Interview 

Backlash Against Anti-Racism Is a Constant Force Shaping US Politics

Philosopher George Yancy analyzes the contemporary expression of what W.E.B. Du Bois called the “entrails of whiteness.”

By Josh Friedberg ,

Truthout

August 26, 2026

Blank walls and empty brackets mark the spots where National Park signs memorializing historical facts about slavery were once mounted, before they were removed by the Trump administration, March 14, 2026, at the Independence National Historical Park in downtown Philadelphia, Pennsylvania.Andrew Lichtenstein / Corbis via Getty Images
Blank walls and empty brackets mark the spots where National Park signs memorializing historical facts about slavery were once mounted, before they were removed by the Trump administration, March 14, 2026, at the Independence National Historical Park in downtown Philadelphia, Pennsylvania.

Andrew Lichtenstein / Corbis via Getty Images

In the United States, the presidential administration is actively working to erase past gains in civil rights and environmental justice. This tendency is manifesting in many ways, including curtailing voting rights, whitewashing U.S. history teaching materials, and rolling back social services that disproportionately affect people of color. In this context, one of the most important books I’ve read from the last decade is George Yancy’s Backlash: What Happens When We Talk Honestly About Racism in America. I strongly believe that every white person needs to read and reckon with the ideas in Yancy’s book. Backlash is a tougher but more necessary book than most popular books on race, especially in the context of 2026 being the 250th anniversary of the United States’ independence. At a time when many cling to the myth of this nation as fair and equal while white supremacy continues to dominate national and global politics, Backlash is a stern yet compassionate reminder of the importance of defining racism in terms of power. Within this context, I conducted this exclusive interview with George Yancy, who is the Samuel Candler Dobbs professor of philosophy at Emory University, a frequent contributor to Truthout, and the author, editor, and coeditor of over 25 books.

This interview has been lightly edited for clarity and length.

Josh Friedberg: Let’s start with the letter you wrote in 2015, “Dear White America.” What motivated you to write it?

George Yancy: In terms of context, the letter itself was my essay contribution to a series of popular interviews that I conducted with philosophers on race for The New York Times philosophy series, “The Stone.” The motivation had to do with the desire to find a critical discursive format for addressing the issue of whiteness. How does one speak to white people about something that is so familiar to them, like water is to fish, so normative and yet opaque, and thereby invisible? Indeed, how does one deploy a mode of address that attempts to speak the truth about their whiteness without resulting in forms of irrational defensiveness on their part? I wanted to communicate something about whiteness that critiqued its normative structure and how it falsely equates itself with humanity per se.

In his book Performing Purity (2003), whiteness scholar John T. Warren argues that “like layers of sand constituting a rock, the repeated enactments of [white] identity become sedimented and seemingly fixed, as if they had always been there.” I wanted to communicate to white people that their identity as white — along with the tropes of whiteness as “superior,” “virtuous,” and “pure” — was historically constructed, and that it is through their complicity that whiteness is sustained as a process of racial privilege, domination, and oppression. And I wanted them to own this hard truth. I thought that through my own show of vulnerability — where I talk honestly about my own sexism — I could perhaps touch the souls of white readers. I wanted to get them to use my capacity to expose my own complicity with sexism as a bridge that encouraged their own capacity to be honest about their whiteness and its link to anti-Blackness.


Trump Relies on Centuries-Old Notions of Whiteness to Activate His MAGA Base
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By George Yancy , Truthout April 4, 2026

I saw the letter as one grounded in love. Like James Baldwin, I wanted to demonstrate a form of love “not in the infantile American sense of being made happy but in the tough and universal sense of quest and daring and growth.” I was asking for nothing short of self-confrontation. As you know, the invitation was met with hatred and denial. I was called by the n-word more times than I would like to remember. Being called that word, and so many other despicable words, lends credence to my pessimism vis-à-vis white America’s honesty with itself and its history of anti-Black violence. Like W. E. B. Du Bois, I possess “a hope not hopeless, but unhopeful.”

You later wrote a booktitled Backlash about the responses you received to your letter. In my blog, I related your book to America 250 and our current incarnation of white supremacy in the U.S. Do you think Donald Trump’s presidency has affected the presence of racism in the U.S.? In other words, is racism worse with him in office or is it more of the “same old, same old?”

