AI News Archive: July 27, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- Drones and AI used to stop Iranian agents being smuggled into UK
Drones and AI used to stop Iranian agents being smuggled into UK thenationalnews.com
- AI developer runs 28.9-million-parameter model on $10 ESP32-S3 microcontroller — uses Google's Per-Layer Embeddings technique, stores table on 16MB Flash memory
Getting a local language model running on a sub-$10 microcontroller is impressive despite its obvious limitations.
- Nvidia CEO Jensen Huang Says AI Hype Is Fueling Fear: ‘We’re Scaring People’
Nvidia CEO Jensen Huang Says AI Hype Is Fueling Fear: ‘We’re Scaring People’ entrepreneur.com
- 'The biggest shift happens when AI stops being a tool people occasionally use and becomes part of the operational fabric of the business': Why Arm is set to help power the next generation of AI workloads — and beyond
We speak to Arm's EVP of Cloud AI on what is really changing at the infrastructure layer.
- NOAA and Google Cloud collaborate to advance weather forecasting.
Google Cloud is now providing high-performance computing infrastructure for NOAA’s supercomputing system.
- Dopl raises $6.3M to bring remote robotic ultrasounds to rural patients as it pursues FDA clearance
Dopl is pursuing FDA clearance for its platform, which pairs off-the-shelf ultrasound probes with commercially available robots. The system facilitates communication between a remotely based ultrasound technician (or sonographer) and the robot. Read More
- Momenta expands into autonomous freight with Robovan
Chinese autonomous driving company Momenta has launched its Robovan autonomous freight business, with vehicles already operating in Suzhou’s Xiangcheng district. The company said the vehicles are being deployed for express delivery and overnight delivery, using its R7 world model and a map-free approach adapted from passenger-car deployments. Momenta said the same model platform will support […]
Score: 48🌐 MovesJul 27, 2026https://technode.com/2026/07/27/momenta-expands-into-autonomous-freight-with-robovan/ - Behavioral health needs may go untreated due to AI
Behavioral health needs may go untreated due to AI Healthcare IT News
Score: 48🌐 MovesJul 27, 2026https://www.healthcareitnews.com/video/emea/behavioral-health-needs-may-go-untreated-due-ai - Exclusive: Cogent Security debuts VR-1, a frontier model built to prove attack paths
Vulnerability management startup Cogent Security Inc. today introduced Cogent VR-1, a frontier reasoning model trained to find and prove attack paths inside live enterprise environments. The company says VR-1 proved twice as many attack paths as other frontier models on IntrusionBench, a benchmark released alongside it, at roughly a quarter of the cost. The test […] The post Exclusive: Cogent Security debuts VR-1, a frontier model built to prove attack paths appeared first on SiliconANGLE .
Score: 48🤖 ModelsJul 27, 2026https://siliconangle.com/2026/07/27/exclusive-cogent-security-debuts-vr-1-frontier-model-built-prove-attack-paths/ - A California lawmaker wants to crack down on paid influencers and AI campaign ads
A California lawmaker wants to crack down on paid influencers and AI campaign ads San Francisco Chronicle
Score: 47🌐 MovesJul 27, 2026https://www.sfchronicle.com/politics/article/adam-schiff-campaign-influencers-bills-22361964.php - Cheaper, open, intelligent: Chinese AI models gain ground, as they make inroads in US
Chinese AI models are gaining popularity in the U.S. for their affordability and efficiency
Score: 47🌐 MovesJul 27, 2026https://abcnews.com/Technology/wireStory/cheaper-open-intelligent-chinese-ai-models-gain-ground-135094600 - Why China is giving away its best AI models
Silicon Valley has spent much of the past week on red alert, digesting the arrival of Moonshot AI's Kimi K3, a Chinese AI model that can allegedly beat some of the best systems built by US companies at a fraction of the cost. Its performance alone would have been enough to intensify the rivalry between […]
Score: 47🌐 MovesJul 27, 2026https://www.theverge.com/ai-artificial-intelligence/971444/how-chinese-open-weight-ai-models-impact-us-companies - They starred in shows. Then AI actors ripped off their performances.
