EU sea defence: the new race to protect cables, pipelines and trade

Copyright AP Photo/Gregorio Borgia

By Elisabeth Heinz & Leticia Batista Cabanas

Updated 26/08/2026 - 

Is the deep blue sea the next frontier for defence? Global economies are increasingly dependent on vulnerable subsea data and energy pipelines. So, the EU and its members are shifting budgets towards maritime defence tools.

In just the last decade, the EU has spent almost €118 billion in maritime defence, with a sharp 23.6 percent increase in 2022 after Russia’s invasion of Ukraine.

Data from the EU Blue Economy Observatory show a sharp rise in domestic maritime defence spending, reaching a record high of €11.6 billion. Submarines alone account for 27 percent of the EU's total production value; the rest includes surface ships, aircraft, and other types of warfare.

The bloc decided to transition from a purely commercial ‘blue’ economy approach to a securitised maritime strategy. In 2023, it updated its European Union Maritime Security Strategy (EUMSS) to protect critical seabed infrastructure.

Then it locked in its strategic defence priorities by launching dedicated European Defence Projects of Common Interest and implementing a Submarine Cable Security Toolbox to counter grey-zone threats and protect underwater networks.

Protecting international trade

The global economy relies almost completely on secure, open seas. More than 95 percent of international digital traffic moves through them. Financial transfers happen through over 1.4 million kilometres of submarine fibre-optic cables, which carry an estimated €9.2 trillion in financial transactions every single day. Approximately two-thirds of the world’s oil and gas is either extracted at sea or transported by water.

80 percent of global trade volume is transported by ocean shipping. For the EU, maritime transport accounts for 75.6 percent of all imports and 73 percent of all exports. It totals around €1.126 trillion in goods annually. A disruption at key maritime chokepoints risks triggering inflation and global manufacturing shortages.

An attack on these sea lanes and underwater assets would paralyse Europe, so it needs more than traditional naval patrols. “Underwater Domain Awareness is a critical activity to know what is happening below the surface, particularly around cables, pipelines and offshore energy infrastructure, prioritising those identified as critical for the security of the Union”, said Jürgen Scraback, Head of the Maritime Domain Unit at the European Defence Agency (EDA).

As threats increasingly come from low-cost drones, uncrewed underwater vehicles and mine warfare, governments are investing in autonomous technologies and surveillance systems. Between 2016 and 2025, the EU's annual production value of crewless vehicles, including aerial and submarine drones, increased by 132 percent to €847 million. Fixed-wing unmanned systems accounted for one third of that output, worth €277 million, while production of traditional unmanned submarine platforms fell by 23 percent over the same period.

Fleet modernisation continues to focus on both conventional naval assets and autonomous platforms. Manned surface ships now account for 65 percent of EU maritime defence vehicle production value and serve as the primary platforms for command, logistics and force projection.

Submarines represent another 27 percent of production, supported by new procurement programmes and investment in next-generation underwater weapons. At the same time, autonomous surface vessels and unmanned underwater vehicles are becoming increasingly important for surveillance, reconnaissance and infrastructure monitoring missions.

EU27 maritime defence industry: production by function of vehicle, 2016-2025

Investments also extend to digital tech for maritime surveillance, with European operators deploying AI-enabled unmanned systems to monitor ports, offshore energy infrastructure, and submarine cables, detecting threats across large areas

For Scraback, “autonomous and unmanned systems combined with AI-enabled data fusion are among the technologies likely to have the greatest impact. They can provide persistent surveillance over large areas without requiring expensive crewed platforms to remain almost permanently deployed”.

How is the EU boosting maritime defence?

The 2014 Maritime Security Strategy guides Europe's maritime strategy, protecting citizens, the economy, infrastructure, and borders, while redefining Europe’s approach to maritime defence.

“The new strategy calls for a greater emphasis on the hard power aspects of maritime defence and security, where the EU had previously faced challenges in establishing a role and identity”, said Chris Kremidas-Courtney, senior advisor at the European Policy Centre and associate fellow at the Geneva Centre for Security Policy. It identifies “the protection of critical infrastructure in the maritime domain as a key priority”, they added.

The Industrial Maritime Strategy, adopted in March 2026, backs this shift. It boosts Europe's naval production through a new EU Industrial Maritime Value Chain Alliance and reinforces naval, underwater, and dual-use capabilities, including a dual-use ferry construction programme.

By July, Europe allocated €325 million to five European Defence Projects of Common Interest, including one maritime and seabed defence project to strengthen its industrial base. Since February, a new Counter-Drone Action Plan has shifted production towards unmanned naval drones and counter-drone systems for aerial, surface, and underwater threats.

The EU funds its naval ramp-up through defence tools, such as the European Defence Industrial Strategy and the €1.5 billion European Defence Industry Programme. The Readiness 2030 roadmap totals over €800 billion, including naval capabilities and sea lines of communication protection.

