Anthropic’s Pentagon dispute and military AI governance in 2026

On 28 February 2026, Anthropic’s Claude rose to No. 1 in Apple’s US App Store free rankings, overtaking OpenAI’s ChatGPT. The surge came shortly after OpenAI announced a partnership with the US Department of Defense (DoD), making its technology available to the US Army. The development prompted discussion among users and observers about whether concerns over military partnerships were influencing the shift to alternative AI tools.

Mere hours before the USD $200 million OpenAI-DoD deal was finalised, Anthropic was informed that its potential deal with the Pentagon had fallen through, largely because the AI company refused to relinquish total control of its technology for domestic mass surveillance. According to reporting, discussions broke down after Anthropic declined to grant the US government unrestricted control over its models, particularly for potential uses related to large-scale surveillance.

Following the breakdown of negotiations, US officials reportedly designated Anthropic as a ‘supply chain risk to national security’. The decision effectively limited the company’s participation in certain defence-related projects and highlighted growing tensions between AI developers’ safety policies and government expectations regarding national security technologies.

The debate over military partnerships sparked internal and industry-wide discussion. Caitlin Kalinowski, the former head of AR glasses hardware at Meta and the hardware leader at OpenAI, resigned soon after the US DoD deal, citing ethical concerns about the company’s involvement in military AI applications.

AI has driven recent technological innovation, with companies like Anduril and Palantir collaborating with the US DoD to deploy AI on and off the battlefield. The debate over AI’s role in military operations, surveillance, and security has intensified, especially as Middle East conflicts highlight its potential uses and risks.

Against this backdrop, the dispute between Anthropic and the Pentagon reflects a wider debate on how AI should be used in security and defence. Governments are increasingly relying on private tech companies to develop the systems that shape modern military capabilities, while those same companies are trying to set limits on how their technologies can be used.

As AI becomes more deeply integrated into security strategies around the world, the challenge may no longer be whether the technology will be used, but how it should be governed. The question is: who should ultimately decide where the limits of military AI lie?

Anthropic’s approach to military AI

Anthropic’s approach is closely tied to its concept of ‘constitutional AI’, a training method that guides how the model behaves by embedding a set of principles directly into its responses. Such principles are intended to reduce harmful outputs and ensure the system avoids unsafe or unethical uses. While such safeguards are intended to improve reliability and trust, they can also limit how the technology can be deployed in more sensitive contexts such as military operations.

Anthropic’s Constitution says its AI assistant should be ‘genuinely helpful’ to people and society, while avoiding unsafe, unethical, or deceptive actions. The document reflects the company’s broader effort to build safeguards into model deployment. In practice, Anthropic has set limits on certain applications of its technology, including uses related to large-scale surveillance or military operations.

Anthropic presents these safeguards as proof of its commitment to responsible AI. Reports indicate that concerns over unrestricted model access led to the breakdown in talks with the US DoD.

At the same time, Anthropic clarifies that its concerns are specific to certain uses of its technology. The company does not generally oppose cooperation with national security institutions. In a statement following the Pentagon’s designation of the company as a ‘supply chain risk to national security’, CEO Dario Amodei said, ‘Anthropic has much more in common with the US DoD than we have differences.’ He added that the company remains committed to ‘advancing US national security and defending the American people.’

The episode, therefore, highlights a nuanced position. Anthropic appears open to defence partnerships but seeks to maintain clearer limits on the deployment of its AI systems. The disagreement with the Pentagon ultimately reflects not a fundamental difference in goals, but rather different views on how far military institutions should be able to control and use advanced AI technologies.

Anthropic’s position illustrates a broader challenge facing governments and tech companies as AI becomes increasingly integrated into national security systems. While military and security institutions are eager to deploy advanced AI tools to support intelligence analysis, logistics, and operational planning, the companies developing these technologies are also seeking to establish safeguards for their use. Anthropic’s willingness to step back from a major defence partnership and challenge the Pentagon’s response underscores how some AI developers are trying to set limits on military uses of their systems.

Defence partnerships that shape the AI industry

While Anthropic has taken a cautious approach to military deployment of AI, other technology companies have pursued closer partnerships with defence institutions. One notable example is Palantir, the US data analytics firm co-founded by Peter Thiel that has longstanding relationships with numerous government agencies. Documents leaked in 2013 suggested that the company had contracts with at least 12 US government bodies. More recently, Palantir has expanded its defence offering through its Artificial Intelligence Platform (AIP), designed to support intelligence analysis and operational decision-making for military and security institutions.

