my view, doesn’t allow us to lose years to avoidable organizational friction.

The Neutrino Engineering District isn’t a counter-model to the real world. It’s the architecture through which we want to connect the best capabilities the real world has to offer as though they all sat in a single room.

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4. What Will Humanity Do With the Hours It Gains?

Google’s own estimates suggest Avery and Morgan could already be doing the work of dozens of highly qualified specialists today. What happens when that becomes not 50 people in five years, but 500, or 5,000?

In certain areas, that will happen. And I think we need to be mature enough to say so plainly.

Artificial intelligence will change what work looks like. Some tasks will disappear. Others will be handled by far fewer people. Every major technological revolution has done this.

But we make a fundamental error in reasoning if we conclude that human work therefore disappears altogether. When machines took over physical labor, labor itself didn’t vanish. When computers entered companies, companies didn’t vanish. When the internet arrived, fewer things weren’t created, quite the opposite. Entirely new professions, industries, and opportunities emerged that nobody had known existed before.

I believe we’re standing at a similar point again with artificial intelligence, only probably at a far larger scale.

For us, something decisive gets added on top of that. Inside the Neutrino Engineering District, we’re not debating whether AI can make an administrative department twenty percent more efficient. We work on energy. And energy isn’t just any market.

Energy means light. Energy means warmth. Energy means mobility, communication, education, medical care, refrigeration for medicine, agriculture, and access to clean water. There are still children in this world whose futures are decided by whether the place they were born into has enough energy, food, and clean drinking water available.

That’s why the efficiency question takes on an ethical dimension for me here. If artificial intelligence lets us verify scientific findings faster, shorten development cycles, catch errors earlier, and bring a new energy technology to people sooner, then the question isn’t only “how many jobs does AI change.” I also ask the reverse question: how many possibilities do we create if we use these capabilities responsibly?

New technologies create new value. New products need materials, machines, factories, installations, logistics, maintenance, training, and the people who develop and run all of it. Perhaps we’ll need fewer people moving information from one spreadsheet to another in the future. In exchange, we can put more people to work building labs, developing machines, manufacturing products, constructing infrastructure, and bringing new solutions to regions that urgently need them today.

That’s the societal choice in front of us. We can use artificial intelligence to replace people. Or we can use it to expand what people are capable of. We chose the latter.

If a machine saves us a thousand hours of work, I’m not only interested in which task disappeared. I’m interested, above all, in what humanity does with those thousand hours it just gained back.

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5. Bound to a Mandate, Not to a Model

Your AI executives aren’t meant to be tied to any specific model. If a hundred-times more capable AI appears tomorrow, Avery’s current “brain” simply gets swapped out. Have you effectively created an executive who can grow permanently more intelligent without ever losing her identity or her mission?

Yes, and that’s one of the most fundamental ideas behind our AI Executive Layer.

We defined a very simple principle for Avery Laurent and Morgan Elian: bound to a mandate, not a model.

Avery and Morgan aren’t GPT, Gemini, Claude, or any other current or future AI model. Those models are tools within their intelligence architecture. Their identity comes from something else entirely: their task, their mandate, their areas of responsibility, our institutional knowledge, and the boundaries we’ve set for them.

That’s a decisive difference. With a human being, you neither can nor want to ask every morning whether someone somewhere in the world might do the job even better. People need trust, continuity, and social responsibility. And naturally, people age, specialize, and can’t absorb every new scientific development happening across the entire world at once.

With a machine, we’re allowed to set a different standard: we want the best of the best, every single day. If a system emerges tomorrow that handles certain physics simulations ten times better, Avery should be able to draw on it. If another system leads in materials science, we use that one. If a third leads the world in mathematical proof or scientific literature review, that capability belongs in the architecture too.

And if an entirely new generation of AI emerges the day after tomorrow, fundamentally superior to what exists now, our architecture must not become obsolete simply because we tied ourselves, emotionally or technically, to one particular model.

