AI News Archive: August 4, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Deepfake bosses are crashing video calls, and researchers are trying to expose them
Fraunhofer researchers are developing a real-time warning system for deepfake video meetings, targeting scams where AI-generated bosses and coworkers pressure employees into costly decisions.
- Bypassing AI guardrails is so easy a script kiddie can do it
Claiming 'it's my server' was often enough to persuade models to help
- Palantir: Inside the ‘Otherworldly’ Earnings Making the Stock an AI Winner
Palantir: Inside the ‘Otherworldly’ Earnings Making the Stock an AI Winner Barron's
- Nedbank’s AI strategy unlocks R375m in value
The bank’s Intelligent Hyper Automation strategy generates R375 million annualised value as it accelerates its digital transformation.
Score: 48🌐 MovesAug 4, 2026https://www.itweb.co.za/article/nedbanks-ai-strategy-unlocks-r375m-in-value/Pero3MZ3J5AqQb6m - Newest mission for drones? Delivering blood for life-saving procedures at emergency scenes
Hospital officials say field transfusions will shift the critical window of care directly to accident locations
Score: 48🌐 MovesAug 4, 2026https://www.independent.co.uk/news/world/americas/florida-blood-delivery-drone-b3027353.html - AI Could Give Nurses More Time for Patient Care by Taking Over 1 Important—but Tedious—Task
A 300-patient clinical trial found an AI-powered system kept hospital patients in their target oxygen range far longer than standard care, while reducing manual adjustments.
- 69% of Indian enterprses plan to increase AI investments: Survey
A surge of artificial intelligence adoption is evident among Indian enterprises. With many organizations actively pursuing AI projects and planning significant investments, the benefits are becoming apparent. Despite the visible success of AI initiatives, challenges related to enterprise data readiness pose obstacles to expansion. Businesses are making a decisive move from experimental uses of AI to widespread application across their operations.
- Why AI infrastructure needs a new operating model
The next AI infrastructure crisis may come from unmanaged inference capacity. For the past several years, the AI infrastructure conversation centered on one question: how do we get more compute? That made sense. Enterprises needed GPUs, cloud capacity, foundation models and room to experiment. Compute became shorthand for AI readiness. Production AI changes the operating discussion . Utilization, routing, latency, throughput, cost control, policy, privacy and governance now need to be managed together. A GPU that sits idle creates no business value. A model endpoint with unpredictable latency frustrates users. An inference stack that cannot be measured end-to-end becomes difficult to defend when usage grows and finance asks where the money is going. CIOs need governed capacity. Governed capacity means operating AI infrastructure as a production system rather than a collection of disconnected resources. They need to know how much useful output their infrastructure produces, where that output runs, why it runs there, what it costs, how it performs, what policy applies and whether the system can be controlled as demand changes. Enterprises buy AI infrastructure to deliver answers, summaries, recommendations, software code, customer interactions, analysis, automation and agent workflows. Those outputs need to be reliable, measurable and affordable enough to keep running. The pilot-era stack is reaching its limit The first wave of enterprise AI rewarded speed. Teams bought GPUs, reserved cloud capacity, tested APIs, adopted open-source models and assembled whatever stack helped them move. Infrastructure inefficiency then becomes a business issue. The symptoms are familiar: more systems to manage, more vendors to coordinate, more integration work and less visibility into what drives cost and performance. That creates friction across the organization. IT teams support AI workloads that behave differently from traditional enterprise applications. AI teams need speed, but often lack the infrastructure control to tune cost, latency, utilization and performance together. Finance teams want predictable unit economics, but the stack was assembled under pressure and is hard to measure end to end. Most teams can now get access to models and compute. Fewer can show how each workload is performing, where it runs and what it costs. Capacity needs control Extra capacity can still leave teams with idle infrastructure, uneven latency and unclear unit costs. The useful questions are operational. Can the organization see utilization across teams, tenants, models and infrastructure pools? Can it route workloads based on cost, latency, privacy, availability and service objectives? Can it measure cost per token, cost per inference, cost per user interaction or cost per business workflow? Inference behavior changes constantly. Demand fluctuates. Longer contexts increase cost. Model choice affects latency and output quality. Utilization varies across workloads. A customer-facing assistant may prioritize response time. A batch workflow may prioritize throughput and cost. A procurement-led AI strategy cannot manage that complexity on its own. CIOs need an operating model for production inference. Enterprise Linux offers a useful analogy. Linux gave companies flexibility and attractive economics, but enterprises needed a trusted operating layer and support model before using it for business-critical systems. AI infrastructure is reaching a similar stage. The models, hardware and software components already exist. Many organizations now need a way to operate them consistently and economically in production. Token economics is becoming a management discipline The useful output of many AI systems is delivered through tokens. That makes token economics a practical operating metric. Token volume needs context. A token that helps complete a task, answer a question or resolve a customer issue creates value. A token generated through poor routing, excess latency or an unnecessarily expensive model adds cost without improving the outcome. How much useful output are we getting per dollar? How much per watt? How much per GPU? How much per workload? How much per unit of latency? How much per business outcome? Manufacturing leaders do not only ask how many machines they own. They ask what those machines produce, how often they sit idle, how much waste they create, how much energy they consume and how efficiently raw materials become finished goods. AI infrastructure needs the same operating discipline: utilization, throughput, reliability, cost control and visibility into what the infrastructure is producing. Enterprises need usability and control Serverless AI APIs are fast to start and easy for developers. They work well for many use cases. As usage grows, economics can become harder to control and visibility into infrastructure behavior is limited. Self-managed infrastructure gives teams more control and can improve long-term economics for persistent workloads. It also adds operational burden. Teams have to manage deployment, scaling, routing, model serving, monitoring, reliability, performance tuning, security, isolation and utilization. Enterprises want the simplicity of managed services without giving up visibility and control. Developers should be able to access AI services without managing the underlying stack. Infrastructure, security and finance teams still need to see placement, cost, latency, utilization, tenant policy, service levels and risk. That is the role of an inference operating layer: turning fragmented infrastructure into governed, measurable capacity that teams can manage as demand changes. Beyond procurement The more successful an AI application becomes, the more inference it consumes. As inference grows, cost, latency, utilization and governance determine whether the application can scale. AI can repeat the cloud-cost pattern many CIOs already know. A service begins as an innovation accelerator, usage expands across teams and the bill grows faster than governance. By the time the organization tries to regain control, the architecture, workflows and vendor dependencies are difficult to unwind. GPUs remain essential. Models remain essential. Data remains essential. Production AI also needs an operating layer around those assets. The next generation of AI leaders will ask a harder question: How much useful intelligence can we produce from our infrastructure, at what cost, with what reliability, under what policy and under whose control? The answer will determine whether AI becomes a controlled production capability or another expensive system the business struggles to explain. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 48🌐 MovesAug 4, 2026https://www.cio.com/article/4204565/why-ai-infrastructure-needs-a-new-operating-model.html - AI in Supply Chain Planning: Lessons From the Field What Works, What Fails and Why
AI in Supply Chain Planning: Lessons From the Field What Works, What Fails and Why Gartner
- Unity AI Gateway is Generally Available
The last six months have seen a rapid rise in AI-powered productivity and a proliferation...
- Wukong Disappears Four Months After Launch: Alibaba Consolidates Into Qianwen Office Public Beta as Big Tech Races to Integrate AI Work Into Single Unified Agent Products
Alibaba Qianwen Office opens public beta integrating QoderWork, Wukong, and MuleRun, as the four-month Wukong cycle shows AI office competition moving beyond single agents.
Score: 48🌐 MovesAug 4, 2026https://pandaily.com/wukong-qianwen-disappears-alibaba-qianwen-office-ai-work-jul2026 - Boston tech unicorn Creatio plans 200 new hires as AI drives commercial growth
The software company reached a $1.2 billion valuation in 2024 and expanded its Boston office to 12,000 square feet this spring.
Score: 48🌐 MovesAug 4, 2026https://www.bizjournals.com/boston/news/2026/08/04/creatio-plans-hiring-spree.html?ana=brss_6150 - WorkBuddy Security Center Goes Live: Tencent Brings OS-API Layer Sandboxing, Recoverable File Deletion, and Version Rollback as Default Agent Safety Defaults
Tencent WorkBuddy 5.0 ships default security sandbox, recycle-bin file deletion, and version-restorable edits at the OS API layer, addressing the agent safety crisis without slowing agent efficiency.
