Frankfurt (Germany) (AFP) – The head of Germany's Isar Aerospace voiced hope Tuesday that the company could one day overtake Elon Musk's SpaceX to become the world's premier space launch company.
Issued on: 10/11/2026 - FRANCE24
Isar launched a rocket into orbit from Andoya in Norway on September 5, 2026 © Handout / Isar Aerospace/AFP/FileDaniel Metzler, who founded the company with two fellow students in 2018, pointed to a huge backlog of launch orders, helped by European military clients.
"SpaceX was founded in 2002. Back then I was playing with Pokemon cards," he said. "Looking at what could happen in the next 18 years, I wouldn't rule out us being further ahead than SpaceX."
Isar became in September the first privately financed European company to get a rocket into orbit.
Europe is betting that competition between Isar and other players such as Rocket Factory Augsburg will bring down the price of satellite launches.
This would help ensure affordable access to space for sectors ranging from telecommunications to defence, independent of entities such as SpaceX, the sector leader valued at $1.9 trillion (1.7 trillion euros).
Speaking at a conference organised by the SZ newspaper, Metzler said Isar had already come a long way, noting that SpaceX needed four attempts to get into orbit compared to Isar's two.
Capacity rather than demand was now the big bottleneck, Metzler added.
He said the company was 15-times oversubscribed for 2028 launches as European governments now preferred European providers to put their defence satellites up as opposed to firms overseas.
"There's 14 customers for every rocket that we have to disappoint," he said.
"About 18 months ago 85 percent of inquiries were commercial and 15 percent were military, and that ratio has since flipped," he added.

Isar is working on developing reusable rockets to boost capacity and bring launch costs down, hoping to catch up with SpaceX's reusable Falcon 9 rocket.
"Already with our next launch, for which we are currently finalising the launch vehicle, we will be taking initial measurements to assess the re-entry of the rocket's first stage," he said.
"Over the next three or four flights, we also plan to start flying hardware specifically designed for reusability."
Asked if Isar could one day be valued at 100 billion euros, Metzler said "people massively underestimate the potential of space".
"To be honest, I reckon the figure is much higher, but then people would be even more convinced that I'm completely mad."
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Key Takeaways:
Starlink, Chinese mega-constellations and others are creating hyper-redundant architectures that also serve as geopolitical tools, with non-state actors already using commercial satcom for grey-zone operations.
Treating satellite terminals as sensitive dual-use technology and imposing stricter international oversight are required to prevent commercial connectivity from enabling orbital conflict and empowering proxies.
The term ‘space economy’, which now features in a broad range of policy and strategy discussions, is not very old. The Organisation for Economic Co-operation and Development (OECD) codified it in 2012, defining it as, “The space economy is the full range of activities and the use of resources that create and provide value and benefits to human beings in the course of exploring, understanding, managing, and utilising space.”
The OECD coined the term ‘space economy’ to draw private capital and commercial capacities into space activities and to boost the national economies of member states. While commerce appears to be the central driver, the rise of the “space economy” was triggered by two anti-satellite (ASAT) kinetic kill weapons tests: China’s test on 12 January 2007 and the US’s response on 21 February 2008. Both tests pitted the US and China against each other in a comprehensive contest across all of Earth’s orbits. This time, it was not merely a space race but one involving the weaponisation of orbit.
The Military Origins of the Space Economy
China’s ASAT test showed the US a stark picture, which it reciprocated: standalone satellites of that era were not only large and expensive, but also sitting ducks and single points of failure that could cripple the whole military architecture, especially for globally expeditionary military activities. It was well known before the term ‘space economy’ was coined that countries intending to maintain dominance in space, or even to be keepers of exclusive capabilities, would need to carry out military activities under the guise of dual-use commercial activities. Commercial players build the assets, militaries are the end users, and the whole orbital architecture gets laid out, with weaponisation occurring smoothly while hiding in plain sight. The US Defence Intelligence Agency’s (DIA) 2026 public release of an image of China’s secretive spaceplane, Shenlong, operating in Earth’s orbit shows that the weaponisation race has been under way for more than a decade but is coming to the fore only now through in-orbit intelligence gathering.
