Starlab Space<\/a> is a US-led global joint venture and partner network that is ensuring a continued human presence in low-earth orbit. Led by Voyager Technologies, Starlab is the most advanced commercial space station under development, bringing together decades of ISS experience, Al-enabled design and global partnerships into a platform designed to scale science and industry in space. From manufacturing and life sciences to defense applications and international collaboration, Starlab is built for real work, real discovery and real continuity, providing a seamless transition from the International Space Station to the new commercial space station era.<\/p>\nClick here for Notes and Transcript<\/p>\n
Time Markers<\/p>\n
00:00 \u2013 Episode introduction
00:39 \u2013 Juan Alonso\u2019s background and role at Luminary Cloud
00:53 \u2013 What computational fluid dynamics is and why it matters
03:06 \u2013 The rise of physics-driven AI in simulation
05:73 \u2013 Risks, accuracy, and the limits of algorithmic design
07:53 \u2013 Silicon Valley vs. global innovation ecosystems
10:10 \u2013 Childhood inspirations and early exposure to aerospace
12:35 \u2013 Hypersonics, national defense, and technological competition
15:31 \u2013 Collaboration with Northrop Grumman
17:04 \u2013 Blurred lines between atmospheric and orbital tech
18:37 \u2013 The next big leaps in aerospace engineering
21:20 \u2013 Society\u2019s and policymakers\u2019 ability to keep pace
23:19 \u2013 The data revolution and why it matters
24:21 \u2013 What\u2019s next? Where are the opportunities?<\/p>\n
Transcript \u2013 Juan Alonso Conversation<\/p>\n
This transcript has been edited-for-clarity.<\/p>\n
David Ariosto \u2013 Juan Alonzo, you are the founder and director of the Aerospace Design Laboratory, and also the co-founder and CTO of Luminary Cloud. It\u2019s so great to have you on the program.<\/p>\n
Juan Alonso \u2013 It\u2019s a real pleasure to be with you, David.<\/p>\n
David Ariosto \u2013 You know, maybe just to start us off, to kind of give the audience who aren\u2019t perhaps experts \u2014 some of them may be, but perhaps not all \u2014 a sense of computational fluid dynamics and the growing need for simulations, for analysis and design, and how all this plays into performance, IP issues, adaptation, and the mountain of things you\u2019re doing at Luminary and at Stanford more broadly. Give us a little primer on what Luminary is and what you do. What\u2019s the nature of computational fluid dynamics and how is it changing? I realize there\u2019s a lot wrapped in there, but maybe let\u2019s start with Luminary. What is it?<\/p>\n
Juan Alonso \u2013 Yeah, Luminary is a company that was created to give customers the end-to-end capability to generate physics AI models. So these are models of the physical world, like computational fluid dynamics, that can be way faster and just as accurate as the models we\u2019ve been using for a long, long time.<\/p>\n
So you asked about computational fluid dynamics. Computational fluid dynamics is nothing but solving the equations of how air flows around, let\u2019s say, a rocket during launch, or inside a rocket engine or a thruster, as we\u2019ll talk about later today \u2014 how to understand all of that through numerical simulation inside a computer.<\/p>\n
Of course, that\u2019s had tremendous impact in our industry for a very long time, but frankly, at Luminary, we believe it\u2019s slowing down the process \u2014 the fact that those simulations take a long time to complete. So at Luminary, we\u2019re generating tremendous amounts of data very quickly, and then turning those simulations, through what we call physics AI, into models that are just as accurate but way, way faster.<\/p>\n
Eventually, the objective of the company is to accelerate design and certification processes so we can deploy much more capable systems more quickly, allowing us to react faster to commercial and defense threats \u2014 and much more.<\/p>\n
David Ariosto \u2013 This is why I found your company so interesting, because it\u2019s almost like you\u2019re the tip of the spear in this new paradigm shift \u2014 the nexus between AI, machine learning, new nanofabrication techniques coming online \u2014 all of which promise an iterative approach to aerospace unlike anything we\u2019ve seen before.<\/p>\n
I find your perspective especially interesting because of the long history of academia, the commercial sector, and defense all working together. I want to explore that ecosystem \u2014 because on one hand we have this brave new era of technologies, and on the other, it\u2019s a tried-and-true recipe for advancement throughout the years, right?<\/p>\n