Writing Backlash was cathartic for me. I’ve been told that the book is hard to read — not because of its philosophical jargon, but because I don’t withhold sharing the raw vitriol that was directed at me by so many white readers. The responses were vile projections spewed from the historical formation of the white imagination — an imagination imbued with myths of “white purity.” Racialized xenophobia and white fear and resentment were unambiguous:

“He should f*** off to Africa if he doesn’t like living in a white country.”

“You’re going back to Africaaaaaa!”

“Deport him to Africa to ponder racism there.”

The irony is that I never asked white people to bring me here, to take me from Africa, to enslave me, to brutalize me, to lynch me, to make me the object of white anti-Black fantasies and desires. It was never about me, about Black people. It was about whiteness and the historical fact that white people created a fictionalized conception of Blackness to conceal their own lack. Whiteness is a structural lie that underwrites and perpetuates its delusions of ontological supremacy.

Linking Backlash to the U.S.’s 250th birthday and to Donald Trump’s presidency (2.0) is important as neither one speaks to the eradication of the toxicity of whiteness. This country’s founding is predicated on the genocide of Indigenous peoples and the denigration of Black people. Those realities speak to the ontological binary structure of whiteness. In short, whiteness creates conditions of ontological apartheid.

For me, whiteness is not anti-Black or racially xenophobic because it has gone awry; whiteness is anti-Black because its very being is anti-Black; this is how it thrives. Racism is not “worse” because of Trump. He is just its latest manifestation, the latest conduit through which the ugliness of white supremacy expresses itself shamelessly. The U.S. is either a white supremacist state, or it isn’t. There is no in-between.

For Black people, this doesn’t mean that juridically there has not been any progress. But what is juridical progress when one remains part of a white state — that reinforces collective white psychic life — that needs you to remain abject? Let’s face it. Trump’s white nationalist xenophobia and anti-Blackness — from his discourse about Black “shithole countries,” to his expressed desire for the U.S. to accept more immigrants from Norway, to his false claim that there is genocide against white South Africans, and to his claim that Haitian immigrants are eating the cats and dogs of their neighbors — is unashamed. I can easily imagine Trump saying to me: If Yancy doesn’t like America, which is the greatest country the likes of which we’ve never seen before, then he should take his woke and radical left ideas to one of those shithole countries. In short, for me, contemporary white supremacy is more of the same old, same old.

Something that struck me in your book was your compassion and empathy, as well as tough-mindedness, for whites, as you acknowledged your own sexism as a man. You see racism as being about power and privilege, not value — your approach is not,“This person is a racist and is therefore a bad person.” Can you expand on that, addressing the tension between the compassion and the toughness in your writing?

Sure. My position is grounded in the prophetic love of James Baldwin when it comes to critiquing whiteness. This doesn’t mean that I love white people because of their whiteness. That is part of the lie and seduction of whiteness, that somehow BIPOC people are expected to love whiteness, its symbolism, its aesthetics, its tropes of “greatness.” I refuse to be a consumer of that poison, to wear a white mask and forget the fact that I’m seen as a “problem” by the white state. That seduction — that act of mask-wearing — can cost me my life.

Baldwin didn’t forget that he was Black. Rather, he resisted the derogatory definitions that white people imposed upon him. So, I would say that the compassion or empathy that you’re talking about is, for me, a manifestation of Baldwinian love that will, as he says in The Fire Next Time, “force [white people] to see themselves as they are, to cease fleeing from reality and begin to change it.” That is Socratic to the core. When white people begin to truly interrogate their whiteness, this will place them within a state of danger. Why danger? Because this will or should generate a powerful form of disenchantment with themselves and the world that has come to accept the lie that whiteness is innocent.

I have had BIPOC students ask me, “Why do you spend so much time talking to white people about their whiteness, when you should be talking to Black people and people of color?” Well, I can do both. More importantly, I talk to white people — I place the ethical burden upon them — because they must take responsibility for the violence of whiteness and its structural binary, its us-versus-them mentality. To get white people to face the lie of their white innocence is to — perhaps — save the lives of Black people.

