Space Data Is Starting to Feel Like Software

A little while ago, I wrote about how the space economy is taking off. You can find it here: https://justme.website/tech/the-space-economy-is-taking-off/. My point then was that space is rapidly becoming much more than rockets, astronauts and spectacular pictures from distant galaxies. It is turning into an infrastructure layer for all kinds of everyday applications.

I recently came across another example that makes that development much more tangible. The video Apparently, Satellite Apps Are Vibe-Codeable Now (https://youtu.be/49EMkFgFzgY?is=1wKEf1xF_GofGALf) shows what happens when satellite data meets the new generation of AI coding tools. At the centre of the story is Tilebox (https://tilebox.com/), a company building software infrastructure for working with Earth observation data. And it is a good illustration of where the space economy may be heading.

The difficult part is increasingly down here

Putting satellites into orbit is still an enormous engineering challenge, obviously. But once they are there, they generate huge amounts of data. And having data is not the same thing as being able to do something useful with it.

Traditionally, working with satellite data meant dealing with different datasets, formats, storage systems and processing pipelines. Tilebox tries to put an abstraction layer around all of that. Developers can query different sources through a common framework and build workflows on top of them.

That in itself is interesting. But AI changes the picture again.

Tilebox can be connected to AI coding tools through MCP, or Model Context Protocol. MCP is an open standard and open-source framework introduced by Anthropic in November 2024. Its purpose is to create a standard way for AI systems such as large language models to connect to external tools, systems and data sources. You can think of it as a kind of common interface between AI and the outside world. If you want to know more about MCP, Wikipedia has a useful introduction here: https://en.wikipedia.org/wiki/Model_Context_Protocol.

Back to Tilebox. Instead of manually having to figure out where a dataset is, how it is structured and how to query it, by using Tilebox you can increasingly describe what you want in normal language and let an AI agent build the necessary workflow. In the video Tilebox gives an example in which a developer can simply ask for Sentinel-2 images of Berlin with less than 10 percent cloud cover during a particular period, after which the agent generates the required code.

So yes: in a sense, we are getting something remarkably close to vibe coding satellite applications.

A much bigger developer community

This is exactly why I find the space economy so interesting. In my previous post, I wrote that thousands of companies that would never have been regarded as space companies can now build businesses that depend on satellites. Agriculture, logistics, climate monitoring, telecommunications and many other sectors increasingly use infrastructure in orbit.

Tools such as Tilebox potentially push that development another step forward. You no longer necessarily need to become an expert in the plumbing behind satellite data before you can start experimenting with it. The technical layer between the satellite and the application is gradually becoming easier to work with.

That is something we have seen before in computing. Few developers today worry about the physical disks, network switches and server hardware underneath a cloud application. Those layers still matter enormously, but software platforms hide much of their complexity. Something similar appears to be happening with space data.

Space becomes a platform

There is another interesting connection with AI here. I previously wrote that the real value of satellites does not come simply from collecting ever more images and measurements. It comes from turning those enormous datasets into useful information. AI is particularly good at helping with exactly that problem. Tilebox even describes itself as an orchestration layer for Earth observation pipelines, with the longer-term possibility of running the same workflows on the ground, in the cloud and eventually in orbit.

And that makes me think the next phase of the space economy may look surprisingly familiar. Satellites provide the infrastructure. Platforms provide access to the data. AI helps us work with it. Developers build applications on top.

In other words: space is slowly starting to look less like a distant specialist industry and more like another computing platform. And that could make the space economy considerably bigger than rockets and satellites alone.

Building Robots Is Not Enough

I keep coming back to the same question when writing about European technology: what exactly do we want to keep in Europe? The usual answer is something like: semiconductor production, cloud infrastructure, AI models and perhaps a few strategically important factories. But after reading about French robotics company Wandercraft (https://en.wandercraft.eu/) and its cooperation with Renault, I think there is another part of the story that deserves much more attention. Europe should not only build products. It should also build the machines that build those products.

That may sound slightly old-fashioned in a world where almost every discussion about technology quickly turns into a discussion about software and AI. But there is a very practical reason for it. If you build your own production machines, you learn from them. You discover that a certain component is unnecessarily expensive. That another part could be lighter. That a production step takes too long. That a tolerance does not need to be quite so precise. Or that changing some software, mechanics or electronics could make the entire production process better.

In other words, a factory is not simply a place where products come off a production line. It is also a rather large laboratory.

Robots building European industrial knowledge

Wandercraft is an interesting example. The French company originally developed robotic exoskeletons for people who have difficulty walking. More than ten years of developing balance systems, actuators, mechanics and control software eventually resulted in something else: Calvin, a humanoid robot designed for industrial work.

And importantly, this isn’t primarily a robot designed to dance on a stage or generate spectacular YouTube videos. It is designed to work in a factory. Renault is already testing Calvin in its factory in Douai, where the robot moves things such as car tyres. Renault plans to have around 350 humanoid robots working in its French and Spanish factories by the end of 2027.

