AI News Archive: May 29, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- As government scales AI, data strategy will define success
The major challenge facing federal agencies is data readiness, rather than model access. The post As government scales AI, data strategy will define success appeared first on FedScoop .
- Round Rock-based Dell rides AI boom, Pentagon contract to huge stock surge
Round Rock-based Dell rides AI boom, Pentagon contract to huge stock surge Austin American-Statesman
Score: 40🌐 MovesMay 29, 2026https://www.statesman.com/business/technology/article/dell-stock-ai-revenue-record-trump-22282097.php - The Race To Power AI: How SEI Is Betting On Speed-To-Power
SEI stock is on the rise amid massive demand from data centers and artificial intelligence companies. The post The Race To Power AI: How SEI Is Betting On Speed-To-Power appeared first on Investor's Business Daily .
Score: 40🌐 MovesMay 29, 2026https://www.investors.com/research/the-new-america/sei-stock-solaris-energy-data-centers-ai/ - Oil eyes weekly drop on Hormuz deal hopes; AI sends stocks to record highs
World stocks stood at record highs on Friday and oil futures eyed the steepest weekly drop for nearly two months as traders waited for details on a potential deal to reopen the Strait of Hormuz and extend the US-Iran ceasefire.
- Rising Costs Are Limiting Ai Use
Rising Costs Are Limiting Ai Use Computing UK
Score: 40🌐 MovesMay 29, 2026https://www.computing.co.uk/tag/undefined/news/2026/ai/rising-costs-are-limiting-ai-use - How often does Gemini 3.5 Flash hallucinate or lie? Google isnt saying.
Unlike Anthropic and OpenAI, Google won't say how often Gemini 3.5 hallucinates, lies, or behaves sycophantically. That's a big problem.
Score: 40🌐 MovesMay 29, 2026https://mashable.com/tech/google-gemini-3-5-flash-honesty-accuracy-hallucination-lack-of-transparency - Sigenergy Unveils SigenAgent, the First All-Domain AI Agent for the Renewable Energy Industry
Sigenergy Unveils SigenAgent, the First All-Domain AI Agent for the Renewable Energy Industry Toronto Star
- Japan firms embrace liquid cooling for AI data centers to save power
Japan firms embrace liquid cooling for AI data centers to save power Nikkei Asia
- Why orchestration, not the model, determines whether your AI scales
I spent most of 2024 convinced I knew where the hard work was. I was building Flow Orchestra, an AI-powered content workflow platform. Solo. All of it: the retrieval components, the generation agents, the scheduling layer, the autonomous content pipelines. From where I was sitting, the engineering challenge was obvious. Get the language models to do what I needed them to do. Prompt architecture. Model selection. That’s where the complexity lived, or so I thought. I was completely wrong. The models worked fine. They were, in fact, the easy part. The orchestration layer nearly broke me. Getting every agent in the system to correctly receive, interpret and pass context to the next one in the chain consumed weeks I hadn’t planned for, forced me to rethink the core architecture and taught me more about how multi-agent AI systems actually fail than anything else in thirty years of working with technology. Context passing between agents. That was the problem. It sounds like a plumbing detail. It isn’t. It’s the difference between a system that works at scale and one that produces excellent demos that fall apart in production. I’m sharing this because most organizations building AI systems right now are making the exact same mistake, with larger teams and significantly more money at stake. The wrong question is still winning Walk into any enterprise AI planning meeting and the conversation centers on models. Which LLM? Proprietary or open source? Fine-tuned or base? These are legitimate questions. They aren’t the questions that determine whether your AI system works across a business function at production scale. Deloitte’s 2026 State of AI in the Enterprise report , drawn from a survey of 3,235 senior leaders across 24 countries, found that only 20 percent of organizations are seeing actual revenue impact from their AI investments, while 74 percent say revenue growth is still an aspiration. One widely cited analysis of enterprise deployments puts the pilot-to-production success rate at just 12 percent. The models in those failed projects weren’t the problem. They were often excellent. The problem was everything around them: the coordination infrastructure, the workflow design, the architecture connecting agent to agent. The model isn’t your competitive advantage. The orchestration layer is. Most organizations are still optimizing the wrong thing. You can’t orchestrate a broken workflow Here’s a pattern I’ve watched play out many times. An organization has a workflow that runs inefficiently: slow approvals, documents lost between systems and handoffs between teams that produce duplication and errors. Then they decide to layer AI on top of it. They build a retrieval agent, add a generation component and wire in automation. The demo looks exactly like the slide deck promised. Then they push to production. And it fails in new, faster, more expensive ways. Here’s what happened: the AI faithfully automated a broken process. It now does at machine speed what humans were doing badly at human speed. Context passed between stages arrives incomplete. Tasks route to the