Also, I would add that it is too easy to say that to be racist is to be a bad person, but white people must, because of their complicity with white supremacy, carry the weight of how their whiteness denigrates me and how they are thereby positioned within a network of social relations that holds them collectively responsible. In this way, white people don’t escape the unethical dimensions of whiteness.

With the current controversies around ethnic studies and diversity, equity, and inclusion (DEI), how do you think the ideas in Backlash have held up since its publication? Is there anything you would change or add to them now?

I think that Backlash captured the unmitigated aspirations that white people possess when it comes to desiring “white purity” and evading the reality of structural white anti-Blackness. Hence, since its publication, I’m reminded that whiteness will do anything to survive, especially through the deployment of the social, political, and existential chimera of being a target of “anti-white racism.”

In his book Ethnic Studies at the Crossroads (2026), George Lipsitz — writing about the fiction of reverse racism — writes: “Once affirmative action was invalidated by the Supreme Court and [critical race theory] lost its luster as privileged villain, it quickly became supplanted by opposition to DEI programs. Beneath the surface, this succession of moral panics about an imaginary ‘reverse racism’ entails opposition to the project of ethnic studies and indeed to any visible manifestation of larger antiracist social formations.” Lipsitz goes on to mark the difference between what I would call a form of political patchwork vis-à-vis racism as opposed to something far more substantive: “It is not that nothing ever changes in history, but that when it comes to racism the changes are often more cosmetic than substantive. W.E.B. Du Bois compared white supremacy in the U.S. to a crack in a plate that was once broken but had been patched up. Whenever the plate was dropped again, it broke along the lines of the patched-up crack because the weakness was structural, not surface.” If I was to add anything different, I would make explicit the insights of historian Jeanelle K. Hope regarding the historical and contemporary ways in which U.S. whiteness and U.S. fascism are inextricably linked.

Does your work in Backlash relate to any current or upcoming projects you are working on? Are there any parts of it that you’d like to see other thinkers and scholars reckon with?

I am currently editing a book under contract with Temple University Press that explores how Black and white philosophers and public intellectuals interpret James Baldwin’s view that white America invented the N*****. So, I continue to explore and expose what W.E.B. Du Bois, in “The Souls of White Folk,” called the entrails of whiteness. In terms of other scholars, I would especially like to see more white philosophers come to terms with their structural positionality as white and how their whiteness manifests as anti-Black. Perhaps how I write — bringing the pain and suffering close — might inspire that kind of deep psychic work, the work of lived experience, that is courageous and honest without resulting in white “innocence” framing race/racism as a distant conceptual object, buried in arcane philosophical works written by Anglo-American and European philosophers of the past.


This article is licensed under Creative Commons (CC BY-NC-ND 4.0), and you are free to share and republish under the terms of the license.

Josh Friedberg
Josh Friedberg is an author, TEDx speaker, storyteller, and music historian. He has over 100 articles published, including as a staff writer at the web magazine PopMatters. He has won many awards from Illinois and national communications contests since 2017. He holds a master’s degree in English from Northeastern Illinois University and lives in Chicago, where he tutors college students in writing.

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.
View all posts by Burak Oktenli

French soldiers disciplined over Rhodesian flags linked to white supremacists

France's Ministry of the Armed Forces has launched disciplinary proceedings against several soldiers photographed holding flags linked to Rhodesia, a former white minority-ruled territory in southern Africa whose symbols have been embraced by white supremacists.


Issued on: 26/08/2026 - RFI

France's Ministry of the Armed Forces has launched disciplinary proceedings against several soldiers photographed displaying Rhodesian flags, which have become associated with white supremacist movements. AFP - LUDOVIC MARIN

Two separate groups of French soldiers – one in Nîmes in southern France and another in the United Arab Emirates – have been pictured displaying the same banner, with the images later shared in online groups glorifying the former British colony – which operated a white-minority ruled system similar to South African apartheid – investigative website Mediapart revealed.

One image posted on social media shows three soldiers holding a flag with three green and white stripes. At its centre is a coat of arms depicting a pick axe on a green background, flanked by antelopes and topped with a golden bird.

Another image shows four French soldiers in front of a tank holding the same banner.

The Ministry of the Armed Forces said the soldiers involved had been identified and disciplinary proceedings were under way. It said the two incidents were separate, with no connection between the soldiers involved or their activities.