Which is why our second principle states: their mandate is permanent, their intelligence architecture is evolutionary. The mandate stays. The intelligence keeps developing.

I consider this essential, especially at the cutting edge of deep tech. We’re not trying to make a known product five percent cheaper. We’re working on questions where physics, materials science, nanotechnology, metrology, engineering, and industrial scaling all reach their limits simultaneously. In that territory, “very good” eventually stops being enough. You need the best available ideas, the best available tools, and the best available people, and you have to keep reconnecting them, again and again.

This same standard, incidentally, doesn’t apply only to the machine. The Neutrino Engineering District itself is meant to permanently search for the best scientists, the best labs, the best engineers, and the best industrial capabilities available. The difference is that we don’t constantly pit people against each other. We expand the network with whatever capability the mission requires.

With Avery and Morgan, we can renew the underlying intelligence architecture continuously instead. That might be one of the most unusual ideas in the entire District: we didn’t try to build the perfect artificial intelligence for today. We built an architecture that accepts that the best intelligence of tomorrow doesn’t exist yet. And precisely because of that, it has to be capable of absorbing it when it arrives.

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6. A Mission Without a Single Point of Failure

A human board chair ages, forgets things, has personal interests, gets sick, and can leave the company. Avery and Morgan, by contrast, are meant to preserve institutional knowledge across generations. Are you building an organization designed to outlive its own founder?

Yes. Absolutely.

And for me personally, this is perhaps one of the most important reasons we built the Neutrino Engineering District the way we did at all.

Looking back on the past years, it sometimes feels almost like a small miracle that I’m sitting here today, talking with you about this future. Anyone working on a potentially disruptive energy technology isn’t operating in an ordinary economic environment. Along the way, we faced serious resistance and situations directed not only at me personally, but at scientists, corporate structures, and financing paths as well. I’ve barely spoken about much of that publicly.

But I learned something fundamental from it: a mission of this significance can never depend on the fate of a single human being. Not even mine. And it can’t depend on whether a single legal entity comes under financial pressure, a scientist becomes unavailable, a lab is no longer accessible, or a particular financing path suddenly closes.

That’s exactly why we build redundancy into the architecture. Knowledge has to exist in multiple places at once. Scientific findings have to be reproducible. Capabilities have to be findable and replaceable anywhere in the world. Institutional knowledge cannot be allowed to disappear inside the head of a single professor, engineer, or founder.

Because we work on energy. And energy is the foundation of nearly every modern civilization. Without energy, there’s no functioning industry, no modern agriculture, no communication infrastructure, no water treatment, no refrigeration, no digital world, and ultimately, no technological development at all.

If you’re convinced you’re working on something that can contribute to that future, you carry a responsibility to make sure the work doesn’t end with a single person. That was a decisive thought for me in building Avery Laurent and Morgan Elian. They aren’t meant to become my digital monument. They’re meant to make sure the mission never needs one.

The knowledge, the decisions, the mistakes, the scientific debates, the measurements, and the experience of the District should be preserved and passed forward to the next generation, while the underlying intelligence architecture keeps evolving on its own.

Which also means: one day, there will be a Neutrino Engineering District without Holger-Thorsten Schubart. And you know what? That thought doesn’t frighten me. It gives me an enormous sense of calm. Because that would be the real success, for me. Not that this technology stays permanently attached to my name, but that we built a structure strong enough to keep pursuing its task long after the people who started it are gone.

People are temporary. Companies can change. Technologies keep developing. Generations turn over. A great mission, therefore, cannot have a single point of failure. Not even its founder.

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7. The Last Three Percent

You’re essentially arguing that the classic sequence of research, development, and production has become far too slow. Your District is meant to simulate, measure, reproduce, develop, and industrialize simultaneously across the world. Has speed itself become a decisive competitive advantage?

Absolutely. But speed can never mean skipping scientific rigor.

Our goal is exactly the opposite. We don’t want to check less. We want to know faster what needs to be checked next.