The bloc also invests in detection and surveillance technologies to fight threats and sabotage to seabed cables. As cables cover large areas, are privately owned and can be easily damaged, “the best approach is a layered resilience system designed to make interference detectable, limit the disruption caused by a successful attack and restore service quickly”, Kremidas-Courtney explained.

This thinking now drives EU policy. The Action Plan on Cable Security (2025) strengthens Europe’s ability to prevent, detect, respond to, and recover from cable incidents that disrupt critical functions like communication and energy supply. The €92 million OceanEye expands maritime awareness using AI, autonomous sensors, and digital twins.

Under Horizon Europe, the UnderSect and Smart Maritime and Underwater Guardian projects invest nearly €6 million each in underwater threat-detection systems for ports and maritime infrastructure. European Defence Fund (EDF) projects, such as SHIELD and SOUND2, develop AI systems to detect threats using underwater acoustic signals.

Ramping up maritime defence goes beyond detecting and repairing cable breaks. For Kremidas-Courtney, authorities should identify behavioural patterns such as unexplained slowing, shipping lane deviations, and manipulation of identification signals.

“The most effective solution to me is an integrated information-and-action network that fuses undersea sensors, AIS data, coastal radar, satellite imagery, intelligence and port records into a continuously staffed existing regional maritime operations centre”, Kremidas-Courtney explained.

Who invests the most?

Between 2016 and 2025, cumulative EU production of maritime defence vehicles and equipment reached €117.8 billion. According to the EU Commission’s Blue Economy Observatory, production remained concentrated in four countries. France, Germany, Italy and Spain together account for 87 percent of the bloc's maritime defence industrial output. The Netherlands, Sweden and Poland contribute a further 8 percent, largely through surveillance technologies and maritime security systems.

In 2025, France generated 37 percent of the EU’s total production of maritime defence vehicles. Germany and Italy each contributed 19 percent, and Spain followed with 8 percent. These four countries accounted for 82 percent of the EU’s total output value and 60 percent of the EU's total defence expenditure. This shows that defence spending remains higher in member states with a long tradition of armaments.

EU maritime defence industry: total output value by member state, 2016-2025, billion EUR

“A few large navies can provide scarce high-end capabilities, but they can’t secure every coastline, patrol the sea lanes, and protect every piece of undersea infrastructure. Europe doesn’t need everyone to build a fleet to match Italy's or France's, but it does need credible, distributed forces connected by interoperable systems and a shared maritime picture. The only way to make that work is a whole-of-Europe approach which includes the UK and Norway”, Kremidas-Courtney warned.

At company level, the France-based Naval Group led with 24 percent of the EU’s total output, producing advanced surface combatants (frigates and corvettes), nuclear-powered submarines, and unmanned surface and underwater systems. Italy’s Fincantieri (15 percent) specialises in warships and underwater defence systems, including torpedoes and sonars.

The German Thyssenkrupp Marine Systems accounted for 8 percent of the EU’s overall market for maritime defence vehicles, focusing on surface vessels and submarine construction. Spain’s Navantia (7 percent) builds multi-mission frigates, AIP-equipped submarines, aircraft carriers and patrol vessels.

The EDF supports European companies in developing joint defence technologies and equipment. It invests €2.7 billion in collaborative defence research and €5.3 billion in collaborative capability development for the period 2021-2027. The 2025 EDF totals €1.07 billion and funds 57 projects, including E-DOMINION, which develops a digital architecture and combat cloud for European navies.

According to Scraback, “we need to continue shifting from fragmented national solutions towards interoperable, scalable and jointly developed capabilities”. He explained that the European Defence Project of Common Interest on Integrated Maritime and Seabed Defence “can be a key vehicle for this, bringing Member States, existing European programmes and investments together under one coherent framework”.

 

EU pours billions into maritime defence as threats to its seas persist

Copyright Euronews

By Evi Kiorri & Mert Can Yilmaz

Published on 26/08/2026 - EURONEWS

From undersea cables to warships, the EU is spending record sums to defend its waters. Watch the video.

The EU is increasing maritime defence spending as threats to its ports, undersea cables and offshore infrastructure grow.

90 percent of EU trade, energy supplies and internet data move by sea. This exposes the bloc to hybrid and cyber attacks, border tensions and infrastructure sabotage, including from Russia.

Brussels updated its Maritime Security Strategy in March 2023 and funding followed. EU countries spent €343 billion on defence in 2024, up 19 percent year-on-year. Equipment procurement jumped 39 percent. Spending hit a record €392 billion in 2025, much of it through the €150 billion SAFE fund under the EU's Readiness 2030 roadmap.