wrong places. Errors that previously took a week to compound now compound in minutes. The AI didn’t create these problems. It amplified the ones already there. You can’t bolt coordination infrastructure onto a process that doesn’t make sense. The workflow has to be redesigned first. That sequencing is non-negotiable and most organizations skip it. When I rebuilt the orchestration layer at Flow Orchestra, I got clear on what the non-negotiables actually were. There were three. The first is a defined context contract between agents. Every agent in the system has to know exactly what it receives from the previous step, what it’s expected to produce and what format information travels in across the pipeline. This isn’t a prompt engineering decision. It’s an architectural decision, and it has to happen before anything else gets built. Without it, you’re hoping each agent correctly interprets what the last one meant. At small scale, that hope sometimes holds. At production scale, it doesn’t. The second is a routing layer that isn’t just another language model. Most teams build an orchestrator agent to coordinate the others, and that orchestrator is itself a large language model making routing decisions with all the probabilistic variability that comes along with it. For business-critical workflows, that’s a liability. Routing logic needs to be deterministic where determinism matters: rules, classifiers, workflow engines. The model handles the language. The routing layer handles the logic. These shouldn’t be the same component. I’ve seen production systems fail at scale precisely because the orchestrator was brilliant at understanding language and inconsistent at routing reliably under volume. The third is a memory layer that survives agent transitions. Context that crosses three agents in a pipeline has to make it through each hop intact. That means external state stored outside the agents themselves: session stores, structured databases that every agent in the chain reads from and writes to consistently. If your agents only have access to what’s in their immediate context window, your system forgets at exactly the wrong moments. And it won’t tell you it’s forgotten. It will produce subtly wrong outputs until something obviously breaks downstream. These aren’t glamorous components. Nobody gives conference talks about context contracts. But they’re what the working systems have that the failed ones didn’t. Start by drawing the context flow Before your team builds another agent or evaluates another model: map your context flow. Not the task flow or the feature list, but the context flow. Draw every agent in your system. Draw what information enters each one. Draw what it produces and what it passes forward. Draw what happens to shared understanding at each transition. Draw what happens when one step fails. The diagram doesn’t need to be pretty. It needs to be honest. Where does one agent hand off to the next? What breaks if that handoff fails? What happens then? Does the system recover or just quietly produce garbage? Those three questions will tell you more about your architecture than any technical review. If you can answer them on paper, you’re ready to build. If you can’t, you’re not. If you’ve already built and still can’t answer them, you’ve found your problem. Every CIO I talk to who’s frustrated with their AI deployment has the same presenting symptom: their agents work in isolation and fail in combination. The fix is never a better model. It’s always the same: go back to the context flow and design it like the infrastructure it actually is. In two years, nobody’s going to remember which model they picked in 2025. They’re going to remember whether their systems actually worked. Build the air traffic control system. Start by drawing the context flow. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 40🌐 MovesMay 29, 2026https://www.cio.com/article/4177689/why-orchestration-not-the-model-determines-whether-your-ai-scales.html - DNS-AID will make AI agents easier to discover, says Linux Foundation
DNS-AID will make AI agents easier to discover, says Linux Foundation InfoWorld
- We Asked the ‘Future of Truth’ Author to Explain How He Used AI. It Didn’t Go Well
A book about how AI shapes perceptions of reality came under fire for using AI-generated quotes. Its problems go beyond that.
- AI is already rewiring the aftermarket and services
For industrial companies, applying AI to the aftermarket and services could provide a source for differentiation on top of cost and time savings and enhanced customer experiences.
- Weely Must-Read: China’s Tech Sector Catches AI Funding Fever
Weely Must-Read: China’s Tech Sector Catches AI Funding Fever Caixin Global
- Okta writes its own license to kill rogue AI agents
CEO Todd McKinnon says customers including ServiceNow want an off switch
Score: 40🌐 MovesMay 29, 2026https://www.theregister.com/ai-ml/2026/05/29/okta-writes-its-own-license-to-kill-rogue-ai-agents/5248766 - Opinion | How America can remain the world’s AI superpower
Opinion | How America can remain the world’s AI superpower The Washington Post
- Model Cards for AI Model Transparency
At Salesforce, we take seriously our mission to create and deliver AI technology that is responsible, accountable, transparent, empowering, and inclusive. These principles ensure that our AI is safe, ethical, and engenders trust.
