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The Future of Warfare: AI-Powered Drones Should the AI-powered drones of the future have a licence to kill? The question is becoming ever more pressing as governments and the defence industry acknowledge that drone systems will play an increasingly crucial role in future warfare. The Moral Dilemma of Autonomous Weapons With drones being deployed in huge numbers in the Ukraine war and AI being used to assist bombing missions in the Iran conflict, there is an expectation among some observers that weapons will have to operate with increased operational autonomy, which means they will need something approximating a moral framework. Expert Opinions on AI and Morality Last year Mustafa Suleyman, chief executive of Microsoft’s AI arm and a co-founder of the UK-based DeepMind, was unequivocal about the issue of machines making moral decisions. He said: “AIs cannot be people – or moral beings.” David Omand, the former head of the UK spy agency, GCHQ, believes AI can create a “moral” configuration for unmanned weapons. The UK armed forces minister, Al Carns, told the Financial Times recently there must be an option to “take the human out of the loop” in decision-making. The Challenges of Programming Morality Zee Talat, an academic specialising in machine learning at the University of Edinburgh’s school of informatics, argues that large language models – the technology that underpins modern generative AI systems such as chatbots – are fundamentally incapable of moral decision-making. “If you have a machine that’s probabilistic by nature it will veer towards the most likely answer in a situation. Do we think that morality follows probabilistic notions?” The Debate on Autonomous Weapons Governance Jessica Dorsey, an assistant professor of international law at Utrecht University in the Netherlands, raises concerns about determining whose morality the drone is following, given the United Nations is still trying to achieve a global consensus on autonomous weapons governance. “War is filled with so many variables and it is a given that things will go wrong. And when that happens at AI-like speed, it is difficult to unravel.” The Future of AI-Powered Drones Some experts argue that giving drones greater autonomy, and programming rules of engagement and morality into them, will be a necessity if other nation states continue to develop and deploy similar technology at pace. Nicholas Wright, a neuroscientist and author of Warhead, a book on the human brain and war, says: “For any military to compete effectively against other high-end militaries it is going to need a large amount of systems that will be required to take decisions on their own.” Olaf Hichwa, the co-founder of Neros, a US drone startup, believes that drones will not replace human decision-makers, but enhance the abilities of their human pilots.

Nvidia's Bold Move into the CPU Market Nvidia opened Taipei's Computex trade show with a significant announcement, unveiling the RTX Spark, a 'superchip' designed to run AI agents securely. This move marks Nvidia's entry into the $200 billion CPU market, with the goal of powering AI PCs from top manufacturers. The RTX Spark: A Powerful PC CPU The RTX Spark is a 1-petaflop chip capable of running AI agents like OpenClaw or Hermes Agent securely. It will be available in Windows PCs from ASUS, Dell, HP, Lenovo, Microsoft Surface, and MSI, with models from Acer and Gigabyte to follow. These PCs will feature secure sandboxes developed with Microsoft to run agents safely. Key Features and Capabilities 1-petaflop processing power Support for local versions of large language models Enough CPU, GPU, RAM, and Nvidia CUDA software for smooth performance Faster performance for AI, better image quality, and support for AI features in over 1,000 games and applications Market Impact and Future Outlook Nvidia's CEO, Jensen Huang, envisions a future where users can simply ask their PCs to perform tasks, eliminating the need for traditional app launching and typing. With over 100 Windows software makers supporting the new chip, including Adobe and Riot Games, Nvidia is poised to make a significant impact in the market. The Road Ahead While previous attempts at Nvidia ARM-based Windows devices have failed, Huang's track record of delivering record revenue quarters makes it difficult to bet against his PC ambitions. As these PCs hit the market, their pricing and competition with affordable options like the Mac Mini will be crucial factors to watch.