“The ministry's position is unambiguous: French armed forces do not tolerate any manifestation of a racist, supremacist or extremist nature,” it said.

“Military personnel are subject to a particular requirement of exemplary conduct and neutrality, which also applies to their behaviour and posts on social media.”

Defying decolonisation

Rhodesia's prime minister, Ian Smith, unilaterally declared independence from the United Kingdom on 11 November, 1965 in an effort to preserve political power for the white European minority – which made up just 5 percent of the population, fewer than 250,000 people.

Rhodesia's green and white flag was first raised on 11 November, 1968, three years after Smith's declaration, which was made without British agreement or international recognition.

“Rhodesia wanted to signal to the world what it regarded as a betrayal: the fact that the United Kingdom was accepting the decolonisation of Africa,” South African historian Saul Dubow, who teaches Commonwealth history at the University of Cambridge, said.

Rhodesian propaganda portrayed the territory as “threatened by Communism” and therefore forced to defend “civilised standards”.

In practice, this meant defending the social and political superiority of white people, Zimbabwean researcher Bruce Berry wrote in his thesis on Rhodesian identity and its symbols.

African nationalist movements resisted Smith's government, particularly Joshua Nkomo's Zimbabwe African People's Union, known as ZAPU, and Robert Mugabe's Zimbabwe African National Union, or ZANU.

The conflict between Rhodesia's white minority government and African nationalist movements became known as the Bush War, or Second Chimurenga. It killed thousands of people, most of them African nationalists and civilians, and led to the militarisation of Rhodesian society.

As this civil war intensified in the country during the 1970s, Rhodesia became heavily dependent on neighbouring South Africa, then operating an apartheid regime, for its survival.

Negotiations and a ceasefire followed, and Rhodesia became the independent and renamed state of Zimbabwe on 18 April, 1980 – with ZANU leader Mugabe as prime minister.

Military mythology

With the white population having rallied behind its soldiers and the country's founding myths, those narratives survived beyond the ceasefire and the birth of the new country, feeding nostalgia for a “heroic” past – one in which the suffering of the black population was concealed.

“There is an enormous body of literature written by people who took part in the Bush War. They celebrate their victory and talk about black people in very insulting terms,” Dubow explained.

Many white Rhodesians, he said, became convinced that they had not lost the war, but had instead been abandoned by their leaders and cheated by politics.

“It is the myth of the small white minority that resisted the black majority for 15 years,” said Hugh W Macmillan, a professor from the region.

That fascination with Rhodesia's military past has also surfaced among soldiers elsewhere.

In 2018, Canada's military opened an investigation into some of its personnel accused of running a website selling equipment and memorabilia inspired by the "Fireforce" – a tactic developed by Rhodesian forces to surround and attack independence fighters in the bush.

Flags from apartheid-era South Africa and Rhodesia were among the items sold by the online shop.

Symbol of a 'lost cause'

In some white supremacist circles, Rhodesia provides a similar function to the southern Confederate States in the United States, with both seen as “lost causes” unjustly condemned by history.

In June 2015, white supremacist Dylann Roof opened fire in a church in Charleston, South Carolina, killing nine African American worshippers who had gathered for bible study.

Roof had written a racist manifesto published on a website called The Last Rhodesian. In a photograph that became notorious, he wore the former orange, white and blue South African flag on his jacket lapel, with the green and white Rhodesian flag immediately below it.

As Berry wrote in his thesis: “These flags – Rhodesian and South African – are popular in white supremacist circles because they allow people to signal their beliefs to others who share the same ideas, without exposing themselves as openly as they would by wearing or displaying a swastika."

This article has been adapted from the original version in French by Liza Fabbian

SPACE ENTHUSIASTS

Looking up: Amateur meteorite hunters search for clues to origins of universe

French astronaut Sophie Adenot made history this month as the first French woman to perform a spacewalk. Back on Earth, space enthusiasts across France are finding their own ways to reach for the stars. In the third of a five-part series, RFI hears from volunteers in south-west France who are searching for meteorites as part of a citizen science project designed to help researchers recover fragments from space.