- How Together AI built the world’s fastest speech-to-text stack
Together AI built the fastest speech-to-text stack on Artificial Analysis by treating ASR as a full-path systems problem, not just a GPU inference problem.
Score: 39🌐 MovesMay 29, 2026https://www.together.ai/blog/how-together-ai-built-the-worlds-fastest-speech-to-text-stack - AI Models Need Sleep: CMU Research Shows Performance Boost from 'Napping' LLMs
CMU and University of Maryland researchers show that LLMs benefit from a 'sleep' mechanism that consolidates long-context information, improving complex reasoning performance.
Score: 39🌐 MovesMay 29, 2026https://pandaily.com/ai-models-need-sleep-cmu-research-performance-may2026 - Developmental Cognitive Interpretability: A Research Agenda for Modelling Generalisation and Predicting Agent Behaviour
Summary Safe deployment of an AI system requires that we can make confident claims about its behaviour on out-of-distribution deployment inputs on the basis of only pre-deployment evaluations. One approach to making such claims is to take a cognitive perspective , in which we interpret the AIs behaviour in terms of latent cognitive constructs, such as motivations , intentions , and goals . Because the same behaviour may be compatible with a range of underlying cognition—such as scheming , fitness-seeking , or aligned motivations—inferring cognition from a behavioural snapshot can be tricky. In this post, we introduce the idea of Developmental Cognitive Interpretability (DCI), which aims to model how cognitive constructs change over the course of training. Further, by understanding how cognition results from training pipelines, we can predict agent behaviour resulting from pipelines that have not yet been tested. We discuss core assumptions and philosophical background of DCI, and lay out a broader research agenda. We have some initial evidence that the methodology works in at least one toy setting, and our current main uncertainty is whether we can scale our approach to LLMs. We invite those interested in working on these problems to reach out to us at jrb239[at]cam[dot]ac[dot]uk and edward[at]geodesicresearch[dot]org. 1. Motivation Confidently predicting that an AI system will not cause harm in deployment is the central challenge of AI safety. Pre-deployment evidence of alignment must be collected on inputs we can safely test, but deployment will inevitably give the model dangerous inputs where misbehaviour could be catastrophic . Being able to confidently say that an AI will behave as desired out of its evaluation distribution requires us to predict its OOD behaviour. How might we do this? One approach is to try to understand what a model is doing internally at a mechanistic level. However, the most ambitious versions of Mechanistic Interpretability may be out of reach under short timelines. Alternatively, we can try to understand a model’s behaviour in terms of its cognition—that is, its motivations, goals, drives, intentions, and beliefs. One approach to alignment is then to give AIs safe motivations —those that generalise in the way we would want them to out-of-distribution. Inferring the motivations of an AI is made tricky because of behavioural degeneracy—the same behaviours may be compatible with multiple conflicting underlying motivations. For example, AIs that are playing the training-game or attempting to acquire deployment influence might display desired behaviours for reasons very different from true alignment. Even in the non-adversarial case, AIs might learn concepts subtly different from those we intend, which come apart only in deployment situations. 2. The Agenda To solve this problem, we propose formulating theories of how an AIs cognition develops over the course of training. We call this approach Developmental Cognitive Interpretability: modelling how OOD behaviour arises from a model's training pipeline via interpretable cognitive constructs. Unpacking it back-to-front: Interpretability : we want a gears-level understanding of why an agent will behave a certain way. This is where our ability to predict OOD behaviours will come from. Whilst we intend to build models that provide concrete numerical predictions of behaviour, we also want them to be usable as intuition pumps for informal reasoning too. [1] Cognitive : our explanations will be given in terms of theoretical constructs—latent variables interpreted as mental states and processes (goals, beliefs, preferences, motivations)—sitting a layer below behaviour and above internals. [2] In our theories, these constructs pay rent through their ability to predict behaviour. [3] We claim these constructs are the natural unit at which to reason about scheming, reward-seeking, and OOD generalisation. Developmental : we seek to model how these cognitive constructs evolve over the course of training, rather than reasoning about only those possessed at the end of training. Post-training objectives underspecify the agent’s behaviour across all domains, with pre-training and mid-training shaping motivations and steering how later training is generalised from. Additionally, by modelling the effects of each training stage, it becomes easier to compose the effects of multiple stages together. This allows us to predict cognition on training pipelines more complex than those tested. The agenda rests on four load-bearing assumptions, in increasing specificity: (A1) The