The Battle for Content Ownership: CNN Sues PerplexityUnited States news channel CNN has initiated a federal lawsuit against Perplexity in New York, alleging that the AI search engine provider is unlawfully distributing its copyrighted content. This legal action marks a significant escalation in the ongoing conflict between traditional media and the rapidly evolving generative AI sector.Allegations of Unlawful Content DistributionThe complaint, filed on Thursday, alleges that Perplexity unlawfully copied thousands of CNN stories, videos, and images to power its products. The lawsuit claims the company distributes "identical or substantially similar" content, effectively repurposing original reporting without permission. CNN is seeking an unspecified amount of monetary damages and a court order to block Perplexity from violating intellectual property rights.The High-Stakes Economics of AI DataThis legal battle centers on the valuation of data versus the protection of creative work. Perplexity, valued at tens of billions of dollars, has defended its practices by stating, "You can’t copyright facts." However, CNN argues that while facts may not be copyrightable, the specific reporting, curation, and presentation of news are protected by copyright law. The lawsuit emphasizes that Perplexity exploits the economic incentives that make original newsgathering possible.Shifting the Paradigm of AI TrainingThis case is not isolated; it is part of a broader industry trend. Since the launch of OpenAI’s ChatGPT in 2022, news publishers have faced existential threats regarding their content being scraped for training large language models. CNN's lawsuit joins a growing list of high-stakes cases brought against AI firms, including The New York Times, Reddit, and Dow Jones. Consequently, many news firms are now pivoting toward signing licensing deals and partnerships with Big Tech to ensure verified access and compensation.The Future of AI-News IntegrationThe outcome of this lawsuit will likely set a precedent for how AI companies handle copyrighted material. As legal challenges mount, the industry is moving away from "scraping" and toward "licensing." We can expect a future where AI search engines must pay for access to premium news content, fundamentally changing the revenue models of digital media.

Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

Google’s AI Overview Stumbles on Simple Letter Counting Google’s newly rolled‑out AI Overview feature in Search incorrectly counted letters in everyday words – claiming there are two “P”s in “Google”, one “r” in “poop”, and even misspelling “journalism”. The blunders highlight a long‑standing weakness of large language models (LLMs) when it comes to exact spelling. The Miscounted Letters Behind the New Search AI “Google” – AI said 2 Ps (actual: 0) “poop” – AI said 1 r (actual: 0) “journalism” – AI said 2 d’s (actual: 0) U.S. President’s last name – AI reported 1 P but rendered “t‑r‑p‑u‑m” Quantifying the Miscounts: Numbers Behind the Errors Beyond the anecdotal examples, the AI also produced a faulty definition for the word “disregard”, responding with “Understood. Let me know whenever you have a new prompt or question!” This illustrates that token‑based encoding can produce nonsensical outputs even when the input is a single word. Implications for Search Trust and AI Adoption Google’s AI‑driven overhaul aims to make generative responses the centerpiece of its 29‑year‑old search product. Repeated factual and spelling errors risk eroding user confidence, especially after earlier AI Overviews cited satirical sources and gave absurd advice such as “eat rocks”. Trust in AI‑generated answers remains a critical hurdle. What’s Next for Google’s Generative Search? Google told TechCrunch it is “working to fix this particular issue” and will likely refine its tokenizer and post‑processing pipelines. Industry observers expect incremental improvements rather than a complete architectural shift, meaning users may continue to see occasional glitches while the broader AI‑search strategy matures.