A large share goes to building ships. The bloc's maritime defence industry produced €13.7 billion worth of vessels in 2025, two-thirds surface ships. France, Germany, Italy and Spain accounted for 82% of output. Shipbuilders Naval Group, Fincantieri, Thyssenkrupp Marine Systems and Navantia cooperate on the European Patrol Corvette project while securing multi-billion-euro export deals with Norway and Indonesia.

Which EU countries invest more in maritime defence and why?

Copyright AP Photo

By Evi Kiorri

Published on 27/08/2026 - EURONEWS

Europe's naval rearmament is rising. Which countries drive the maritime defence boom and why the map may be misleading.

Europe’s militaries are pouring money into the sea. EU defence expenditure rose to €418 billion in 2025, a 20 percent increase from the previous year, and is projected to reach €454 billion in 2026, equivalent to 2.4 percent of GDP. Maritime defence is one of the fastest-growing sectors. Production of naval vehicles and equipment across the bloc has reached €117.8 billion since 2016, with output hitting €13.7 billion in 2025 alone.

Who’s building Europe’s navies

On paper, four countries dominate that output. France, Germany, Italy and Spain account for 87 percent of the EU’s maritime defence industrial base and captured 82 percent of its total output value last year. France alone produced 37 percent of the bloc’s maritime defence vehicles in 2025, followed by Germany and Italy at 19 percent each, and Spain at 8 percent. Together, the four also account for 60 percent of the EU’s total defence expenditure.

For Christophe Tytgat, Secretary General of SEA Europe, the shipyards and maritime equipment association, that pattern is no accident: “the concentration is real and structural, not incidental,” reflecting decades of naval-industrial history and geography concentrated in a handful of states. Submarines are also a growth area, now 27 percent of EU maritime defence output, with the same four countries producing 93 percent of the bloc’s naval exports.

A skewed picture?

But industrial output isn’t the same as military commitment, according to Chris Kremidas-Courtney, senior advisor at the European Policy Centre, who argues the four-country narrative overlooks some of Europe’s most exposed navies. “Industrial concentration is not the same as maritime-defence commitment,” they said, naming Greece and Sweden as “conspicuous omissions.”

Greece runs one of Europe’s strongest conventional submarine fleets and maintains a demanding operational posture across the Aegean, Eastern Mediterranean and Red Sea. Sweden’s smaller navy is purpose-built for the Baltic and backed by a serious domestic defence industry.

The real test, Kremidas-Courtney says, is integration rather than size. “Europe doesn’t need everyone to build a fleet to match Italy or France, but it does need credible distributed forces connected by interoperable systems and a shared maritime picture”, an approach they argue must extend beyond the bloc to include the UK and Norway.

Measured against GDP rather than raw output, the map zooms towards the east. Poland spends the largest share of any EU state on defence at 4.48 percent of GDP, ahead of Lithuania (4.00 percent), Latvia (3.73 percent) and Estonia (3.38 percent), all frontline states bordering Russia or its ally Belarus. Germany has more than doubled its share of GDP since 2021, from 1.27 percent to 2.14 percent, and aims to reach €162 billion in annual defence spending by 2029.

Tytgat argues neither the industrial giants nor the frontline states can carry EU maritime security alone: “only four EU countries cannot substitute for broad-based EU maritime security, because collective security strategy requires interoperable capability, resilient supply chains and genuine burden-sharing across the whole Union.”

What’s driving this spending

Behind all this spending is Russia’s war on Ukraine and the maritime threats that followed. A “shadow fleet” of sanctioned tankers allegedly used for surveillance and sabotage has put the EU on alert. A series of undersea cable cuts in the Baltic Sea, including the BCS East-West Interlink, C-Lion1 and Estlink 2 incidents in late 2024, pushed Brussels to adopt a Cable Security Action Plan in 2025, alongside NATO’s “Baltic Sentry” naval patrol mission.

The EU revised its Maritime Security Strategy in 2023. The previous strategy was built with a focus on “piracy, illegal fishing, migration flows”; the updated one is built to confront state-based threats, Tytgat explains. He also warns the current strategy lacks teeth: “the tools have multiplied, but the financing and governance architecture to actually translate the strategy into tangible action is still lacking.”

How is the EU helping with funding?

A Commission subsea infrastructure package announced in February 2026 carries €347 million, alongside a separate €92 million ocean-observation initiative launched mid-2026. Tytgat calls both “a first step,” but says the sums are “far from enough if the EU wants to face the daily threats it deals with appropriately.”

Brussels is trying to close that gap through other channels: the €150 billion SAFE loan facility under its “Readiness 2030” roadmap, the European Defence Fund, 68.4% of which has gone to France, Germany, Italy and Spain, and PESCO’s joint shipbuilding projects, including the Italian-led European Patrol Corvette.

In March 2026, the EU also launched an Industrial Maritime Strategy, folding shipbuilding into a bloc-wide industrial framework for the first time rather than leaving it to national champions and earmarked €325 million for naval and undersea defence projects.