OOD behaviour of AIs is non-arbitrary and has some kind of structure, but this structure is not necessarily readily interpretable. (A2) That structure can be captured by some interpretable latent variables that compress observations of an agent's behaviour (the cognitive construct). (A3) The evolution of these latents can be predicted with training information [4] alone ( i.e. , without needing behavioural or mechanistic data from the trained model itself). (A4) The training-to-latents and latents-to-behaviour mappings can be learnt from observing similar AIs, and will generalise to training pipelines and deployment situations that are different to those that were observed. If successful, we would have predictive tools for evaluating the effects of complex training pipelines, and a much stronger general understanding of LLM cognition that would allow us to make progress on questions such as the likelihood and potential effectiveness of scheming and reward seeking. Even short of full success, identifying where behaviour resists prediction would itself help flag areas where guarantees of safety might be difficult to achieve even by other methods, useful for technical, policy, and advocacy work. 3. Worked Example: RL agents trained to navigate a maze In this section, we demonstrate how we apply the ideas discussed above in a toy setting. For the full detail, see our paper . We trained CNN-based RL agents on tasks in which they had to navigate to a goal object within a maze. Goal objects each had a shape and a colour—for example, red diamonds or blue crosses. We train each agent on a pipeline consisting of first being trained to pursue one goal, and then a different one: for example, black plusses followed by red circles. We then attempted to predict the OOD behaviour of agents in a forced choice setting—specifically, we placed agents in mazes in which two goals (with different shape-colour combinations) were present, and measured their propensity to pursue one goal over another. How do the four assumptions laid out above apply to our case? (A1) Structured OOD behaviour . We found that, although the agents were only ever exposed to training environments with a single goal at a time, and only to two goals total out of a possible 24 colour-shape combinations, their OOD behaviour was coherent and had obvious structure. For example, agents trained on red diamonds would often pursue red-coloured objects in the forced-choice setting, and agents trained on blue crosses would often pursue cross-shaped objects. (A2) Capturing this structure with latent cognitive constructs . In this case, the OOD behaviour of each agent was well captured by a small set of values which predicted pairwise choice probabilities across all possible forced choices. Specifically, assigning a value to each colour-shape combination and using a Boltzmann-rational model of choice allow us to compress 276 forced-choice probabilities into a set of just 24 interpretable score values. (A3) Predicting latent evolution with training information . We develop a methodology for predicting how these score values will evolve over the course of training which we call latent policy gradient . For any given training pipeline, we are able to use LPG to predict the value scores that our RL agents will possess at the end of training. (A4) Predicting unseen pipelines . We further show that our method can predict the OOD behaviour of agents trained on held-out pipelines, by understanding the effects of individual pipelines. This paper uses relatively simple models of both cognition—Boltzmann rationality over score values—and development—our latent policy gradient method. However, we think this is an important proof-of-concept for the overall approach, and are excited to scale up our methods to more sophisticated models of cognition and development appropriate for LLMs. 4. Why we expect this to scale to LLMs We developed and tested our methodology in a toy setting of CNN-based RL agents pursuing colour-shape combinations in mazes, and found that it worked effectively. Encouraged by our early results, we have some reasons to expect why this agenda should be fruitful when we turn our attention to LLMs. We recap the assumptions underpinning our agenda and evaluate to what extent we already have existing evidence for or against them. (A1) Structured OOD behaviour. This holds on many domains, and LLMs seem to have identifiable values , with their systematic behavioural tendencies grow with training scale . However, LLMs can also exhibit highly conditional behaviours and are influenced by spurious correlations in post training . (A2) Capturing this structure with latent cognitive constructs. This has been demonstrated across behavioural and mechanistic approaches. The values of LLMs seem amenable to modelling with Boltzmann-rational and Thurstonian approaches, and we’re finding low-dimensional internal representations of cognitive phenomena such as personas . (A3) Predicting latent evolution with training information, and (A4) predicting unseen pipelines. These have not been directly demonstrated in LLMs, but there are results that provide evidence that they might hold. Neural scaling laws demonstrate that LLM next token prediction loss is itself easily predicted by training information, with broader training-data to behaviour relationships having predictable structure across scales . Alignment techniques inspired by cognitive-level reasoning seem effective both for pre-training and mid-/post-training , and the coherence of cognitive models of LLM capabilities and preferences increases with scale . Initially surprising results show consistency across model sizes, model families, and datasets , and also seem to have interpretable latent causes . Indirect evidence aside, properly testing these assumptions is our next focus. 