I am disappointed with the state of space technology. By all reasonable projections of the 20th century, we should be a spacefaring civilization by now, yet we are still far from it. What happened — and more importantly, how can we still achieve that goal in our lifetimes?

I grew up with pictures of futuristic rockets launching, leaving the ground, piercing the skies.

Pictures on the wall of my grandmother’s living room.

Where other families displayed photos of their grandchildren, she proudly framed test flights of orbital launchers designed by my uncle, Lutz Kayser. In 1975, Lutz founded OTRAG — the world’s first private space launch company. From a vast test site in Africa (a territory the size of Austria), he launched his low-cost rockets skyward.

To me, it seemed inevitable that by the time I grew up, space travel would be routine. We’d be commuting to orbit, docking with massive space infrastructure lofted by my uncle’s rockets. Cities in space felt imminent — and it was clear that private companies would be the ones to build this vast ecosystem.

It was not to be.

OTRAG collapsed under intense geopolitical pressure. Governments wanted to own access to space, and they did. The end of the Cold War ushered in a more modest era of spaceflight, and innovation in the sector stagnated for decades. Space became a self-fulfilling bureaucracy. Only in the late 1990s did the tide begin to shift — driven by bold entrepreneurs who had made their fortunes elsewhere and were now foolish enough to invest in their dreams of space.

Foolish enough to try — yet determined enough to succeed.

Today, in the age of SpaceX, we are beginning to dream big again. A new generation of thinkers, builders, and inventors are reviving the ambitions once championed by visionaries like Jules Verne, Tsiolkovsky, Oberth, Sänger, Korolev, von Braun (who sat on OTRAG’s board), Arthur C. Clarke, and countless authors of texts which we call “science fiction” — fiction often so technically sound it reads more like a masterplan than a story.

Konstantin Tsiolkovsky famously said: “The Earth is the cradle of humanity, but one cannot live in a cradle forever.”

But leaving the cradle is hard — not for lack of vision, but because of economics. Dreams are inspiring, but at some point, value to humans is measured in money. Launch costs have come down dramatically, thanks to SpaceX and its first example of a functioning large-scale space economy: the Starlink constellation. Yet for most who follow in their footsteps, the barrier to entry remains forbiddingly high. Space is unforgiving — and inherently costly.

Unless we can design, build, and deploy systems — rockets, space stations, extraterrestrial infrastructure in general — rapidly and affordably, and offer real value for humanity in return, today’s space enthusiasm risks fading out again.

It’s less a question of whether it makes economic sense to extend civilization to space — of course it does. There’s infinite energy, infinite natural resources, ideal conditions for advanced manufacturing — alongside everything we’ve already grown dependent on: space-based navigation, observation, communication.

Once we have a commercially self-sustaining off-planet ecosystem in place, we will take extraterrestrial resource access for granted — just like we cannot imagine a world without global satellite navigation.

But at the core of this challenge lies the enormous amount of engineering required to design spacecraft and off-planet infrastructure. Cold War-era efforts were fueled by national pride, security concerns, and massive budgets. Hundreds of thousands of engineers were mobilized to beat the other guy to the Moon. And, somewhat foolishly, governments today are trying that trick again with varying amounts of success.

But that model isn’t sustainable. Today, it’s not a sprint to a singular goal — it is a long-term, never-ending effort to extend human civilization skywards. There is no “winning party.” The winner will be humanity itself.

But how do we get over this seemingly insurmountable hurdle of having to finance all the manpower that will have to go into engineering?

There’s another way.

For the last decade, I have been asking myself a single question: why is engineering considered something that is laborious, hard, and slow? Is there a way we can engineer differently — faster, more consistently — tapping into a field’s entire body of engineering, not just the fragmented, often tribal knowledge inside an engineer’s head?

Could we preserve engineering knowledge systematically? No mistake should be made twice. No insight should vanish in someone’s notebook. Can we not turn engineering into a process that compounds — like software?

Engineering has not followed the same exponential curve as information technology. While modern rockets are impressive, they don’t differ radically from those of the 1960s, and the effort to design them is quite similar. Meanwhile, a computer from the Apollo or Energia era is a laughable antique. Nobody would use one today — yet we’d still happily fly on a Saturn V (I would!).

Computers are powered by microchips, and microchips are arguably the most complex machines ever devised by man. And yet, we can design a custom chip in a matter of weeks. So why, in contrast, does it still take years to develop a new rocket engine?

The difference lies in how engineers work.

Engineering remains trapped in a visual paradigm. Today’s Computer Aided Design tools are powerful, but they still enforce a process as old as the ancient Romans: the engineer imagines the design — and then draws it by hand, in the past on paper, today on a computer screen.