The real test for burden-sharing will be when the European Commission releases its progress report on the maritime strategy in October 2026. For now, Tytgat says the EU should focus on ensuring it has “the necessary tools and investment to meet the current challenges it faces in its vicinities but also in all global chokepoints that create threats to the EU's security of supply, trade and economy."

AI Can Discover New Materials Faster Than Science Can Validate Them – Analysis


Image: ChatGPT

August 27, 2026

By Burak Oktenli

Key Takeaways:

Generative AI is turning materials discovery from scarcity into abundance—proposing vast numbers of candidate structures far faster than labs can synthesize, characterize, and manufacture them.

A computational prediction of stability is not the same as a usable technology; real materials must clear successive hurdles of synthesizability, reproducible manufacturing, and performance under operating conditions.

The author argues the next bottleneck is evidence infrastructure—such as a standardized “materials AI evidence passport” that tracks model uncertainty, synthesis, independent measurement, and qualification—so scarce experimental resources go to the candidates most worth proving.

Generative models are rapidly expanding the search for new batteries, semiconductors, catalysts, aerospace materials and defense technologies. The next bottleneck is no longer finding candidates. It is proving that a predicted material can actually be synthesized, manufactured and trusted in the real world.

Materials science has traditionally suffered from a scarcity problem. Researchers could explore only a small fraction of the almost unimaginable number of possible compounds, structures and compositions that might possess useful properties.

Artificial intelligence is beginning to reverse that problem.

The emerging challenge may be abundance.

Machine-learning systems can now screen enormous chemical spaces, predict properties and increasingly generate candidate materials designed around specified characteristics. What once required researchers to select a relatively small number of hypotheses can increasingly become a computational search across thousands, millions or even more possible structures.

That is a remarkable scientific advance.

It also creates a new question: What happens when artificial intelligence can propose promising materials much faster than laboratories can determine whether those materials are real, manufacturable and useful?

The answer matters far beyond materials science.

Advanced materials sit beneath many of the technologies governments now consider strategically important: batteries, semiconductors, solar cells, carbon capture, aerospace systems, nuclear technologies, medical devices and defense equipment. The countries and companies that learn to convert AI-generated candidates into reliable physical materials will possess an advantage that cannot be measured simply by the number of structures their algorithms produce.

The new race is therefore not only to discover materials faster.

It is to validate them faster without confusing prediction with proof.
From Scarcity to Abundance

The scale of the shift became visible with Google DeepMind’s GNoME project.

In a 2023 Nature paper, the researchers reported more than 2.2 million crystal structures stable relative to previously known materials, with approximately 381,000 appearing on an updated stability frontier. The study represented an extraordinary expansion of the computationally accessible materials landscape.

But the same work also illustrates the distinction between discovering a computational candidate and possessing a usable material.

The paper reported 736 structures that had been independently experimentally verified. Its authors also identified synthesizability, dynamic stability and phase behavior among the remaining challenges between computational discovery and real-world application.

That gap is not a weakness of the research. It is the next scientific problem.

The transition is already visible in practical materials research. AI-guided, high-throughput experiments have been used to search enormous molecular spaces for improved photovoltaic materials, combining computational selection with automated synthesis and direct measurement in working solar cells. The important part of that workflow is not AI alone. It is the closed loop between prediction, synthesis and experiment.

Microsoft’s MatterGen provides another indication of where the field is heading. Rather than merely screening existing candidates, MatterGen can generate inorganic materials conditioned on desired characteristics, including mechanical, electronic and magnetic properties.

Its researchers went an important step further: they experimentally synthesized one AI-designed material and found its measured property to be within roughly 20 percent of the intended target.

That experiment is significant precisely because it crossed the boundary from computational proposal to physical evidence.

As generative systems improve, however, the number of proposals could grow much faster than the number of candidates that can receive comparable experimental attention.

A model can create another structure almost instantly. A laboratory cannot create another characterization campaign almost instantly.

Synthesis requires equipment, expertise, raw materials and time. Characterization requires instruments. Manufacturing introduces defects and process variability. Environmental testing requires additional facilities. Component qualification may take months or years.

AI can compress one part of the scientific pipeline without automatically compressing the rest.
A Stable Crystal Is Not Yet a Technology

One source of confusion is that the word “discovery” can describe very different stages of evidence.

A machine-learning model may predict that a crystal structure is energetically stable. That is scientifically useful. But stability under a computational method does not automatically establish that the material can be synthesized economically, manufactured reproducibly or maintained under operating conditions.

Different layers of uncertainty enter at different stages.

First comes model uncertainty. A machine-learning system is most reliable in regions sufficiently represented by its training and validation data. Materials discovery is difficult precisely because genuinely interesting candidates may lie outside that familiar domain.

