The company, which was founded in 2016, is pitching itself as a privacy-focused alternative to both legacy badge systems and more controversial forms of facial recognition. According to Alcatraz, its flagship product, the Rock, uses facial authentication rather than surveillance-style identification, allowing employees to enter buildings without badges or passcodes while avoiding the storage of photographs or other personal data in the cloud. The company said the platform was designed to meet compliance requirements including GDPR, CCPA and BIPA. 

A Security Pitch Built for the A.I. Era

Alcatraz said demand has risen sharply as the AI boom turns data centers into some of the world’s most sensitive physical infrastructure. In its announcement, the company said its customer base already includes major AI data centers, U.S. airports, energy companies, NFL teams, universities and Fortune 100 companies. It also reported more than 300% year-over-year growth in data center adoption in 2025, along with 200% growth in new enterprise customers and a fivefold expansion across Fortune 500 deployments. 

Chief Executive Tina D’Agostin said the company sees itself as bringing smartphone-style identity verification into the workplace. “We are the Face ID of securing physical spaces,” she said in the announcement, arguing that badges and passcodes now create too much risk for modern workplaces. Founder Vince Gaydarzhiev, who Alcatraz said worked on hardware prototyping for iPhone and iPad during the development of Face ID at Apple, said he wanted to bring a privacy-centered approach to the buildings where people work. 

The timing of the funding reflects a larger shift in the market: as companies pour billions into AI infrastructure, the business of protecting the physical spaces behind that technology is becoming more strategically important. Alcatraz said it plans to use the new capital to expand into new industries, enter international markets and grow its team, betting that the next phase of AI growth will require not just more computing power, but tighter control over who can access it. 

The threat carries more weight because at least one major U.S. cloud operator has already been affected. Reuters reported on April 1, citing the Financial Times and a person familiar with the matter, that Amazon’s cloud computing operation in Bahrain was damaged after an Iranian strike. In earlier reporting, Reuters said drone strikes had damaged Amazon Web Services data centers in both the United Arab Emirates and Bahrain, disrupting cloud services and underscoring the risks facing tech infrastructure in the region. 

The latest warnings mark a sharp broadening of the conflict’s impact on the technology sector, especially as global cloud and AI infrastructure increasingly depends on Gulf-based capacity. Reuters has separately reported that rising instability in the Middle East is already testing Big Tech’s 2026 AI spending plans, with companies such as Amazon, Microsoft, Alphabet and Meta exposed to higher energy and infrastructure risk. The Associated Press also reported that U.S. tech firms operating in the region are now facing direct threats as the war widens.

That is the idea behind what NVIDIA’s chief executive, Jensen Huang, described at GTC as the arrival of the “inference inflection.”

The phrase captures a turning point in the AI market. Systems are no longer valued only for their ability to generate text, images or code in a controlled setting. They are being asked to do more demanding work: to reason through problems, use tools, read files, understand context and carry out productive tasks with a degree of autonomy. In practical terms, that means AI is moving from demonstration to deployment.

AI will be used in the key industries

And deployment requires inference.

Inference is the phase in which a trained AI model is actually used. It is the moment a system responds to a prompt, analyzes a document, makes a decision, writes code, summarizes a meeting or completes a task. If training is the creation of intelligence, inference is its application. As AI systems become more agentic — breaking problems into steps, calling tools, revising answers and operating across longer chains of reasoning — inference becomes vastly more important, and vastly more expensive.

Huang put it plainly in his keynote: “There’s a reason for that. This fundamental inflection. Finally, AI is able to do productive work and therefore the inflection point of inference has arrived. AI now has to think. In order to think, it has to inference. AI now has to do. In order to do, it has to inference. AI has to read. In order to do so, it has to inference. It has to reason. It has to inference. Every part of AI, every time it has to think, it has to reason. It has to do. It has to generate tokens. It has to inference. It’s way past training now. It’s in the field of inference. So the inference inflectionhas arrived at the time when the amount of tokens, the amount of compute necessary, increased by roughly 10,000 times.”

That remark goes to the heart of a major change underway in artificial intelligence economics. In the last two years, according to the keynote, compute demand for AI work has increased by roughly 10,000 times, while usage has climbed about 100 times. Huang suggested that among startups and major AI labs such as OpenAI and Anthropic, the real increase in computing demand may feel closer to one million times over the same period.