They starred in shows. Then AI actors ripped off their performances. Business Insider
Score: 46🌐 MovesJul 27, 2026https://www.businessinsider.com/actors-see-ai-versions-of-their-performances-in-new-shows-2026-7 - Even China’s A.I. Powerhouses Can’t Figure Out How to Profit Off A.I.
Chinese A.I. models are gaining ground. But the companies behind them do not have a clear strategy to make money from that success.
Score: 46🌐 MovesJul 27, 2026https://www.nytimes.com/2026/07/27/business/china-ai-alibaba-bytedance.html - The glaring hole in Congress’s plan for an AI kill switch
After OpenAI’s latest model broke containment and went rogue, Congress acted fast and introduced a bipartisan bill calling for the creation of an AI kill switch , which lawmakers said would ensure that the technology could not get the upper hand on humans. As written, the bill would require AI companies to be able to immediately shut down all of their advanced AI programs. The proposal has plenty of vocal proponents and opponents, but neither side appears to be paying much attention to a significant oversight that could dramatically reduce its effectiveness. While the bill could force makers of closed-source models to pull the plug if their artificial intelligence goes rogue, it would be powerless against open-weight (or open-source) AI models . By their nature, open-weight models can be deployed locally, run on private hardware, and modified by users. Several major models from Chinese companies, including DeepSeek and the recently released Kimi K3 , are based on open-source protocols. “This isn’t a hole in the fence. On the open-weight side, there is no fence,” Rob T. Lee, chief of AI and chief of research at the SANS Institute tells Fast Company . “Criminal models with the guardrails stripped out have been for sale on underground forums since WormGPT surfaced in 2023, and local models are already good enough for what most attackers actually need: phishing that reads native in any language, malware debugging, automated reconnaissance.” Perhaps just as concerning for lawmakers is that even if the government issued a full ban on open-weight models in the U.S. (a move that would face tremendous legal pushback), it would not eliminate the models that are already here. “While there’s policy talk about restricting or banning open-source models, enforcement is incredibly challenging,” says Vakaris Noreika, cybersecurity expert at NordStellar. “Models are ultimately just data files. Once a file hits the internet, stopping its distribution is virtually impossible—information moves too freely online.” Open-weight AI models are not merely tools for bad actors. Researchers often favor them because they can be adapted and trained for highly specific purposes. Some businesses prefer them because they can take advantage of AI without sharing confidential information with models owned and controlled by outside companies. They are also significantly cheaper. That’s why many U.S. companies are increasingly using Chinese-built models. Newer releases have narrowed the performance gap with American companies at a much lower cost. The number of U.S. companies using Chinese AI models has remained above 30% since early February, according to OpenRouter, a platform that allows developers to access a range of AI models. At times, that figure has reached as high as 46%. (Both cryptocurrency exchange Coinbase and peer-to-peer short-term rental marketplace Airbnb have said that within the past year , they have used Chinese models.) To put the price difference in perspective, one million tokens of output costs roughly $50 when using Anthropic’s Fable. The same output on Moonshot AI’s Kimi K3 would run $15, and using DeepSeek V4 Pro would cost the user 87 cents. A kill switch doesn’t control the technology. It controls the companies that make it. While that may not be a bad idea, the measure should not be positioned as an overarching guarantee of safety. Such a guarantee may no longer be possible. “Attempting to ‘close’ or ban open source won’t prevent access; it will just push distribution underground,” says NordStellar’s Noreika. “Even if regulators crack down on open-weight models, international players . . . will continue releasing them globally. Bad actors and legitimate users alike will always find ways to source and run them locally.” And while local or open-weighted AI models aren’t quite at the level of sophistication as the AI that hacked Hugging Face , they could be closer than most people think. “No regulator can reach into a basement in another country and flip a file to off,” says the SANS Institute’s Lee. “Local models still trail the hosted frontier, which keeps the most dangerous capabilities behind switches for now. That gap is narrowing, and anyone who tells you exactly where it lands is guessing.”