Then comes reference-physics uncertainty. Machine-learning models are often trained against calculations based on methods such as density functional theory. Those calculations are extraordinarily valuable, but they remain approximations whose accuracy can vary with chemistry, structure and the property being predicted.

Next comes physical and manufacturing uncertainty. A perfect computational crystal is not necessarily the material produced by an industrial process. Defects, grain boundaries, impurities, temperature histories and manufacturing tolerances can change behavior.


Finally comes application uncertainty.

A battery material must survive repeated electrochemical cycling. A turbine material must tolerate extreme heat and mechanical loading. A semiconductor must perform reliably at manufacturing scale. A space material may face radiation, vacuum and severe temperature cycling. A defense material may need to survive shock, vibration, corrosion, aging, extreme thermal conditions or other highly demanding environments.

No single confidence score from a discovery model can represent that entire chain.
The Missing Infrastructure Is Evidence

This suggests that the next important innovation in AI-enabled materials science may be less glamorous than another generative model.

The field needs a common way to record what has actually been demonstrated.

One approach would be a materials AI evidence passport: a standardized evidence record that follows an AI-generated candidate from computational discovery through experimental validation and, where appropriate, manufacturing and qualification.

The passport would not decide whether a material is “good” or “bad.” It would make the status of the evidence legible.

At the computational stage, it could record the model and version used, the relevant training-data domain, the reference calculation, the conditions under which the model was validated and an empirically tested estimate of uncertainty.

If a candidate lies substantially outside the model’s validated domain, that fact should travel with the candidate rather than disappearing behind a high prediction score.

Recent work on AI-assisted alloy discovery points in the same direction: useful discovery systems increasingly need to distinguish confidence from uncertainty and identify regions where the available evidence is insufficient, rather than merely rank candidates.

The next layer would document independent computational verification where appropriate.

After that would come the physical record: whether synthesis has been achieved, whether composition and structure have been confirmed, whether the predicted properties have been measured and whether results have been reproduced independently.

Later stages could record whether a manufacturing route has been demonstrated and whether the material has survived testing under conditions representative of its intended use.

A scientist, investor, manufacturer, government laboratory or program manager should be able to look at a candidate and immediately distinguish between three very different statements:


The model predicts this should work.

We have made it and measured the relevant property.

We can manufacture it reproducibly and it works in the environment for which it is intended.

All three statements are valuable. They are not equivalent.
Why This Would Accelerate Science Rather Than Slow It

Standardized evidence can sound bureaucratic, particularly in a field whose attraction lies partly in accelerating discovery. But the purpose would be the opposite.

As computational candidate generation becomes cheaper, experimental capacity becomes relatively more scarce.

The scientific problem becomes one of allocation.

Which candidates deserve expensive synthesis? Which deserve synchrotron time? Which should proceed to manufacturing experiments? Which require additional calculation first? Which apparently spectacular result is simply too far outside a model’s validated domain to justify immediate investment?

An evidence passport would allow laboratories to direct scarce physical resources toward candidates with the strongest combination of potential value and credible supporting evidence.

It could also make results more portable.

Different universities, national laboratories and companies do not need to use identical AI models or surrender proprietary datasets. But they could use a common grammar for describing what a model has established and what physical tests remain incomplete.

That distinction is important. Scientific standardization does not require methodological uniformity.

Researchers can disagree about models while still agreeing that provenance, uncertainty, synthesis and physical validation should be visible.

The same principle already operates throughout mature engineering disciplines. A component rarely becomes trustworthy because its designer announces a confidence score. Trust accumulates through documented testing, traceability, calibration, independent measurement and experience under increasingly representative conditions.

AI-generated materials should not be exempt from that logic simply because the front end of discovery has become computational.

The Strategic Implications Are Larger Than Defense

My original interest in this problem came from defense applications, where the consequences of weak validation can be unusually severe.

AI can help search for energetic compounds, thermal-protection materials, armor, radiation-tolerant components and materials designed for extreme environments. But a computational prediction cannot substitute for the destructive and environmental testing required before such materials enter operational systems.

Defense is therefore a useful stress test for the broader problem. It is not the only sector facing it.

The energy transition will depend on new battery chemistries, catalysts, photovoltaic materials and materials for electricity transmission and storage.

Semiconductor progress increasingly depends on materials with carefully controlled electrical and thermal characteristics.

Fusion systems require materials capable of surviving environments that are extraordinarily difficult to reproduce.

Space exploration requires lightweight structures, radiation tolerance and long-duration reliability.

Medical technologies introduce their own requirements for safety, biocompatibility and reproducibility.

Across all of these fields, AI can accelerate the search. Physics still decides whether the result works.
The Next Bottleneck

The history of technological development repeatedly shows that discovery and deployment operate at different speeds. Artificial intelligence may make that mismatch far more visible.

The computational side of materials science is entering an era in which proposing a new candidate can become extremely cheap. That does not make experimental science obsolete. It makes experimental science more valuable.