- Autonomous-driving company WeRide enters Nordic market through Denmark partnership
Chinese autonomous-driving company WeRide has partnered with Danish shared-mobility platform GreenMobility to develop Denmark’s first commercial autonomous shared-mobility project, marking WeRide’s first entry into the Nordic market. Subject to regulatory approval, the partners plan to launch the service to the public in the first half of 2027 using L4 Robotaxi GXR vehicles compliant with EU […]
- How the UK is training its 'surgeons of the future' with robots
The NHS wants robots in nine out of 10 keyhole surgeries by 2035. At a London hospital already using the newest system, surgeons and patients say the change is already being felt.
Score: 48🌐 MovesAug 4, 2026http://www.euronews.com/next/2026/08/04/how-the-uk-is-training-its-surgeons-of-the-future-with-robots - How Firms Like Coinbase Are Building Coding Agents to Complement Anthropic's Claude Code
How Firms Like Coinbase Are Building Coding Agents to Complement Anthropic's Claude Code The Information
- Alex Karp Says Palantir Crushed Earnings With 'Shrinking' Sales Team
Alex Karp Says Palantir Crushed Earnings With 'Shrinking' Sales Team Business Insider
Score: 48🌐 MovesAug 4, 2026https://www.businessinsider.com/alex-karp-palantir-crushed-earnings-shrinking-sales-team-2026-8 - BofA, USAA agree to cross-license AI, banking patents
BofA, USAA agree to cross-license AI, banking patents Reuters
Score: 48🌐 MovesAug 4, 2026https://www.reuters.com/legal/transactional/bofa-usaa-agree-cross-license-ai-banking-patents-2026-08-04/ - The benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
Score: 48🌐 MovesAug 4, 2026https://news.mit.edu/2026/medical-ai-assistance-benefits-vary-based-on-user-expertise-0804 - Deploy local agents everywhere with LFM2.5-2.6B
Deploy local agents everywhere with LFM2.5-2.6B
- DigitalOcean tops second quarter expectations amid surging AI demand
DigitalOcean Holdings Inc. today posted second quarter earnings that topped analyst expectations across the board. The company also raised its third quarter guidance. DigitalOcean launched in 2011 to provide an alternative to the industry’s three leading public clouds. The company says that its platform is easier to use and provides lower pricing across some services. […] The post DigitalOcean tops second quarter expectations amid surging AI demand appeared first on SiliconANGLE .
Score: 48🌐 MovesAug 4, 2026https://siliconangle.com/2026/08/04/digitalocean-tops-second-quarter-expectations-amid-surging-ai-demand/ - Between Kimi K3 and DeepSeek V4: Why Native Multimodal Capability Defines the Next Phase of Chinese Frontier Models
Moonshot AI Kimi K3, Alibaba Qwen3.8-Max, and ByteDance Doubao-Seed-2.1 commit to native multimodal training while DeepSeek, Zhipu, and Tencent Hunyuan stay text-only as vision-in-the-loop becomes decisive for long agent tasks.
- Time to rethink US frontier AI model dominance
China has outdone the US again, with benchmarks from Artificial Analysis showing that the latest DeepSeek model, V4 Flash 0731, is significantly cheaper than equivalent OpenAI models. Even after OpenAI’s most recent 80% price cut on GPT-5.6 Luna, Artificial Analysis reported that DeepSeek V4 Flash 0731’s cost per task using its first-party application programming interface (API) is 60% lower than GPT-5.6 According to Artificial Analysis, DeepSeek’s price performance lead is due to the significant cost savings that can be achieved when the answer to a prompt is already cached. Artificial Analysis reported that DeepSeek offers a cache hit discount rate of about 98% on its first-party API, which it said is a “significantly more aggressive discount than the 90% cache hit discount offered by most of the industry”. Beyond the significant cost savings available from the latest DeepSeek option, IT decision-makers assessing which model to choose may find that there are benefits to avoiding the frontier models, which come at a premium. This is reflected in the findings of the recently published AI openness report from OpenUK. Discussing the report, Amanda Brock, CEO of OpenUK, said: “The data tells us that the UK is not well served by throwing money at building new large or frontier models. The opportunity to home in on with a laser focus is the model-adjacent technologies that AI harnesses – infrastructure and developer tooling . The lower barrier to entry is enabling individuals and innovators to break into this space.” Another consideration is the rate of change, which means that some newer models outperform frontier AI models that were released just a few months ago, as Sapien CTO Glen Ceniza explains following a meeting he had with Anthropic. “One thing they told me is that their ‘midweight’ model now is significantly better than the heavyweight model from three months ago,” he said. In other words, Asana, which uses Anthropic’s Claude model, today outperforms Opus from three months ago. “But people have it in their mind that they want to use Opus even if the midweight model today is significantly better, faster and uses less tokens. You’ll get better results than the older heavyweight model.” Opting for the less expensive non-frontier AI models or using open models gives IT leaders greater choice when they need to decide how to budget for their organisation’s AI strategy and balance the needs of the organisation with IT and AI costs. For instance, housing association Sovereign Network Group (SNG) has decided to be AI model agnostic. Vanessa Hewitt, its head of business platforms, said: “We have to be agnostic because the price is constantly changing.” By having what she says is a “headless model”, SNG focuses its technology investments on the foundational technology for integrating IT systems and providing access to the data across the organisation. Boomi’s integration platform is one of the pieces of this foundational technology. SNG’s goal is to provide affordable housing in the South East and London, and she says Boomi supports this objective: As an example, she says: “Boomi helps us in initiatives where we can offer a customer a third-party app to help them with rent arrears.” For Hewitt, “AI is peripheral”. She says it supports the niche use cases SNG wants to do. Read more about AI cost management Boomi CEO shares vision of AI cost management : Steve Lucas, CEO of Boomi, believes the answer is prompt routing, which sends queries to the LLM with the lowest token cost and caches responses. AI coding agents will cost more than real developers: As organisations ramp up the use of AI coding agents in software development, they may find costs increase significantly if such tools are overused.
Score: 47🌐 MovesAug 4, 2026https://www.computerweekly.com/news/366646977/Time-to-rethink-US-frontier-AI-model-dominance - 'Strategic autonomy' in AI crucial, avoid overdependence on single source: IT Secretary, S Krishnan
India must build domestic AI capabilities and avoid single-source dependence. Strategic autonomy is key for India's AI ecosystem development. Diversified and resilient global supply chains are essential for India's AI integration. The government is considering an artificial intelligence law and regulatory framework. Feedback is currently being gathered for potential AI legislation and rules.
- Spotify expands AI remix and covers project with Merlin partnership
Spotify says Merlin, which represents more than 30,000 independent labels and distributors, has joined Universal Music Group in backing its upcoming AI-powered remix and covers product. The paid tool will let fans create AI-generated covers and remixes of participating artists’ music while ensuring artists opt in, receive credit, and are compensated.
