AI-generated political advertisements are becoming increasingly visible in Texas election campaigns, highlighting gaps in existing laws designed to regulate deepfakes in political messaging.
Texas was the first state in the United States to adopt legislation restricting the use of deepfakes in campaign advertisements. However, the law applies only to state-level races. It does not cover federal contests, including the US Senate race that has dominated advertising spending in Texas and featured several AI-generated campaign ads.
Some lawmakers and experts warn that the growing use of AI-generated political content could complicate election campaigns. During recent primary contests, campaign advertisements featuring manipulated or synthetic images of political figures circulated widely across media platforms.
State Senator Nathan Johnson, who has proposed legislation to strengthen the state’s rules regarding deepfakes, said the rapid evolution of AI technology makes the issue increasingly urgent. Johnson argues that voters should be able to make decisions based on accurate information rather than manipulated media.
The current Texas law, adopted in 2019, contains several limitations. It only applies to video content, requires proof of intent to deceive or harm a candidate, and covers material distributed within 30 days of an election. Critics say these restrictions make the law difficult to enforce and limit its practical impact.
Lawmakers from both parties attempted to address some of these issues during the most recent legislative session. Proposed reforms included removing the 30-day restriction, requiring clear disclosure when AI is used in political advertising, and allowing candidates to pursue legal action to block misleading ads. Although both chambers of the Texas legislature passed versions of the legislation, the proposals ultimately failed to become law.
Supporters of stricter regulation argue that the rapid advancement of generative AI tools is making it harder to distinguish synthetic media from authentic content. Some political leaders warn that increasingly realistic deepfakes could eventually influence election outcomes.
Others, however, caution that regulating political content raises constitutional concerns. Some lawmakers argue that many AI-generated political ads resemble satire or parody, forms of political speech protected by the First Amendment.
At the federal level, regulation of congressional campaign advertising falls under the Federal Election Commission’s authority. In 2024, the agency declined to begin a formal rulemaking process on AI-generated political ads, leaving states and policymakers to continue debating how to address the emerging issue.
Experts warn that as AI tools continue to improve, distinguishing authentic political messaging from deepfakes and other forms of synthetic content will likely become more complex.
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AI competition among leading AI developers intensified in early 2026 as major companies expanded their models, platforms, and partnerships. Companies including Google, OpenAI, Anthropic, and xAI are introducing new capabilities and integrating AI systems into broader ecosystems.
Google has continued to expand its Gemini model family with updates to Gemini 3.1 Pro and 3.1 Flash, designed to support complex tasks across applications. The company is also integrating Gemini into services such as Docs, Sheets, Slides, and Drive, allowing users to generate documents and analyse data across multiple Google services.
Gemini has also been embedded into the Chrome browser and integrated with Samsung’s Galaxy devices, expanding its distribution across consumer platforms as AI competition among major developers accelerates.
Anthropic has focused on advancing the Claude model family while positioning the system for enterprise and professional use. Recent updates include Claude Sonnet 4.6, which introduces improvements in reasoning and coding capabilities alongside an expanded context window currently in beta. The company has also launched a limited preview of the Claude Marketplace, allowing organisations to use third-party tools built on Claude through partnerships with several software companies.
OpenAI has continued to update ChatGPT with the release of the GPT-5 series, including GPT-5.2 and GPT-5.4. The newer models combine reasoning, coding, and agent-based workflows, while also introducing computer-use capabilities that allow the system to interact with applications directly.
OpenAI has also introduced additional services, including ChatGPT Health and integrations designed to assist with spreadsheet modelling and data analysis, further intensifying AI competition across enterprise and consumer tools.
Meanwhile, xAI has expanded development of its Grok models while increasing computing infrastructure. The company has reported growth in Grok usage through integration with the X platform and other applications. Recent announcements include upgrades to Grok’s voice and multimodal capabilities, as well as continued training of future models.
Across the industry, developers are increasingly positioning their systems not only as conversational assistants but also as tools integrated into enterprise workflows, creative production, and software development. New releases in 2026 reflect a broader shift toward multimodal systems, agent-based capabilities, and deeper integration with existing digital platforms, highlighting how AI competition is shaping the next phase of AI development.
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OpenAI has introduced a new ChatGPT feature called dynamic visual explanations, allowing users to interact with mathematical and scientific concepts through real-time visuals.
Instead of relying solely on text explanations or static diagrams, the feature enables users to manipulate formulas and variables and immediately see how those changes affect results. For example, when exploring the Pythagorean theorem, users can adjust the triangle’s sides and see the hypotenuse update instantly.