When millions of possible candidates compete for limited laboratory attention, the ability to determine which computational claims deserve physical verification becomes a strategic scientific capability in its own right.

The most successful AI-for-science systems will therefore not simply generate the largest number of new materials. They will create better loops between prediction and experiment.


Models will propose. Experiments will test. Failures will return information to the models. Manufacturing will reveal forms of uncertainty invisible in idealized calculations. Real operating environments will expose limits that neither simulation nor laboratory characterization could fully anticipate.

That closed loop – not generation alone – is where the real acceleration of materials science will occur.

AI may be able to propose tomorrow’s battery cathode, semiconductor, radiation shield, catalyst or armor material in hours.

The important question is whether science can tell, nearly as quickly, what has actually been proved.

NATO’s Next Supply-Chain Vulnerability Has No Factory Floor – Analysis


Credit: NATO

August 25, 2026

By Burak Oktenli

Key Takeaways:

NATO’s post-Ankara industry strategy correctly stresses scalable, interoperable, and resilient physical production, but the same standards must now extend to the digital layer—AI models, cloud services, identity systems, and software dependencies—that modern military capability increasingly rests upon.

Vendor diversity does not equal dependency diversity: a portfolio can appear multinational while still containing single points of failure in models, clouds, authentication, updates, or jurisdictions; commercial digital services can become unavailable overnight due to export controls, outages, or policy changes even when hardware remains intact.

Critical AI-enabled systems should carry an explicit dependency budget and be stress-tested for substitution time rather than supplier count, so allies can practice controlled dependence—using the best available technology while ensuring rapid, operable fallbacks—before the October implementation plan is finalized.

Ankara’s new industry strategy is designed to make defense production more scalable, interoperable, and resilient. The same discipline should apply to the AI models, clouds, identity services, and software dependencies that can disappear without a factory shutting down.

A missile shortage is easy to see. A missing cloud service may not become visible until the capability depending on it stops working.

That difference matters as NATO turns the commitments made at its July summit in Ankara into an implementation plan for a stronger transatlantic defense industrial base. The Alliance’s new Strategy for Industry-NATO Cooperation is unusually concrete: it calls for modularity and open architectures, stronger interoperability, more resilient supply chains, and tabletop exercises that stress-test whether production can surge and endure under crisis conditions.


Ankara also produced two practical mechanisms. The NATO Front Door for Industry is intended to simplify how companies find procurement, innovation, testing, and engagement opportunities. The NATO Engine is meant to connect industrial demand with available manufacturing capacity across the Alliance. Both respond to the same strategic reality: deterrence depends not only on possessing capability, but on being able to scale, sustain, and replace it.

There is one layer of the industrial base that deserves the same treatment before the October implementation plan is completed: the digital infrastructure underneath AI-enabled military capability.

Modern military AI increasingly arrives as a stack rather than a box. A system may depend on one company for the model, another for cloud hosting, a third for identity and access management, proprietary interfaces for integration, external services for updates and evaluation, and data pipelines governed in yet another jurisdiction. The nationality of the prime contractor tells only part of the story.

A procurement portfolio can therefore look diversified while retaining a single digital point of failure.

Vendor Diversity Is Not Dependency Diversity

The problem became visible in June when a U.S. export-control directive required Anthropic to restrict access to its Fable 5 and Mythos 5 models for foreign nationals. Because the order took effect immediately and the company said it had no reliable way to verify nationality in real time, Anthropic suspended the models for all users. The controls were lifted on June 30, and access began returning the next day.


The point is not that NATO should avoid American AI services, nor that this particular episode predicts a future alliance crisis. The lesson is narrower and more useful: a commercially available digital capability can change availability because of a government order, export restriction, licensing decision, security incident, provider outage, contract dispute, or technical change even when every physical component remains intact.

Replacing the service may require much more than buying another subscription. A second model may expose different interfaces. Its outputs may need fresh validation. Security controls may have to be rebuilt. Data may need to move between jurisdictions. Operators may require retraining. Existing software may have been optimized around one provider’s architecture.

A replacement that exists commercially may therefore be unavailable operationally for weeks or months.

This is the digital equivalent of discovering that several weapon systems depend on the same scarce component. NATO already treats concentration risk in physical supply chains as a resilience problem. Digital concentration deserves the same precision.

Five contractors do not create resilience if all five ultimately rely on the same cloud, the same model provider, the same authentication layer, the same update service, or the same jurisdiction for a mission-critical function. A multinational supply chain can still contain a single switch.

Give Critical AI Capabilities a Dependency Budget

NATO’s implementation plan offers an opportunity to make that exposure measurable. Every critical AI-enabled capability should carry a dependency budget: an explicit account of how much operational capability rests on any single provider, technical service, interface, or jurisdiction, and how quickly that dependence can be substituted.