But the really interesting part is the relationship between the two companies. Renault is not simply buying robots from Wandercraft. It is also helping the company figure out how to manufacture those robots at scale. Wandercraft brings robotics and AI knowledge. Renault brings decades of experience in mass production, supply chains, quality control and designing products that can actually be manufactured at an acceptable cost. That combination makes a lot of sense.

Keep the feedback loop close

There is another detail I particularly like. Wandercraft says the components in its current robot all come from Europe. That matters. Because if the robot is designed in Europe, manufactured using European components, deployed in European factories and improved based on what happens in those factories, you create a very useful feedback loop: engineers see what breaks, factories discover what works, production specialists improve how the robot itself is manufactured and developers adjust the software and control systems. And then you do it all over again. That is industrial knowledge being created continuously. Move too many parts of that chain elsewhere and eventually part of that knowledge moves with it.

Europe already has an enormous high-quality manufacturing base, many specialised machine builders and companies that know how to manufacture complicated products at scale. We sometimes seem strangely eager to underestimate how valuable that is.

Perhaps technological sovereignty is not only about owning an AI model or running a European cloud. Sometimes it is also about knowing how to build the machine that builds the machine.

Here is a video of Andreas Klinger from the European investment fund PROTOTYPE (https://www.prototypecap.com/) visiting Wandercraft.

Europe Is Better at Tech Than We Like to Admit

I am personally very interested in digital sovereignty, digital autonomy, European technology and open-source software. Partly because I believe Europe needs more control over the technology on which our companies, governments and societies increasingly depend. The tools I use myself — from my laptop and phone to software applications — are almost exclusively European and/or open source.

At the same time, I sometimes — perhaps even often — get frustrated by the way this discussion is conducted, especially in the Netherlands. There is a persistent assumption that European technology does not amount to very much, that the United States has already won the technology race, and that open source is mostly something complicated for technically minded enthusiasts. I think that picture is far too simplistic.

There is certainly a technology gap in some areas. American hyperscalers dominate cloud computing, many of the most visible AI companies are American, and China has built formidable capabilities in areas such as manufacturing, batteries and robotics. Pretending otherwise would be pointless.

But there is another side to the story. Sometimes I also think CIOs and IT managers make things unnecessarily easy for themselves. If Microsoft, AWS or another established supplier already provides almost everything you need, it is tempting simply to buy whatever your preferred vendor puts in front of you. That is convenient. It reduces the number of suppliers, contracts and technologies that need to be evaluated. But convenience can easily turn into dependency.

European and open-source alternatives are therefore sometimes dismissed as being too difficult before they have even been seriously investigated. And in the Netherlands in particular, I sometimes feel there is too much pessimism surrounding European technology. We talk extensively about everything Europe cannot do, while paying considerably less attention to the areas in which European companies are genuinely world-class.

That was one of the reasons I was particularly interested in visiting Siemens Realize Live Europe in Amsterdam. I knew beforehand that Siemens would offer a rather different perspective on the global technology race.

Europe is not absent from the technology race

I recently wrote about the event for Computable.nl in an article titled “Siemens: Europa is ijzersterk op het gebied van industriële tech” (text is in Dutch). The central message is worth repeating: Europe may not always dominate the headlines surrounding artificial intelligence, cloud platforms or consumer technology, but in industrial technology its position is considerably stronger than the popular narrative sometimes suggests.

One of the most striking examples during the event involved UBTech, a well-known Chinese manufacturer of humanoid robots. At first sight, it appears to be another illustration of Europe’s supposed technological weakness. China builds advanced humanoid robots while Europe watches from the sidelines. Except that this is not what is actually happening.

Siemens demonstrated how UBTech uses technology from the Siemens Xcelerator portfolio to design, simulate, test, manufacture and manage its robots throughout their lifecycle. In other words, some highly advanced Chinese technology is being created with European industrial software and engineering technology.

That changes the picture considerably. Yes, the most visible product may be Chinese, while much of the engineering infrastructure behind it comes from Europe.

Industrial technology deserves more attention

This is perhaps one of the problems with the broader technology debate. We tend to judge technological strength by looking at the companies that are most visible to consumers. Google, Microsoft, Amazon, Apple, OpenAI, Meta and Nvidia naturally receive enormous amounts of media attention. Industrial technology is much less visible. Yet behind virtually every physical product is an enormously complex digital environment. Aircraft, cars, chips, machines, factories, energy systems and robots must be designed, simulated, tested, manufactured and maintained. And Europe has exceptionally strong companies in precisely those areas.

At Realize Live Europe, Siemens repeatedly emphasised this complete industrial digital chain. Its strategy increasingly revolves around what the company calls the digital thread: connecting information from chip and electronics design through mechanical engineering, simulation and manufacturing all the way to factory automation and maintenance.

That includes technologies from companies Siemens has acquired over the years, including Mentor Graphics, Altair and the originally Dutch low-code company Mendix. Instead of presenting these as separate software products, Siemens increasingly positions them as parts of a connected industrial technology platform.