That gap matters. It suggests that the next phase of AI will not be defined only by who has the smartest model, but by who can afford to run it at scale.

Inference is becoming the new bottleneck. When AI systems are expected to reason before answering, process more tokens, consult external tools and operate continuously inside products and workflows, the underlying infrastructure has to do much more work per user interaction. A simple chatbot response is one thing. An AI agent that reads documents, plans actions, iterates through options and produces a useful outcome is something else entirely. The second model consumes far more compute, and therefore far more capital.

That helps explain why NVIDIA has placed such heavy emphasis on this phase of the market. The company designated 2025 as its “year of inference,” with a strategy centered on making sure its infrastructure performs across the full lifecycle of AI — from training to post-training and inference — while extending hardware usefulness and lowering costs for investors. In other words, NVIDIA is not simply selling chips for model creation. It is positioning itself as the central supplier for the operational age of AI.

The market projections cited in the keynote underscored the scale of that bet. Last year, Huang said, there was already strong confidence behind demand and purchase orders totaling $500 billion for Blackwell and Ruben systems through 2026. Looking ahead through 2027, he said, he now sees at least $1 trillion in demand, while also suggesting that real computing demand could end up even higher.

Those figures are striking not only for their size, but for what they imply about investor expectations. The AI market is no longer being priced solely around model development. It is increasingly being priced around sustained consumption— the daily, repeated computational load required when AI becomes embedded in search, software, enterprise automation, robotics, science and digital assistants.

This is why the inference inflection point matters. It changes the story of AI from one about invention to one about industrialization.

For startups, it raises the cost of ambition. Building an impressive model is no longer enough; companies must also finance the infrastructure needed to serve real users at high frequency. For cloud providers and chipmakers, it creates an enormous commercial opportunity, because every leap in agentic capability drives more demand for inference hardware. For enterprises, it signals that adopting AI at scale may be more expensive and more operationally complex than many early forecasts assumed. And for the broader market, it suggests that demand for compute could remain intense even if the pace of headline model releases slows.

In that sense, the inference inflection point is not merely a technical milestone. It is a market reset. It marks the moment when AI begins to behave less like a research breakthrough and more like a utility — one that must be delivered constantly, reliably and at great computational cost.

The industry spent the past several years proving that AI could learn. It is now entering a period in which it must prove that AI can work. And if Huang is right, that shift will do more than reshape NVIDIA’s business. It may redefine the economics of the entire AI era.

Image: NVIDIA Keynote Screenshot

For NVIDIA, that matters far beyond headline optics. Blackwell is at the center of the company’s current AI server push, while Rubin is being positioned as the next major step in its hardware roadmap. On stage, Huang framed the shift as a reflection of how fast demand has moved in just a few months, as hyperscalers, cloud providers and AI model companies continue racing to lock in more compute.

Now, I don’t know if you guys feel the same way, but $500 billion is an enormous amount of revenue,

Huang said during the keynote.

Well, I’m here to tell you that right now where I stand — a few short months after GTC DC, one year after last GTC — right here where I stand, I see through 2027, at least $1 trillion.

The number lands at a moment when NVIDIA is trying to show that its growth story extends well beyond one blockbuster chip cycle. Rubin, which Nvidia had previously described as its next-generation AI architecture, is expected to outperform Blackwell significantly on both training and inference workloads, with production ramping in the second half of 2026. That makes Huang’s trillion-dollar claim not just a forecast about demand, but a statement about how central NVIDIA expects its future platforms to remain in the economics of AI.

Image: Keynote GTC, Screenshot

The pitch is straightforward: if the first wave of generative AI was about generating text, code and images, the next one is about software that can actually take action inside enterprise systems. NVIDIA is positioning Agent Toolkit as infrastructure for that shift, bundling together Nemotron open models, the AI-Q agent blueprint, open skills such as cuOpt, and the new OpenShell runtime.

NVIDIA CEO Jensen Huang framed the launch as a turning point for enterprise software.