To use the tool, users can ask ChatGPT questions such as ‘What is a lens equation?’ or ‘How can I find the area of a circle?’ The chatbot responds with both a written explanation and an interactive visual module that users can manipulate directly.
The feature currently supports more than 70 topics in mathematics and science. The topics include binomial squares, Charles’ law, compound interest, Coulomb’s law, exponential decay, Hooke’s law, kinetic energy, linear equations, and Ohm’s law.
OpenAI says it plans to expand the range of topics over time. The feature is already available to all logged-in ChatGPT users. The launch marks a shift in how ChatGPT supports learning. Instead of simply providing answers, the tool now encourages users to explore underlying concepts by experimenting with interactive models.
AI tools have become increasingly common in education, although their role remains widely debated. Some educators worry that students may become overly dependent on AI tools, while others see them as valuable learning aids.
According to OpenAI, more than 140 million people use ChatGPT every week to help with subjects such as mathematics and science, which many learners find challenging. Other technology companies are also experimenting with similar tools. Google’s Gemini introduced interactive diagrams and visual explanations last year.
The new feature joins several other ChatGPT learning tools, including study mode, which guides users through problems step by step, and QuizGPT, which allows users to create flashcards and test themselves before exams.
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Google’s cybersecurity division, Mandiant, has warned about the growing threat of AI-driven adaptive malware, highlighting how AI is reshaping the cyber threat landscape.
According to a recent report, adaptive malware can modify its behaviour and code in response to the environment it encounters, thereby evading traditional security tools. By analysing the security systems protecting a target, the malware can rewrite parts of its code to bypass detection.
Unlike traditional malware, which typically follows fixed instructions, adaptive malware can adjust its behaviour during an attack. This capability makes it more difficult for conventional cybersecurity tools to detect and block malicious activity.
Mandiant noted that such malware is increasingly associated with advanced persistent threat (APT) groups that conduct long-term, targeted cyber operations. These groups often pursue espionage objectives or financial gain while maintaining prolonged access to compromised systems.
AI is also being used to automate elements of cyberattacks. Machine learning algorithms allow malicious software to anticipate defensive measures and adjust its behaviour in real time. In some cases, attackers are integrating AI into broader automated attack chains. AI-driven malware can gather information, adapt its strategy, and continue operating with minimal human intervention.
Security researchers say autonomous AI agents may be capable of managing multiple stages of an attack, including reconnaissance, exploitation, and persistence, while remaining undetected.
To address these evolving threats, Mandiant recommends that organisations strengthen their cybersecurity strategies by deploying advanced detection and response tools, including AI-based systems that can identify anomalous behaviour. As AI capabilities continue to develop, cybersecurity experts say understanding adaptive malware and automated attack techniques will be essential for organisations seeking to protect their systems and data.
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Cybersecurity risks are increasing as digital connectivity expands across governments, businesses and households.
According to Thales Group, a growing number of connected devices and digital services has significantly expanded the potential entry points for cyberattacks.
AI is reshaping the cybersecurity landscape by enabling attackers to identify vulnerabilities at unprecedented speed.
Security specialists increasingly describe the environment as a contest in which defensive systems must deploy AI to counter adversaries using similar technologies to exploit weaknesses in digital infrastructure.
Security concerns also extend beyond large institutions. Connected devices in homes, including smart cameras and speakers, often lack robust security protections, increasing exposure for individuals and networks.
Policymakers in Europe are responding through measures such as the Cyber Resilience Act, which will introduce mandatory security requirements for connected products sold in the EU.
Long-term risks are also emerging from advances in quantum computing.
Experts warn that powerful future machines could eventually break widely used encryption systems that currently protect communications, financial data and government networks, prompting organisations to adopt quantum-resistant security methods.
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Researchers at MIT have developed a hybrid AI framework designed to improve how robots plan and perform complex visual tasks. The approach combines generative AI with classical planning software, allowing machines to analyse images, simulate actions, and generate reliable plans to reach a goal.
The system relies on two specialised vision-language models. One model analyses an image, describes the environment, and simulates possible actions, while a second model converts those simulations into a formal programming language used for planning.
Generated files are then processed by established planning software to produce a step-by-step strategy.
Testing showed a significant improvement compared with existing techniques. The framework achieved an average success rate of about 70 percent, while many baseline methods reached roughly 30 percent.
Performance remained strong in unfamiliar scenarios, demonstrating the system’s ability to adapt to changing conditions.
The method could support applications such as robot navigation, autonomous driving, and multi-robot assembly systems. Continued development aims to handle more complex environments and reduce errors caused by AI model hallucinations.
