NVIDIA is using Palantir Foundry and cuOpt to automate its hardware supply chain allocation decisions across global manufacturing sites.
The company measures operational delivery from wafer-out to first token. This window splits into time-to-rack (the transit from fab output to an assembled data centre system) and time-to-token (which covers power, cooling, networking, and day-one software readiness.)
Managing NVL72 and Vera Rubin component flows
Hardware scaling has magnified supply constraints. An NVIDIA Grace Blackwell NVL72 rack contains 18 compute trays, with each tray requiring two Grace CPUs, four Blackwell GPUs, and 32 HBM3e memory packages sourced across thousands of suppliers, OEMs, and contract design partners.
The upcoming supply chain constructed for NVIDIA’s Vera Rubin architecture is twice as large as the network supporting Grace Blackwell.
Assembly cannot proceed until parts arrive from three designated channels: direct inventory, consignment stock, and external suppliers. Early shipments must wait on delayed components, extending the metric NVIDIA terms ‘Time of Ownership’ (the duration from when a facility receives materials to when finished sub-assemblies depart.)
Factory allocations are reworked weekly over rolling two-quarter horizons to resolve part availability, throughput limits, and customer fulfilment schedules.
Mixed-integer linear programming via cuOpt
To coordinate these dependencies, the NVIDIA operations team built the ‘Digital Supply Chain Intelligence’ command centre using Palantir Foundry. Foundry’s Ontology models facilities, supplier commits, component stocks, and production targets as interconnected objects and links.
NVIDIA cuOpt, an open-source library for GPU-accelerated decision optimisation, reads this operational layer directly. Formulating distribution as a mixed-integer linear program designed to minimise TOO, the solver evaluates parts constraints across every tier of the bill of materials.
Beyond outputting weekly delivery schedules, cuOpt identifies active factory limits, such as regional assembly capacity caps versus raw memory availability.
Training Nemotron on qualitative operational records
Mathematical optimisation alone failed to capture unstructured operational variables observed by human planners, including supplier call transcripts, regional weather forecasts, partner email exchanges, and geopolitical events.
NVIDIA addressed this by post-training Nemotron 3.5 Lightning, an open-weight mixture-of-experts model featuring 30 billion total parameters and approximately three billion active parameters per forward pass.
The engineering pipeline processes historical records through NeMo Anonymizer to redact sensitive operational fields, NeMo Data Designer to balance training examples with synthetic capacity disruption scenarios, and NeMo AutoModel to apply low-rank adaptation (LoRA) parameters while keeping base model weights frozen. Palantir Autopilot manages data lineage, model tracking, and recommendation delivery.
Production benchmarks and future reinforcement learning
Evaluated on historical allocation records, the post-trained Nemotron 3.5 Lightning model achieved 86.7 percent decision accuracy, compared to 55.5 percent for the larger Nemotron 3 Ultra model and 17.5 percent for the un-tuned Lightning base model.
The post-trained model achieved a 58.6 percent balanced accuracy and a 57.5 percent macro-F1 score, outperforming Nemotron 3 Ultra’s 42 percent balanced accuracy and 39.5 percent macro-F1 score.
Fine-tuning completed on two NVIDIA B200 GPUs within minutes. Domain fine-tuning improved allocation decisions, though production risk forecasting further into the future remained difficult.
Operational choices, planner revisions, overrides, and observed factory outputs are continuously written back to the Palantir Ontology.
NVIDIA confirmed this dataset will form preference pairs for reinforcement learning routines – scoring recommendations on allocation precision, policy compliance, and evidence grounding – with production models remaining strictly isolated from live and unmonitored retraining.
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JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones.
The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed for tasks across warehousing, sorting, transport, and delivery.
Specialised systems include equipment designed to operate in temperatures as low as minus 20 degrees Celsius, automated pharmacy dispatch systems, autonomous delivery vehicles, and drones.
The five-year procurement plan builds on automation that JD Logistics already has in operation. As of June 30, its LangzuTech Goods-to-Person automated warehousing system had been deployed in more than 30 warehouses across China, with deployments also launched in the UK and Germany.
JD Logistics’ broader warehouse network included more than 1,800 self-operated warehouses and more than 2,000 third-party cloud warehouses on its Open Warehouse Platform as of June 30. The network covered more than 36 million square metres in aggregate.
The company also had thousands of unmanned vehicles in regular operation across more than 20 Chinese provinces by the end of June. More than 100 domestic drone routes were operating across applications including parcel and food delivery, emergency medicine transport, and disaster relief.