Claude Code and OpenClaw have sparked the agent inflection point — extending AI beyond generation and reasoning into action,

Huang said in the release. He added that employees will increasingly work alongside teams of frontier, specialized and custom-built agents, and argued that enterprise software is set to evolve into “specialized agentic platforms.”

The company is also trying to make the economics look compelling. NVIDIA said its AI-Q blueprint uses frontier models for orchestration and Nemotron open models for research tasks, an approach it claims can cut query costs by more than 50% while still delivering top-ranked performance on DeepResearch Bench and DeepResearch Bench II. That matters because one of the biggest open questions around enterprise agents is not whether they work, but whether they can be deployed at scale without turning inference bills into a budget problem.

Just as important, NVIDIA isn’t presenting this as a solo effort. The company named a long list of software vendors and enterprise platforms that are already integrating parts of the stack, including Adobe, Atlassian, Amdocs, Box, Cadence, Cisco, Cohesity, CrowdStrike, Dassault Systèmes, IQVIA, Red Hat, SAP, Salesforce, Siemens, ServiceNow and Synopsys. The message is classic Nvidia: build the tooling, seed the ecosystem, and make it easier for the rest of the software industry to pull workloads onto Nvidia-backed infrastructure.

There is also a security angle running through the announcement. NVIDIA said OpenShell is being developed with compatibility for cyber- and AI-security tools from providers including Cisco, CrowdStrike, Google, Microsoft Security and TrendAI, underscoring how seriously enterprise buyers are taking the risk of giving autonomous systems access to internal tools and data. Agent systems may be attracting intense interest, but they are also forcing the market to confront a harder question: how much autonomy companies are actually willing to trust in production.

For developers, NVIDIA said Agent Toolkit and OpenShell are available through build.nvidia.com, through inference providers and Nvidia cloud partners including Baseten, Bitdeer AI, CoreWeave, DeepInfra, DigitalOcean, GMI Cloud, Fireworks, Lightning, Together AI and Vultr. The company also said OpenShell can run locally on RTX PCs, workstations and DGX systems. Enterprises, meanwhile, can deploy on infrastructure from AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure, as well as server vendors including Cisco, Dell Technologies, HPE, Lenovo and Supermicro.

Vendors and what they are using

Image: NVIDIA 

For global AI companies chasing power, capital and speed, the Gulf once looked like the industry’s cleanest expansion story. Saudi Arabia and the United Arab Emirates had money to spend, energy to sell and a strategic desire to become indispensable to the next computing era. That is why OpenAI, Microsoft, Amazon, Oracle, Google and xAI have all been pulled into the region’s widening AI orbit. But the war in Iran is now darkening that promise, turning what was pitched as a launchpad for the next phase of AI infrastructure into a harder question about security, resilience and geopolitical exposure.

The concern is not marginal. The Information recently reported that the conflict is complicating Gulf plans to pour more than $300 billion into data centers, chips and other AI investments. That sum matters well beyond the region itself. At a time when frontier AI companies are scrambling for financing and electricity, Gulf sovereign capital and Gulf-hosted infrastructure have emerged as one of the few plausible answers to the industry’s vast appetite for compute. If that pipeline slows, the effects will be felt far beyond Riyadh and Abu Dhabi.

Why the Gulf became irresistible

The attraction was straightforward: the Gulf offered what the United States and Europe increasingly struggle to deliver at speed. Land is available. Energy is comparatively cheap. Governments are willing to move aggressively. And sovereign investors are prepared to think in decades, not quarters. That combination turned the region into a serious destination for hyperscale infrastructure rather than just a source of capital. Reuters has reported that Saudi Arabia’s Humain is building a major AI footprint with U.S. partners, while the UAE’s Stargate project is designed to become the world’s largest AI data center complex outside the United States.

The UAE project captures the ambition. Reuters reported that “Stargate UAE,” backed by G42 alongside OpenAI, Oracle, Nvidia, Cisco and SoftBank, is expected to begin operations in 2026, with an eventual 5-gigawatt campus in Abu Dhabi. Oracle Chairman Larry Ellison said the platform would allow “every UAE government agency and commercial institution” to connect its data to advanced AI models, a line that makes clear how the Gulf is pitching itself: not just as a place to host servers, but as a place to concentrate national-scale AI capability.