Another prominent player is Anduril Industries, a US defence technology company focused on developing AI-enabled defence systems. The firm produces autonomous and semi-autonomous technologies, including unmanned aerial systems and surveillance platforms, which it supplies to the US DoD.

Shield AI, meanwhile, is developing autonomous flight software designed to operate in environments where GPS and communications may be unavailable. Its Hivemind AI platform powers drones that can navigate buildings and complex environments without human control. The company has worked with the US military to test these systems in training exercises and operational scenarios, including aircraft autonomy projects aimed at supporting fighter pilots.

The aforementioned partnerships illustrate how the US government has increasingly embraced AI as a key pillar of national defence and future military operations. In many cases, these technologies are already being used in operational contexts. Palantir’s Gotham and AIP, for instance, have supported US military and intelligence operations by processing satellite imagery, drone footage, and intercepted communications to help analysts identify patterns and potential threats.

Other companies are contributing to defence capabilities through autonomous systems development and hardware integration. Anduril supplies the US DoD with AI-enabled surveillance, drone, and counter-air systems designed to detect and respond to potential threats. At the same time, OpenAI’s technology is increasingly being integrated into national security and defence projects through growing collaboration with US defence institutions.

Such developments show that AI is no longer a supporting tool but a fundamental part of military infrastructure, influencing how defence organisations process information and make decisions. As governments deepen their reliance on private-sector AI, the emerging interplay among innovation, operational effectiveness, and oversight will define the central debate on military AI adoption.

The potential benefits of military AI

The debate over Anthropic’s restrictions on military AI use highlights the reasons governments invest in such technologies: defence institutions are drawn to AI because it processes vast amounts of information much faster than human analysts. Military operations generate massive data streams from satellites, drones, sensors, and communication networks, and AI systems can analyse them in near real time.

In 2017, the US DoD launched Project Maven to apply machine learning to drone and satellite imagery, enabling analysts to identify objects, movements, and potential threats on the battlefield faster than with traditional manual methods.

AI is increasingly used in military logistics and operational planning. It helps commanders anticipate equipment failures, enables predictive maintenance, optimises supply chains, and improves field asset readiness.

Recent conflicts have shown that AI-driven tools can enhance military intelligence and planning. In Ukraine, for example, forces reportedly used software to analyse satellite imagery, drone footage, and battlefield data. Key benefits include more efficient target identification, real-time tracking of troop movements, and clearer battlefield awareness through the integration of multiple data sources.

AI-assisted analysis has been used in intelligence and targeting during the Gaza conflict. Israeli defence systems use AI tools to rapidly process large datasets for surveillance and intelligence operations. The tools help analysts identify potential militant infrastructure, track movements, and prioritise key intelligence, thus speeding up information processing for teams during periods of high operational activity.

More broadly, AI is transforming the way militaries coordinate across land, air, sea, and cyber domains. AI integrates data from diverse sources, equipping commanders to interpret complex operational situations and enabling faster, informed decision-making. The advances reinforce why many governments see AI as essential for future defence planning.

Ethical concerns and Anthropic’s limits on military AI

Despite the operational advantages of military AI, its growing role in national defence systems has raised ethical concerns. Critics warn that overreliance on AI for intelligence analysis, targeting, or operational planning could introduce risks if the systems produce inaccurate outputs or are deployed without sufficient human oversight. Even highly capable models can generate misleading or incomplete information, which in high-stakes military contexts could have serious consequences.

Concerns about the reliability of AI systems are also linked to the quality of the data they learn from. Many models still struggle to distinguish authentic information from synthetic or manipulated content online. As generative AI becomes more widespread, the risk that systems may absorb inaccurate or fabricated data increases, potentially affecting how these tools interpret intelligence or analyse complex operational environments.

Questions about autonomy have also become a major issue in discussions around military AI. As AI systems become increasingly capable of analysing battlefield data and identifying potential targets, debates have emerged over how much decision-making authority they should be given. Many experts argue that decisions involving the use of lethal force should remain under meaningful human control to prevent unintended consequences or misidentification of targets.

Another area of concern relates to the potential expansion of surveillance capabilities. AI systems can analyse satellite imagery, communications data, and online activity at a scale beyond the capacity of human analysts alone. While such tools may help intelligence agencies detect threats more efficiently, critics warn that they could also enable large-scale monitoring if deployed without clear legal and institutional safeguards.

It is within this ethical landscape that Anthropic has attempted to position itself as a more cautious actor in the AI industry. Through initiatives such as Claude’s Constitution and its broader emphasis on AI safety, the company argues that powerful AI systems should include safeguards that limit harmful or unethical uses. Anthropic’s reported refusal to grant the Pentagon unrestricted control over its models during negotiations reflects this approach.