Issued on: 26/08/2026 - RFI

Concentration, patience and sharp eyesight are needed to find a meteorite no bigger than a golf ball. © Baptiste Coulon / RFI

At the Fleurance Astronomy Festival in south-west France, 50 volunteers have gathered across four freshly cut fields of wheat. The group, kitted out in caps, rucksacks and trainers, includes space enthusiasts as well as families with young children.

The volunteers have been told that a meteorite fell to Earth during the night, just a few hundred metres from where they are standing.

“We saw a huge flash of light at an altitude of 30 kilometres, then the fireball broke into many pieces. The aim is to find them,” said planetary scientist Sylvain Bouley, who is leading the search.

This meteorite hunt is actually just an exercise, a fact the volunteers are aware of. An hour before the public arrived, Bouley hid 20 meteorite fragments across the fields. The group then split into four teams and began searching.

The task quickly proves tougher than it first appears. The fragments are no bigger than a dice and the search area covers about five hectares.

“When we have to find a two-centimetre stone over such a large area, we need members of the public – citizens who've learned how to observe,” Bouley said. “So the aim is to train people all over France so we can call on them when a real meteorite falls to Earth.”

That is not a hypothetical scenario. In February 2023, a 650-kilogramme fireball broke apart over Normandy and scattered around 100 fragments across an area seven kilometres wide.

Within hours of the impact, Bouley and his team had gathered 200 volunteers to search the area. Several pieces were found.

A meteorite fragment about two centimetres across and weighing around 10g, found by a participant. © Baptiste Coulon / RFI

'Fan of rocks'

The apprentice hunters in Fleurance are hoping to do the same. Their search involves two hours of walking in closely spaced lines, eyes fixed on the ground.

Aurélie has joined the exercise with her daughter Abygaëlle, an energetic little girl who describes herself as a “fan of rocks”.

“I'm a very big collector,” Abygaëlle says, making her mother laugh.

“She has a whole shelf in her bedroom,” her mother says, suggesting the collection has become somewhat overwhelming.

So far, Abygaëlle has found only “pretty rocks” rather than meteorites. She thinks she knows how to tell them apart.

“Meteorites don't have too many holes, whereas other rocks have very deep holes,” she says.

The reality is a little more complicated.

“A meteorite has a black fusion crust, caused by the heating of the rock through compression and friction as it enters the atmosphere," Bouley explains. "It also has flat, rounded and fairly clean surfaces that can be distinguished from rocks that have been there for some time. They might look like meteorites, but they aren't.”

The possibility of finding a meteorite only to discover that it is an ordinary rock makes the search demanding. After an hour of careful observation, none of the meteorites has been found.

The teams turn around and begin searching in the opposite direction, led by Claire Loubière, one of the supervisors. A physics and chemistry teacher at a school in the Paris region, Loubière says she was excited by the citizen science project.

“It's great to think that we've helped a scientist move forward with their research. It's not at all the image of the researcher alone in their laboratory or out in the field," she explains. "You realise that it's a field open to everyone, including 10-year-olds who can find rocks that are useful to science."

Adults and children take part in the hunt for meteorites. © Baptiste Coulon / RFI

Scientists unearth meteorite from the birth of the solar system

Clues from space

The first find is eventually made by 10-year-old Emmanuel, to applause from his group. He immediately puts the fragment into their bag.

Brigitte Zanda, the astrophysicist who first recovered the fragment – dated to 4.56 billion years old – says it could provide information about the origins of the universe.

“Most meteorites come from small bodies that have changed very little since the formation of the solar system and were therefore present when the sun was forming," Zanda explains. "Until we've analysed a new meteorite, we can't know whether it might have incredible information to teach us."

Involving members of the public has become increasingly important because the number of meteorites recovered in France has fallen sharply over the past 200 years.

"We found 45 during the 19th century, but only nine during the 20th century," Zanda says. Yet the number of impacts remains stable, at about two to three per year in France and several tens of thousands worldwide each year.

“To find a meteorite, you have to see it fall,” Zanda says. “But our lifestyles have changed. We look at the sky less than we used to because fewer of us live in the countryside. And television and smartphones also distract us from the stars.”