From AI decisions to physical execution
JD Logistics is connecting its physical equipment with Meta Brain, an AI system used across warehousing, transportation, and delivery. JD said Meta Brain 3.0 can calculate optimal routes for hundreds of millions of parcels in seconds, compared with minutes previously.
Meta Brain also powers JD Logistics’ LangzuTech Packer robotic arm, which combines the model with multimodal sensor data to track, grasp, and place parcels with different shapes.
JD Logistics said in a first-quarter regulatory filing that the Packer uses parallel reinforcement learning in simulated environments to optimise parcel-placement sequences and loading layouts. The company said the system is designed to improve sorting efficiency and the use of available carrier space.
JD upgraded the robotic arm’s force-control technology during the second quarter to support more precise cage-loading operations. By June, the Packer was operating around the clock at multiple JD Logistics parks, according to the company’s interim report.
JD is also adding computing capacity to support AI development. JD Cloud plans to work with Chinese chipmaker Moore Threads on a cluster containing 100,000 GPUs for large-model training, inference, and embodied-AI workloads.
The two companies have previously worked on a 10,000-GPU cluster, according to Data Center Dynamics. Details of which Moore Threads GPU models will be used in the planned 100,000-GPU system have not been disclosed.
JD Cloud also plans to collect more than 10 million hours of video showing real-world human activities over the next two years for embodied-AI training.
Beyond warehouse operations, JD Logistics has expanded its autonomous vehicle network into night-time delivery. Its interim report said the company had launched night-time autonomous routes in Shenzhen, allowing vehicles to operate around the clock.
JD is also using drones in rural logistics. In June, JD Logistics launched a drone delivery network in Zizhong, Sichuan province, covering 78 administrative villages, and said deliveries to some mountain villages could be completed in as little as seven minutes.
Scaling automation across JD’s logistics network
JD did not disclose the total expected cost of the five-year procurement programme at JDDiscovery or provide a network-wide return-on-investment target.
JD Logistics spent RMB2.3 billion on research and development during the first half of 2026, up 23.7% from RMB1.9 billion a year earlier. The company attributed the increase to continued investment in technology and innovation but did not provide a breakdown showing how much was spent specifically on AI or robotics.
Depreciation of property and equipment and amortisation of other intangible assets rose 18.7% to RMB2.6 billion during the first half of 2026, from RMB2.2 billion a year earlier. JD Logistics attributed the increase mainly to additional logistics equipment and vehicles.
Purchases of property and equipment and investment properties totalled RMB3.09 billion over the same six-month period, compared with RMB2.70 billion a year earlier. Those figures cover the wider logistics business and are not disclosed as spending specifically associated with the new physical AI programme.
JD’s automation plans also come as China’s major ecommerce platforms expand fulfilment infrastructure. Reuters reported on September 3 that competition between JD.com, Alibaba, and Meituan had moved from heavy spending on delivery subsidies towards logistics infrastructure, broader supply, and order-level economics.
Alibaba and JD have been opening dark stores and fast-fulfilment “lightning warehouses” in densely populated areas to support deliveries within an hour, while Meituan has been building supermarkets to expand its grocery operations. Ministry of Commerce research cited by Reuters estimates China’s instant-retail market will reach RMB1.2 trillion, or about $178 billion, by the end of 2026.
JD and companies within its ecosystem employ around 700,000 delivery and logistics personnel, according to the South China Morning Post.
JD founder Richard Liu said earlier this year that robots would eventually take over parcel-delivery work now carried out by human couriers. The Financial Times reported in June that JD had signed agreements with around 120 educational institutions to retrain workers for roles including robot repair and maintenance.
JD said JD Logistics currently operates eight robot repair centres in China and plans to expand its robotics after-sales capabilities over the next five years. The company expects the expansion to support more than 100,000 robotics service engineer jobs.
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Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.
Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.
To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware, and AI. Initial ecosystem participants include AWS, ECARX, Hugging Face, Liquid AI, NXP, PlusAI, PSYONIC, QNX, Qwen, Siemens, and Unitree Robotics.
The initiative targets physical systems that combine AI models, runtime software, compute silicon, sensors, and actuators to sense, reason, and act in operational environments. Hardware manufacturers and software developers require standardised baselines to reduce integration risk, optimise compute workloads, and move from proof-of-concept testing to deployment at scale.