AI, but not AI for AI’s sake

Artificial intelligence naturally played a major role at the event. What I found interesting, however, was that Siemens did not primarily present AI as yet another chatbot. The focus was much more practical. AI is being incorporated into engineering tools, simulation software, manufacturing systems and product lifecycle management. According to Siemens, AI already reduces the time required to create certain technical 2D drawings by around fifty percent, while parts can be classified in Teamcenter roughly ten times faster.

The company is also introducing AI agents capable of carrying out parts of engineering workflows. But engineers remain responsible for supervision and final decisions. This is an important distinction. In industrial environments, AI cannot simply generate something that looks plausible. Engineering information needs to be accurate, traceable and connected to the correct version of a product or production process. That is why Siemens places so much emphasis on data.

Intelligence Center X

One of the major announcements during Realize Live Europe was Intelligence Center X, which Siemens positions as an intelligent layer across its software portfolio. The idea is that AI agents can analyse information, initiate processes and support engineers while remaining connected to the underlying industrial data. Instead of copying enormous quantities of information into separate AI environments, the agents work with data stored in systems such as Teamcenter.

That is significant because industrial data changes continuously. A product configuration may change. A component may be replaced. A new engineering version may become available. Manufacturing parameters may be adjusted. AI systems therefore need more than access to data. They need access to the right and current data, including the context in which that information exists.

Traceability is equally important. Companies need to understand where information came from and why an AI system reached a particular conclusion. These requirements are rather different from those of consumer-oriented generative AI.

Simulation becomes part of design

Another fascinating development is happening in simulation. Traditionally, engineers often designed something first and then used simulation to determine whether the design would actually work. That process is changing. According to Siemens, the combination of GPU acceleration and what it calls Physics AI can make some simulations hundreds or even thousands of times faster.

Examples presented at the event illustrated the impact. Continental has reportedly reduced a complex airbag simulation from around two hours to less than thirty seconds. Magna reduced another simulation from fourteen hours to approximately ten seconds. Rolls-Royce is using Simcenter Simsolid to analyse engine designs much more quickly.

When simulations take seconds rather than hours, they become something very different. An engineer can test many different design options during the design process rather than using simulation mainly as a final verification step. That can potentially change the way products are developed.

AI enters the factory

The same development is happening inside factories. Siemens demonstrated how AI can analyse abnormalities in manufacturing processes and combine information from engineering systems, ERP software and production environments. Instead of requiring engineers to manually investigate every possible relationship between datasets, AI can identify correlations and suggest possible improvements. (Computable)

Again, this does not mean removing humans from the process. It means automating parts of the investigative work while leaving engineers in control. That appears to be an important theme in Siemens’ approach to industrial AI: moving from systems that simply answer questions towards software that can help execute complete processes.

And then there is digital sovereignty

For me, one of the most interesting elements of the Siemens story was its connection with digital sovereignty. Mendix plays an important role here. The Dutch low-code platform, acquired by Siemens in 2018, is becoming part of the company’s broader AI architecture. It can be used to create applications and agentic workflows in which humans and AI agents work together.

But there is also an infrastructure dimension. Siemens is working with German cloud provider STACKIT to allow certain Mendix environments to run on European cloud infrastructure. According to Siemens, this currently primarily concerns projects involving Dutch government organisations, but the approach is expected to expand. Siemens has also been talking with European cloud provider OVHcloud about hosting additional Siemens solutions on European infrastructure.

That is exactly the kind of development I believe deserves considerably more attention. Digital sovereignty should not simply mean complaining about Microsoft, AWS or Google. Nor should it mean replacing American Big Tech with European Big Tech. It should mean creating genuine technological choice. That requires European cloud infrastructure, open standards, open-source technologies, interoperable software and strong European technology companies.

Perhaps we should stop underestimating ourselves

The visit to Siemens Realize Live Europe reinforced something I have thought for some time. Europe certainly has technological weaknesses. We need more investment, more scale, stronger capital markets and probably much more ambition in several areas of digital technology.

But constantly repeating that Europe has already lost the technology race is not particularly helpful either. Because it is not entirely true.

Europe has deep expertise in industrial automation, engineering software, semiconductor technology, manufacturing systems, digital twins, telecommunications, embedded systems, machine building and many other technologies that rarely generate the headlines enjoyed by ChatGPT or the latest American cloud service.

And these technologies matter enormously. Sometimes they are even the technologies being used to create the highly visible American and Chinese innovations that we subsequently point to as evidence that Europe is falling behind. The Siemens event was a useful reminder that technological power is not always found where the headlines are.

You can read my original Dutch article for Computable, “Siemens: Europa is ijzersterk op het gebied van industriële tech” here.

Perhaps the discussion about European digital sovereignty would become more productive if we spent a little less time explaining why things cannot be done — and a little more time looking at the technology that Europe already has.