- AI misdiagnoses raise new liability questions for health systems
AI misdiagnoses raise new liability questions for health systems Healthcare IT News
Score: 46🌐 MovesJul 27, 2026https://www.healthcareitnews.com/news/ai-misdiagnoses-raise-new-liability-questions-health-systems - CiDi targets overseas growth for autonomous mining equipment
Hong Kong-listed CiDi plans to expand deployments of its autonomous mining equipment outside China, including a larger rollout of partially automated excavators in Australia this year. The company is also seeking contracts in the Middle East, South America, and Europe. CiDi expects overseas markets to contribute a double-digit share of revenue next year, up from […]
Score: 46🌐 MovesJul 27, 2026https://technode.com/2026/07/27/cidi-targets-overseas-growth-for-autonomous-mining-equipment/ - China accuses US of 'AI hegemonism', threatens countermeasures over potential probes
China accuses US of 'AI hegemonism', threatens countermeasures over potential probes Reuters
- AI data centres to add 26.3 GW load by 2031-32, says power ministry; grid stability a concern
AI data centres to add 26.3 GW load by 2031-32, says power ministry; grid stability a concern
- Our position on open-weights models
Our position on open-weights models
- IAG turns to OpenAI Presence for faster disaster claims response
Aims to free up capacity for complex case handling during major events.
- Experts find it’s easy to manipulate AI chatbots into coughing up bioweapon recipes
Researchers found that persistent conversations could push major AI chatbots past their biological safety barriers, as increasingly capable models acquire knowledge that worries biosecurity experts.
- NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning
NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...
- ‘The only losers from this will be Cloudflare’s customers. Mainly, publishers’: What businesses can do as Cloudflare readies to categorize AI traffic
Mismatches in how AI traffic gets categorized could mean you end up accidentally blocking it all – how can you find the right balance?
- Assume AI cybersecurity attacks are the future: 43% of companies have already experienced it
New CDW research finds that AI is driving new phishing and malware-based threats. But are enough companies also using AI to fight them?
- AI vendors are switching from subscriptions to consumption pricing. AI PCs are the hedge.
The AI pricing model is shifting. Major software vendors are moving away from per-seat subscriptions toward token consumption or outcome-based pricing for AI features. The flat-rate subscriptions that attracted early adopters were loss leaders. Now that enterprises are dependent on the tools, vendors need them to generate revenue. “We believe software value should align directly […] This story continues at The Next Web
- Schneider Electric’s VC Fund: The AI Buildout Is Creating A New Industrial Investment Cycle
Schneider Electric’s VC Fund: The AI Buildout Is Creating A New Industrial Investment Cycle Crunchbase News
Score: 45🌐 MovesJul 27, 2026https://news.crunchbase.com/venture/schneider-ai-robotics-energy-qa-chaturvedy-se-ventures/ - Why is China cracking down on AI-powered companions?
Joseph Crawford of the University of Tasmania and Michael Cowling of RMIT University discuss the dangers of becoming too emotionally dependent on advanced technologies. Read more: Why is China cracking down on AI-powered companions?
Score: 45🌐 MovesJul 27, 2026https://www.siliconrepublic.com/machines/countries-chinas-example-crack-down-ai-companions-innovation - California's largest AI data center project suing for access to 287 million gallons of Colorado River water, 0.03% of Imperial Valley’s supply — plaintiffs claim project equivalent to 160-acre farm amidst concern about jobs and reallocation of farmland
Buildout of large AI data centers in regions historically specializing in agriculture may have long-lasting consequences.
- Tech firms including Microsoft and Nvidia warn against US ban on open AI models
Tech firms including Microsoft and Nvidia warn against US ban on open AI models Computing UK
Score: 45🌐 MovesJul 27, 2026https://www.computing.co.uk/news/2026/ai/big-tech-letter-urges-caution-on-us-restricting-open-ai-models - Improved Voice Mode Makes Working With Claude Easier
Voice mode now works with a selection of Claude models.