The disagreement between Anthropic and the US DoD therefore highlights a broader tension in the development of military AI. Governments increasingly view AI as a strategic technology capable of strengthening defence and intelligence capabilities, while some developers seek to impose limits on how their systems are deployed. As AI becomes more deeply embedded in national security strategies, the question may no longer be whether these technologies will be used, but who should define the boundaries of their use.

Military AI and the limits of corporate control

Anthropic’s dispute with the Pentagon shows that the debate over military AI is no longer only about technological capability. Questions of speed, efficiency, and battlefield advantage now collide with concerns over surveillance, autonomy, human oversight, and corporate responsibility. Governments increasingly see AI as a strategic asset, while companies such as Anthropic are trying to draw boundaries around how far their systems can go once they enter defence environments.

Contrasting approaches across the industry make the tension even clearer. Palantir, Anduril, Shield AI, and OpenAI have moved closer to defence partnerships, reflecting a broader push to integrate advanced AI into military infrastructure. Anthropic, by comparison, has tried to keep one foot in national security cooperation while resisting uses it views as unsafe or unethical. A divide of that kind suggests that the future of military AI may be shaped as much by company policies as by government strategy.

The growing reliance on private firms to build national security technologies has made governance harder to define. Military institutions want flexibility, scale, and operational control, while AI developers increasingly face pressure to decide whether they are simply suppliers or active gatekeepers of how their models are deployed. Anthropic’s position does not outright defence cooperation, but it does expose how fragile the relationship becomes when state priorities and corporate safeguards no longer align.

Military AI will continue to expand, whether through intelligence analysis, logistics, surveillance, or autonomous systems. Governance, however, remains the unresolved issue at the centre of that expansion. As AI becomes more deeply embedded in defence policy and military planning, should governments alone decide how far these systems can go, or should companies like Anthropic retain the power to set limits on their use?

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Codex Security expands OpenAI’s push into cybersecurity tools

OpenAI has launched Codex Security, an AI-powered application security agent that detects hard-to-find software vulnerabilities and proposes fixes through advanced reasoning. By providing detailed context about a system’s architecture, the tool identifies security risks that are often missed by conventional automation.

The system uses advanced models to analyse repositories, construct project-specific threat models, and prioritise vulnerabilities based on their potential real-world impact. By combining automated validation with system-level context, Codex Security aims to reduce the number of false positives that security teams must review while highlighting high-confidence findings.

Initially developed under the name Aardvark, the tool has been tested in private deployments over the past year. During early use, OpenAI said it uncovered several critical vulnerabilities, including a cross-tenant authentication flaw and a server-side request forgery issue, allowing internal teams to quickly patch affected systems.

The company says improvements during the beta phase significantly reduced noise in vulnerability reports. In some repositories, unnecessary alerts fell by 84 percent, while over-reported severity dropped by more than 90 percent, and false positives declined by more than half.

Codex Security is now rolling out in research preview for ChatGPT Pro, Enterprise, Business, and Edu customers. OpenAI also plans to expand access to open-source maintainers through a dedicated programme that offers security scanning and support to help identify and remediate vulnerabilities across widely used projects.

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AI legal advice case asks whether ChatGPT crosses legal boundaries

A newly filed lawsuit against OpenAI raises a key issue: Does allowing generative AI systems like ChatGPT to provide legal advice violate laws that bar the unauthorised practice of law (UPL)? UPL means providing legal services, such as drafting filings or giving advice, without the required legal qualifications or a state licence.

The case claims an individual used ChatGPT to prepare legal filings in a dispute with Nippon Life Insurance, prompting the company to argue OpenAI should be held responsible for the outcome.

The lawsuit claims ChatGPT helped the user challenge a settled legal dispute. As a result, the company had to spend additional time and resources responding to filings produced with ChatGPT. The claim alleges tortious interference with a contract, which is the unlawful disruption of an existing agreement between two parties by causing one of the parties to breach or alter it.

Ultimately, this disrupted another party’s contractual relationship. The suit also claims unauthorised practice of law and abuse of the judicial process, which means using the legal system improperly to gain an advantage. It argues OpenAI should be liable because ChatGPT operates under its control. The dispute centres on whether AI systems should analyse disputes and offer legal advice like a lawyer.

Advocates argue the tools could widen access to legal advice. They could make legal support more accessible and affordable for those who cannot easily hire a lawyer. However, US legal frameworks restrict the provision of legal advice to licensed lawyers. The rules are designed to protect consumers and ensure professional accountability.