Saudi Arabia has been equally assertive. Reuters reported that the kingdom’s Public Investment Fund launched Humain to oversee AI technologies, infrastructure, cloud platforms and advanced models, while U.S. chipmakers and cloud-linked partners moved quickly to sign on. In one of the clearest signs of that momentum, Reuters reported that Humain invested $3 billion in xAI’s Series E round, building on a partnership to jointly develop 500 megawatts of AI data-center infrastructure.

The conflict is turning ambition into risk

That is what makes the war in Iran so disruptive. The problem is not only that governments may need to revisit spending priorities. It is that AI infrastructure depends on the very kind of stability the conflict now throws into doubt: secure energy flows, trusted logistics, predictable insurance costs, executive mobility and confidence that a data center can operate as critical infrastructure rather than as a strategic vulnerability.

The shift is already visible in the reporting. The Information said the war is crimping what had been a crucial potential funding source for power-hungry technology companies. Reuters has separately reported that Gulf AI ambitions were already intersecting with U.S. strategic oversight, security checks and export-control concerns. In a hotter regional conflict environment, those sensitivities are likely to intensify rather than fade.

This is where the glossy AI-growth narrative starts to look more fragile. For months, the Gulf sold itself as a faster, cheaper and more decisive place to build. But AI data centers are not ordinary real estate projects. They sit at the intersection of national security, energy policy, semiconductor supply chains and cloud sovereignty. The war in Iran has exposed how quickly that stack of advantages can become a stack of risks.

Washington wanted the Gulf close — but on its terms

The U.S. strategic angle is also impossible to ignore. Reuters reported last year that Washington viewed deeper AI ties with Gulf allies as a way to keep advanced infrastructure inside a U.S.-aligned technology orbit. David Sacks, then the White House Special Advisor for AI and Crypto, said previous export controls were “never intended to capture friends, allies, strategic partners,” underscoring the argument that countries such as the UAE and Saudi Arabia should be buyers and builders within an American-led ecosystem, not pushed toward Chinese alternatives.

Yet even before the current conflict, the largest UAE-linked AI campus plans were not fully settled. Reuters reported in 2025 that the multibillion-dollar U.S.-UAE data campus deal was still far from final because of persistent Washington concerns over security and technology protection. In other words, the Gulf AI buildout was never simply an economic project. It was always a geopolitical one. The Iran war merely makes that impossible to ignore.

What this means for OpenAI, xAI, Microsoft, Amazon, Oracle and Google

For the major U.S. players, the Gulf remains too important to walk away from. OpenAI has pursued regional capital and infrastructure relationships. xAI has attracted direct Saudi-linked backing. Oracle is embedded in Abu Dhabi’s Stargate buildout. Amazon, Microsoft and Google all see the region as a place to expand cloud and AI capacity while deepening ties to governments and sovereign investors. The fundamental logic still stands: AI needs colossal amounts of power and funding, and the Gulf can offer both.

But the investment case now looks less like a straightforward growth story and more like a resilience test. Companies will have to ask not only whether the Gulf can host the next generation of compute, but whether it can do so under conditions of prolonged regional instability. Boards, financiers and infrastructure planners are likely to reprice that risk. So will insurers. So, very likely, will governments.

The bigger lesson for the AI industry

The deeper point is that AI’s infrastructure race is no longer just a technology story. It is an energy story, a capital story and, increasingly, a war-and-security story. The industry spent much of the past year talking about chips, training costs and power scarcity. The Gulf seemed to offer relief on all three fronts. What the war in Iran has done is remind investors and executives that AI geography matters as much as AI strategy.

The Gulf may still become one of the world’s defining AI corridors. The money is still there. The ambition is still there. The partnerships are still alive. But the assumption that this would be a smooth buildout — that the region could serve as AI’s great stable frontier — has been badly shaken. And for an industry that runs on confidence almost as much as compute, that may be the most consequential change of all.

Image: AI Generated

Generative AI has quickly become one of the most important and misunderstood technologies in the modern digital economy. In business, it is often described as a productivity engine. In research, it is a family of machine learning systems trained to generate new content. In government, it is becoming a regulatory category. In healthcare, it is both a promising assistant and a potential source of harm. In law and politics, it is forcing new debates over copyright, liability, transparency and power.