The Lead A phenomenon dubbed "AI psychosis" is reportedly affecting tech executives, particularly CEOs, who are overestimating artificial intelligence capabilities while simultaneously implementing mass layoffs. This disconnect between perception and reality is creating organizational chaos in the tech industry. The CEO AI Delusion Box founder Aaron Levie has suggested that CEOs are uniquely prone to "AI psychosis" because they're sufficiently distant from the implementation details of AI systems. When executives "play with AI" by developing prototypes or generating contracts, they often make the leap to believing AI agents can fully handle complex work without understanding the limitations. Unlike their technical teams, CEOs aren't responsible for reviewing code, discovering bugs, or training AI models on company-specific requirements. This lack of firsthand experience with AI's limitations doesn't stop them from making decisions based on overoptimistic assessments of AI capabilities. The Layoff Numbers In the first five months of 2026 alone, the tech industry has already seen 115,430 people fired from 152 tech companies. This nearly matches the 124,636 people let go by 275 companies throughout all of 2025, according to industry tracker Layoffs.fyi. The majority of these layoffs have been attributed to AI, though many argue that companies are engaging in "AI washing" - crediting AI productivity gains when other business decisions are really driving the cuts. The ClickUp Experiment Zeb Evans, CEO of project management software startup ClickUp, proudly declared on X that he had laid off almost a quarter of his employees (22%) after implementing approximately 3,000 AI agents for internal work. Evans insisted this wasn't a cost-cutting measure but rather an attempt to create what he calls a "100x org" composed of people who run and review AI agents' work. The Productivity Paradox Research on AI and productivity presents a complex picture. A meta-analysis published in UC Berkeley's California Management Review found "no robust relationship between AI adoption and aggregate productivity gain." Meanwhile, research from the National Bureau of Economic Research concluded that while AI adoption does improve productivity, there's a "productivity paradox" in which perceived gains exceed measured improvements. MIT researchers studying thousands of AI agents found they aren't yet producing human-quality work in many cases. They predict that at the current rate of improvement, large language models will "be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level," with additional time needed to outperform humans. The Executive Bottleneck Research published in the Harvard Business Review suggests that when everyone in an organization uses AI to produce more output, the bottleneck simply shifts to executives. Their work awaits authorization of all the content being generated by AI-empowered employees. If everyone is empowered to act, the system risks becoming overwhelmed, as evidenced by OpenAI's experience last year. As Levie advises, CEOs should use AI extensively to understand both its capabilities and limitations. However, with the current trend of mass layoffs and organizational restructuring based on overoptimistic AI assessments, the tech industry may face continued chaos until this balance is achieved.

The Architecture of Agentic FinanceRobinhood is fundamentally redefining the user experience by launching support for AI agentic trading and a new agentic credit card. This initiative allows users to create separate accounts for their AI agents, connecting them to a dedicated wallet. While these agents can analyze portfolios and suggest strategies, they are restricted to executing trades using only pre-loaded balances. The platform ensures safety through a mandatory approval workflow for trade previews and employs a dedicated fraud detection team to review suspicious activities.Protocol Integration: Agents connect via the Model Context Protocol (MCP) to analyze concentration risk and sector exposure.Control Mechanism: Users receive real-time notifications and can monitor all agent activities within the app.Current Scope: The beta feature is currently limited to stock trading.Expanding the Agentic EcosystemThe rollout of these tools represents a significant expansion of Robinhood's capabilities. The company is not only enabling autonomous trading but also introducing a virtual credit card for AI agents to facilitate payments. Currently, this card is exclusive to Robinhood Gold Card holders, who can link their accounts to set monthly limits and approval preferences. The platform has also outlined a clear roadmap for future asset classes.Upcoming Assets: Support for options, crypto, event contracts, futures, and prediction markets is planned for the near future.Platinum Access: The Robinhood Platinum Card will receive similar agentic card features later this year.Redefining the Role of the TraderThis development marks a pivotal shift in the financial services industry, moving from active manual trading to agentic finance. By adopting the Model Context Protocol (MCP), Robinhood allows users to integrate third-party Large Language Models (LLMs) directly into their investment workflow. This reduces the friction of manual data analysis and positions Robinhood as a central node in the growing network of autonomous financial agents.The Future of Autonomous FinanceAs major players like Stripe, Amazon, and Google race to build similar capabilities, the barrier to entry for AI-driven financial management is rapidly dropping. We predict that by the end of the year, the distinction between a traditional trading account and a managed portfolio will blur, with AI agents becoming the primary interface for routine financial transactions and payments.

