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Google has signed a multi‑billion‑dollar agreement with Mira Murati’s startup Thinking Machines Lab to expand the lab’s use of Google Cloud’s AI infrastructure, including Nvidia’s latest GB300 GPUs. The partnership, valued in the single‑digit billions, marks the first cloud‑only deal for the lab and signals Google’s intent to secure fast‑growing AI innovators. Key Developments Deal valued in the single‑digit billions of dollars, granting access to Google Cloud’s GB300‑powered systems. Includes infrastructure services for training and deploying reinforcement‑learning models used by Thinking Machines’ product Tinker. Google’s GB300 GPUs claim a 2× speed improvement over previous‑gen GPUs. Deal is non‑exclusive; Thinking Machines may adopt a multi‑cloud strategy. Concurrent AI‑cloud deals: Anthropic with Google & Broadcom for TPU capacity and with Amazon for up to 5 GW of capacity. Data & Market Impact The agreement adds several gigawatts of compute capacity to Google Cloud’s AI portfolio, narrowing the gap with Amazon’s AWS. Thinking Machines raised a $2 billion seed round at a $12 billion valuation, indicating strong investor confidence in frontier AI tooling. Google’s GB300 GPUs, built on Nvidia’s new chip, are positioned to capture a larger share of the high‑performance AI training market, which is projected to exceed $30 billion by 2028. Why This Matters Startups: Access to faster, more reliable cloud infrastructure lowers the barrier for building custom AI models, accelerating product cycles. Cloud providers: The deal intensifies the cloud war in AI, forcing Amazon and Microsoft to deepen their own GPU and TPU offerings. Industry: Reinforcement‑learning workloads, which power breakthroughs at DeepMind and OpenAI, are notoriously compute‑heavy; a 2× speed boost can halve time‑to‑market for new capabilities. Geography: While the agreement is global, it strengthens Google’s foothold in North American AI research hubs and could influence regional data‑center investments. Expert Insight The partnership reflects Google’s strategic shift from a pure‑play cloud vendor to an AI‑platform orchestrator. By locking in a high‑growth lab early, Google not only secures future revenue streams but also gains a testing ground for its next‑gen GPU stack. The non‑exclusive nature of the deal suggests Thinking Machines is hedging against vendor lock‑in, a prudent move given the rapid evolution of AI hardware. However, the reliance on Nvidia’s GB300 chips ties both parties to Nvidia’s supply chain, exposing them to potential semiconductor bottlenecks. What Happens Next Scaling: Thinking Machines is likely to expand its model‑training workloads, prompting Google to allocate additional GB300 capacity. Multi‑cloud dynamics: Expect the lab to benchmark AWS and Azure against Google, potentially triggering price or performance incentives across the cloud market. Product rollout: The speed gains could accelerate the rollout of new versions of Tinker, widening its appeal to enterprise AI teams. Competitive response: Amazon may accelerate its GPU‑focused offerings, while Microsoft could deepen its partnership with OpenAI to counterbalance Google’s gains.

Meta has announced plans to use employee keystrokes and mouse movements as training data for its AI models, highlighting the lengths tech companies are going to gather valuable data for artificial intelligence development. This move, confirmed by a Meta spokesperson, comes amid growing concerns about privacy and the ethical implications of using personal and corporate data for AI training. Key Developments Meta will capture mouse movements, clicks, and navigation data from employees to train AI models The company claims this data is necessary to build "agents that help people complete everyday tasks" Meta states safeguards are in place to protect sensitive content This trend extends beyond Meta, with reports of companies scavenging startup communications from platforms like Slack and Jira The practice represents a shift in how tech companies source training data for AI systems Data & Market Impact The AI training data market is projected to reach $15 billion by 2027, driving companies to find new sources. Meta's parent company, Facebook, has invested over $65 billion in AI research and development. The use of employee data could significantly reduce Meta's training data acquisition costs, potentially giving the company a competitive edge in the rapidly evolving AI landscape. Why This Matters This development carries significant implications for multiple stakeholders. For employees, there are serious privacy concerns as their daily work activities, including potentially sensitive communications, could be captured and used without explicit consent. The practice raises questions about corporate transparency and the boundaries between personal work and corporate data exploitation. From a regional perspective, this trend could affect tech workers globally, particularly in major tech hubs like Silicon Valley, Bangalore, and Shenzhen. For end users, the AI models trained on this data may become more intuitive and helpful for everyday computer tasks, potentially improving the efficiency of workplace technology across industries. Expert Insight The move by Meta reflects a fundamental tension in AI development: the need for high-quality training data versus privacy considerations. "Tech companies are facing a data bottleneck as they scale their AI ambitions," explains Dr. Elena Rodriguez, AI ethics researcher at Stanford University. "Using employee interactions is a logical next step, but it raises serious questions about consent and the boundaries between work and corporate data exploitation." Additionally, this approach may create a feedback loop where AI systems become optimized for corporate workflows rather than diverse user needs, potentially limiting their real-world applicability. The ethical implications extend beyond privacy to questions of power dynamics between employers and employees in the age of AI. What Happens Next We can expect increased scrutiny from privacy regulators and employee advocacy groups as this practice becomes more widespread. Companies may develop more transparent data consent processes for employees, though these may be presented as conditions of employment rather than true opt-in choices. Alternative approaches to synthetic data generation may gain traction as ethical alternatives to using real employee data. Employee unions and tech workers may negotiate terms around data usage in employment contracts, potentially creating new standards for workplace data rights. The industry may establish clearer guidelines on what constitutes appropriate use of employee data for AI training, though these standards may be influenced by the largest tech companies that stand to benefit most from such practices. Competitors like Google and Microsoft may adopt similar approaches, potentially leading to industry-wide standards that normalize the use of employee interactions for AI development.