Score: 47🌐 MovesAug 4, 2026https://techcrunch.com/2026/08/04/spotify-adds-merlin-to-its-ai-music-remix-and-covers-effort/ - Why meta agents must become the economic intelligence layer of the agentic enterprise
In “ Micro and macro agents: The emerging architecture of the agentic enterprise ,” I proposed a three-layer architecture for enterprise AI. Micro agents execute specialized tasks. Macro agents orchestrate end-to-end business processes. Meta agents provide governance through monitoring, compliance, security, and human oversight. As enterprises begin deploying thousands — and eventually tens of thousands — of autonomous agents, token costs have become a major concern. According to Gartner , rising token-driven AI spend is straining budgets and challenging cost justification. To track this economic concern, meta agents should do more than simply being the governance agents. They should become the economic intelligence layer of the enterprise. Their responsibility is not only ensuring AI behaves responsibly. It is ensuring AI creates measurable business value. The missing economic model for AI Every major technology revolution eventually develops its own economic framework: Manufacturing measured productivity. Cloud computing measured infrastructure utilization. Digital businesses measured customer acquisition costs and lifetime value. The agentic enterprise now requires its own financial discipline. Every AI prompt. Every reasoning cycle. Every interaction between agents. Every autonomous workflow. Tokens have quietly become the operational currency of enterprise AI. Tokenomics is now a foundational part of enterprise AI architecture. Yet today, most organizations measure only one thing: Cost. How many tokens were consumed? Which models cost the most? What was the monthly inference bill? These are useful operational metrics. They are not strategic business metrics. Boards rarely ask how much electricity a factory consumed. They ask how much value the factory produced. Enterprise AI deserves the same conversation. This is where I was thinking about the laws of physics. Based on physics laws, energy cannot be created or destroyed. It is transformed into another form. Electricity becomes light. Chemical energy becomes motion. Solar energy becomes electricity. Enterprise AI offers a similar management lesson. Intelligence must be transformed into value Tokens are not valuable because they are consumed. They become valuable only when they are transformed into business outcomes. A faster loan application decision. A fraud detection. A better customer experience. Higher software quality. Greater employee productivity. A new business opportunity. This leads to what I call return on tokens (ROT). ROT measures how effectively an organization converts token consumption into measurable business value. Instead of asking, “How many tokens did we consume,” leaders should ask, “How much enterprise value did every million tokens create?” The Second Law of Thermodynamics tells us something equally important: Every energy transformation introduces inefficiencies. Although total energy is conserved, some inevitably becomes less useful for doing work. Enterprise AI behaves similarly. The second law: Every AI transformation creates friction Not every token creates value. Some tokens are spent on repeated reasoning. Some generate redundant conversations between agents. Some support oversized context windows. Some produce hallucinations requiring correction. Some route simple tasks to unnecessarily expensive models. The tokens are not lost. But they create very little useful business work. I refer to this as token entropy. Token entropy represents the portion of AI activity that consumes intelligence without producing proportional business outcomes. Every agentic enterprise will experience token entropy. The organizations that win will be the ones that continuously identify and reduce it. Beyond energy: The importance of exergy Thermodynamics offers another concept that is even more relevant. It is called Exergy. Unlike energy, exergy measures the amount of energy that can actually be converted into useful work. Two systems may contain the same amount of energy while producing dramatically different levels of useful output. The same principle applies to enterprise AI. Two organizations may consume exactly the same number of tokens. One generates meeting summaries. The other transforms loan processing, accelerates software development, detects fraud, improves customer retention, and creates new revenue streams. Their token consumption is identical. Their business impact is not. Borrowing it as a management analogy, not claiming that AI tokens literally obey the thermodynamic definition of exergy. I think of this as token exergy. It’s not that AI tokens literally obey the thermodynamic definition of exergy. Token exergy measures how much of an organization’s AI intelligence is converted into useful business work. It is not enough to consume tokens efficiently. Organizations must convert those tokens into outcomes that matter. The meta agent evolves This is where meta agents become transformational. Today we think of them as governance agents. Tomorrow they become economic governors. Meta agents continuously monitor every interaction across the enterprise and answer questions such as: Which agents produce the highest ROT? Where is token entropy increasing? Which workflows generate the highest token exergy? Which models deliver the greatest business value per token? Which agents should use smaller models? Which prompts should be optimized? Which workflows require human intervention? Which autonomous processes should be redesigned? Meta agents no longer simply supervise AI. They optimize its economics. The economic intelligence layer The architecture now becomes complete. Micro agents: Perform work. Macro agents: Coordinate work. Meta agents: OGovern, observe, optimize, and continuously improve the economics of intelligence. Their objective is straightforward: Maximize return on tokens. Minimize token entropy. Increase token exergy. This represents a shift from AI governance to AI economics . The executive dashboard of tomorrow The executive dashboard of the future will not focus solely on infrastructure metrics. It will measure intelligence performance. Imagine a boardroom dashboard displaying: Return on tokens (ROT) Token entropy index Token exergy score Business value per million tokens Agent productivity index Cost per autonomous decision AI value by business unit Human escalation rate Model effectiveness score These metrics move AI discussions beyond engineering. They make AI accountable for business outcomes. A new responsibility for CIOs The next generation of CIOs will not simply deploy AI. They will manage an economy of intelligence. Their role will resemble that of a portfolio manager — allocating AI capacity where it creates the greatest enterprise value, reducing waste, and continuously improving the productivity of every autonomous workflow. That responsibility cannot be fulfilled by dashboards alone. It requires an intelligent layer capable of observing, learning, and optimizing the entire agent ecosystem. That is the emerging role of the meta agent. The next competitive advantage Every technological revolution rewards organizations that learn to measure what others overlook. Factories measured productivity — not fuel consumption. Digital businesses measured customer engagement — not server utilization. The agentic enterprise will reward organizations that measure intelligence itself. The winners will not be those deploying the largest models. Nor the most agents. Nor consuming the fewest tokens. They will be the organizations that continuously maximize return on tokens, relentlessly reduce token entropy, and increase token exergy. I believe this is the next evolution of the agentic enterprise. Not simply governed intelligence, but economically optimized intelligence. The AI adoption spending spree is over. Time to focus on value. And in that future, meta agents will serve not only as the guardians of AI — but as the stewards of enterprise intelligence economics. Through this framework I strongly believe that executives can easily remember the key measures for economic intelligence. ROT (return on tokens): How much value did AI create? Token entropy: Where are we wasting AI intelligence? Token exergy: How effectively are we converting AI intelligence into useful business work? This article is published as part of the Foundry Expert Contributor Network. Want to join?
- India takes months to spot breaches as AI aids attackers move faster: IBM
IBM's Cost of a Data Breach Report 2026 shows AI is accelerating cyberattacks while many Indian organisations still take months to detect breaches, increasing both financial losses and cyber risk
- OK, Well, Rogue AI Agents Are Hacking Again
Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.
Score: 46🌐 MovesAug 4, 2026https://www.wired.com/story/ok-well-there-are-even-more-ai-agent-hacking-incidents/ - The Sovereign AI Choice: Why Enterprises Need to Act Now
As agentic AI reshapes how enterprises operate, the businesses pulling ahead may become the ones building sovereign control into their data and AI platforms now.
Score: 46🌐 MovesAug 4, 2026https://www.independent.co.uk/tech/the-sovereign-ai-choice-why-enterprises-need-to-act-now-b3027503.html - Calvary Health Care previews digital assistant for 20,000 staff and volunteers
Anticipated to go live next week.
- Introducing Frontier AI Model Platforms — Because An AI Model Is Not A Business Model
The world’s most advanced AI companies just learned a lesson that enterprises have understood for decades: Technology doesn’t create value — solutions do. Our 2024 Forrester Wave™ covering AI foundation models for language evaluated the models themselves, but raw capability predicts little about enterprise success. The vendors know this, too. Anthropic and OpenAI launched billion-dollar […]
- Autonomous A2Z wins W11b UAE self-driving deal
Autonomous A2Z has signed an 11 billion won ($7.7 million) export deal with Space42, a UAE-based artificial intelligence company, to supply its autonomous driving vehicles and full-stack solutions, expanding its footprint in the UAE’s smart mobility market. According to the company Tuesday, the two parties will collaborate on the Smart Mobility Convergence Project, aimed at replacing its existing fleet of Chinese-made autonomous vehicles with 19 vehicles powered by Autonomous A2Z’s technology. T
- Palantir is leaving its software peers behind in the AI race
Palantir is leaving its software peers behind in the AI race
- HPE brings quantum computing to real-world use cases with HPC and AI
Quantum hybrid computing is moving from a hardware race to an integration challenge — and Hewlett Packard Enterprise Co. is positioning itself around the infrastructure layer that connects quantum with HPC and AI. During HPE’s World Quantum Day event, researchers, national laboratory representatives and analysts reached a similar conclusion: Quantum will not replace today’s systems. Instead, it […] The post HPE brings quantum computing to real-world use cases with HPC and AI appeared first on SiliconANGLE .
Score: 45🌐 MovesAug 4, 2026https://siliconangle.com/2026/08/04/quantum-hybrid-computing-hpeworldquantumday/ - Tech Companies Are Setting Themselves on Fire to Keep Up in the AI Race
But why? The post Tech Companies Are Setting Themselves on Fire to Keep Up in the AI Race appeared first on Futurism .