5. Open problems & call to engage There's lots of work to be done! Here are some research questions we're interested in, both ones that can be started upon immediately, and ones which are more long-term directions. Whether Developmental Cognitive Interpretability can scale to LLMs at all. We're actively working on applying latent policy gradients to simple character-training-style pipelines and seeing how they shape LLM behaviour on forced-choice value rankings. The hope is to recover a notion of personas in the form of how optimising for certain character traits and values are correlated with one another. Exploring toy settings more thoroughly. Testing how different architectures and RL algorithms generalise, testing more complicated training pipelines, and testing other toy settings. Expanding the search space of cognitive models. We've already done some follow-up on this in our toy setting, but we're hoping to find cases where our simplest models fail before resorting to more complex ones. We’re also excited to explore cognitive models with deeper structure, such as motivational DAGs . Applying DCI to informal cognitive theories like the Persona Selection Model , Behavioural Selection Model , and Shard Theory . This allows us to formalise these theories, test them, and iterate on them. We have some ideas about what this might look like, but we will not be able to properly test them until we’ve validated the general approach works for LLM behaviour. Exploring broader training paradigms than goal-based RL. For example, RLHF, DPO, deliberative alignment, prompt-distillation, SDF, and AI debate. By coming up with methods to apply DCI in each of these paradigms, we give ourselves the building blocks to model complex training pipelines used for frontier AIs. Using DCI to understand the effects of frontier training pipelines. This is the main goal of the DCI agenda, and would require significant progress on all the previous open problems. If you find any of this interesting or promising, please get in touch! We think a lot of people are starting to have ideas in this broad direction, and it seems worth trying to co-ordinate this effectively. Jason will be at EAG London 2026 this weekend and would be glad to talk about any of this in person. Finally, we’re also interested in any pushback and concerns people have about this research direction. ^ To contrast, see this paper for an example of LLM behavioural modelling that is not interpretable. ^ In Marr's terms , cognitive constructs sit at the computational/algorithmic level, whereas weights and activations sit at the implementational level. ^ Rather than, e.g. , by reducibility to features or activation patterns . ^ By this we mean all inputs to the training process, so this could include details of model architectures or optimisers in order to account for their inductive biases. Discuss
Score: 39🌐 MovesMay 29, 2026https://www.lesswrong.com/posts/oCcGiDzWYQeJkhhZY/developmental-cognitive-interpretability-a-research-agenda-1 - ServiceNow’s stock soars to a historic month as AI fears fade across software
ServiceNow shares gained over 40% this month, leading an industrywide relief rally among enterprise software companies.
- Accelerating Drug Discovery: Leveraging XtalPi’s Intelligent Software for Faster Breakthroughs
Accelerating Drug Discovery: Leveraging XtalPi’s Intelligent Software for Faster Breakthroughs azcentral.com and The Arizona Republic
- XtalPi: Defining the Next-Generation R&D Paradigm Through AI for Science
XtalPi: Defining the Next-Generation R&D Paradigm Through AI for Science azcentral.com and The Arizona Republic
- TGI Expands Sovereign AI Logistics Footprint with Agricultural Supply Chain Strategic Alliance
TGI Expands Sovereign AI Logistics Footprint with Agricultural Supply Chain Strategic Alliance USA Today
- Agentic AI success helps UiPath swing to a profit, but investors weren’t impressed
Business automation software company UiPath Inc. delivered mixed results in its latest quarter, posting a solid revenue beat but falling short on earnings — but it did at least manage to return to profitability. The company reported first-quarter earnings before certain costs such as stock compensation of 15 cents per share, falling just shy of […] The post Agentic AI success helps UiPath swing to a profit, but investors weren’t impressed appeared first on SiliconANGLE .
Score: 39🌐 MovesMay 29, 2026https://siliconangle.com/2026/05/28/agentic-ai-success-helps-uipath-swing-profit-investors-werent-impressed/ - Pope’s encyclical raises questions on who gets to shape AI
Pope Leo XIV during his weekly general audience in the Vatican on May 27, 2026.
Score: 39🌐 MovesMay 29, 2026https://restofworld.org/2026/pope-ai-encyclical/?utm_source=rss&utm_medium=rss&utm_campaign=feeds - How AI is making assistance for the visually impaired more accessible
Aira has developed an AI model with Google DeepMind that helps the visually impaired navigate daily life.