The Lead: A Personal Manifesto Against AI ConvenienceWendy Liu explains why she deliberately avoids generative‑AI tools, insisting that the struggle of thinking is what makes us human. In an era where large language models can produce code and prose in seconds, Liu contends that the convenience comes at the cost of cognitive sovereignty.The Early Coding Journey: Learning by Hand in the Mid‑2000sGrowing up with unmonitored access to a family computer, Liu taught herself to build websites using only a basic text editor. The process involved countless hours of debugging and poring over documentation, which she describes as “painstaking” but ultimately rewarding.Mid‑2000s: Self‑taught web development using a simple editor.Result: Deep appreciation for the craft of coding despite imperfect outcomes.The Rise of AI‑Assisted Development: From “Vibe‑Coding” to Mass RedundanciesToday, tools like OpenAI’s Codex and Anthropic’s Claude Code enable anyone to generate functional code through natural‑language prompts. Liu notes that this “vibe‑coding” trend has led many tech firms to justify large‑scale layoffs, using AI as a pretext for workforce reductions.The Cognitive Off‑Loading Concern: Protecting Our Thinking MusclesLiu warns against “cognitive off‑loading,” the habit of delegating mental tasks to AI for convenience. She cites emerging research suggesting that even brief interactions with AI chatbots can negatively affect problem‑solving abilities.The Societal Implications: From Corporate Greed to Environmental TollThe article links AI’s rapid expansion to broader issues:Trillions of dollars projected for data‑centre construction.Corporate revenues used to fund mass redundancies while pushing AI adoption.Environmental concerns tied to the energy consumption of massive AI models.Potential widening of socioeconomic inequality as AI becomes a “utility” controlled by a few corporations.The Path Forward: Embracing Inefficiency as a Moral ChoiceChoosing to work without AI, Liu argues, is a deliberate act of preserving humanity and building character. She acknowledges the personal trade‑offs—being a less efficient coder and writer—but frames the inconvenience as a safeguard against corporate‑driven efficiency that threatens individual agency.

The Lead When the founders of a mental health app for men called Mental discovered that one feature — AI interactive audio — was resonating strongly with users, they recognized a significant opportunity. This insight led to the creation of The Path, a new AI therapy application co-founded by renowned motivational speaker Tony Robbins and former Calm employees, which has now secured $14.3 million in seed funding. The Birth of a Safer AI Therapy Platform The Path emerged from observations made by co-founder and CEO Anson Whitmer and co-founder Tyler Sheaffer, who previously worked together at meditation app Calm. Whitmer's personal experiences with suicide in his family inspired him to pursue mental health technology. After working at Calm until 2021, he felt he could make a greater impact by addressing the unique, personal nature of people's mental health challenges. Whitmer sees large language models (LLMs) and AI as the bridge to providing personalized mental health care to everyone, especially given the shortage of therapists worldwide. "What's exciting and game-changing is that, for the first time in my career, I've seen that there's actually this possibility for every single person to have the personalized sort of access and care that they need to really get the help," he said. Funding and Celebrity Endorsement The Path has successfully raised $14.3 million in seed funding, led by Prime Movers Lab where Tony Robbins is a partner. Other notable investors include Olympic speed skater Apolo Anton Ohno, boxer Deontay Wilder, and Designer Fund. After Prime Movers invested, Robbins initially consulted on branding but his enthusiasm grew, leading to him becoming a co-founder. The author has since helped shape The Path into a therapy-plus-coaching app that incorporates his popular self-improvement methods. The app currently offers 11 virtual AI therapists that users can customize based on their preferences for directness and other details. While it's currently free to gain users, The Path plans to eventually charge $40 per month for the service. Superior Safety Benchmarks A key differentiator for The Path is its specially trained AI model, which has scored a 95 on the Vera-MH mental health safety AI benchmark. This significantly outperforms consumer chatbots, which top out at 65 on the same benchmark. According to Whitmer, consumer chatbots are "optimized for engagement," which is counterproductive to effective therapy and coaching that should focus on deep understanding rather than quick solutions. "It's meant to challenge you. It's not just meant to agree with you," Whitmer explains. The Path's AI is designed to help users dig out their assumptions and discover their own solutions rather than simply reinforcing ideas to keep users engaged. The startup's model is post-trained from open source models and doesn't use major consumer LLMs, positioning it as a specialized therapeutic tool rather than a generic chatbot wrapper. Market Potential and Future Outlook The mental health tech market is experiencing significant growth, with OpenAI reporting that at least 900 people use ChatGPT for mental health-related queries every week. This demonstrates the clear demand for AI-powered mental health solutions. However, The Path aims to capture a specific segment of this market by focusing on therapeutic rigor and safety. As mental health awareness continues to grow and technology becomes more sophisticated, AI therapy platforms like The Path could play an increasingly important role in addressing global mental health challenges. The combination of Tony Robbins' brand recognition, the technical expertise of the Calm alumni team, and the specialized focus on therapeutic safety positions The Path as a notable contender in the emerging field of AI-powered mental health care.