Critics argue that limiting legal advice to licensed lawyers preserves an expensive monopoly and hinders access to justice. AI-driven legal tools highlight this tension over the future of legal services.

The outcome of this lawsuit will likely hinge on whether AI-generated responses constitute intentional legal advice and if OpenAI can be held liable for such outputs. Even if it fails, the case foregrounds the broader debate about granting generative AI a legitimate role in legal guidance.

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ChatGPT ‘adult mode’ launch delayed as OpenAI focuses on core improvements

OpenAI has postponed the launch of ChatGPT’s ‘adult mode’, a feature designed to let verified adult users access erotica and other mature content.

Teams are focusing on improving intelligence, personality and proactive behaviour instead of releasing the feature immediately.

A feature that was first announced by Sam Altman in October, with an initial December rollout, aiming to allow adults more freedom while maintaining safety for younger users.

The project faced an earlier delay as internal teams prioritised the core ChatGPT experience.

OpenAI stated it still supports the principle of treating adults like adults but warned that achieving the right experience will require more time. No new release date has been provided.

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OpenAI explains 5 AI value models transforming enterprise strategy

AI is beginning to reshape corporate strategy as organisations shift from isolated technology experiments to broader operational transformation.

According to OpenAI, businesses that treat AI as a collection of disconnected pilots risk missing the bigger structural change that the technology enables.

A new framework describes five value models through which AI can gradually reshape companies. The first stage focuses on workforce empowerment, where tools such as ChatGPT spread AI capabilities across teams and improve everyday productivity.

Once employees develop fluency, organisations can introduce AI-native distribution models that transform how customers discover products and interact with digital services.

More advanced stages involve specialised systems. Expert capability integrates AI into research, creative production, and domain-specific analysis, allowing professionals to explore a wider range of ideas and experiments.

Meanwhile, systems and dependency management introduce AI tools capable of safely updating interconnected digital environments, including codebases, documentation, and operational processes.

The final stage involves full process re-engineering through autonomous agents. In such environments, AI systems coordinate complex workflows across departments while maintaining governance, accountability, and auditability.

Organisations that successfully progress through these stages may eventually redesign their business models rather than merely improving efficiency within existing structures.

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OpenAI upgrades ChatGPT conversations with GPT-5.3 Instant

The most widely used ChatGPT model has received an update from OpenAI, introducing GPT-5.3 Instant to make everyday conversations more coherent, useful, and natural.

An upgrade that focuses on improving tone, contextual understanding, and the flow of dialogue rather than only benchmark performance.

One of the main improvements concerns how the model handles refusals and safety responses. Earlier versions sometimes declined questions that could have been answered safely or delivered overly cautious explanations before responding.

GPT-5.3 Instant instead gives more direct answers while still maintaining safety constraints, reducing interruptions that previously slowed conversations.

The update also improves the way ChatGPT uses information from the web. Instead of simply summarising search results or presenting long lists of links, the model now integrates online information with its own reasoning.

Such an approach aims to produce more relevant answers that highlight key insights at the beginning of responses.

Reliability has also improved. Internal evaluations conducted by OpenAI show reductions in hallucination rates across multiple domains.

When using web sources, hallucinations dropped by roughly 26.8 percent in higher-risk fields such as medicine, law, and finance. Improvements were also recorded when the model relied only on its internal knowledge.

Beyond factual accuracy, the model is designed to feel more natural in conversation. OpenAI says the system now avoids overly preachy language, unnecessary disclaimers, and intrusive remarks that previously disrupted dialogue.

The goal is a more consistent conversational personality across updates, while maintaining the familiar user experience of ChatGPT.

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Medical chatbots spark powerful debate over serious health risks and benefits

Medical chatbots are rapidly becoming part of digital healthcare as technology companies expand AI tools into health services. Companies such as OpenAI and Anthropic are introducing chatbot features designed to answer medical questions using personal data.

Medical chatbots can analyse information from medical records, wearable devices and wellness applications. By incorporating details such as prescriptions, age and prior diagnoses, they aim to provide more personalised responses than a standard internet search.

However, companies stress that these tools are not substitutes for professional medical care. They are not intended to diagnose conditions but rather to summarise results, explain terminology and help users prepare for appointments.

Supporters argue that medical chatbots can improve patient understanding. Experts from the University of California, San Francisco, note that the tools may clarify complex reports and highlight essential health trends when used responsibly.

Despite these benefits, significant limitations remain. AI systems can hallucinate or generate inaccurate advice, and users may struggle to distinguish reliable guidance from subtle errors.