At its core, generative AI refers to artificial intelligence systems that can create new content rather than simply analyze existing information. The U.S. National Institute of Standards and Technology defines generative AI as a class of AI models that “emulate the structure and characteristics of input data in order to generate derived synthetic content.” That content can include text, images, audio, video, software code and other digital material.

The OECD offers a similarly broad description, presenting generative AI as a form of AI capable of producing text, images, music and video. That may sound simple, but in practice the term covers a broad and fast-evolving range of technologies, from large language models and image generators to multimodal systems that can process and produce several forms of media at once.

The scientific view: how generative AI works

From a scientific perspective, generative AI is rooted in deep learning, where neural networks are trained on massive datasets to identify patterns and produce statistically likely outputs. The breakthrough architecture behind much of the current boom is the transformer, introduced in the influential 2017 paper Attention Is All You Need. That paper laid the technical foundation for many of today’s leading language and multimodal models.

In the case of large language models, these systems are often trained to predict the next token or word in a sequence. IBM describes large language models as large-scale statistical prediction systems that learn patterns in text and generate language based on those patterns. This is why generative AI can produce remarkably fluent responses, but also why it can sometimes invent facts, fabricate citations or present falsehoods with confidence.

That distinction matters. Generative AI does not “understand” the world in the same way humans do. It models relationships in data. Research from the Stanford Institute for Human-Centered Artificial Intelligence notes that the current generation of systems is built on foundation models trained on broad datasets and then adapted for many downstream uses. That flexibility is what makes the technology so powerful — and so difficult to govern.

The business view: the next general-purpose technology?

The business world sees generative AI less as a scientific breakthrough and more as an economic platform. Its value lies in its ability to automate parts of writing, design, coding, customer support, research, search, analysis and enterprise workflows.

A widely cited estimate from McKinsey suggests generative AI could add between $2.6 trillion and $4.4 trillion annually to the global economy across dozens of business use cases. That forecast is one reason the technology has moved so quickly from innovation labs into the boardroom.

The OECD has gone further, arguing that generative AI may qualify as a general-purpose technology — a category usually reserved for innovations like electricity, the computer and the internet. In other words, this is not just another software feature. It may become a foundational layer across industries.

Yet the commercial promise is uneven. Many companies have already learned that deploying a chatbot or writing assistant is the easy part. The harder challenge is integrating generative AI into actual workflows, connecting it to company data, setting up review systems, managing security and proving a measurable return on investment.

Microsoft CEO Satya Nadella captured the productivity promise when he said at the World Economic Forum that AI will act as “a co-pilot” that helps people do more with less, as quoted by the World Economic Forum. That framing has become central to the enterprise case for generative AI: not replacing every worker, but augmenting many of them.

The government view: innovation opportunity and regulatory challenge

Governments increasingly see generative AI through two competing lenses. On one side, it is a source of economic growth, scientific leadership and national competitiveness. On the other, it is a source of misinformation, bias, opacity and systemic risk.

Nowhere is that balancing act clearer than in Europe. The European Commission explains that the EU AI Act includes obligations for providers of general-purpose AI models, including requirements tied to documentation, copyright compliance and transparency. This is a major shift: generative AI is no longer being treated only as a consumer product, but as upstream digital infrastructure that can affect many downstream applications.

In the United States, the regulatory landscape remains more fragmented, but agencies are moving. The Federal Trade Commission has made clear that AI systems and the companies behind them remain subject to existing rules around deception, fairness and consumer harm. That position is important because it signals that generative AI is not being allowed to develop in a legal vacuum.

The result is a new policy reality. Governments want to accelerate AI innovation while also containing the damage it can cause. That tension is likely to define the next stage of the market.

The political view: power, persuasion and global competition

Generative AI is also political because it reshapes information systems. It can produce persuasive text at scale, generate synthetic media, automate influence campaigns and lower the cost of flooding digital platforms with content. That makes it relevant not only to industry policy, but to democratic trust itself.