Arm standardises capability tiers for robotics systems
Robotics currently lacks a common method to describe, compare, and communicate system capabilities, according to an architectural manifesto (PDF) published by Arm chief architect Richard Grisenthwaite. This fragmentation makes robotic systems harder to design, integrate, and scale across industrial deployments.
In response, Arm has introduced the Robotics Capability Framework as a collaborative starting point for a shared technical vocabulary, patterned after the SAE Levels used for driving automation.
Arm’s new framework categorises robotic systems across progressing tiers of operational sophistication, mapping machines from reactive setups to context-aware, cognitive, and self-improving systems.
Each capability tier links real-world use cases to machine behaviours, outputs, and hardware constraints. These criteria establish parameters for system latency, compute placement, memory allocation, power constraints, determinism, and safety standards.
Arm developed the initial baseline using feedback from across the robotics sector. Participating organisations contributing to the framework include Anaxi Labs, ANYbotics, FMC³ Robotics, Fourier, GALBOT, Gravis Robotics, Lenovo, McKinsey, and Robotec.ai.
Virtual platforms accelerate pre-silicon automotive physical AI development
Arm Total Design for Physical AI extends a collaborative development structure previously used for cloud AI infrastructure. The programme brings together AI models, virtual platforms, digital twins, sensors, compute silicon, and software stacks to enable earlier development and testing cycles.
Autonomous transport and robotics face common technical requirements across sensory perception, AI processing, real-time control, safety, and power-efficient compute. Arm demonstrated this collaborative methodology in the automotive sector alongside AWS, Google, HERE, RemotiveLabs, and Siemens.
The participating automotive companies developed an integrated digital cockpit reference solution. This environment enabled software engineering teams to develop, test, and validate complex automotive code on the Arm Zena CSS platform prior to physical silicon availability.
Arm is now soliciting technical contributions from the wider engineering community to expand the Robotics Capability Framework as physical AI implementations progress.
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Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI.
The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method, called the Concept-Wrapper Network or CW-Net, aims to translate the internal calculations of a self-driving system’s neural network into concepts a human can actually read.
If a current self-driving car brakes hard on a clear road with no obvious hazard in sight, neither the driver nor a passenger has any way of knowing why. Modern self-driving systems increasingly rely on neural networks trained on large volumes of driving data. Those networks can perform well, but they don’t expose their reasoning, which is why engineers describe them as black boxes.
Translating neural network logic into human concepts
CW-Net works by converting a self-driving system’s internal logic into concepts such as “Approaching Stopped Vehicle” or “Close to Cyclist.” These could, according to Motional, appear on a dashboard showing which concepts are influencing the vehicle’s driving decisions as they happen.
The system is designed so the explanations aren’t generated after the fact as a guess at what the network might have been doing. Instead, the vehicle’s final decision-making system takes action based directly on these human-interpretable concepts, so a braking event traces back to a specific concept that triggered it. Motional describes this as causally faithful, distinguishing it from approaches that generate natural-language explanations, which can read as plausible without necessarily being accurate.
Laura Major frames the case for this kind of interpretability against the alternative of relying purely on end-to-end deep learning to handle driving decisions.
“The general end-to-end only approach can get to a really good 80-90 percent – maybe even 95 percent – solution, but that’s not good enough to remove a driver or to earn the trust of cities, communities, and customers,” she said.
Testing explainable AI for self-driving cars around Las Vegas
Explainable AI research has largely stayed confined to computer simulations in lab settings, according to Motional. The Motional and MIT team instead deployed CW-Net on an autonomous vehicle with an experienced safety operator in the driver’s seat, collecting data on a private test track and on public roads around Las Vegas.
The team used an earlier experimental version of its deep-learning-based planning system, described as showing competitive performance but with notable shortcomings that CW-Net could help surface. Two incidents from the testing illustrate what the system caught.
In one, the autonomous vehicle repeatedly stopped near a traffic cone, and the vehicle operator assumed the cone itself was triggering the behaviour. Researchers removed the cone and the car stopped anyway. CW-Net’s display showed the actual cause: the experimental planning system was hallucinating a stopped vehicle ahead, a pattern traced back to its training data. That explanation let the researchers understand, predict, and resolve the issue.
A second test involved a cyclist. The autonomous vehicle detected and stopped for the cyclist as expected, but CW-Net revealed that the experimental planning system wasn’t actually basing its decision on the cyclist’s presence. The safety driver responded by exercising more caution around cyclists after noticing this. Follow-up analysis confirmed that caution was warranted, because the vehicle’s braking in that case came from a safety backup system rather than the experimental deep-learning-based planner.