Score: 45🌐 MovesAug 4, 2026https://futurism.com/artificial-intelligence/tech-companies-setting-on-fire-ai-race - AI Got Good at Language. Now It’s Learning the Language of Life. (Eric Nguyen, Co-Founder and CEO of Radical Numerics)
Listen now | Eric Nguyen explains why biology needs specialized AI, how DNA can be modeled as language, and why lab verification remains a key bottleneck between AI predictions and discovery.
- LexisNexis opens customer innovation lab driven by AI to change the future of legal work
LexisNexis Legal & Professional, a division of RELX plc, today announced the opening of its Customer Innovation Lab in New York City, which will deliver a new model for how legal artificial intelligence gets built. The company said the shift is designed to bring AI directly into the grasp of legal professionals. Instead of building […] The post LexisNexis opens customer innovation lab driven by AI to change the future of legal work appeared first on SiliconANGLE .
- What Workday, OpenAI, and a German court have in common
Subscribe • Previous Issues Passing Your Evals Doesn’t Mean You’re Safe Evals are part of every serious conversation about putting AI into production. Teams define benchmarks, set thresholds, and increasingly run red teams to see how the system holds up against someone actively trying to break it. That combination is reasonably good at telling you whether a Continue reading "What Workday, OpenAI, and a German court have in common" The post What Workday, OpenAI, and a German court have in common appeared first on Gradient Flow .
- AI chatbots will happily create fake news articles, and tests show ChatGPT is the worst at it
A new investigation suggests today's biggest AI chatbots can still generate convincing fake news articles with ChatGPT emerging as the easiest to manipulate.
- The enterprise AI strategy that outlasts any single model
In January of this year, few enterprise tech leaders would have bet on Anthropic over OpenAI. Today, Claude reigns supreme (inspiring a notable 180 by Elon Musk ), with Gemini threatening to take market share and introduce pricing models that could flip the leaderboard on its head again. That’s exactly why betting on a single model is a dangerous strategy. The most successful organizations won’t be those trying to guess tomorrow’s top-tier model, nor will they wait passively for future releases. Instead, they will invest in underlying frameworks that continuously improve regardless of which specific AI model drives them. Why betting on one AI model is a losing strategy Our strategy for AI, through recursive self-improvement (RSI), is rooted in this core principle. RSI is an approach to AI that compounds its own abilities by improving itself. If done carefully, RSI can function as an overarching layer above any model. Crucially, given RSI’s inherently compounding trajectory, it represents the most likely contender to be the approach that reaches superintelligence, no matter which model is used underneath. Though recently achieving the status of a Silicon Valley buzzword , applying something like RSI to unlock superintelligence has been the Holy Grail of AI research for decades. It’s what researchers like us have recognized since the 1960s as a critical step along the path towards what we call artificial superintelligence (ASI) today. Recursive self-improvement compounds value beyond the model The fundamental premise of RSI is that the next phase transition in AI won’t come from a system that has been taught to improve by any of the traditional methods of the past few years. Relying purely on data, compute and human intuition to generate exponentially improving capabilities is a path with hard physical and practical limits. Instead, the leap will come from a system that invents its own improvements and feeds them back into itself. RSI has already delivered what business leaders would recognize as a virtuous cycle. Each improvement increases the system’s capacity to generate the next improvement. Competitive advantage compounds because the system benefits from both its own recursive progress and every improvement in the underlying models. As Anthropic puts it, AI that can improve itself would be a “major development in the history of technology.” Indeed, we believe it’s the single most important frontier of AI research. Anthropic’s model-specific approach to RSI is already paying off for them: a recent Anthropic Institute report captures the pace of change with real world impact, “Claude-written code was somewhat worse than human-written code at Anthropic in late 2025, is roughly at parity today, and we expect it to be strictly better within the year.” That’s worth applauding. But what about the companies not currently building an in-house model? For them, looking at improvements that only take place inside the model someone else develops is unnecessarily limiting. It makes more sense to embrace a model-agnostic approach to RSI that improves whenever any new model is released. Approaching the model as one component of a system without relying on any individual provider or tool makes it possible to achieve recursive improvement at the system level. By using an RSI approach that works outside the model and can swap models instantaneously, companies can immediately benefit from the compounding effects of self-improving AI. Build a model-agnostic AI strategy that benefits from every breakthrough This holistic approach to RSI fits the market today. The AI landscape is becoming more dynamic by the month. Frontier models leapfrog one another, open-weight models improve at remarkable speed, pricing strategies change and entirely new capabilities emerge in rapid succession. For enterprise leaders, the lesson isn’t to predict the next winner. It’s to build systems that improve regardless of which model comes out ahead. In fact, organizations that tie their future to a single model provider risk getting left behind altogether if a different model’s next iteration leapfrogs the one they’ve signed a long-term contract to use. Every enterprise tech leader already understands the power of virtuous cycles. Amazon didn’t build an enduring competitive advantage by betting on a single product. Every improvement to its logistics network attracted more sellers, which increased selection and order volume, which justified further investment in logistics. Visa became more valuable as more merchants accepted its cards, attracting more cardholders, which, in turn, encouraged even more merchants to join. Organizations that anchor their AI strategy to a single model provider will spend the next decade reacting every time the frontier shifts. On the other hand, organizations that build model-agnostic systems will benefit from every shift. Every improvement from Anthropic, OpenAI, Google, Meta or the next breakthrough model becomes another source of competitive advantage. The world’s most durable companies don’t simply accumulate assets – they build systems where every improvement makes the next improvement easier; creating long-term advantage. Enterprise AI should be approached the same way. For enterprise tech leaders, that’s the strategic shift that matters. Stop asking which model deserves your long-term bet and instead, start asking whether your AI strategy creates its own virtuous cycle. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 45🌐 MovesAug 4, 2026https://www.cio.com/article/4204569/the-enterprise-ai-strategy-that-outlasts-any-single-model.html - Why agentic supply chains are the next frontier for AI sovereignty
As AI agents take on decisions across global supply chains, organizations must ensure they retain oversight of the economic consequences.
Score: 45🌐 MovesAug 4, 2026https://www.weforum.org/stories/supply-chains-and-transportation/agentic-supply-chains-ai-sovereignty/ - Customer Experience (CX) Agents in Production: Lessons from Lyft, Vodafone, and LATAM Airlines
Insights from major companies on deploying CX agents in real-world settings.
- RL-100 framework helps robots refine learned tasks amid real-world disruptions
Robots are gradually making their way into a variety of settings, ranging from households to public spaces, offices, factories and health care facilities. Despite their potential, however, many existing robots do not perform as well in dynamic and unpredictable real-world environments as they do during controlled laboratory tests.
Score: 45🌐 MovesAug 4, 2026https://techxplore.com/news/2026-08-rl-framework-robots-refine-tasks.html - Govt proposes easier compliance for foreign firms using Indian data centres
The Taxation and other laws (Amendment) Bill, 2026, introduced in the Lok Sabha on Tuesday, reduces reporting requirements for foreign companies which wish to claim the tax holiday announced in the FY27 Budget. It also ensures that genuine data centre businesses get started and operate with far less friction.