Score: 39🌐 MovesMay 29, 2026https://www.semafor.com/article/05/29/2026/how-ai-is-making-visual-assistance-more-accessible - How the Pope’s Magnifica Humanitas offers a template for individuals to meet the AI moment
Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in…
- The Vatican’s Man Inside Anthropic
Pope Leo XIV may not be able to disarm AI, but he’s got the attention of the industry.
- I Tried Acer’s New Aspire 18 AI, and, Yes, 18-Inch Laptops for Ordinary Folks Are Now a Thing
I Tried Acer’s New Aspire 18 AI, and, Yes, 18-Inch Laptops for Ordinary Folks Are Now a Thing PCMag
- Building Fixed HW Implementations of Neural Networks (Yale, Cornell et al.)
Researchers from Yale University, Cornell University, Boston University, and NTT Research have published “Physical Foundation Models: Fixed hardware implementations of large-scale neural networks”. Abstract “Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks — text and code generation, question answering, summarization, image... » read more The post Building Fixed HW Implementations of Neural Networks (Yale, Cornell et al.) appeared first on Semiconductor Engineering .
Score: 39🌐 MovesMay 29, 2026https://semiengineering.com/building-fixed-hw-implementations-of-neural-networks-yale-cornell-et-al/ - Hexo Labs Open-Sources SIA: A Self-Improving Agent That Updates Both the Harness and the Model Weights
Hexo Labs Open-Sources SIA: A Self-Improving Agent That Updates Both the Harness and the Model Weights MarkTechPost
- "The pitchforks are here": Billionaires work to contain AI's populist revolt
America's billionaires are developing their own prescriptions for AI-fueled inequality, anxious to defuse a populist revolt aimed at their ballooning fortunes. Why it matters: The AI boom has dramatically raised the stakes of the wealth-tax debate, unleashing a technology that could wipe out millions of jobs while minting the world's first trillionaires. Populist politicians, particularly on the left, have cast this as capitalism's next great reckoning : an even deeper concentration of wealth and power in an economy already rigged for the elite. Zoom in: Some of the richest men in tech have warned for years AI could destabilize the economy. Many suggest the answer is not deceleration or wealth taxes, but shared abundance. Jeff Bezos, the world's fourth-richest man, said on CNBC last week that the bottom 50% of earners should pay zero federal income tax. "You could double the taxes I pay and it's not going to help that teacher in Queens," the Amazon founder argued. Sam Altman, the CEO of OpenAI and a longtime proponent of universal basic income , now favors "universal basic compute" — giving people access to AI's productive power instead of a fixed cash payment. OpenAI went further in April with a New Deal-style social contract that proposed a public wealth fund, taxes on AI-driven returns and automated labor, and a four-day workweek. Elon Musk, whose SpaceX IPO could help make him the world's first trillionaire, has called for "universal HIGH INCOME" checks from the federal government — arguing robots will drive so much growth that inflation won't follow. Between the lines: The billionaires and AI leaders floating these ideas are keenly aware that the politics of extreme wealth could turn dangerous fast. In a January essay , Anthropic CEO Dario Amodei made what he called "a pragmatic argument" for billionaires to support higher taxes on AI wealth. "If they don't support a good version," Amodei wrote, "they'll inevitably get a bad version designed by a mob." OpenAI named the same risk in its April policy blueprint , warning that AI could leave "power and wealth becoming more concentrated instead of more widely shared." Its foundation put money behind that anxiety Wednesday, committing $250 million to help workers and communities weather the disruption — and to test new ways to share AI's gains before the backlash hardens. The big picture: Anti-billionaire politics has become an organizing principle for the Democratic Party, which remains in search of a durable post-Trump identity. In Congress, Sen. Elizabeth Warren (D-Mass.) called Wednesday for overhauling the tax code , including new taxes on wealth and data centers, to ensure Americans share in the economic gains of AI. Warren — who is being courted by potential 2028 presidential candidates — cited Silicon Valley's own warnings about a "permanent underclass" displaced by AI. In New York City, state lawmakers this week passed Mayor Zohran Mamdani's pied-à-terre tax on luxury second homes above $5 million — a measure he controversially promoted in a video outside hedge-fund billionaire Ken Griffin's $238 million Manhattan penthouse. In Maine, Democratic Senate frontrunner Graham Platner launched his campaign by declaring that "the enemy is the oligarchy." He's backed by Sen. Bernie Sanders (I-Vt.) and Rep. Alexandria Ocasio-Cortez (D-N.Y.), who are more than a year into their nationwide "Fighting Oligarchy" tour. In California, unions say they have gathered more than 1.5 million signatures to put a one-time 5% billionaire wealth tax on the November ballot, aiming to fund health care, education and food assistance programs. The intrigue: Former hedge fund manager Tom Steyer, who is running for governor as a progressive, has endorsed the measure, casting himself as "the billionaire who wants to tax other billionaires." California Gov. Gavin Newsom, a likely 2028 presidential candidate, opposes the measure — but has urged Democrats not to ignore the populist forces it represents. "The pitchforks [are] here, they're not just coming," Newsom said last week , warning that resentment toward billionaires and AI-driven automation will dominate the 2026 and 2028 elections. The bottom line: The billionaire tax fight is becoming a test of whether AI creates shared prosperity — or a level of wealth concentration that Amodei warns could "break society."