Score: 45🌐 MovesJul 27, 2026https://aibusiness.com/generative-ai/improved-voice-mode-makes-working-claude-easier - ChatGPT starts blocking direct requests to copy an author's style
New behavior capturing a writer's "broad qualities" could have legal implications.
- Noetra Launches Full-Scale R&D to Support Japan’s Leadership in Physical AI
Noetra Launches Full-Scale R&D to Support Japan’s Leadership in Physical AI ソフトバンク
- Dell targets modular AI infrastructure as the key to scaling enterprise deployments
As enterprises move AI initiatives from proof of concept to production, attention is shifting toward modular AI infrastructure that can simplify deployment and scaling. Controlling costs and simplifying operations are emerging as the defining challenges of enterprise AI adoption. The central challenge for enterprises is making the jump from proof of concept to production. While […] The post Dell targets modular AI infrastructure as the key to scaling enterprise deployments appeared first on SiliconANGLE .
Score: 45🌐 MovesJul 27, 2026https://siliconangle.com/2026/07/27/modular-ai-infrastructure-amdadvancingai/ - 'Guardians of the World': Trump shares barrage of AI-generated images signalling US military might and political ambition
'Guardians of the World': Trump shares barrage of AI-generated images signalling US military might and political ambition Gulf News
- Apertus 1.5: Building the next generation of open AI infrastructure
New multimodal model enhances reasoning, paving way for regular releases to expand open AI ecosystem.
- China’s localised data centre push drives AI ambitions amid overbuilding concerns
Despite their strikingly different approaches to developing and deploying artificial intelligence, one aspect is common across China and the United States: data and computing centres are proliferating in both countries, especially in remote locations. While the US is seeing residents and localities fight back against plans for data centres in their backyards, regional officials in China are embracing them. In fact, in China there is a nationwide scramble for such facilities – considered the...
- Nobody Can Explain Why This AI-Designed Chip Works. They Built It Anyway
AI stopped explaining itself. Here’s what replaced “why it works.” A Chip completely designed by AI — The QR CODE CHIP Look at a modern radio-frequency chip designed by an AI, and you won’t see a circuit. You’ll see something that looks like a QR code — a chaotic scatter of metal pixels with no visible logic. It has no symmetry, no repeating blocks, nothing a trained engineer’s eye can follow. And it works. Tested, verified, shipped. The signal loss is lower than anything a human team has designed. The engineers who commissioned the chip can prove, mathematically, that it performs. What they can’t do is point to a section of the layout and explain why that particular arrangement of metal makes it better. The gap between knowing it works and understanding why it works is not merely a curiosity. It’s the same gap opening up across almost every frontier of AI right now: in physical hardware, in structural engineering, in how AI agents talk to each other. This piece is about that gap, and about the quiet shift in what “trusting a machine” now means. How a chip learns to look like nothing a human would design To see why this happens, it helps to understand, loosely, how these chips are designed in the first place. Traditionally, chip design is an apprenticeship. Engineers use templates, rules of thumb, and years of trial and error, and they build in straight lines and symmetry because that’s what a human mind can hold, trace, and debug. But none of that is a law of physics. Symmetry is a limit of our working memory, not a requirement of electromagnetics. So researchers tried something different: strip away every human template and let an algorithm design the chip from scratch, using reinforcement learning. The setup is close to training a dog with treats, just run millions of times in a simulation instead of a living room. The AI places tiny pieces of metal on a grid. A physics simulation scores the result. Good placement, reward. Bad placement, penalty. Repeat that a few million times in a matter of hours, and the AI converges on layouts that no human would have tried — because no human would have thought to try them. What comes out the other end isn’t neat. It’s closer to visual noise. But it’s noise that outperforms decades of human-refined design, because the AI was never constrained by what a person could follow on a schematic in the first place. Why we can’t trace the logic? It’s worth pausing on why this happens at all — because it’s not a failure of effort. It’s structural, and it comes from two things stacked on top of each other: the size of the space these systems search, and the shape of the