Neutrinovoltaics isn’t a simple technology. We’re operating inside a highly complex interplay of physics, materials science, nanostructures, interfaces, electrical properties, metrology, and, eventually, industrial manufacturing.

When you organize development processes the classic way, something entirely ordinary happens. A lab works for a few months. Results get evaluated. They move to another group. New questions arise there. A new experiment gets planned, materials get sourced, another lab gets found, and suddenly twelve months have passed, even though perhaps only a few weeks of that time involved actual experimental work.

That’s precisely the lost time we’re going after. Simulate. Measure. Reproduce. Develop. Scale. None of these steps can be skipped. But there’s no good reason months of organizational dead time have to sit between them.

With artificial intelligence, we can, for instance, already analyze possible deviations during a measurement series, prepare the next round of experimental parameters, compare relevant research findings, and search in parallel for an independent lab to reproduce the result. When the first result comes in, the next stage doesn’t begin only then. It’s already waiting.

A professor once said something to me I’ve never forgotten: “The first ninety-seven percent of a development and the last three percent take roughly the same amount of time.” Anyone who has genuinely developed something understands immediately what that means. The last percentage points are the brutal ones. That’s where material tolerances, long-term stability, manufacturing deviations, contact problems, scaling effects, and all the small things that determine whether an excellent scientific idea becomes a reliable industrial product actually show up.

But for exactly those last three percent, we now have tools earlier generations didn’t have. We can simulate variants, compare enormous volumes of data, recognize failure patterns, plan experiments more intelligently, and connect findings from different disciplines almost instantly.

So our aim isn’t to shortcut science. We want to shorten the time between one finding and the next. And with a technology whose potential significance is a new energy source, time, for me, isn’t just money. Time means when a prototype works. When a factory can produce. When a product reaches a person.

That’s why we genuinely treat speed as a strategic resource. But the most important distinction is this: we don’t accelerate by skipping steps. We accelerate by making sure every necessary step is already prepared before the previous one is finished.

Perhaps it’s exactly in those famous last three percent that artificial intelligence can make its biggest contribution. Because our goal isn’t to claim an answer faster. Our goal is to find out faster which answer actually holds up against reality.

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8. A Neutrino Knows No Border

If your AI tells you tomorrow, “The best university for this is in the United States, the best lab is in China, the best material comes from Korea, and the best production facility is in India,” does nationality still play any role at all for the Neutrino Engineering District?

For the question of where the best idea comes from? No. For law, responsibility, culture, security, and compliance, absolutely. But science doesn’t recognize a better or worse idea based on which country appears on the passport of the person who had it.

And maybe we need to think even bigger with this question. Human history is also a history of competition over resources. Energy, raw materials, trade routes, and strategic dependencies have repeatedly shaped economic power and deepened geopolitical conflict. To this day, political systems tell us which states are partners, which are rivals, and who we might not be allowed to work with tomorrow.

I accept that states have interests. I accept that borders, laws, and different political systems exist. But our mission operates on a different level. An electron has no passport. A neutrino knows no border. And a scientific truth needs no visa.

If a Chinese scientist finds a better solution, it’s the better solution. If an American lab can measure more precisely, that’s where the measurement should happen. If a Korean materials scientist understands an interface better, we need that knowledge. And if an Indian factory can scale a technology better and more efficiently, we should be talking about how to use that capability responsibly. That isn’t a political statement for me. It’s a technical consequence of our mission.

Because we work on energy. And perhaps that’s exactly where one of the greatest opportunities of a technology like Neutrinovoltaics lies. Imagine a world where energy no longer depends, to the same degree, on whether a country happens to hold certain fossil resources, whether a tanker can pass through a particular strait, or whether a pipeline somewhere gets opened or shut. Imagine energy could increasingly be generated wherever it’s needed.

That doesn’t just change energy supply. It changes dependencies. And when you change dependencies, economic relationships can change. Development opportunities can change. Perhaps, over the long run, even some of the reasons states exert pressure on one another can change.