This was also how microchips were designed — until the 1970s. First by hand, then with CAD. But as chips became more complex, it became impossible to draw them manually. This forced a radical shift: instead of sketching, engineers began to encode the logic of chip design into software. Design became computational. Chip structures were no longer laboriously drawn — they were computed by algorithms based on high-level input. That is why modern microprocessors can be created in days.

That same transformation now needs to happen in every field of engineering.

But currently, engineers don’t work this way. Even with today’s sophisticated CAD tools, the burden of reasoning, physics, and logic still rests on the engineer’s brain. There’s no consistent method for preserving or sharing this knowledge. We rely heavily on individual experience, undocumented intuition, and trial and error. And some of that experience goes into retirement and vanishes with the individual.

Why can’t engineers, instead of translating their knowledge visually into a 3D drawing, instead abstractly explain the reasoning behind their designs? Engineers follow a logical process while they build something — why can’t they transcribe that process and then re-run it in seconds instead of redoing it manually in weeks?

At LEAP 71, we have spent the last few years building this new kind of system — a system that encodes engineering knowledge, physics, logic, manufacturing constraints, and practical lessons into computational models. The goal is to go from high-level requirements to manufacturable geometry, documentation, machining instructions, and test protocols — all using a deterministic and reproducible process. Rooted in engineering expertise, science, manufacturing experience, and practical feedback.

Some have called our system “the first AI that builds machines.” But that framing is misleading. It evokes black-box neural networks that hallucinate answers. Our approach is not that. It’s a computational system. The same input always yields the same result. Every step is explainable. Every assumption is documented — and, ideally, any assumption is replaced over time by validated test data and better algorithms.

While it has taken us many months — years, by now — to build up these rules, the execution of the system takes only minutes. Over the past two years, we’ve applied it to many fields, from electric motors to micromechanics. But the field we are most active in — probably driven by family history — is rocket propulsion. Over the course of 12 months, we hot-fired a new thruster design on average every four weeks, including a 5 kN Aerospike engine, which some regard as the hardest proof of engineering prowess.

After a rocket test, we often have a new engine version by the time we drive back from the stand, recomputed with freshly calibrated coefficients derived from real-world data.

Computers excel at computation. The key is to encode the design logic once, then reuse it effortlessly. With this approach, engineering becomes fast, iterative, and reliable — exactly what we need in space.

In fact, it brings us back to how space engineering once was: fast-moving, iterative, and rooted in physical testing. In the 1960s and ’70s, engineers often spent a lot of time in the dust near a test stand. Without modern tools, theory only got you so far — you had to build, test, and learn. And do it again and again… Perhaps today, people spend too much time in the comfort of their numerical simulation data. Why not build and test? Reality is the ultimate feedback loop. But I understand the hesitation: if you spend months in CAD to design something, it better work the first time.

Ironically, by embracing computational models, we can return to this rapid experimentation cycle. Iteration is no longer slow or expensive. Innovation depends on iteration — the mother of invention. By designing quickly, building things, failing fast, and consistently encoding our learnings for everyone to see, we can build machines that have never been imagined before. Engineering can become almost playful. We will not imitate the 1960s — we will design the 21st century.

There’s nothing in physics that prevents us from building large space stations or mining asteroids. But economics might — at least in the short term. How can we bootstrap our way to space instead of needing a NASA budget from a bygone era?

The key factor is how quickly we can go from an idea to a testable prototype. If that takes years and only works for proven concepts, then the economics collapse and require a Jeff Bezos budget. If it takes weeks or months, and supports rapid experimentation, then all bets are off.

Launch costs will continue to drop, especially as emerging competitors — many embracing Computational Engineering — accelerate development. Yet, you cannot compete with SpaceX by copying SpaceX. You can’t catch the leader by running at the same speed. You have to be more nimble. Using computers for computation is infinitely faster and more powerful than using them as a manual sketchpad.

That’s the core of Computational Engineering.

At the beginning of the computer revolution, people could not imagine how fast things would change under Moore’s Law of exponential speed. Nowhere is this more apparent than when reading a science fiction novel from that time. While the authors envisioned nuclear propulsion and orbital space stations in the 21st century, they found it unimaginable that computers would have replaced pen and paper, that communications would be digital, that all information would be accessible everywhere. It’s hard to imagine exponential speed — which is the compounding effect of a computational approach.

By consistently applying this paradigm to engineering, we can move the evolution of our physical world under that accelerating curve which made Gordon Moore famous.

Once that happens, then, in a few decades, we will be building things that today’s most imaginative writers are failing to predict.

There’s never been a better time to build for space.