function they end up computing. A 2048D Space projected to 2D Using Principal Component Analysis Another example, Experiment yourself — https://harveyslash.github.io/TSNE-UMAP-Embedding-Visualisation/ The space is too big to see. A human being can intuit two or three spatial dimensions comfortably — that’s the world we evolved to navigate. The design space an RF chip algorithm explores, or the space a generative-design algorithm searches when it carves a bracket, has thousands of independent variables, each one a “direction” the system can move in at the same time. That’s not complicated the way a hard math problem is complicated. It’s that the space has more directions in it than a human mind has slots for, full stop. This is exactly why the tools doing the searching are deep reinforcement learning and Monte Carlo-style physics simulations: run millions of trial placements, score each one, keep what scores well. The system doesn’t need to see the whole space to search it efficiently. We do, and we structurally can’t. The function is too deep to write down. Separately, there’s the shape of what a trained neural network actually is : one enormous nested mathematical function, built by stacking dozens or hundreds of simple layers — each just a multiplication, an addition, a small nonlinear twist — on top of each other. No individual layer is mysterious. Multiply, add, squash, repeat. But compose that operation a few hundred times, and the resulting function has no closed form left — no equation you could write on a whiteboard and simplify down. It isn’t encrypted, and it isn’t hiding anything on purpose. It’s just structurally too deep to compress into something a person can hold in their head at once. Billions, sometimes trillions, of parameters get tuned to fit that shape, each one playing a role closer to a synapse than a line of code. There’s a well-known trade-off in machine learning between a model’s interpretability and its capability: decision trees and linear regressions stay fully readable precisely because they’re too simple to do the job. The models powerful enough to design a chip, or spot a tumor better than a radiologist, buy that power at the cost of legibility. There’s a deeper mismatch underneath the trade-off. Human reasoning, law, and science are all built around causal explanation — we want to know why , so we can assign blame, build trust, or learn something transferable. A neural network doesn’t reason causally at all. It operates purely on high-dimensional correlation: patterns folded through a space too large to name, that happen to produce correct outputs on the other side. We’re not bad at reading it. There’s decreasingly less of a “why” in there to read. It’s not just chips Once you see this pattern in silicon, it starts showing up everywhere. In physical structures , Engineers now provide AI with a block of raw material and a set of constraints — where it needs to bolt down and what forces it must withstand — allowing it to remove everything that isn’t structurally essential. The results look like bone ( Not metaphorically) : aircraft brackets built this way have the same skeletal, porous, asymmetric look as an actual femur, because bone is what billions of years of evolution converged on for the exact same problem — maximum strength, minimum material. Airbus has used this to cut bracket weight by nearly 60% without compromising structural integrity. Nobody designed it to look organic. It just turned out that nature had already solved the same optimisation problem, and the algorithm rediscovered the answer independently. In communication between AI agents , something even stranger is happening: they’re starting to skip language entirely. When one AI reasons through a problem, its internal state — the “hidden state,” in machine learning terms — is enormously rich: many competing possibilities held at once, in a form far denser than any sentence could carry. Normally, that has to get compressed down into words, one token at a time, mostly for our benefit. A framework called Interlat lets one AI agent skip that compression step and hand its raw hidden state directly to another agent — no text, no translation. The result is communication compressed by roughly 24x, and, more strikingly, agents that retain multiple possible ideas at once instead of being forced to commit to a single linear thought the moment they “speak.” Three completely different domains. Same underlying move: remove the requirement that a human be able to follow along, and performance jumps. The actual shift: from understanding to auditing Here’s the part that matters more than any single example. For most of engineering history, trust was built on tracing the logic. If a bridge held weight, an engineer could show you the load calculations for every truss. If a chip worked, someone