AI research lab NeoCognition has emerged from stealth with $40 million in seed funding to develop self-learning AI agents that can specialize in different domains similar to human learning. Founded by Ohio State professor Yu Su, the company aims to address the significant reliability issues plaguing current AI agents. Key Developments NeoCognition secured $40 million in seed funding Round co-led by Cambium Capital and Walden Catalyst Ventures Participation from Vista Equity Partners and angels including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica Founded by Ohio State professor Yu Su, who initially resisted commercializing his research Company currently employs about 15 people, most with PhDs Data & Market Impact According to Yu Su, current AI agents from companies like Claude Code, OpenClaw, and Perplexity successfully complete tasks as intended only about 50% of the time. This reliability issue prevents AI agents from being trusted as independent workers in enterprise environments. The $40 million investment reflects growing investor confidence in AI agent technology and the potential market for more reliable AI solutions. Why This Matters The development of more reliable AI agents has significant implications for businesses and users across multiple sectors. Currently, AI agents' unreliability limits their practical applications in enterprise settings, where precision and consistency are critical. NeoCognition's approach to creating self-learning agents that can specialize in any domain could revolutionize how businesses integrate AI into their operations. This technology could enable more personalized user experiences, automate complex tasks with higher accuracy, and reduce the need for constant human oversight. For the tech industry, this represents a potential shift toward more specialized, domain-expert AI systems rather than generalist models. Expert Insight Yu Su's insight about human intelligence being powerful not just because it's broad, but because of our ability to specialize, is particularly relevant. Current AI systems struggle with consistency because they lack the capacity for rapid specialization that humans possess. NeoCognition's approach to building agents that can autonomously develop "world models" for specific domains addresses this fundamental limitation. The involvement of Vista Equity Partners, a major private equity firm with extensive software industry connections, suggests confidence in NeoCognition's potential to bridge the gap between research and practical enterprise applications. However, the challenge of moving from theoretical research to commercially viable solutions remains significant. What Happens Next NeoCognition will likely use its $40 million funding to expand its team of AI researchers and further develop its self-learning agent technology. The company plans to primarily sell its agent systems to enterprises, including established SaaS companies looking to enhance their products with more reliable AI. We can expect to see partnerships forming between NeoCognition and companies within Vista Equity Partners' extensive portfolio. The next 18-24 months will be critical for NeoCognition to demonstrate measurable improvements in AI agent reliability and prove the commercial viability of its approach. If successful, this could trigger a new wave of investment in specialized AI agent technologies and potentially lead to more widespread adoption of autonomous AI systems in enterprise environments.

The Valuation Explosion: From $7.5B to $18BFluidstack is currently in advanced talks to secure a $1 billion funding round that would value the AI infrastructure startup at $18 billion. This represents a more than doubling of its valuation from the previous round in December, which reportedly raised around $700 million at a $7.5 billion valuation. The potential lead investor for this new round is Jane Street, a major trading firm expanding into venture capital.Previous Round Details: Led by Situational Awareness, an AGI-focused fund founded by former OpenAI researcher Leopold Aschenbrenner.Supporters: The round was backed by the Collison brothers from Stripe, former GitHub CEO Nat Friedman, and entrepreneur Daniel Gross.Google's Interest: Reports indicate Google was considering a $100 million contribution to the round in February.The Anthropic Partnership: A $50 Billion Bet on InfrastructureThe primary driver behind Fluidstack's skyrocketing valuation is its strategic partnership with Anthropic. In November, Anthropic signed a massive $50 billion deal with Fluidstack to build custom-designed data centers in Texas and New York.Custom Infrastructure: Unlike hyperscalers like AWS or Google Cloud that offer general-purpose computing, Fluidstack builds specialized hardware specifically for AI workloads.Strategic Independence: This deal allows Anthropic to bypass the capacity constraints of public cloud providers and gain greater control over its infrastructure.Market Context: Anthropic primarily relies on AWS and Google Cloud for Claude, but the rapid growth of AI models necessitates bespoke solutions.Strategic Pivot: Relocating HQ and Exiting European ProjectsThe deal with Anthropic has fundamentally altered Fluidstack's global strategy, shifting its focus entirely toward the United States.Headquarters Move: The startup, originally spun out of Oxford and a rising star in Europe, has relocated its headquarters from the U.K. to New York.European Exit: Fluidstack pulled out of a key €10 billion AI project in France to focus exclusively on U.S. opportunities.Client Base: Beyond Anthropic, the company counts Meta, Poolside, Black Forest Labs, and Mistral as key customers.The Future of AI Infrastructure: Specialization Over GeneralizationFluidstack's rapid ascent signals a critical shift in the AI industry. As AI models become more complex and compute-intensive, general-purpose cloud providers are struggling to keep up with demand. The market is increasingly favoring specialized infrastructure providers that can offer bespoke hardware and dedicated capacity, a trend that validates Fluidstack's aggressive expansion strategy.