Independent research reinforces these concerns. A 2024 study by the University of Oxford found that participants who used chatbots for hypothetical health scenarios did not make better decisions than those who relied on online searches or personal judgement.

Performance was strong when analysing structured written cases. Yet effectiveness declined during real-world interactions, where communication gaps affected outcomes.

Privacy presents another major issue. Medical chatbots often require users to upload sensitive health information to deliver personalised responses.

Unlike doctors and hospitals, AI companies are not bound by HIPAA, the US federal health privacy law. Although platforms state that data is stored separately and not used to train models, privacy standards differ from those in traditional healthcare.

Experts from Stanford University advise users to understand these differences before sharing medical records. Transparency and informed consent are critical considerations.

Medical chatbots are also inappropriate in emergencies. Individuals experiencing symptoms such as chest pain, shortness of breath or severe headaches should seek immediate medical attention instead of consulting AI tools.

Even in non-urgent cases, specialists recommend maintaining healthy scepticism. Consulting multiple AI systems may provide a form of second opinion, but it does not replace professional medical advice.

Medical chatbots, therefore, represent both opportunity and risk. As their capabilities expand, users must carefully weigh convenience and personalisation against accuracy, oversight and data protection concerns.

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OpenAI and Microsoft strengthen their long-term AI collaboration

Microsoft and OpenAI have reaffirmed their long-standing collaboration after new funding and partnerships raised speculation about their relationship.

Both firms stressed that recent announcements leave their original agreements intact, preserving a framework built on technical integration, trust and shared ambitions for AI development.

Microsoft’s exclusive licence to OpenAI’s intellectual property remains untouched, as does its position as the sole cloud provider for stateless APIs powering OpenAI models.

These APIs can be accessed through either company. Yet all such calls, including those arising from third-party partnerships such as OpenAI’s work with Amazon, continue to run on Azure rather than on alternative clouds. OpenAI’s own products, including Frontier, also stay hosted on Azure.

Revenue-sharing arrangements are unchanged, alongside the contractual definition and evaluation process for artificial general intelligence.

Both companies emphasised that the partnership was designed to allow independent initiatives while preserving deep cooperation across research, engineering and product innovation.

OpenAI retains the freedom to secure additional compute capacity elsewhere, supported by large-scale initiatives such as the Stargate project.

Even with broader collaborations emerging across the industry, both firms present their alliance as central to advancing responsible AI and expanding access to powerful tools worldwide.

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AI misuse in online scams involving OpenAI models

OpenAI has reported new instances of its models being exploited in online scams and coordinated information campaigns. The company detailed actions to remove offending accounts and strengthen safeguards, highlighting misuse in fraud and deceptive content creation.

Several cases involved romance and ‘task’ scams, in which AI-generated messages built emotional engagement before requesting payment. One network, dubbed ‘Operation Date Bait,’ used chatbots to promote a fictitious dating service targeting young men in Indonesia.

Another, ‘Operation False Witness,’ saw actors posing as legal professionals to solicit advance fees for non-existent recovery services.

The report also outlined coordinated campaigns leveraging AI to produce articles, social media posts, and comments on geopolitical topics. In ‘Operation Trolling Stone,’ AI-generated content on a Russian arrest in Argentina was shared widely in multiple languages to mimic grassroots engagement.

OpenAI stressed that AI was sometimes used, but reach and account size largely drove engagement.

The company continues monitoring misuse and collaborates with partners and authorities to curb fraudulent or deceptive activity. Systems have been updated to decline policy-violating requests, and not all suspicious content online was generated using its tools.

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OpenClaw creator Peter Steinberger urges playful approach to AI coding

Peter Steinberger, creator of the viral AI agent OpenClaw and now at OpenAI, urged developers to approach AI experimentation with curiosity rather than rigid plans. On the Builders Unscripted podcast, he said progress often comes from exploration rather than expertise.

He said OpenClaw began without a roadmap. Early tests included a WhatsApp integration he paused, expecting major labs to build similar tools. When that did not happen, he developed his own prototype and refined it through real-world use.

Using the tool in low-connectivity environments helped clarify its value. Through trial and iteration, he observed how modern AI models can generate workable solutions without explicit programming, reshaping how developers think about problem-solving and workflows.

He cautioned that coding with AI is a skill that requires practice. Comparing it to learning guitar, Steinberger said early frustration is common, but persistence leads to improved intuition and efficiency over time.

Steinberger argued that developers who focus on solving problems and creating useful tools will remain in demand. Treating AI as a collaborative instrument rather than a shortcut, he said, is essential in a rapidly shifting technology landscape.

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