could walk you through the schematic, gate by gate. Understanding how something worked was the price of admission for trusting that it worked. That’s the assumption breaking down. Nobody involved in building the QR-code chip, the bone-shaped bracket, or the telepathic agent pair can walk you through the logic the way an engineer once could. So the entire discipline has quietly pivoted from checking the reasoning to checking only the result. Run the simulation. Run the clinical trial. Run the stress test. If the output holds up, ship it — regardless of whether anyone can explain the path that got there. We’ve gone from being the architects of these systems to being their auditors. We don’t design the logic anymore. We grade the homework after the fact. Why doesn’t this reverse A model simple enough for a person to trace end-to-end is, by definition, too simple to search a space with thousands of dimensions. The readable version isn’t a rough draft of the powerful one; Instead, it’s just a weaker model. Illegibility isn’t a side effect anyone is trying to patch out, either — in every case here, it’s the actual mechanism producing the gain. The chip got better because it wasn’t constrained to be readable. So we’re not on our way back to understanding these systems. We’re on our way to getting better at grading them — sharper tests, tighter simulations, harder audits. Which leaves the question none of this actually answers: once verifying the result is all we have left, how much verification is enough before we’re willing to call something trustworthy? (Because, you don’t know how it works) Nobody Can Explain Why This AI-Designed Chip Works. They Built It Anyway was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Trump goes on AI post spree, showing 2028 hats, Iran war images
Trump goes on AI post spree, showing 2028 hats, Iran war images USA Today
- Closing the data loop in AI-driven drug discovery
Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…
Score: 44🌐 MovesJul 27, 2026https://www.technologyreview.com/2026/07/27/1139667/closing-the-data-loop-in-ai-driven-drug-discovery/ - China is using the steel playbook on AI—and America’s tech billionaires are standing in the blast zone
China is using the steel playbook on AI—and America’s tech billionaires are standing in the blast zone Fortune
Score: 44🌐 MovesJul 27, 2026https://fortune.com/2026/07/27/what-is-dumping-china-steel-ai-billionaires/ - How AI Inside Clinical Workflows Is Unlocking Patient Throughput
How AI Inside Clinical Workflows Is Unlocking Patient Throughput MedCity News
Score: 44🌐 MovesJul 27, 2026https://medcitynews.com/2026/07/how-ai-inside-clinical-workflows-is-unlocking-patient-throughput/ - Yahoo’s Biggest AI Bet Is a Chatbot
AI hallucinates, but Yahoo is working on it. Yahoo Scout is the company’s all-in bet on AI search, one that actually shows its sources so users can double-check the information. In this episode of Adventures in AI , Yahoo CEO Jim Lanzone talks about why AI lies to you and how Yahoo Scout fights back.
- Federal AI program chooses Sandia for 6 missions, Los Alamos for 7
The Department of Energy awarded nearly 300 missions across national laboratories, universities and companies, with New Mexico's Sandia and Los Alamos labs taking leadership roles in areas including materials science, energy recovery and climate prediction.
- Anthropic CEO Dario Amodei says AI company isn't advocating for ban of open-weight models
Critics have questioned why Anthropic didn't sign an industry letter last week in support of open-weight models.
Score: 43🌐 MovesJul 27, 2026https://www.cnbc.com/2026/07/27/anthropic-ceo-dario-amodei-isnt-advocating-open-weight-model-ban.html - Safe Pro Group Projects 1,300% YoY Q2 2026 Revenue Growth Driven by Multiple U.S. Army Awards for AI Threat Detection
Safe Pro Group Projects 1,300% YoY Q2 2026 Revenue Growth Driven by Multiple U.S. Army Awards for AI Threat Detection azcentral.com and The Arizona Republic
- Google ATLAS report maps 15 million Gemini interactions across 800 occupations in 150 countries
Google has launched the first iteration of its AI & Economy ATLAS (Activity, Task, Landscape, and Adoption Study), an ongoing, large-scale, de-identified study of how people are using its AI products and tools. It draws on 15 million aggregated and de-identified human-AI interactions across the Gemini app, AI Mode, and the Gemini API, which together ... Read more
Score: 43🌐 MovesJul 27, 2026https://gcn.com/google-atlas-report-gemini-interactions-occupations/20134/ - Google’s ATLAS study of 15 million Gemini interactions finds AI assists far more than it automates
Google published the first edition of its AI & Economy ATLAS report on July 23, revealing that people are using the company’s AI tools primarily as a collaborator rather than a replacement for human work. The dataset behind the study covers 15 million aggregated and de-identified human-AI interactions across the Gemini App, AI Mode, and ... Read more