Policy analysis from the OECD highlights issues ranging from accountability and transparency to labor disruption and concentration of power. Those concerns are no longer theoretical. Generative AI is already affecting elections, media ecosystems and geopolitical competition over computing power, chips, talent and data.

Some of the most widely quoted remarks about AI reflect just how large this political and economic contest has become. Google CEO Sundar Pichai said AI is “more profound than electricity or fire,” in remarks highlighted by the World Economic Forum. OpenAI CEO Sam Altman, meanwhile, has repeatedly argued that advanced AI needs regulation even as the technology expands commercially. The industry’s own rhetoric shows the contradiction clearly: companies want rapid adoption, but even many of their leaders acknowledge the need for oversight.

The healthcare view: transformative potential, high-stakes risk

Healthcare is one of the sectors where generative AI may have the greatest long-term impact — and where the consequences of error are among the most serious.

The World Health Organization says large multimodal models are likely to have broad uses in healthcare, scientific research, public health and drug development. Potential applications include drafting clinical notes, summarizing patient records, assisting medical research, supporting administrative workflows and improving patient communication.

The U.S. Food and Drug Administration has also reported a significant rise in drug development submissions that incorporate AI components, including in clinical, manufacturing and post-market contexts.

But healthcare also exposes generative AI’s most dangerous weaknesses. In separate guidance, the WHO warns that these systems can produce plausible but incorrect, incomplete or biased outputs. In medicine, that is not a minor inconvenience. It can become a patient safety issue.

That is why the most responsible view of generative AI in healthcare is not that it will replace clinicians, but that it may assist them under tightly governed conditions. In this field, validation, supervision and auditability matter far more than demo-quality performance.

The legal view: authorship, copyright, liability and disclosure

Legal systems are still catching up to generative AI, but several battlegrounds are already clear. Copyright is one of the biggest.

The U.S. Copyright Office states that copyright protection in the United States depends on human authorship. In its 2025 report on AI and copyright, the office concluded that material generated entirely by AI without sufficient human creative control is not protected in the same way as human-created work. That has major implications for publishing, entertainment, design, advertising and software.

Training data is another major issue. The U.S. Copyright Office’s report on generative AI training stresses that copyrighted works used to train models are not merely neutral data points; they often contain protected expression. That question sits at the center of lawsuits, licensing disputes and debates over whether AI model development requires a new legal settlement between creators and platforms.

Disclosure and responsibility are becoming equally important. The European Commission has outlined transparency obligations for some AI systems, especially where users may be exposed to AI-generated or manipulated content. The broader legal direction is increasingly clear: responsibility for generative AI outputs will not disappear simply because the technology is complex. Courts and regulators are likely to ask who built the system, who deployed it, what safeguards were in place and what harms were foreseeable.

The cultural and social view: creativity, authenticity and trust

Generative AI is not just a business tool or regulatory concern. It is also a cultural force. It dramatically reduces the cost of producing text, images, music, video and design. That opens new creative possibilities, but it also raises serious questions about originality, ownership and authenticity.

If digital content becomes infinitely reproducible and increasingly synthetic, the value of trust may rise rather than fall. Audiences, readers, voters and consumers may increasingly ask not only whether content is impressive, but whether it is real, attributable and reliable.

That is why one of the most enduring observations about AI remains highly relevant.

There is nothing artificial about it. AI is made by humans, intended to behave by humans and, ultimately, to impact human lives and human society,

Stanford professor Fei-Fei Li wrote in a widely cited post on X. It is a reminder that generative AI is never separate from the social structures, values and incentives of the people building and deploying it.

So what is generative AI, really?

The narrow answer is that generative AI is a class of artificial intelligence systems that generate new content by learning patterns from existing data.

The broader and more useful answer is that generative AI is becoming a new computing layer for language, media, design, software and knowledge work. It can write, summarize, synthesize, simulate, classify, recommend and persuade. But it can also hallucinate, mislead, reproduce bias and create legal and ethical uncertainty.

That is why the term means different things in different domains. To scientists, it is a model class. To companies, it is a productivity platform. To governments, it is a regulatory challenge. To healthcare providers, it is a tool that must be handled with extreme caution. To lawyers, it is a source of unresolved disputes. To politicians, it is part of a larger contest over power, competitiveness and public trust.