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YouTube is expanding its likeness-detection technology designed to identify AI-generated deepfakes, extending access to a pilot group of government officials, political candidates, and journalists.
The tool allows participants to detect unauthorised AI-generated videos that simulate their faces and request removal if the content violates YouTube policies. The system builds on technology launched last year for around four million creators in the YouTube Partner Program.
Similar to YouTube’s Content ID system, which detects copyrighted material in uploaded videos, the likeness detection feature scans for AI-generated faces created with deepfake tools. Such technologies are increasingly used to spread misinformation or manipulate public perception by making prominent figures appear to say or do things they never did.
According to YouTube, the pilot programme aims to balance free expression with safeguards against AI impersonation, particularly in sensitive civic contexts.
‘This expansion is really about the integrity of the public conversation,’ said Leslie Miller, YouTube’s vice president of Government Affairs and Public Policy. ‘We know that the risks of AI impersonation are particularly high for those in the civic space. But while we are providing this new shield, we’re also being careful about how we use it.’
Removal requests will be assessed individually under YouTube’s privacy policy rules to determine whether the content constitutes parody or political critique, which remain protected forms of expression. Participants must verify their identity by uploading a selfie and a government-issued ID before accessing the tool. Once verified, they can review detected matches and submit removal requests for content they believe violates policy.
YouTube also said it supports the proposed NO FAKES Act in the United States, which aims to regulate the unauthorised use of an individual’s voice or visual likeness in AI-generated media. AI-generated videos on the platform are already labelled, though label placement varies depending on the topic’s sensitivity.
‘There’s a lot of content that’s produced with AI, but that distinction’s actually not material to the content itself,’ said Amjad Hanif, YouTube’s vice president of Creator Products. The company said it plans to expand the technology over time to detect AI-generated voices and other intellectual property.
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AI, cloud computing, and cross-border data flows have made questions about control and jurisdiction increasingly important for governments and businesses. In Asia, the debate around digital sovereignty often focuses on ‘US versus non-US cloud’ providers or data localisation.
Such simplifications miss the practical challenges organisations face when choosing hosting locations or training AI models while navigating diverse regulatory regimes.
At the same time, Asia’s digital economy is building its own regulatory foundations. In Vietnam and Indonesia, new rules such as Vietnam’s Decree 53 and Indonesia’s data protection framework show how governments are shaping data governance while still relying on global cloud and AI platforms. Most organisations across the region continue to operate using a mix of local, regional, and international providers.
Organisations must address key questions about data jurisdiction and workload mobility when risks change. They must also control who can access sensitive systems during incidents. Digital sovereignty is clearer when seen through three pillars: data sovereignty, technical sovereignty, and operational sovereignty.
Data sovereignty is about jurisdiction, not just data storage. As AI regulation expands, businesses need to know which authorities can access their data and how it may be used. Technical sovereignty is the ability to move or redesign systems as regulations or geopolitics shift. Multi-cloud and hybrid strategies help organisations remain adaptable.
Operational sovereignty focuses on governance and control. It addresses who can access systems, from where, and under what safeguards, thus linking sovereignty directly to cybersecurity and incident response.
For Asia-Pacific organisations, digital sovereignty should not be a simple procurement checklist. Instead, it should guide cloud and AI strategies from the start, ensuring legal clarity, technical flexibility, and operational trust as the digital landscape evolves.
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Clemson University has introduced ChatGPT Edu to its students, faculty, and staff, providing them free access to the secure, institutionally managed version of the AI platform.
The rollout is part of Clemson’s partnership with OpenAI. It forms part of the university’s broader AI Initiative, which aims to develop a human-centred approach to AI across education, research, and operations.
University officials said the ChatGPT Edu environment will expand access to generative AI tools while ensuring institutional data remains protected and is not used to train external AI systems.
Members of the Clemson community who want to use the platform must request access through a ChatGPT Edu account request form. Once approved, accounts are automatically created, and users can sign in through Clemson’s single sign-on system.
Even if students or staff members already have a ChatGPT account linked to their Clemson email, they will still need to request access to ChatGPT Edu. After approval, they can merge your current account or download your chat history before creating a new one.
The university said the launch reflects its view that access to emerging technologies should be paired with clear guidance and responsible use. Users are advised to review Clemson’s updated AI guidelines before using the system.
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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.
I resigned from OpenAI. I care deeply about the Robotics team and the work we built together. This wasn’t an easy call. AI has an important role in national security. But surveillance of Americans without judicial oversight and lethal autonomy without human authorization are…
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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