The Race for Orbital Dominance
In August 2013, the US Air Force Space Command released a paper titled “Resiliency and Disaggregated Space Architectures” which advocated for a disaggregation approach by moving from standalone and exquisite satellites to constellations of small satellites. Constellations have been preferred since then as a deterrent to ASAT tests. They introduced hyper-redundancy, removed single points of failure, established a mathematical deterrent by lowering the cost of making and launching satellites, put the onus of orbital destruction entirely on the ASAT attacker, and created a proliferated warfighting architecture.
SpaceX was the first company to file with the US Federal Communications Commission (FCC) and the International Telecommunication Union (ITU) for an orbital shell of more than 1,000 satellites, specifically 4,425 satellites, in 2016. China filed for an enormous constellation of 12,992 satellites in September 2020. This was also the period when China began nurturing many quasi-private commercial space players under the watchful eye of its state-controlled industries, under the State Council. While China’s Belt and Road Initiative has always had a space component, the first explicit mention of a satellite constellation as a national priority came in the 14th Five-Year Plan, which was approved in March 2021.
At present (as of October 2026), Starlink has more than 10,783 satellites, followed by OneWeb with 654 satellites, Amazon’s Kuiper with 394 satellites, and Qianfan with 238 satellites. In the next few years, the world could see more than 20,000 satellites hovering around in low Earth orbit, but that is not all: unmitigated numbers are envisaged. E-space, founded by OneWeb founder Greg Wyler, has registered with ITU for a satellite constellation of a whopping 116,640 satellites. China’s State-owned Assets Supervision and Administration Commission of the State Council has filed for an enormous constellation of more than 200,000 non-geostationary satellites. This raises questions about the limits to the growth of orbital constellations, whether this expansion is a mad rush, a zero-sum game or a race to nowhere, and where this orbital ‘great power’ rivalry for absolute dominance is ultimately headed.
In the past year, several governments have issued strongly worded statements urging states to step back from an escalation ladder that could lead to a space war. In October 2026, Russia’s former President Dmitry Medvedev warned that if the US blocks Russian satellites, the Ukraine-Russia conflict would escalate into a full-fledged space war, with Russia targeting Starlink.
In September 2026, UK Defence Secretary Wes Streeting cautioned that space-based threats are growing at an alarming rate and in sophistication, including lasers, electromagnetic pulses and kinetic ASAT weapons. After US Air Force Secretary Troy Meink made the startling revelation that the US has placed “on-orbit space control weapons capable of defending the Joint Force against hostile adversary action,” China’s foreign ministry spokesperson, Guo Jiakun, urged the US to stop preparing for war in space and turning the orbits into a battlefield. The US is certain that its space dominance is being challenged by Russia and China through a strategy of deceiving, disrupting, denying, degrading and destroying. In a 2024 statement, the US Space Force’s Space Systems Command argued that proliferated LEO constellations are the way to avert a single point of failure. Moreover, each of them intends to complicate its adversary’s targeting calculus.
From Orbital Warfare to Geopolitical Leverage
The weaponisation of orbits is a great-power game — and a space war is fast becoming a possibility. The more chronic issue, however, is that great powers use the placement of weapons in space to exploit space assets in pursuit of their geopolitical and geoeconomic goals. India should treat proxies, non-state actors, terror groups and criminal organisations with access to the downstream services of satellite constellations not as peripheral, anomalous actors but as strategic force multipliers of space powers.
With access to LEO satellite communications (satcom), these four groups have already shown several signs of engaging in grey-zone operations, in which their backers can claim plausible deniability or exploit the difficulty of attribution. They use LEO satcom in zones with a governance vacuum, where they trample the telecom sovereignty of legitimate but weak governments. In some situations, they could also create front enterprises with a digital backbone provided by LEO satcom, which then fund the larger terror and criminal enterprises
History has shown that proxies are rarely obedient to their benefactor states. At some point, they go rogue, having acquired weapons, wealth, power and self-interest. Their independent actions, which could be reckless given the absence of accountability, can force their sponsors into conflict with a major rival power while further eroding the international order and the institutions that safeguard the sanctity of space law.