This does not require one universal percentage. A logistics-planning tool can tolerate a different dependency profile from cyber defense, intelligence analysis, air defense, or command-and-control support. What matters is that concentration becomes visible before a system is embedded deeply enough to make replacement prohibitively difficult.

For each critical capability, planners should map the model provider, compute environment, cloud operator, identity service, update authority, proprietary interfaces, data dependencies, evaluation services, cryptographic credentials, jurisdictional constraints, and fallback options.

Then ask operational questions. How much capability remains if the primary provider disappears for 24 hours? What remains after 30 days? Can an allied operator move to another model without rebuilding the surrounding software? How long would revalidation take? Can the system continue in a degraded local mode? Who controls the credentials, keys, updates, and interfaces required to make the transition?

Those questions turn digital sovereignty from a political slogan into an engineering property.

They also create a more useful measure than national origin alone. An American service may be entirely appropriate for a European military mission if substitution paths are credible and the conditions governing access are understood. A nominally European system may create greater vulnerability if its compute, software dependencies, or update chain ultimately converge on one external provider.
Stress-Test Substitution Time, Not Supplier Count

The Ankara strategy already calls for tabletop exercises that stress-test defense production under heightened demand and crisis conditions. Digital dependency should be added to those exercises.

One scenario could remove a major cloud or model provider from an allied workflow without warning. Another could impose a jurisdictional restriction on a critical software component. A third could assume that a commercial provider remains online but stops issuing trusted security updates.

The metric should be substitution time, not whether an alternative vendor exists on paper.

A fallback model that requires three months of integration and validation offers little resilience during the first week of a crisis. The same is true of an alternative cloud environment that cannot accept existing data, identities, credentials, or workloads without extensive reengineering.

This is where NATO’s emphasis on modularity, open architectures, digital standards, testing, verification, and lifecycle interoperability becomes strategically important. Open interfaces reduce switching costs. Common evaluation procedures make alternative models easier to qualify. Portable data and identity architectures reduce migration time. Contract terms can require providers to document critical dependencies and preserve workable exit paths.

Controlled Dependence, Not Digital Autarky

None of this requires NATO to abandon American technology or ask every ally to reproduce the frontier-AI ecosystem nationally. That would consume enormous resources and could fragment the Alliance technologically.


The more practical objective is controlled dependence.

Allies can continue using the best available models, clouds, and software while designing systems that remain operable when one layer changes. The United States benefits as well: allied confidence in American technology is stronger when reliance comes with tested continuity arrangements rather than an assumption of permanent availability.

The strategic issue is larger than procurement preference. Software services and AI infrastructure will increasingly determine whether physical military assets can be coordinated, maintained, upgraded, and used effectively. A defense industrial strategy that measures only factories, inventories, and production lines will miss part of the capability chain.

Ankara gave NATO a serious framework for strengthening the industrial base behind deterrence. The implementation plan due in October should recognize that part of that industrial base has no factory floor.

A missile shortage is visible in the warehouse. A digital dependency becomes visible when the mission stops. NATO should find it first.

 

Science Has Discovery Thresholds, It Also Needs Stop Rules – OpEd

Image: ChatGPT

 August 25, 2026

By Burak Oktenli

Key Takeaways:

• Science is skilled at defining what would count as a discovery but often fails to specify in advance what evidence would close, downgrade, or pause an extraordinary claim; historical cases such as OPERA and BICEP2 show that the disappearance of an anomaly is itself a scientific success.
• AI makes anomaly hunting nearly limitless by generating unlimited candidates from vast datasets, which shifts the scarce resource from detection to adjudication and risks turning research programs into narratives that continually relocate rather than face decisive tests.
• Critical AI-enabled or high-profile searches should therefore carry explicit exit conditions—discriminating observables, conventional alternatives, calibration failures, independent replication standards, and null-result sensitivity thresholds—so that closure, downgrade, or pause become recognized scientific outputs rather than after-the-fact improvisations.

AI can make anomaly hunting nearly limitless. Researchers should define in advance what would close, downgrade, or pause an extraordinary claim not only what would count as a discovery.

Science is very good at celebrating the moment a result becomes interesting. It is less practiced at deciding when continued pursuit is no longer justified.

When the OPERA experiment reported a neutrino timing result that appeared to challenge the speed of light, the scientific response was not to protect the anomaly. Researchers attacked the timing chain, checked the instrumentation, and sought independent measurements. Later measurements were consistent with neutrinos traveling at light speed. The disappearance of the anomaly was not a failure of science. It was the science.

The BICEP2 episode made the same point in a different way. An apparent B-mode polarization signal was widely discussed as possible evidence of primordial gravitational waves. A joint BICEP2/Keck and Planck analysis later found strong evidence for dust and no statistically significant evidence for tensor modes in the analyzed data. The valuable result was not simply that a spectacular interpretation weakened. Science had narrowed what the observation could responsibly mean.