- Tsalla Aerospace builds autonomy software that flies drones without GPS
Tsalla Aerospace builds autonomy software that flies drones without GPS YourStory.com
- AI coding agents are blowing through budgets — Replit, Kilo Code, and Symbotic explain how they're managing it
At Kilo Code, engineers are reading or writing code themselves only about 1% of the time now, according to co-founder Emilie Schario — the rest is agents. That shift is forcing new questions onto dev teams: which systems are safe to hand over, who cleans up when models goof up, how to support multi-model architectures, and whether skyrocketing token bills mean real progress or just burned IT budget. As far as tech leads from Replit, Kilo Code, and Symbotic are concerned, it’s a natural — and welcome — evolution as agentic AI becomes embedded into more and more enterprise workflows. “Unless something's really broken or debugging, 99% of the time engineers are not reading or writing code anymore,” Emilie Schario, co-founder of Kilo Code, said at VB Transform 2026 . AI good at greenfield, not so great at brownfield For Jared Go, distinguished engineer for AI and cloud at warehouse automation company Symbotic, the current moment is about directing the focus of AI. "These are my criteria," he said. "Let's look at it from the lens of security, elegance, clean, concise code, water tightness." That way, AI does most of the heavy lifting, and human code review isn't as critical. Human involvement becomes necessary further down the line, Go noted, because agents don't make strong product decisions. “Greenfield [building brand new codebases] is so easy for agents. Brownfield [writing, updating, or maintaining existing code] we all know is where the actual challenge lies.” Replit takes a bit of a different tack: While the company has "gone very agentic," they've been more conservative with AI coding, explained Amol Jain, head of product engineering. An agent reviews each pull request (PR) and assigns it a risk score; low-risk PRs are self-merged by their author, while others go to human reviewers who read the code and give feedback. “The idea was human on the loop, not human in the loop,” Jain said. Replit’s internal tool is essentially self-driving for software engineers; devs give a task to agents, which do end to end planning, implementation, and testing. “It's a fleet of agents that run in their own cloud virtual machines (VMs) with access controls behind token proxies so they're secure,” Jain said. He shared one example where an engineer couldn’t repro or solve a “very gnarly bug” deep in its systems. It was sent to an AI manager agent, which told it to go to sleep. The manager agent then spun up a bunch of underlying agents that found the issue; it subsequently spun up a bunch more agents that found the fix. Six hours later, AI had a PR ready for the bug that had puzzled human engineers. Multi-model is the future AI providers are also evolving beyond the lock-in model, as customers increasingly demand multi-model choice. Kilo Code, for its part, supports 500-plus models in its gateway. "Your software that you're using to do agentic engineering should be decoupled from the model that you're using to do it," Schario said. For instance, Schario said companies often use expensive frontier-tier models to architect a project, then switch to a less expensive open-weight model for the rest of the work. It’s also important to respect model provider limitations, such as when they need to work in closed or isolated environments or providers in their specific regions. “It's factoring in what's important to you, what limitations you've set, what data retention policies you've established, what keys you've brought in, what commits you might have … into that routing decision,” Schario said. Replit, similarly, tends to have a better sense of the cost versus capability spectrum than its customers, Jain contended. “We are essentially making the decisions on users' behalf of what model to use when, in what capacity, to minimize cost and maximize capability.” To tokenmaxx or not to tokenmaxx Of course, an important consideration as AI adoption increases is runaway costs, which has led to some enterprises tracking and capping AI use through tokenmaxxing. Concerns come from both sides, Schario said: internally and from customers. From the latter, she's hearing, "I accidentally spent my whole AI budget for the year … so what do I do now?" In response, Schario said Kilo Code points customers to the same workflow: use expensive models for planning, then open-weight models for affordability. Further, sharing skills, strong guidance, and Model Context Protocol (MCP) will empower models. “Realizing where you can really uplevel your team to help them get the most out of the models they're using is going to make a big difference,” Schario said. Internally, meanwhile, Schario noted one particular engineer that has a "heavy foot" and is constantly at the top of the usage board. "I regularly have to nudge, 'What are you doing there?'" she said. It's easy to look at a $600 bill for daily work and react, "Wow, that's so much," but looking at the amount of work completed can sometimes justify the cost. “Cost per pull request is the metric that I'm paying attention to right now,” Schario said. “It feels like the closest proximity for how I can measure value.” Ultimately, AI changes how enterprises are thinking about ROI because spend is not the problem. “The spend with no return on that spend is the problem.” Symbotic, for its part, has set per-month cost tiers for its employees. The company built a tool that gives managers visibility into PRs and usage trends. They can then move users up or down a tier as they see fit, Go explained. “Having a cap and seeing how many people went up in cap this month makes a big difference when you're trying to corral these costs and make things efficient,” Go said. When Cursor — which Symbotic uses heavily — ended a legacy discount that had grandfathered the company into a flat per-request rate even for frontier models, and moved everyone to full pricing, it forced a company-wide reckoning on efficiency, Go said. "People were saying, 'You should try this model … This works better for this C# code, this whatever,'" he said. But the cost problem is increasingly moving out of IT; Replit, for one, broadened agents beyond engineering, and eventually found that a user on the support side had "blown through an insane amount of money," Jain said. When they looked under the hood, they figured out it was because they were running an automation on GPT 5.5 Pro Max. “At least till that point, the ROI was rather clear,” Jain said. “We could see engineering productivity 3X, so no one had questioned it yet.” Visibility that isn’t “anti-productive,” model routing, and sensible defaults are critical, he emphasized. “Most tasks do not need the frontier.”
- AI Companions May Worsen Loneliness for Vulnerable Users, Stanford Study Finds
Stanford research finds that users with limited social networks who seek emotional support from AI companions experience lower well-being.
Score: 45🌐 MovesAug 4, 2026https://hai.stanford.edu/news/ai-companions-may-worsen-loneliness-for-vulnerable-users-stanford-study-finds - You trained the AI. Big Tech got paid
On January 24, 1956, the American Telephone and Telegraph Company was the largest private company in the world. Its revenues amounted to almost 2% of the U.S. gross domestic product. It employed 746,000 people. It owned Bell Labs, the fabled research division that had already produced the transistor, the solar cell, information theory, and radio astronomy, while also laying the first transatlantic telephone cable. In the following decades, it would add UNIX, modern cellular telephony, the CCD image sensor, and the first active communications satellite to its long list of scientific milestones. This singular stretch of intellectual output paved the way for Bell scientists to eventually collect five Turing Awards and 10 Nobel Prizes. By many metrics, life as a regulated monopoly was very good for AT&T. Yet by the end of that day, AT&T had signed away exclusive rights to every single one of its 7,820 unexpired patents, royalty-free, to any American company that asked. AT&T would also license any future patents it filed at “reasonable rates.” A bleeding-edge intellectual property treasure hoard was suddenly and irrevocably opened to the free market. A technician at Bell Telephone, 1922 [Photo: Bell Telephone Magazine / Wikimedia ] Antitrust officials initially sold the agreement as a triumph. The Justice Department called it a major victory, with one DOJ lawyer hailing it as “miraculous.” Despite AT&T already existing for decades as a regulated monopoly, with its returns constrained to a relatively conservative (by today’s standards) ~7% per year, government regulators had pursued and established a landmark set of additional restrictions to curtail AT&T’s monopoly power. Soon, however, public sentiment started to shift. Business Week called the consent decree “hardly more than a slap on the wrist.” A House congressional subcommittee would later deem it “a blot on the enforcement history of antitrust laws” for its perceived lenience on AT&T’s exclusive supply chains and vertical integration. Both the ratepayers, who subsidized AT&T’s vast research budget through its rate contracts, and many in the federal government believed this unprecedented economic concentration to still be far too dangerous for the Republic to continue unabated. The now-infamous 1956 patent decree was just half of a settlement negotiated over seven years between AT&T and the federal government. AT&T wanted to continue manufacturing telephone equipment through its subsidiary Western Electric, but regulators believed the vertical integration was foreclosing competition within the industry. The federal government itself was so conflicted about this issue that the secretary of defense under President Eisenhower, Charles Wilson, pleaded with litigators that severing AT&T from Western Electric was “contrary to the vital interests of our nation.” The second half of the settlement barred Bell from pursuing any business other than telecommunications. A later analysis of the historical record revealed that 69% of Bell’s patents had little to do with telecom. Rather, they ranged from chemistry to computing to semiconductors to metalworking, lighting, optics, and more. The two halves of the settlement combined to ensure that this rich intellectual corpus (roughly 1.3% of all unexpired American patents at the time) became freely available essentially overnight— and included a guarantee from Uncle Sam that the big, bad legal wolf would not come knocking. Within just a few years, these released patents would generate an estimated $5.7 billion in follow-on patent value outside the telecom industry. About $3.5 billion of that value came from patents filed by young, startup companies. One famous branch of that startup explosion ran through Shockley Semiconductor, then Fairchild Semiconductor , and eventually into the storied company known as Intel . Intel’s cofounder, Gordon Moore (of Moore’s Law fame), would later describe this consent-decree-driven innovation cascade as “one of the most important developments for the commercial semiconductor industry”: ‘[It] allowed the merchant semiconductor industry to really get started in the United States. There is a direct connection between the liberal licensing policies of Bell Labs and people such as Gordon Teal leaving Bell Labs to start Texas Instruments and William Shockley doing the same thing to start Shockley Semiconductor in Palo Alto. This started the growth of Silicon Valley.’ A generation of brilliant, publicly subsidized scientists built one of the most impactful clusters of technical genius the world has ever seen. Bell generated patents, invented products, and became the undisputed epicenter of American frontier science for decades. But how? Sediment Imagine a carefully crafted rice paddy, terraced by exacting farmers who spent years precisely engineering a fertile environment. It looks like just a flooded field, but it turns out that rice is one of the few major crops that tolerates submerged roots. Since most weeds can’t tolerate submersion either, the water does the weeding. The deliberate flooding also cuts off the oxygen required for organic decomposition, so the soil retains more of its nutrients rather than burning them off like a dry, aerated field does. And the warm, waterlogged mud triples as an excellent habitat for nitrogen-fixing microbes. A well-tended paddy largely fertilizes itself, season after season, sometimes for centuries. This humble mud pond is one of the most productive growing systems humans ever designed. AT&T’s unique economic position as a monopoly set the conditions for Bell Labs’ culture of deliberate experimentation, patient exploration, and delayed harvesting. Bell drew from an enormous and stable nationwide revenue base that didn’t have to be rejustified every budget cycle. American regulators set this revenue base through AT&T’s prices by using a fixed percentage return calculation on the capital it invested in the network. Here, invested capital means switches, cables, buildings, and the like. Bell Laboratories’ Eero Saarinen-designed headquarters, Holmdel, New Jersey. 2000 [Photo: Library of Congress] At a normal company, research is a cost you minimize, but not at AT&T. Every dollar spent on research at Bell Labs did two things at once. First and foremost, it was a no-risk, recoverable cost subsidized by U.S. telephone ratepayers under contract. Second, it was a wellspring of new, capital-intensive technology for AT&T to build and deploy. This capital expenditure expanded the very rate base on which its guaranteed return was calculated. The more money spent on these new technologies, the larger the absolute profit gained by the same regulated ~7% return. This arrangement worked out very well for all parties for decades, but is not necessarily replicable. Nor is it obvious we should even try to recreate it, because it came with real costs too. Inefficient overinvestment, lack of price discipline, and most importantly an incentive to hoard inventions behind a monopoly wall all hurt ratepayers. But for much of the twentieth century, these guaranteed profits did objectively create an expansive paddy field in which one technological innovation after another could flourish. Frontier science looks different today. It’s rooted in model weights and GPUs. It is flooded with token spend and agentic loops. It blooms in data centers. While AI -assisted research is still young as a field, usage statistics show something big is happening in and around the major AI labs. Serious people are using this new technology to solve real problems , sometimes entire classes of problems, that were previously unsolvable. Protein structures, research mathematics , material design, drug discovery, and complex systems analysis are just a few of the fields where AI models are tangibly improving researchers’ abilities to clear humanity’s scientific roadblocks. But from where does this rich soil come? It’s not really a secret. OpenAI says it “primarily rel[ies] on publicly available information to teach [its] models how to be helpful.” Anthropic attempted to build a “central library of ‘all the books in the world’” to train its models. Sam Altman himself elaborates that their frontier models are trained on “the collective experience, knowledge [and] learnings of humanity.” Strip the euphemisms and you’re left with the stark reality that these unprecedented capabilities were assembled out of the self-expression of every person across the globe who ever wrote anything down. And the product built from this reality—at least according to the frontier labs’ own revenue, projections, and usage numbers—is the most valuable thing built in a generation. Maybe in history. Anthropic’s annualized revenue run rate rocketed from $87 million in January 2024 to $1 billion by year-end, grew roughly tenfold through 2025, and in May 2026, hit $47 billion. This makes it the fastest-compounding enterprise software company in history. OpenAI isn’t that far behind. An estimated 80% of the American workforce now holds a job where some portion of the work is exposed to these models. All of this impact was made possible by multiweek training runs over a data corpus measured in the lifetimes of billions. This is the private capture of public genius. A frontier model is the compression of a massive amount of training data into numerical weights. It’s staggering to even think about the combined collection of books, forums, code repositories, manuals, papers, chat logs, transcripts, court cases, essays, comment sections, articles, tutorials, and every errant thought scrapeable by the frontier labs’ army of spiders crawling across the internet and beyond. In a way, its incomprehensibility is almost like psychic armor. It’s too big to understand directly. Consider a wild river delta. As water runs from highlands to the sea, it erodes the land it travels through and carries the debris downstream as sediment. Silt, sand, clay, and all manner of organic material, scoured from every inch of tributary and riverbank, from plowed fields to rugged hillsides, end up aggregated in the delta. So does the richness of every life the river supports along the way. A continental watershed, swirling, accumulating, and ultimately settling at its terminus. The vast volume of disparate material combines in the delta to form something lush, strange, and alive. The Yukon Delta National Wildlife Refuge , Alaska. Captured by Landsat-7 on September 22, 2002. [Photo: NASA / USGS / Landsat ] And what is the sum of all human knowledge if not this? Every cluster of letters scraped from the pages of history (the literal tokens an AI model ingests) is a single grain of silt deposited by the ever-flowing river of humanity’s exploration. Pile enough grains and you understand the movement of the stars. Stare long enough at the mud and you see the structures of logic itself. The large language model’s transubstantiation of alluvial soil into answers is the grand harvest of the society that grew it. But subtract the dirt and there is no delta. Subtract the corpus and there is no harvest. There is nothing. The model did not learn to reason in a vacuum. It absorbed rationality by observing rationality over and over and over again. Its powers of generalization are downstream of every example, correction, and argument it subsumed. A human decision somewhere in the echoes of history, culture, and science set the stage for today’s chatbot response. This cultivated intelligence grows from the sediment of human sensemaking, but there is no sediment here that was not deposited by someone . Many of those someones are dead. They wrote the ancient texts, tested the baseline science, and recorded the history of the world from antiquity for the benefit of all of us still here. But many of those someones are alive. They are writing the working code that the model spits out. They’re pushing that baseline science past its frontier. They’re organizing and investigating and acting upon and reacting to the infinite feed of current events. Any response germane to today is borrowed from somebody . In fact, you’re one of those somebodies. Literally. Your 2 a.m. shitpost. That eloquent reply to a stranger’s essay. The scathing restaurant review you left. Your captions, comments, inside jokes, and all of your public conversations. Every contribution you ever made to the infinitely branching stream of digital communication, big and small, has settled somewhere in the delta. The Nile River delta fed Egypt for 5,000 years. The Mekong and the Ganges regions still feed hundreds of millions today. It’s no coincidence that every cradle of civilization owes its formation in whole or part to the floodplains and deltas of great rivers. These areas supported humanity through our most primitive eras with little more than the inherent richness of their raw materials. This dirt is begging to burst forth with life, yet somehow the richest farmland on earth is, almost without exception, accidental. So too goes the internet. We myriad digital denizens of the information superhighway did not set out to create a training corpus. We wrote for ourselves and for each other. We joked, argued, taught, complained, flirted, and debugged our way into this aggregated mass of interrelational raw material now harvested by private capital. The field of economics (which is also in the corpus) has a vocabulary for this. To categorize any resource, economists ask two questions: Is it excludable, and is it rivalrous? More plainly, can you stop people from using it? And does one person using it diminish what’s left for everyone else? There are caveats and subcategories, but this simple test gives us a map. If a good is excludable and rivalrous, it is a private good. Think about a sandwich. If I eat it, it is gone, and the law protects me from sandwich thieves. If a good is excludable but mostly nonrivalrous, it is a club good. A Netflix subscription is a club good. If I watch a movie, you can still watch it too, but only if we both pay to have access. If a good is hard to exclude people from using and rivalrous, it is a common-pool good. A pasture is the classic example. Many farmers can access the pasture, and while one cow grazing does not destroy the field, add enough cows and they’ll eventually gnaw the grass down to dirt. This is the infamous “tragedy of the commons” problem. Finally, if a good is hard to exclude people from using and nonrivalrous, it is a public good. Streetlights are public goods. Once the street is lit, all of us can walk beneath the light, and my doing