Weaponised LEO constellations are becoming a Sword of Damocles over international security. While the space economy drives global digital connectivity, that connectivity transcends jurisdictions and becomes a geopolitical weapon. Commercially, it grants bad-faith actors the status of data subscribers, giving them secure, high-speed communication backbones that outpace the capabilities of traditional national law enforcement. Closing this security gap requires a paradigm shift: satellite terminals must be treated not merely as commercial consumer electronics but as sensitive weaponised technology, demanding strict international regulatory oversight, cooperative corporate governance and constructive international diplomacy.
None of these seems to be working despite the rise of the space economy, since great powers are becoming autarkic and hegemonic, relinquishing their responsibilities towards shared international well-being and being driven more by domination in space. The question is whether the global space economy, beyond the trillions of dollars it aims to generate, will deny bad-faith actors access to space and commit to accountability, impartial justice and the well-being of those in need, a necessity in today’s world, or whether it will continue to abet space weaponisation unchecked.
Source: This article was published by the Observer Research Foundation.
About Observer Research Foundation
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By Burak Oktenli
Key Takeaways:
Recent detections, including additional gravitational waves and a disappearing MeerKAT pulsar, illustrate how changing signals and instrumental rhythms require hybrid approaches that combine physics templates, spectral methods, and machine learning.
Researchers are urged to test time–frequency stages against existing methods on known and injected signals, while remaining alert to the risk of mistaking instrument artifacts for genuine discoveries.
Gravitational waves, disappearing pulsars and automated sky surveys show that nature often reveals itself through patterns in time. Scientific AI should recognize the rhythm without mistaking the instrument for the universe.
The universe does not always announce a discovery by showing astronomers a new shape. Sometimes it sends a faint pulse, a pattern of silence, or a rising note buried in noise. What matters is not what an object looks like, but how a physical signal changes through time.
That distinction feels newly important in 2026. In May, the international LIGO–Virgo–KAGRA collaboration reported 161 additional gravitational-wave detections, bringing its catalog to 390 confirmed signals. In September, radio astronomers working with South Africa’s MeerKAT telescope reported a pulsar that repeatedly disappears from view. Both achievements depended on looking for structure in measurements rather than recognizing an object in a photograph.
Artificial intelligence can help with this work, but it needs the right scientific question. A system trained to classify images and a system designed to find a weak, changing frequency are solving different problems. Neither should be mistaken for a universal way of discovering the unknown.
The black hole was found in a changing frequency
On September 14, 2015, two instruments in the United States registered the first directly detected gravitational wave. The signal called GW150914 swept upward from about 35 to 250 hertz as two black holes approached merger. Its effect on the detector was extraordinarily small, but the changing waveform matched a family of predictions from general relativity. Matched-filter analysis found it with a signal-to-noise ratio of 24.
This was not an algorithm discovering a black hole merely because it had seen pictures of similar ones. Nor was it simply noticing one frequency. The search used an evolving waveform, the instruments’ frequency-dependent noise, and independent observations from two locations. Investigators also tested environmental and instrumental explanations. The recognizable object came later, as a physical interpretation of the measured signal.
The lesson is not that physics has no need of machine learning. Quite the opposite. A January 2026 study in Physical Review Letters combined information from multiple gravitational-wave search pipelines using machine learning and uncertainty calibration. Existing astronomy already mixes physical templates, frequency-domain analysis, anomaly searches and learned methods. The next opportunity is to make those complementary strengths more accessible across instruments and disciplines.
A disappearing pulsar is still a signal
Another example arrived from South Africa. In a September 2026 paper, researchers described a pulsar found in archived MeerKAT observations toward the Small Magellanic Cloud. An image-based search noticed that a radio source appeared and disappeared over roughly minute-long intervals. Follow-up measurements established a rotation period of about 0.55 seconds. The team estimated that its radio emission was absent for roughly one-third of the observed pulses.