The 364-kg Mont Dieu meteorite, found in the Ardennes region in 2010, is 4.65 billion years old and one of the largest ever found in France. Sold for €51,000 at auction, the owner refused to let it go, considering the price too low. AP - Thibault Camus

Watching the skies

Some of the search work is now automated. In 2014, Zanda and Bouley created the FRIPON programme – a network of about 100 cameras installed at observatories, universities and museums. The cameras monitor the sky day and night to detect meteorite falls.

Since then, similar networks have been established across Europe as well as in Africa.

After two hours, the organisers signal the end of the hunt. Not all the hidden meteorites have been found. Fortunately, Bouley and his teams had recorded the exact location of every fragment they had hidden, so the remaining pieces can be recovered.

At the end of the day, all the participants – including those who found nothing – leave with a meteorite and a certificate of authenticity.

Abygaëlle and her mother Aurélie found two fragments.

“We'll be able to add them to our collection, but these ones will have a special value,” Aurélie says with a smile.

Abygaëlle already knows where they will go: “On the shelves of the bookcase in the living room.”

This article has been adapted from the original version in French by Baptiste Coulon.

Looking up: French collector gathers lunar photos that changed our view of space

Astronaut Sophie Adenot made history this month as the first French woman to perform a spacewalk. Back on Earth, space enthusiasts across France are finding their own ways to reach for the stars. In the last of a five-part series, space historian and collector Victor Martin-Malburet talks to RFI about his passion for Nasa's Moon missions and the photos they produced. He began searching around the world for original prints aged 15 and has now amassed a collection of 1,500 photographs.


Issued on: 27/08/2026 - RFI

Martin-Malburet holding one of 1,500 photographs he's collected from Nasa's lunar programmes. He inherited his passion from his father, a collector of modern and contemporary art. © Baptiste Coulon / RFI

Martin-Malburet keeps his precious photo collection in 50 acid-free boxes, protected by plastic film. They're stacked away from the light on a shelf in his Paris living room.

The pictures trace 11 years of Nasa's race to the Moon, from 1961 to 1972. They include the US space agency's Mercury, Gemini and Apollo missions, all painstakingly catalogued.

He carefully takes the "Apollo 11" box, documenting the first mission to put humans on the Moon in July 1969, and selects the picture that kicked off his collection.

“In this image, we see Buzz Aldrin in his spacesuit on the lunar surface. In the reflection of his visor, we can see Neil Armstrong taking the photograph. It's a bit like the Mona Lisa of the Moon,” Martin-Malburet says.

An original Nasa print showing Buzz Aldrin in his spacesuit, photographed by Neil Armstrong, whose reflection can be seen in the astronaut's visor. © Baptiste Coulon / RFI


A new perspective

The photographs show all of Nasa's crewed space missions from Mercury to Apollo.

“We don't realise that these missions were also photographic missions,” Martin-Malburet says. “At that time, every minute was a first and every mission was an opportunity for a new, extraordinary view that humans had never seen before and that the astronauts captured.”

Nasa produced the prints a few days after the astronauts returned to Earth. Their value comes from their authenticity, the texture of the paper and the quality of the colours captured by Kodak cameras.

“They record light in a way that digital doesn't,” Martin-Malburet says.

Each photograph also has an identification number and an original Nasa stamp, adding to the value of the prints.

The collector began the painstaking search for the photographs when he was 15. In some cases, it took him as long as 10 years to find the original prints.

“I went to get them from the archives of astronauts, former Nasa employees or scientists who worked on these lunar programmes,” he says.

Nasa astronaut and Artemis II mission specialist Christina Koch peers out of one of the Orion spacecraft's main cabin windows, looking back at Earth, as the crew travels towards the Moon, 2 April 2024. @ NASA via REUTERS

Astronaut artists

Part of the reason the search took so long is that Nasa simply threw away some of the prints. Their value was not always recognised at the time.

“Photography was mainly used for scientific purposes. We didn't realise its artistic value,” Martin-Malburet says.

He regards the astronauts themselves as artists.

“They have the status of explorers. They brought humanity a new visual vocabulary that goes beyond science and becomes art,” he insists.

The collection is not intended to remain in Martin-Malburet's living room.

He is in contact with museums about displaying the photographs to the public and sharing this visual record of the space race.

The idea is particularly fitting, the collector says, as Nasa prepares for another Moon landing in 2028.

This article was adapted from the original version in French by Baptiste Coulon.