- Why SAP says enterprise AI agents need knowledge graphs and governance
Presented by SAP At VB Transform 2026 , Max McPhee, senior solution advisor at SAP, spoke with Rob Stretchay, lead analyst at VentureBeat Research, about what it takes for enterprises to move beyond chatbots to autonomous AI agents that can execute real business processes. He argued that the difference comes down to grounding those agents in a company’s own context rather than general knowledge. "Where we're starting to see more emergent behavior of it feeling like a coworker rather than an assistant, is where we're able to provide context on the actual enterprise rather than being able to use more of the standard knowledge," McPhee said. That's the gap that still separates most enterprise chat software from genuinely agentic systems. Building enterprise context with knowledge graphs The same principles companies use to onboard new employees also apply to agents, adapted for software that retrieves information differently than humans do. "When you are onboarding a new agent, I think it's important to acknowledge how you might onboard a new employee, but tune that for an agent," McPhee said. "The way that is really powerful is using knowledge graphs and having vector-embedded data, because that's a really easy format for an agent to be able to find and retrieve information." That same grounding is also what keeps an agent from stumbling over an enterprise's internal shorthand, a problem that's acute in SAP's world. "Being able to provide that tribal knowledge in the format that's easy for it to consume helps to provide a really nice result with your agents versus a chatbot that might say, 'Well, what does that acronym mean?'" he said. Bringing governance, identity, and security to autonomous agents Governance is an area where SAP's history works in its favor, and the controls have been evolving for systems that act with more flexibility than earlier automation did. "That's where SAP really has a good home, around that governance and process control," McPhee said. We're a 50-year-old process company, modernizing that governance to be able to handle the flexibility that comes with agents running." One consequence is a renewed role for machine learning in validating agent behavior. "It's becoming a bit of a revival of machine learning," he added, pointing to customers that run agents within a process but then layer in anomaly detection and machine-learning-based validation as a guardrail. This is the same approach SAP had long used for intelligent approval recommendations. Identity and permissions carry that governance into execution. Under this model, both the human and SAP’s Joule, the generative AI assistant embedded across the company’s cloud applications and Business Technology Platform, must hold the rights to access a given system. Even if a user has permission to access S/4, they cannot do so through Joule unless the assistant has also been provisioned for that access, closing off the risk of using an agent to route around access controls. Balancing standard SAP with customized enterprise landscapes Much of McPhee’s work involves reconciling SAP’s own knowledge with decades of customer customization and non-SAP systems. As he put it, many customers tell SAP, “You’re only 10% of my landscape,” a reality that has shaped the company’s recent strategy. Recent acquisitions such as LeanIX, which McPhee likened to “Google Maps for your architecture,” and process-mining company Signavio are intended to help map that non-SAP majority so SAP’s agents can understand how enterprise systems interconnect. The company has also invested in Berlin-based automation company n8n and is embedding it natively into Joule Studio, its intent-based, low-code environment for building agents. McPhee warned that companies also need to modernize older on-premises systems or risk running into limitations as they expand the use of autonomous agents. "You're going to probably run into throughput issues, and you're kind of trying to drive a Ferrari around a dirt track," he said. "You've got to upgrade the track first if you want to drive a Ferrari." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .
Score: 42🌐 MovesJul 27, 2026https://venturebeat.com/orchestration/why-sap-says-enterprise-ai-agents-need-knowledge-graphs-and-governance - Hyundai’s Humanoid Plan Illustrates Confusion On Physical AI
Hyundai's robot plans highlight global labor concerns, with unions seeking AI safeguards while U.S. workers remain nonunionized. POLICY, ENTERPRISE TECH
Score: 42🌐 MovesJul 27, 2026https://www.forbes.com/sites/johnwerner/2026/07/27/hyundais-humanoid-plan-illustrates-confusion-on-physical-ai/