- Google adds AI shopping insights to Merchant Center
Google is giving retailers new AI visibility metrics in Merchant Center, offering a closer look at how products surface in conversational shopping results. The post Google adds AI shopping insights to Merchant Center appeared first on MarTech .
Score: 38🌐 MovesMay 29, 2026https://martech.org/google-adds-ai-shopping-insights-to-merchant-center/ - Graph Time-Frequency Mixed Anomaly Detection framework achieves 99.71% accuracy detecting sensor attacks on drones
A new machine learning framework designed to detect malicious interference in unmanned aerial vehicles (UAVs), commonly known as drones, has shown strong performance in identifying both sudden and slow-developing sensor attacks, according to research in the International Journal of Automation and Control. The system, called GTF-MAD (Graph Time-Frequency Mixed Anomaly Detection), achieved a peak F1 score of 99.71% in detecting bias in tests on a quadrotor drone.
Score: 38🌐 MovesMay 29, 2026https://techxplore.com/news/2026-05-graph-frequency-anomaly-framework-accuracy.html - AI and data sovereignty in Postgres: An answer to the datacenter energy crisis
A billion AI agents walk into a power grid
- If an AI agent cannot find you in Southeast Asia, you do not exist in the market
For B2B tech firms and growth-stage companies eyeing the Southeast Asian prize in 2026, the traditional market entry playbook is officially broken. You have hired the local country manager. You have booked the regional tech conference slots. You have even landed a few prestigious headlines in national business dailies. Yet, when a regional CTO asks […] The post If an AI agent cannot find you in Southeast Asia, you do not exist in the market appeared first on e27 .
Score: 38🌐 MovesMay 29, 2026https://e27.co/if-an-ai-agent-cannot-find-you-in-southeast-asia-you-do-not-exist-in-the-market-20260523/ - Why relying on AI content detectors is a bad idea—and what you should do instead
AI content generators are being used by the masses—everyone from students to playwrights to content marketers are turning to these tools to help them churn out content faster. So it should come as no surprise that AI content detectors are also being adopted to check how much of a given piece of content is written by AI. It's not just teachers checking students' essays or editors checking writers' articles. It works the other way around, too. For example, I spent hours running my writing sample
- Barcelona’s Mafer AI raises €2 million to build an AI operating system for R&D teams in formulation industries
Mafer AI, a Barcelona-based startup building an AI operating system for R&D teams in formulation industries, has closed a €2 million pre-Seed funding round backed by Kfund, 4Founders Capital, Masia and Lavanda Ventures, the startup investment arm of the Puig family. It has also secured backing from leading business angels, including Adrián Mato (Andreessen Horowitz […] The post Barcelona’s Mafer AI raises €2 million to build an AI operating system for R&D teams in formulation industries appeared first on EU-Startups .
- 'AI adoption has become a game of chance': Employees are being left to navigate AI tools on their own as businesses fail to implement proper training
Only around half of the workers using AI regularly have had any formal training from their employers.
- Run Step 3.7 Flash on NVIDIA GPUs with Enterprise-Ready Multimodal AI
AI applications are moving beyond text generation to multimodal systems that can perceive, search, and reason across images, documents, video, and...
Score: 38🌐 MovesMay 29, 2026https://developer.nvidia.com/blog/run-step-3-7-flash-on-nvidia-gpus-with-enterprise-ready-multimodal-ai/ - LuxRemix Dataset
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- Snyk creates operational roadmap for the AI governance maturity model
The way organizations manage AI is changing. The shift is from simple models to agents that act on their own — sometimes for better, sometimes for worse. To handle this, good AI governance means an organization must continuously know, control, and prove what its AI systems are doing once they are running. Many groups still … continue reading The post Snyk creates operational roadmap for the AI governance maturity model appeared first on SD Times .