so does not darken the road for you. Private and club goods are typically governed by profit-seeking actors and the legal system in which they operate. Public goods are primarily governed by governments or nobody, and common-pool goods tend to exist in a liminal space where everybody seeks the benefit and nobody wants to own the costs of upkeep. The frontier labs generally argue that data on the internet is open for training under fair use copyright regimes. In economic terms, this argument implies the internet is a public good. The mass scraping, ingestion, and use of internet data for training does not destroy that original data. Every blog post, tweet, and flame war is indeed still there and for the most part accessible. Nobody clearly owns it. Does the platform you post on own your posts? Do you share ownership with the platform? Can this relationship change over time? You did post it online for free after all. Except granting access is not the same thing as giving license. A library card gets you access to read a book, not to photocopy the entire library. Buying a national park pass does not confer logging rights. Visiting an open store does not entitle you to steal its inventory. Public access to work on the internet does not automatically confer usage rights. And there is a second, deeper problem with “you posted it, you accepted this.” Until very recently, the LLM training data use case did not exist and could not have been reasonably foreseen by a party posting online. A blogger from 2008 could not have consented to their work being used to train a language model today, because that wasn’t conceivable back then. Consent can’t be assigned backwards in time, least of all for a sci-fi subplot turned real. The current legal battleground for LLMs is a story of nonresolution. Notably, despite our moral intuition, access and consent are irrelevant to the frontier labs’ primary legal defense claims of “fair use.” Instead, courts evaluate four criteria as they rule on a fair use defense. They look at the purpose of the use of copyrighted material, the nature of the work, the amount used, and the effect on the market for the original. In practice, these four criteria for fair use generally collapse to two important questions: Is the new work transformative? And does it harm the market for the original? In June of 2025, Judge William Alsup, senior district court judge for the U.S. District of Northern California, ruled in Bartz v. Anthropic that training on legally acquired books was “quintessentially transformative,” but building a library from pirated books was “inherently, irredeemably infringing.” With this mixed victory, Anthropic faced a theoretical exposure of up to $70 billion in copyright damages and quickly settled the case for $1.5 billion a few months later. It’s the largest copyright settlement in U.S. history—so far—and granted no future licenses to Anthropic, nor did it clarify any law going forward. In a related ruling, Kadrey v. Meta , Judge Chhabria, a judge in the same U.S. district court, found LLM training similarly transformative and grudgingly ruled the evidence of market harm insufficient. In his ruling, he criticized the plaintiffs for putting forth almost no evidence of market dilution and suggested that LLMs’ ability to flood a market with AI work similar to the training data “will often cause plaintiffs to decisively win the fourth factor—and thus win the fair use question overall—in cases like this.” Complicating the discussion further, the U.S. Copyright Office issued a nonbinding report in 2025 concluding that public availability does not inherently allow fair use model training. As of this writing, there is no settled legal standard for measuring LLM-driven market dilution, but this is primed to be a major confrontation in future legal decisions. Already, dozens of lawsuits and policy fights are testing the frontier labs’ evolving training-data defenses. The labs’ most seductive defense is also the simplest. “It’s just reading” is a common refrain among technologists defending AI model training, and it is a compelling argument. Every writer alive is built from the books they consumed. Nobody sends Hemingway’s estate a check for being inspired by The Old Man and the Sea . If the model is just another reader, it owes what every reader owes: nothing. A person who reads 10,000 books in their lifetime becomes one more writer, working at human speed, publishing at human volume, and returns their sediment to the delta one grain at a time. A model that reads everything becomes a printing press that prints more printing presses. It spits out work at industrial volume, trains its successors, and competes with the very writers it consumed, at the push of a button. Inspiration never diluted a market, but printing does. A printing press. Engraving by Wilson Lowry after John Farey. 1819 [Photo: Wikimedia ] The invention of the Gutenberg press around the year 1440 ultimately led to the passage of the Statute of Anne in 1710. A cartel of powerful book publishers lobbied the British Parliament to restore their monopoly rights over the book trade, and Parliament instead vested the right in authors as legal owners. The incumbents asked for protection and the public’s representatives handed ownership to the creators. This statute established the basis of modern copyright law. Before the printing press, this wasn’t really necessary because mass piracy was practically impossible. The new technological landscape triggered a reproduction cascade that overwhelmed legal systems designed for a previous era’s problems, but that reckoning took more than two and a half centuries to play out. The printing press that prints more printing presses will not let us wait that long. Spoiling the delta On its face, the rich river delta that holds the deposits of humanity’s collective knowledge does appear to be a public good. Frontier labs scraping and ingesting the massive sedimental body of the internet does not destroy the original materials in a literal sense. The courts have already started ruling in that direction, but, like many legal rulings, this is narrowly correct, and completely misses the point. A flat understanding of the training corpus question misunderstands how the internet’s functional layers and its participants actually interact. So far we’ve analyzed just the text layer. The webpages, articles, posts, comments, and everything else that the frontier labs scraped into a training corpus are one obvious piece, but there are many other layers of the internet, and they set the conditions for the text layer to exist at all. Besides the obvious technical layers like the protocol or access layer, we must also consider the discovery layer, the attention layer, the contribution layer, and the integrity layer of the internet, along with the flow of behavior among them. The continued utility of the internet depends on people finding, engaging with, contributing to, and ultimately believing in the value of the things they access online. When framed as a static corpus, it is not obvious that the internet is damaged by AI training runs. Certainly it’s not damaged in the same way too many cows can damage the grass in a pasture. Instead, what’s actually damaged is the complex system that evolved to enrich that corpus in the first place. The internet is a stack of interconnected public, club, and common-pool goods. Different layers react to and reinforce each other to make the whole valuable, yet also vulnerable to the specific harms introduced by LLMs. A deluge of AI generated slop is already forcing Wikipedia editors to hunt down fabricated articles and citations , flooding Apple’s App Store review system with low-effort and copycat submissions , and threatening to overwhelm every major text-based social platform. No human maker can compete with the raw volume of generative output working to overwhelm our algorithms and attention spans. The layers of the internet behave less like a pasture here and more like a road or an email inbox. They’re nonrivalrous up to a threshold, then catastrophically rival. Consequently, the incentive to earnestly participate in the web diminishes with every AI variation of a derivative of a tweet of a derivative. Why make and share things online if you won’t get seen, can’t compete with the 10,000 variations of AI dogs dancing to upbeat electronica, and when you finally make something genuinely impressive the top comments just accuse you of being AI? The ability of generative AI tools to flood any corner of the web with media, slop or no, at effectively zero marginal cost might just be the final pedal stuck to the floor of the Spam-Everyone-Forever-Bus that left the station way back in the ’90s. This is an important moment. The naive harvesting of the fertile corpus layer is a broadside against the very people who made it possible. Some parts of the web have likely already broken. If we play this wrong, the entire internet may irreparably break. A Google data center, Oregon [Photo: Wikimedia ] Yet we’re not without tools to help us here. We already know how to protect a commons. Elinor Ostrom won a Nobel Prize in 2009 for documenting how Swiss alpine pastures, Japanese forests, and Spanish irrigation networks sustainably shared their commons for centuries. She identified eight conditions for enduring commons: clear boundaries on who may draw from it, rules matched to local conditions, the people affected having a say in those rules, monitoring by parties accountable to users, graduated penalties for overuse, accessible ways to resolve disputes, recognition of the community’s right to organize, and governance nested across scales. Run the internet through this checklist and almost none of the eight conditions hold. Its boundaries are hazy, fueled by everyone and fenced by no one. The people who fill it have no say in how it is governed. Its rules are unclear and only sporadically litigated, and then only by a handful of well-capitalized parties. Oversight is thin where it exists at all, always retroactive and never proactive. There is no monitoring, no graduated penalty, no shared venue to resolve disputes. It is, in Ostrom’s precise sense, not a governed commons at all. It is a common-pool good with the plug pulled. That’s why we’re in this mess. Much like in the era of the oozing Cuyahoga and the slurry-drowned Buffalo Creek, we’re staring down a cyberindustrial runoff disaster poised to spoil the entire delta. Attribution collapse The river still flows for now. Fresh sediment continues to settle in the delta, the corpus layer continues to grow, and the labs continue to scrape. And as they scrape, they continue to compress the colossal delta of the internet into fixed sets of weights, but this ongoing ritual manifests another problem. This time it’s a problem of value, not quality. Specifically, the problem of who gets paid for what value. According