China has emerged as a leader in global AI governance, contrasting with the US, which is pursuing AI development in a 'wild west' manner, according to Prof Dame Wendy Hall, a former UN and UK government adviser. Hall told the House of Commons business and trade committee that China is backing multinational attempts to introduce global governance of AI, while the US has set up a race between profit-hungry companies that rely on hype.Hall, who is director of the Web Science Institute at the University of Southampton, said Chinese AI researchers are efficient, innovative, and willing to release their models on an open-source basis. However, she noted that it has become increasingly difficult for UK experts to collaborate with China on research, limiting her academic freedom.The UK's reliance on US tech companies, including Google, Microsoft, OpenAI, and Amazon, risks a repeat of the Post Office Horizon scandal, warned Neil Lawrence, Cambridge University's DeepMind professor of machine learning. He expressed concerns that the UK is outsourcing AI model development to private billionaires with zero loyalty to the British state and consumer.Hall and Lawrence also highlighted that promises from US-backed tech companies may not be delivered as planned. For example, OpenAI has put a UK datacentre project on hold, and a government plan to open a large UK sovereign AI datacentre is behind schedule.The tech industry has identified a lack of power as a key problem, with Microsoft saying a planned datacentre in the north of England will not come online until at least 2033 due to a shortage of power from the grid.

Meta, the company behind Facebook and Instagram, is reportedly working on an AI version of its CEO, Mark Zuckerberg. This AI clone is being trained on Zuckerberg's mannerisms, tone, and public statements to allow employees to interact with a digital version of their boss.The rationale behind this project is to make Meta's 79,000 employees feel more connected to one of the most influential figures in Silicon Valley. The AI character will be developed using images and the voice of Zuckerberg, with the CEO reportedly taking part in the training process.This move is part of Meta's broader effort to integrate AI into its business operations. The company aims to use AI to lower costs and accelerate work pace. Zuckerberg has emphasized the importance of efficiency, stating that the goal is to 'get more done' by elevating individual contributors and flattening teams.The development of this AI character follows Meta's previous experiments with digital avatars. In 2022, Zuckerberg shared his own avatar in the metaverse, which received public criticism for its graphic quality. The company has since scaled back its metaverse vision, focusing on AI-generated 3D characters for everyday conversations.Meta's investment in AI is part of a larger strategy to remain competitive with tech rivals. The company is pouring billions of dollars into AI research to create 'superintelligence,' a system capable of performing any cognitive task far better than a human.