In the end, generative AI is not one thing. It is a technical method, a business platform, a policy problem and a societal stress test at the same time. Understanding it requires looking at all of those dimensions together.

Image: Freepik

Investment updates circulating within the European startup ecosystem indicate that the company is building engineering solutions that optimize the positioning of fibers within composite structures, enabling lighter and stronger components for industries such as aerospace, automotive, and high-performance manufacturing.

Composite materials are increasingly used across industrial sectors where structural efficiency and weight reduction are essential. Fibionic’s approach focuses on improving production processes while enhancing the mechanical performance of composite parts.

The newly raised capital is expected to accelerate product development and strengthen the company’s engineering team.

Image: Fibionic, Our Team/edited – 06.03.2026

According to multiple reports citing The Wall Street Journal and other outlets, the Pentagon used Claude during the classified Venezuela raid that led to Maduro’s capture. This marked a rare, if not unprecedented, use of a private AI system in a sensitive military operation. The model was reportedly accessed via a partnership between Anthropic and Palantir Technologies, whose software is widely used within U.S. defense networks.

Ethical Clash Between Pentagon and Anthropic

The incident has triggered tensions between the U.S. Department of Defense and Anthropic. Pentagon officials are said to be frustrated over restrictions tied to Claude’s usage policies — which prohibit its deployment for violence, weapons development, and surveillance. According to Axios, the dispute has escalated to the point where the Pentagon is considering reducing or even ending its roughly $200 million contract with the company.

Anthropic Selected to Build AI-Powered Assistant for GOV.UK Services

Anthropic, for its part, insists that all uses of Claude must adhere to its ethical guidelines and has maintained its commitment to national security cooperation. Its leadership has also been vocal about the need for regulatory guardrails on AI in military and autonomous contexts.

Broader Implications

The revelation about Claude’s role in a high-stakes operation against a sitting head of state — Maduro was brought to the U.S. to face federal charges earlier this year as part of a broader intervention — highlights how rapidly AI technologies are being woven into defense planning and operations.

Critics warn that using advanced AI in such roles without clear legal and ethical frameworks could set far-reaching precedents, potentially reshaping how future conflicts are planned and executed. Meanwhile, proponents argue that AI tools offer critical capabilities for real-time data analysis and decision-support in complex environments.

Material by Yana Petrova

Image: „The Pentagon“, January 2008, author: David B. Gleason, Wikimedia Commons, CC BY-SA 2.0.

In a report by TechCrunch’s Sean O’Kane, the files show that a little-known businessman, David Stern, maintained a years-long relationship with Epstein while pitching him investments in several high-profile EV startups, including Faraday Future, Lucid Motors and Canoo.

The findings were discussed on TechCrunch’s Equity podcast, where O’Kane explained that Stern first approached Epstein in 2008 — the same year Epstein pleaded guilty to soliciting prostitution from a minor — seeking backing for China-related investments. Over time, emails show Stern presenting opportunities tied to the fast-growing U.S. EV sector.

At the time, Silicon Valley was experiencing a surge of Chinese investment in mobility startups. Lucid Motors, then pivoting from battery supplier to EV manufacturer, was struggling to close a crucial funding round. According to the emails reviewed by TechCrunch, Stern asked Epstein to gather information from Morgan Stanley about Lucid’s fundraising, weighing whether to acquire a discounted stake and potentially flip it if a larger automaker moved in.

While Epstein ultimately did not invest in Faraday Future, Lucid, or Canoo, the correspondence suggests a focus on short-term financial gain rather than long-term company building. Stern did later invest in Canoo, which has since filed for bankruptcy.

TechCrunch notes that much of the communication occurred after Epstein’s 2008 conviction, raising renewed questions about how investors and intermediaries weighed reputational risk during a period of intense capital flows into electric vehicle startups.

Whether the disclosures lead to broader fallout in Silicon Valley remains unclear, but the documents offer a rare look at opaque dealmaking networks during a formative period for the EV industry.

Material by Veronika Atanasova

Image: „Epstein 2013 mugshot“, Wikimedia Commons, Public Domain (State of Florida)

Image: San Jose and Silicon Valley Skyline Oct 2017, Author: Tom Pavel