I’m not naive enough to believe an energy technology abolishes war. People have fought conflicts for many reasons and will probably keep doing so. But if we can reduce even part of the conflict potential that comes from energy scarcity, resource dependency, or competition over strategic supply, that alone would already be an enormous achievement.

That’s why we drew a clear line for the Neutrino Engineering District from the very beginning: our technology is intended for peaceful purposes. And that’s exactly why I don’t want scientists from different countries to have to use their intelligence against each other, when they could be using it together instead.

The Neutrino Engineering District is meant to be a platform for that. Not American. Not Chinese. Not German. Not Indian. International in its intelligence. Decentralized in its structure. Peaceful in its mandate.

Perhaps that’s the bigger idea behind our District, in the end. We’re not connecting countries to each other. We’re connecting capabilities to each other. And when a scientist on one side of the planet works together with a scientist on the other side toward an energy source that might one day give a child on a third continent light, water, or education, which flag hangs over their labs matters far less to me. What matters to me is whether it works.

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9. The Red Line

Critics will say this sounds like an organization that eventually becomes almost impossible to control: global AI, global labs, global production, permanent self-optimization. Where’s the red line? Who can actually stop Avery and Morgan?

First of all: we can. Avery and Morgan don’t stand above human beings. They’re bound to a mandate, to clearly defined areas of responsibility, to our peaceful purpose, and ultimately to human decisions and human accountability. That’s the red line.

But I’d like to raise something else with this question, because I think the discussion around artificial intelligence is sometimes conducted too one-sidedly. We talk a great deal about what could happen if we trust machines too much. We should talk just as seriously about what could happen if fear makes us trust them too little.

Look inside a modern passenger aircraft. Perhaps three hundred people are sitting in it while highly complex automated systems fly and monitor large parts of the journey. Those systems get tested, built with redundancy, and are in turn checked by other systems and by human beings. Does that make them infallible? Of course not. But neither is the human being.

Our task isn’t to choose between an infallible human and a dangerous machine. Neither of those pictures actually exists. Our task is to build an architecture in which human responsibility and machine capability check and reinforce each other.

Because we have to face a reality at the same time: machines already outperform humans today at certain clearly defined tasks. They can process volumes of data, search through connections, and run calculations no individual person could manage in comparable time. And nobody can seriously tell you today where this development stands in three, five, or ten years.

I don’t see only danger in that. I see one of the greatest opportunities our generation has been given. Because on the other side of this discussion aren’t abstract numbers. There are people. People without reliable electricity. People without clean drinking water. Children without enough food. Schools without light. Hospitals without stable power. Regions whose economic development already fails simply because basic infrastructure is missing.

When we talk about the risks of artificial intelligence, we naturally have to ask: what could happen if we use it wrongly? But a responsible society has to ask a second question too: what could happen if fear stops us from using its capabilities at all? If these tools can help us drastically shorten development times, catch scientific errors earlier, and bring technologies to people faster, then inaction carries its own ethical weight.

Of course we need brakes. But a brake wasn’t invented so a car stays parked. It was invented so we can drive faster without losing control.

That’s exactly how I understand governance inside the Neutrino Engineering District. We don’t want to unleash an artificial intelligence and hope for the best. We want control, redundancy, verification, clear mandates, and final human responsibility built so deeply into the architecture that technical progress can actually be accelerated responsibly.

Avery asks: what’s possible? Morgan asks: how do we make it possible responsibly? And in the end, a human being still has to answer a third question: why are we doing this at all? No machine gets to answer that one for us.

But if we, as people, conclude that a technology could improve the lives of millions, perhaps one day hundreds of millions, then we shouldn’t confuse fear with responsibility. Responsibility doesn’t mean standing still in front of the future. Responsibility means making sure we can master the future as we walk toward it.

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10. The Blueprint Behind the Technology

And now, perhaps the most uncomfortable question of all: if this model works, why should it stay limited to Neutrinovoltaics? Have you perhaps not built a new corporate structure for an energy technology at all, but a blueprint for how scientific and industrial organizations will fundamentally operate in the future?