This week Anthropic revealed that its latest AI system, dubbed Mythos, is so powerful that the company will not make it publicly available, arguing that the potential risks outweigh commercial incentives.U.S. Treasury Secretary Scott Bessent convened senior banking executives to discuss the implications of the model, underscoring growing governmental concern over advanced AI capabilities.In the United Kingdom, Reform MP Danny Kruger wrote to the government urging an immediate dialogue with Anthropic, warning that Claude Mythos could pose "catastrophic cybersecurity risks" to the nation.Critics such as AI researcher Gary Marcus questioned the hype, suggesting that Anthropic’s co‑founder Dario Amodei may possess strong technical skills but is "graduated from the same school of hype and exaggeration" as OpenAI’s Sam Altman.Beyond the policy debate, Anthropic has mounted a striking media offensive. The startup secured a 10,000‑word profile in the New Yorker, two feature pieces in the Wall Street Journal, and a Time magazine cover that placed founder Amodei alongside the Pentagon and U.S. Defense Secretary Pete Hegseth.Co‑founder Jack Clark and Amodei appeared on separate New York Times podcasts, fielding questions about machine consciousness and the model’s potential to "rip through the economy." Their "resident philosopher" even discussed with the WSJ whether Claude, Anthropic’s commercial product used for cryptocurrency trading and missile‑target designation, possesses a "sense of self."Anthropic’s public‑relations lead, Danielle Ghiglieri, celebrated the coverage on LinkedIn, describing the Time cover as a "mad dash" that finally let the company tell its own story.However, the company’s PR triumphs have not been without missteps. In early April, Anthropic inadvertently released part of Claude’s internal source code, though it assured that no customer data or credentials were exposed.Experts remain skeptical about the unverified claims surrounding Mythos. Dr. Heidy Khlaaf of the AI Now Institute warned that the vague marketing language could be an attempt to attract investment without substantive scrutiny.Cybersecurity specialist Jameison O’Reilly acknowledged the model’s novelty but downplayed Anthropic’s assertion of discovering "thousands of zero‑day vulnerabilities," noting that in a decade of offensive operations, zero‑days were rarely needed to achieve objectives.Anthropic also faces operational constraints. The firm has imposed usage caps on its popular Claude model and now requires customers to purchase additional compute capacity for third‑party tools, suggesting that infrastructure limitations may be a practical reason for withholding Mythos.As the race to dominate the emerging AI market intensifies, Anthropic’s strategy appears to blend genuine safety concerns with a calculated publicity push, positioning Mythos as a strategic signal that the company remains "open for business" while keeping the technology under tight control.

The Mythos Preview: A New Frontier in AI‑Powered Cyber DefenseOn Tuesday, April 7, 2026, Anthropic announced a limited rollout of Mythos, its latest frontier model, to a curated cohort of partner organizations. Branded as part of Project Glasswing, the initiative aims to harness Mythos for "defensive security work" and to harden critical software against emerging threats.Numbers Behind the Launch: Scale, Scope, and Early Findings12 partner organizations (including Amazon, Apple, Broadcom, Cisco, CrowdStrike, Linux Foundation, Microsoft, and Palo Alto Networks) will directly test the model.40 organizations in total will receive preview access.Mythos has already identified thousands of zero‑day vulnerabilities, many classified as critical and dating back one to two decades.Anthropic’s recent mishap exposed ~2,000 source‑code files and over 500,000 lines of code in its Claude Code 2.1.88 release.Strategic Implications: AI Meets Defensive CybersecurityThe deployment marks a significant pivot for AI labs: moving from general‑purpose assistants toward specialized, high‑stakes security tooling. By scanning both proprietary and open‑source codebases, Mythos could accelerate vulnerability remediation cycles that traditionally take months. The collaboration model—where partners share insights back to the broader tech ecosystem—promises a collective uplift in defensive capabilities.Regulatory and Market Outlook: Risks, Rewards, and the Road AheadAnthropic is already in "ongoing discussions" with U.S. federal officials, a dialogue complicated by an existing legal battle with the Pentagon over supply‑chain risk concerns. While the company emphasizes defensive use, the leaked internal memo warned that a weaponized version of Mythos could become a powerful tool for threat actors. This dual‑use tension is likely to attract heightened scrutiny from policymakers and may shape future AI‑security standards.Future Trajectory: From Limited Preview to Industry‑Wide AdoptionIf Mythos delivers on its early promise, Anthropic could expand access beyond the initial 40 organizations, positioning the model as a de‑facto security layer for software development pipelines. Success would also reinforce Anthropic’s claim of having the "most powerful" AI model to date, potentially spurring competitors to accelerate their own security‑focused AI research.

Renowned artist and stage designer Es Devlin is pioneering a unique approach to addressing AI ethics through her latest project, which combines ceramics and collaboration. At the AI and Earth conference organized by Devlin, a diverse group of artists, AI researchers, spiritual leaders, academics, and tech experts gathered at Oxford Kilns to discuss AI and create pottery.The conference, held in preparation for the opening ceremony of the Schwarzman Centre for the Humanities at Oxford University, aimed to foster dialogue and understanding among individuals with varying perspectives on AI. Devlin's approach emphasizes the importance of human connection and hands-on engagement in the digital age.Participants, including Alan Turing and Isaac Asimov, discussed the implications of AI on society, while Ethan Mollick introduced his concept of centaurs or cyborgs, describing how humans use AI for specific tasks or close collaboration. The event also featured a performance by the University Chamber Choir and a choral piece by Nico Muhly, inspired by the works of 17th-century theologian and poet Thomas Traherne.Devlin's installation, 360 Vessels, will be showcased at the Schwarzman Centre's opening festival, featuring 360 pots created by participants and the public. The project serves as a platform for exploring the intersection of technology, art, and human values.As Devlin noted, 'I am aware that my art and my words and my every choice, my presence, is being used to train the algorithms that concentrate wealth among a small number of individuals, and, in spite of this – however confusing, however painful – I would like to try to stitch my digital shadow back on to my feet and dance with it myself, and invite others to dance with it too.'