This is a particularly revealing case because images and timing were partners, not rivals. Changing images raised the initial question. Precise timing helped establish what kind of astrophysical source the team had found. A still image alone could not describe that behavior. But a narrow search for a perfectly uninterrupted tone could also have missed an object that sometimes falls silent.
China’s FAST radio telescope offers a related lesson on a longer clock. In October, researchers reported a pulsar in an evolving triple-star system whose characterization drew on years of timing measurements and supporting observations. Minute changes in the arrival of pulses carried information about companions that could not be obtained simply by taking a sharper picture.
Periodic signals, drifting frequencies, interrupted bursts and one-time transients demand different tools. Fourier methods can concentrate stable periodic information; folding can reveal weak repeated pulses; time–frequency methods can follow a changing chirp. Learning systems can help combine the evidence. What matters is choosing a representation that fits the physics, rather than expecting every phenomenon to announce itself in the same data format.
Outside astronomy, this way of thinking already supports vibration diagnostics for machinery and the analysis of oscillations in electrical grids. Those fields have used spectral tools for decades. Their lesson for scientific AI is to represent physically meaningful timing and frequency relationships explicitly, rather than assume every useful pattern will emerge from a generic classifier.
The instrument has a rhythm too
There is a less glamorous reason to pay attention to frequencies: detectors generate patterns of their own. Electrical supply harmonics, mechanical resonances, calibration signals and interference can appear in the same observations as the phenomenon being studied. A narrow peak in a spectrum is not automatically a discovery. Sometimes it is the machine measuring itself.
That is why spectral analysis must be accompanied by calibration, comparison between instruments and explicit checks against known noise. A real signal can also be broadband or nonperiodic, so blindly subtracting every unusual frequency would destroy some of the evidence scientists want. The right practice is to model, test and document competing explanations, not to turn a frequency plot into a truth machine.
The scale of incoming data makes that discipline urgent. In February 2026, the Vera C. Rubin Observatory issued roughly 800,000 public alerts in a single night, with a stream expected eventually to approach seven million nightly alerts. Rubin detects changes by comparing images, and its community alert brokers can add light-curve analysis, machine-learning classifications and context. Not every alert contains a periodic signal. The broader lesson is that discovering a change and understanding its temporal behavior are separate tasks that should support each other.
Teach AI to search for physical behavior
A practical next step is not to declare image recognition obsolete or invent a “frequency-only AI.” Researchers should ask how much a physically informed time–frequency stage improves a complete discovery pipeline relative to strong existing alternatives. Test the idea on archival records where the answer is known and on blinded data with injected weak signals, changing frequencies, intermittent emissions and realistic instrumental artifacts.
Compare image-based, raw-time-series, classical signal-processing and hybrid machine-learning approaches on equal terms. Report not just the number of candidates found, but the false-alarm rate, the delay before detection, robustness across instruments and the kinds of real signals each method misses. A system should also say when no proposed interpretation survives the available evidence. Such comparisons would measure an engineering advantage rather than assume one.
There is a limit to every physics-informed search: an unexpected phenomenon may not conform to the models used to find it. Model-free searches and human investigation remain essential. But science can widen the search space by paying attention not only to appearances and labeled categories, but to oscillation, timing, correlation and change.
The next unfamiliar thing discovered in physical data may not look like anything in a training set. Before asking what, it resembles, scientists should ask what it repeats, what changes, and whether that pattern belongs to the universe or to the instrument.
About Burak Oktenli
Burak Oktenli holds an MBA and a Master of Professional Studies in Applied Intelligence from Georgetown University. His research addresses the governance of authority in autonomous and AI-enabled systems, and his writing has appeared at the Modern War Institute at West Point, RUSI, RealClearDefense, RealClearMarkets, and Geopolitical Monitor. He is the author of Authority Architectures for Autonomous Systems, a ten-volume series on how authority in autonomous systems is delegated, monitored and recovered, at authority-architecture.me
View all posts by Burak Oktenli

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