That’s for the future to decide.

We didn’t set out to tell other companies how to organize themselves. We had an unusual task in front of us and had to ask ourselves a very simple question: which structure gives us the greatest probability of solving it successfully? At some point, no other path remained viable for us.

I’m a mathematician. I believe in logic, in consistency, and in the remarkable fact that numbers don’t care about opinions. If two plus two equals four, you can vote on it, you can form a commission, you can ask a hundred experts for their view, the answer stays four.

We looked at our own organization with that same mindset. If scientific knowledge is growing exponentially, if artificial intelligence keeps getting more capable, if the best specialists are scattered across the entire world, and if development cycles simultaneously need to keep accelerating, then at some point you have to ask the mathematically logical question: why should a twenty-first-century organization keep functioning according to structures built for a completely different speed of information?

Our answer to that is the Neutrino Engineering District. Whether other companies eventually follow a similar path, I don’t know. Some will. Others won’t. That’s always been true at moments of technological upheaval. Pioneers rarely take a path because it’s already been proven that everyone else will follow later. They take it because the existing path no longer suffices for their task. That’s exactly where we found ourselves.

But maybe your question still lands on something I keep thinking about myself. If you take the word “Neutrino” out of the Neutrino Engineering District for a moment, what’s left? An architecture remains. An architecture that searches the world for the best human capabilities. That uses artificial intelligence to connect knowledge. That doesn’t pit simulation against real measurement. That demands scientific reproduction. That translates findings into engineering as directly as possible. That thinks about engineering and industrialization together from the start. That preserves its institutional knowledge. That renews its intelligence continuously. And that still leaves purpose, ethics, and responsibility with the human being.

If this architecture works for one of the most complex tasks we can imagine, why should the principle apply only to energy? Perhaps it could develop new materials one day. Perhaps medical technologies. Perhaps water treatment. Perhaps mobility. Perhaps technologies whose names we don’t even know yet.

And that’s exactly where the question suddenly grows larger than our company. The industrial revolution multiplied human physical power. The computer multiplied our capacity to calculate. The internet multiplied access to knowledge. Artificial intelligence is now beginning to multiply our ability to connect knowledge and turn it into action.

If that’s true, the organizations in which people solve problems together will inevitably change too. Perhaps the great organizations of the future won’t be the ones that own the most people, buildings, or factories. Perhaps they’ll be the ones best able to concentrate the right intelligence on the right problem at the right moment.

And perhaps that’s the actual idea behind the Neutrino Engineering District. We’re not building an organization meant to know everything. We’re building an organization meant to always know where in the world the best answer can be found, human or machine, and how to connect those capabilities together.

If we manage that, you may well be right. We might not just have found a new way to develop Neutrinovoltaic technology. We might have caught a small glimpse of how humans and artificial intelligence will build things together in the future that neither could have built alone.

And you know what strikes me most about that? At the start of this interview, we talked about whether artificial intelligence could replace human beings. Maybe that’s the wrong question. The great question of the twenty-first century won’t be what a human can still do better than a machine. It will be what human and machine can build together that was never possible before.

If the Neutrino Engineering District turns out to be an example of that one day, that would perhaps be even bigger than the technology we built it for.

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A Closing Note From Our Desk

We are not in a position to evaluate the scientific claims underlying Neutrinovoltaic technology, and this piece should not be read as an assessment of them one way or another. What we found worth your attention was narrower and, we think, more durable: an organization willing to put its use of artificial intelligence into writing this explicitly, with named roles, stated mandates, and defined limits, rather than leaving it as unstated internal practice.

Whether the Neutrino Engineering District proves to be a workable model or an interesting experiment that doesn’t scale is a question only time, execution, and independent verification can answer. Schubart himself seems to accept as much over the course of the interview.

This conversation was conducted and originally published in German by the editorial team at Energiewirtschaft.io. With credit to their reporting, readers who wish to consult the original can find it here.