Juan Alonso \u2013 I think that\u2019s a fair characterization. Engineering has always been a data-driven discipline. From the early days of splines, we\u2019d put data points together in some way and fit models through them with straight or curved lines.<\/p>\n
Over the last 10 years, the amount and availability of data has exploded \u2014 nothing like what we used to have. At the same time, the world of AI \u2014 not for the physical world, but for other applications \u2014 has taken the world by storm, together with a revolution in machine-learning methods that are very different from what we used to use.<\/p>\n
Some ideas came from academia, some from leading companies trying to incorporate these methods into design, and some from collaborations between startups like us and large companies like Northrop Grumman. It\u2019s taking the world by storm \u2014 the ability to query the physical world and ask, \u201cHow is this rocket going to perform?\u201d and within a second or two get an answer, when we used to wait 10 to 12 hours for just that.<\/p>\n
David Ariosto \u2013 Does that give you pause \u2014 the increased reliance on simulations and algorithms that inform design and processes? The speed is remarkable, and the level of detail you can scrutinize is incredible. But is there a part of you that wonders whether we might rely too much on this?<\/p>\n
Juan Alonso \u2013 Over the last 15 years, numerical simulation and high-performance computing have made simulations more accurate. But no simulation is perfect. Sometimes we miss things in the real world. We know simulation plays a big role in design, but we often still need tests to verify predictions.<\/p>\n
The physics-AI revolution takes us to the next level by adding more data \u2014 both simulations and experimental data \u2014 into a single model. It can be better than simulation alone, more credible, and give more confidence in predictions and designs.<\/p>\n
So yes, I\u2019m always concerned about accuracy and uncertainty in simulations, but I\u2019m hopeful because the new paradigm lets us reduce those risks by combining data sources.<\/p>\n
David Ariosto \u2013 What about the physicality of location? In this modern era, the world has shrunk, yet proximity still seems to matter \u2014 like with Taiwan\u2019s semiconductor hub. Could you do what you\u2019re doing outside Silicon Valley? Do physical meetings, proximity, and time zones matter for a startup like Luminary?<\/p>\n
Juan Alonso \u2013 That\u2019s an extremely interesting and insightful question. Silicon Valley is a unique place. Talent comes together in ways that allow giant leaps \u2014 cloud computing, GPU computing, physical analysis, security, data visualization \u2014 all these disciplines have to come together.<\/p>\n
But talent also exists globally. The Zoom revolution lets us bring in that talent to complement the team.<\/p>\n
David Ariosto \u2013 Talent is global, but it also comes with shared stories. Many people enter aerospace because of early experiences. I know you wanted to be an astronaut. Your father worked in aerospace. These childhood impressions matter. Walk me through that.<\/p>\n
Juan Alonso \u2013 I always wanted to be an astronaut. As a young child, I devoured books about space. Lots of Asimov, pictures of the U.S. space program, the competition with the Soviet Union. Jules Verne. My mother indulged me \u2014 bought me a new book every week.<\/p>\n
My father worked for an airline \u2014 not a pilot, but in charge of scheduling. We had many friends who were pilots. As a six- or seven-year-old, I\u2019d fly from the Dominican Republic, where I was born, to Spain, in the cockpit of a 747. You can imagine how transformative that was.<\/p>\n
But at age eight, I realized two things: I wore glasses, and I lived in a country without a space program. So I decided the next best thing was to design these systems.<\/p>\n
David Ariosto \u2013 Isn\u2019t that interesting? That was then \u2014 but now dozens of countries have space programs, and access to orbit is more accessible than ever. It feels like we\u2019re at the onset of a new age \u2014 maybe even a factory floor in orbit.<\/p>\n
But you also mentioned the Soviet Union and the first space race \u2014 and how today\u2019s competition is with Beijing. Hypersonics were pioneered in the U.S., but others have leapfrogged ahead. On battlefields in Ukraine and in programs in Iran and North Korea, hypersonics are proliferating.<\/p>\n
It seems that to push the envelope \u2014 not just in development but also in response \u2014 computational simulations and design are paramount. Can you speak to that?<\/p>\n
Juan Alonso \u2013 We are much closer to space than ever. Our students build satellites, put them in orbit, and operate them from the building we\u2019re in.<\/p>\n