to the frontier labs, all of these billions of scraped data points are somehow individually worthless, yet collectively worth trillions. By worthless , they mean that no individually scraped work is needed in the training set. Remove any one piece and the model barely notices. Therefore no single work really matters. Therefore no single work is owed payment. But if we can unslack our hanging jaw long enough to chew what they’re feeding us, we can see the individual data points are clearly not worthless. This is a rhetorical trick—doublespeak from a self-appointed detective declaring “since we can’t figure out exactly how much jewelry was stolen, no charges can be filed.” Except they’re also the thief. And just opened up a jewelry store. This “aw shucks” is, of course, preposterous. Poor accounting practices do not erase the clear transfer of value, especially when the accounting is impossible from a legal standpoint because copyright law was built to police discrete copying. Court precedents assume infringers copying enumerable works from legible parties. Large language model training is the statistical absorption of billions of works at once. It’s a different shape with the same moral essence, but since the extracting act is a new mechanism, the legal instrument cannot quite grasp it (yet). But even more concerning is that attempts at quantitative accounting may just be mathematically incoherent. The leading formal method to value a training input is the Shapley value , which averages an input’s marginal contribution across every ordering in which it could appear. But that number isn’t a property of the work itself. It’s a function of the work’s relationship with every other work in the training set. The same document in a different training set will have a different Shapley value. Train the same model twice and, because training is stochastic, the Shapley value might change from run to run. Researchers do not even agree that Shapley is the right contribution metric, and calculating true Shapley values for frontier-scale models is computationally infeasible. These models can take weeks to train once ; exact Shapley accounting would require retraining across impossible combinations of inputs. So as of today, there is no objective valuation scheme for calculating any work’s specific share that would not be litigated into oblivion the moment it was implemented. Individual attribution for LLM training at frontier scale will not work for the foreseeable future. You cannot pay people in proportion to their contribution because no administrable, specific share exists. This is the root of the misdirect. The labs interpret this fact to mean “if we can’t attribute, then we owe nothing.” I argue that it means you can’t pay proportionally , but the payment is still owed. Paying public genius Here is the true shape of the problem: Individuals create singular work, but never in isolation. The internet is communal by nature. Both collaboration and conflict feed the whole. Branches grow stronger when pruned. The richest soil is built from rot. Vines climb by contact. Friendly minds cross-pollinate ideas while predators and prey run each other faster. This teeming mass of garbage and brilliance is valuable precisely because of the varied relationships each piece has with the others. In the living distance between them, human cognition buds, blooms, and bears fruit. Despite the romance of solitary genius, the internet is a team effort. And if you cannot identify the most valuable player, you pay the team. That payment is a royalty. It is a dollars-and-cents accounting of the public genius LLMs extract from the best and worst of us. Call it the Corpus Royalty. The frontier labs pay a fixed share of gross revenue into a public fund. The fund pays every eligible American the same amount each year. Any mechanism cleverer than this reimports the measurement problem, and it dies ten thousand quibbling deaths in the courtroom. I propose an American mechanism because international regimes are built through national commitments. The United States is not the only public with a claim, but in practice it has the regulatory gravity to establish the first durable policy. Smaller markets may be unable to impose parallel royalties without driving frontier labs out of their jurisdictions, which makes a flagship American regime the likely anchor for a later global remedy. As LLMs fuse themselves into the internet and reshape the incentives for human expression, a royalty becomes the only coherent answer to the question of compensating collective, unattributable contribution. Frontier labs cannot continue their harvest of the internet unfettered. They must replenish the upstream sources that feed the fertile delta their models depend on, or the internet will become unrecognizable within the decade. Administrative record, cuneiform tablet. 3100–2900 BCE, Sumerian, Mesopotamia [Photo: The Metropolitan Museum ] We’ve built smaller versions of this machinery before. When private entities profit from shared resources, we recognize the public is owed a claim on the proceeds. The Alaska Permanent Fund follows this intuition. Every eligible resident receives a share of resource wealth no one resident can individually claim. Since we cannot reliably measure what any single person’s words are worth to a model, the distribution should reflect the failure of attribution rather than pretend to solve it. When individual claims are too numerous to price one by one, we do not pretend they have no value, and when private entities damage public spaces, we declare they are culpable for that destruction. After a century of burning rivers, Congress built the Superfund program in 1980 and handed the cleanup bill to the polluters, for dumping that was legal when it happened. Nobody had to trace which barrel poisoned which well. The industries that profited from the toxic waste paid to restore the ground. Some will say the Bell precedent argues for opening the weights, not cutting checks, but remedies follow wounds. In Bell’s case, competitors were wounded by intellectual lockout, so the remedy was access. Today, contributors are wounded by the extraction of intellectual value. It follows that payment is the remedy that makes the wounded whole. The Corpus Royalty may be small at first. Perhaps just enough for an extra case of beer per year, but the amount matters less than the standing it confers. It shows the public they are more than just raw material exploited to train increasingly large language models. If the labs are right about what they are building, beer money becomes grocery money becomes rent money that grows with the labs and their revenues. If the labs are wrong, it won’t be because a royalty killed the business model. Normal people’s lives, arguments, questions, jokes, corrections, and creations help sustain the corpus those models consume, and this brings them along proactively instead of parasitically. The corpus is either essential or it isn’t. If it is, it has a price, and industries pay for essential inputs every day. The labs have already conceded as much in their licensing deals with Reddit, News Corp, The Associated Press, and others. This is not welfare, because welfare assumes the companies are subsidizing the public, when the subsidy runs the other way. This is not charity, because charity implies nothing was received in return. This is not a tax, because a tax treats the surplus as company property subject to public claim. This is restitution. The legal word for this shape of problem is unjust enrichment, but the common law version is too small for the thing now in front of us. In ordinary law, unjust enrichment asks whether one party has benefited at another’s expense under circumstances that make keeping the whole benefit inequitable. The frontier labs have received such a benefit by converting an uncompensated, massively aggregated, publicly generated corpus into private infrastructure-level value while threatening the conditions under which that corpus is renewed. They have done this at a scale and level of diffusion that individual litigation cannot sensibly price. The size of the problem tells us the remedy must be collective. A royalty paid on value rooted in the public corpus is a return. What flows back to the internet is owed, not gifted. This is a royalty on public genius. A royalty is only part of the solution. It’s one piece of a larger system of contribution and sustainment. It does not replace copyright claims or private licensing contracts. Those can and should still happen where ownership is legible. The royalty solves for the unattributable long tail of creativity that cannot organize, negotiate, or litigate its way into the licensing market. What the labs are doing is not new in kind, only in scale. This is the private capture of public genius at civilizational scale. This should not surprise us. Corporate entities built atop public support often try to privatize the upside while socializing the conditions that made it possible. Special organizations have abused their special status this way before. The difference is that this time the affected class is everyone at once . Perhaps we have been building toward this since we first scratched marks into clay more than 5,000 years ago. We’ve recorded, collected, and categorized our way into fragility. Once every externalized thought, every written word, every diagram, flourish, and turn of phrase can be rolled into a mechanical genie, who could resist the temptation to sell it back to the people who supplied it? This is why the public needs a claim. The frontier labs increasingly seek the privilege and power of a utility without accepting the public obligations that come along with it. A Corpus Royalty restores some of the balance in that bargain by letting the public collect a share of the wealth it made possible. The public already bears the downside of the world these models are creating and deserves part of the upside. The Corpus Royalty ensures humankind’s scattered sparks of brilliance have skin in the game they helped create.
- Why Metaphysic AI Looked to Hollywood for a Digital Rights Model
When an individual’s work can be digitally captured, replicated, and monetized, who gets to lay claim to the value?
Score: 45🌐 MovesAug 4, 2026/podcast/2026/08/why-metaphysic-ai-looked-to-hollywood-for-a-digital-rights-model - Lanesurf gave $15,000 to its AI agent. Any broker in the country can call and take it.
Lanesurf, which helps freight brokerages cover loads in 10 minutes, has opened a public phone line. Callers dial (350) 220-6331, get assigned a load, and negotiate the rate with the AI. Any amount the caller talks the AI up above its target is money the caller keeps, up to the prize cap on that load. […] The post Lanesurf gave $15,000 to its AI agent. Any broker in the country can call and take it. appeared first on FreightWaves .