These cases expose a missing half of falsifiability. Scientists spend enormous effort defining what would count as evidence for a claim. High-cost and high-profile searches should also define what would count as enough evidence to close, downgrade, or pause one.

Falsifiability Needs an Exit Condition

Before an extraordinary physical claim consumes years of attention, researchers should be able to answer uncomfortable questions in advance. What observation would materially weaken the hypothesis? What calibration failure would invalidate the signal? What conventional explanation would be sufficient to end the extraordinary interpretation? At what sensitivity would a null result close the parameter range being tested? How many genuinely independent failures to replicate would lower the priority of the claim?

“More data” is not a falsification criterion.

There are good reasons to resist rigid stopping rules. Premature termination can bury real discoveries. Instruments improve. Background models change. A null result at one sensitivity may become a detection at another. Some theories remain scientifically valuable even when the decisive experiment is not yet technically possible.

But the opposite failure is real as well: a research program can become structurally incapable of losing. An anomaly appears and a conventional explanation removes most of it, so attention moves to a residual. The residual disappears and the search moves to another dataset. A replication fails and the failure is attributed to different conditions. A predicted signature is absent and the parameter range moves. None of those moves is automatically illegitimate. Taken together without an exit condition, however, they can turn an empirical program into a narrative that changes faster than it can be decisively tested.

A scientific program that cannot say what would make it stop is in danger of protecting a claim rather than testing it.

Stopping also does not have to mean abandoning a field. It can mean changing the status of a claim. “Discovery” becomes “candidate.” “Candidate” becomes “calibrated anomaly.” “New physics” becomes “model preference under stated assumptions.” One parameter region can close while another remains open. A team can conclude that an instrument lacked sufficient sensitivity, that a proposed signature was not discriminating, or that a conventional mechanism explains the observation without meaningful residual structure.

Those are scientific outputs. Closure is not the opposite of discovery; it is one way evidence becomes useful.

AI Makes Anomalies Cheap

Particle physics already recognizes part of this problem through stringent significance conventions and corrections for the look-elsewhere effect. Searching many channels makes an apparently striking local fluctuation less surprising, which is why the scope of the search has to be part of the evidence rather than an afterthought.

Artificial intelligence makes the broader stopping problem more urgent because it changes the economics of anomaly hunting. A system can scan enormous collections of spectra, images, light curves, detector events, candidate materials, or simulated physical states and rank the strangest examples. That is useful. It also means the supply of interesting outliers can become effectively unlimited.

When finding candidates becomes cheap, adjudicating them becomes the scarce resource.

An AI system can always produce another unusual point, another model fit, another candidate cluster, or another corner of parameter space worth inspecting. If every failed lead merely authorizes the next search without changing the status of the underlying claim, automation can make an already weak scientific habit scale much faster.

Synthetic data sharpen the problem. Imagine a classifier trained to distinguish simulated wormholes from simulated black holes. It performs spectacularly on a synthetic test set and then flags a real astronomical observation as wormhole-like. That may justify follow-up. It does not establish that a wormhole was detected. There are no confirmed wormhole examples on which to validate the label. The classifier may have learned differences between simulation pipelines, omitted astrophysical effects, or artifacts of the generators. Its success proves that it can separate the synthetic worlds it was given. Nature has not promised to resemble either one.

This is why anomaly detection and claim authority should remain separate. A search system can help decide where scientists look next. It should not decide what the observation is called—or whether a search has earned unlimited continuation.

Make Stopping a Scientific Output

The practical discipline is straightforward. Before the result becomes institutionally or emotionally expensive to lose, a project should specify the discriminating observable, the serious conventional alternatives, the calibration failures that would invalidate the signal, the search scope that must be corrected for, what replication would count as independent, the sensitivity at which a null result closes the tested region, and what evidence would trigger a downgrade or pause.

For expensive or extraordinary-claim programs, funders and review panels could ask for those continuation and termination conditions alongside the discovery criteria. The purpose would not be to impose a bureaucratic kill switch on scientific curiosity. It would be to make it harder to invent a new survival condition only after the old one fails.

The same principle should shape publication. Null results should be treated as positive scientific products when they close a meaningful parameter range. A calibration failure can close an anomaly. A conventional explanation can close an extraordinary interpretation. A failed replication can identify which dependency mattered. A search that reaches its prespecified sensitivity without detecting the predicted effect has produced information even if it does not produce a headline.

This matters increasingly in AI-assisted science because search capacity is growing faster than the scientific community’s capacity to investigate every candidate. The bottleneck is shifting from finding unusual things to deciding which unusual things deserve continued belief, money, instrument time, and attention.

Discovery is one way science advances. Elimination is another.

A mature research program should know not only what would make it celebrate, but what would make it stop.

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.
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