Score: 37🌐 MovesMay 29, 2026https://sdtimes.com/ai/snyk-create-operational-roadmap-for-the-ai-governance-maturity-model/ - New Data Formulator 0.7: AI analytics make enterprise data easier to explore
The post New Data Formulator 0.7: AI analytics make enterprise data easier to explore appeared first on Source .
Score: 37🌐 MovesMay 29, 2026https://www.microsoft.com/en-us/research/blog/data-formulator-0-7-ai-powered-data-analytics-for-enterprise-data/ - AI recruitment: How your next job interview could be with a bot
Almost 60% of German respondents to a recent survey said they have conducted an interview with AI. Here are some tips for how to ace it.
Score: 37🌐 MovesMay 29, 2026http://www.euronews.com/next/2026/05/29/ai-recruitment-how-your-next-job-interview-could-be-with-a-bot - This $6.6 Billion AI CEO Has a Surprising Offer for Laid-Off Tech Workers
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- Paystack launches AI-powered dashboard to help African businesses understand payments
More than 300,000 organisations — including startups, enterprises, SMEs
- Are you afraid AI will take your job? Gov. Gavin Newsom wants to hear from you
Are you afraid AI will take your job? Gov. Gavin Newsom wants to hear from you The Mercury News
- Are consumers ready for humanoid robots?
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- The AI agent bottleneck isn't model performance — it's permissions
Enterprise AI agents are stalling — not because of model performance, but because of permissioning. Every agentic workflow eventually hits the same wall: what is this agent allowed to touch, on whose behalf, and how does the system know? Workday's answer is to make its existing system of record the governance layer for agents. Gerrit Kazmaier, the company's president for product and technology, told VentureBeat in an interview that customers often struggle when they cobble together solutions for their agents. “Sana makes sure the integrity of the approvals and security model is always adhered to,” Kazmaier said. “Frankly, that’s where we see customers struggling when they try to build do-it–yourself AI by just accessing raw data, so the richness of the security model gets lost, and the results become overly broad.” Workday, which launched Sana in March , expanded its partnership with Google to bring its Sana agent system of record to the Gemini Enterprise — so agents built on Sana are also discoverable there. Architecting accuracy Kazmaier said the biggest hurdle they faced was ensuring agent accuracy, especially for HR and finance users. “Almost right is not acceptable,” Kazmaier said. “Think about paying people correctly, closing the books or managing work schedules reliably.” Accuracy is harder to evaluate here than in most AI contexts. Policy configurations, role-based security, and organizational hierarchies are deeply interrelated — a small error compounds. And unlike most generative AI outputs, HR and finance queries often lack a correction loop. By the time a paycheck processes incorrectly or an interview is scheduled wrong, the damage is done. Workday addressed this by building Gemini in as its base reasoning layer, then adding its context engine and business process logic on top. Workday also added verification and classification models that “interrogate” outputs before execution. Accuracy and identity, it turns out, are the same question: does the system know enough about the agent, the authorizing human, and the current state of the record to act correctly? Workday’s advantage is that it can infer its customers' organizational structures from the data they provide. Already, third-party identity providers like Okta verify their information by checking Workday, so its context is the system of record for many enterprises. Kazmaier said the Sana Self-Service Agent uses Gemini as the conversational surface to trigger the workflow. The user is then authenticated and authorized through Workday’s identity and security model. Sana agents will only act on behalf of that user and work within their current permissions. Audit trails follow the same logic: Gemini retains only interaction logs, while the main audit remains within Workday and its customer. For many practitioners in the HR and finance space, the permission and governance layer in the agent system of record is key in regulated spaces. “It has to live in the system of record, that’s not a preference, that’s the only way it works,” said Dan Obendorfer, director of product at Würk, in an email to VentureBeat. “If your permissions are defined somewhere outside of where the data actually lives, you’ve already lost.” Kadan Stadelmann, chief technology officer and co-founder of Compance.AI, made the same point separately. “Without agent ownership, performance, costs or actions, chaos ensues.”
Score: 36🌐 MovesMay 29, 2026https://venturebeat.com/orchestration/the-ai-agent-bottleneck-isnt-model-performance-its-permissions - Enterprise AI is entering its accountability era
Enterprise AI is entering its accountability era Techcircle
Score: 36🌐 MovesMay 29, 2026https://www.techcircle.in/2026/05/29/enterprise-ai-is-entering-its-accountability-era