On hypersonics and systems with national-defense implications, yes \u2014 it is a race. People recognize the value of these systems and the advantages they confer. We need to design systems more intelligently and more quickly. That\u2019s what simulation enables \u2014 and what physics AI accelerates.<\/p>\n
Our collaboration with Northrop Grumman is an example of rethinking how to embed new technologies into engineering workflows to explore more design space and come up with better solutions faster.<\/p>\n
David Ariosto \u2013 Let\u2019s talk about the collaboration with Northrop.<\/p>\n
Juan Alonso \u2013 Luminary aims to be the first company providing end-to-end capabilities for physics AI. This involves generating data through numerical simulation and absorbing data from physical testing, then training models that the entire organization can query instantly.<\/p>\n
Northrop Grumman is one use case \u2014 in the aerospace and space side. But we\u2019ve also worked on aircraft, industrial manufacturing, turbomachinery, automotive, and more.<\/p>\n
David Ariosto \u2013 It also strikes me that the line between orbital and atmospheric technologies is blurring. These used to be distinct worlds, but now the ecosystems around them feel more interconnected. Is that your sense?<\/p>\n
Juan Alonso \u2013 Yes, that\u2019s a good observation. The fundamentals are similar across commercial aviation, hypersonics, space-access systems, and re-entry. If you have the capability to ask how a system will perform without ever building it \u2014 accurately and quickly \u2014 then you can apply that capability across disciplines.<\/p>\n
It all comes down to physics.<\/p>\n
David Ariosto \u2013 Thinking about the lifecycle of new technologies \u2014 how long it takes to bring ideas to market \u2014 what\u2019s next? What are the biggest untapped opportunities, especially given how fast things are moving?<\/p>\n
Juan Alonso \u2013 We believe the data revolution leading to the physics-AI revolution will take the engineering world by storm. People don\u2019t quite understand how much this will change design.<\/p>\n
Immediate information about hypothetical systems, exploration of hundreds of alternatives, finding solutions far better than before \u2014 it\u2019s a true revolution. Companies must rethink how they embed these tools into their workflows, not just make things 10% faster, but 10\u00d7 faster.<\/p>\n
Northrop Grumman was a great collaborator because they had advanced thinkers willing to prototype new workflows.<\/p>\n
David Ariosto \u2013 The engineering is better, competition faster \u2014 but can policymakers, culture, regulation keep up? As systems move at warp speed, are we equipped to adapt?<\/p>\n
Juan Alonso \u2013 When I was younger, I thought these systems might replace humans. Now I realize the number of alternatives we can explore is growing but still limited. I see these tools as an assistant \u2014 like Iron Man\u2019s Jarvis \u2014 providing options, helping engineers rethink problems.<\/p>\n
These technologies assist clever designers, helping them reach higher confidence and lower risk, much faster.<\/p>\n
My first PhD student in this area finished in 2012. At the time, we thought there wouldn\u2019t be enough data, or methods wouldn\u2019t generalize. But that\u2019s changing every day. Over the next five to ten years, it will be transformative.<\/p>\n
David Ariosto \u2013 Data itself \u2014 the investment in data centers, the bottlenecks in handling it \u2014 where are the opportunities?<\/p>\n
Juan Alonso \u2013 Competitors are coming up to speed quickly, so what matters is accumulated knowledge \u2014 represented by data. Companies realizing that their value lies in their data will be the winners.<\/p>\n
It\u2019s daunting because training physics-AI models requires massive amounts of data. It isn\u2019t \u201ca bunch of folders\u201d anymore \u2014 it requires data strategies, data centers, curation, availability, controls.<\/p>\n
Physics AI depends on data generated inside Luminary or through experiments. Companies must think seriously about data organization, storage, controls, accessibility \u2014 a corporate-wide effort.<\/p>\n
David Ariosto \u2013 I think that is a good place to leave it. Juan Alonso, CTO and co-founder of Luminary Cloud, founder and director of the Aerospace Design Laboratory \u2014 it\u2019s been an absolute pleasure. I could talk to you for hours. This is fascinating and really feels like the vanguard of where we\u2019re heading. Thanks so much for being here.<\/p>\n
Juan Alonso \u2013 A real pleasure to be with you, David. Thank you so much.<\/p>\n
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