Performance held steady against explainability
Adding layers of explainability to an AI system carries a known cost in speed and performance, and Motional acknowledges that risk. However, when researchers benchmarked CW-Net against leading autonomous driving algorithms, the difference in driving capability came in at less than one percent.
The Las Vegas incidents show why that trade-off matters operationally rather than just academically. A safety driver who can see that a stop is caused by a hallucinated vehicle, or that a backup system rather than the primary planner is responsible for a manoeuvre, can respond and report with more precision than one working from behaviour alone.
That visibility feeds directly into how quickly an engineering team can diagnose a system, and how confidently a safety operator can distinguish between an intended behaviour and a fault.
Motional connects the CW-Net work to broader pressure on autonomous vehicle operators as the technology extends into new markets and jurisdictions. Regulators are naturally asking for more transparency about how AI systems reach their decisions, and it expects tools like CW-Net could move from research projects toward a baseline requirement.
Beyond passenger vehicles, autonomous drones and even robotic surgery are cited as other safety-critical domains where operators and developers will need ways to understand a system’s capabilities, limitations, and unexpected behaviours.
Learn more about physical AI during thePhysical AI Expoheld in Amsterdam, London, and North America.
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Human Archive, a startup founded by UC Berkeley and Stanford researchers, is paying gig workers in India to wear camera-equipped caps and sensor devices to collect the real-world physical training data that AI and robotics labs are racing to acquire.
Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments.
Most existing AI governance frameworks have focused on online harms and model outputs, including bias, misinformation, and harmful content. Embodied AI systems carry risks in physical environments, where failures can affect infrastructure, property, or human safety.
Singapore’s Infocomm Media Development Authority published version 1.5 of its Model AI Governance Framework for Agentic AI on May 20. The framework sets out guidance for organisations deploying AI agents that can plan, make decisions, and take actions across multiple steps to complete user-defined goals.
The framework says agents can interact with tools, external systems, and other agents, including systems that update databases, write files, control devices, or perform transactions. It lists access controls, monitoring, and human approval among governance measures for deployment.
AI moves into physical systems
At an AI summit in Singapore last week, discussions around robotics and embodied AI focused on operational safety issues more commonly associated with aviation, industrial systems, and critical infrastructure oversight than conventional software regulation.
Speakers also discussed whether autonomous systems can operate safely and reliably in unpredictable real-world environments over extended periods.
Dr. Ya-Qin Zhang, founding dean of the Institute for AI Industry Research at Tsinghua University, said embodied AI systems amplify risks already associated with autonomous software. He said failures can directly affect transport systems, drones, logistics networks, and critical infrastructure.
“Any risk in the digital domain will be amplified in the physical domain, and the physical domain will have a physical consequence,” Zhang told MLex on the sidelines of the summit.
He added that vehicles, drones, smart grids, and other infrastructure could become exposed as AI systems are embedded more deeply into physical operations.
Speakers discussed reliability, operational monitoring, and post-deployment assurance as governance concerns. Summit discussions pointed to deployment-based governance models built around simulation, telemetry, and iterative testing, rather than one-time certification alone.
IMDA’s framework also recommends gradual rollouts, continuous monitoring, and further testing after deployment. It says agents interact dynamically with their environment and not all risks can be anticipated before release.
Monitoring becomes a deployment issue
Grab, which is piloting autonomous vehicles and delivery robots in Singapore’s Punggol district, said deployment governance depends heavily on simulation, testing, and continuous monitoring.
“We do a lot of simulation, we do a lot of testing in closed courses and open courses in order to make sure our robots are reliable,” Suthen Thomas Paradatheth, Grab’s chief technology officer, said during one of the summit panels.
“Before we scale to hundreds of robots, we make sure we crack it first in simulation and with a few robots,” he added.
Grab also pointed to monitoring systems designed to track robot performance and detect unexpected failures after deployment.
“There’s a long tail of issues that could emerge,” Paradatheth said.
The IMDA framework says organisations should assess agentic AI use cases based on data access, external system access, autonomy, and task complexity. It also points to the scope and reversibility of agent actions, third-party involvement, and overall system complexity.
It also recommends limiting agent access to tools and systems, applying least-privilege permissions, and defining standard operating procedures for agent workflows. Organisations should also set mechanisms to take agents offline when they malfunction.
Accountability spreads across more actors
MLex reported that embodied AI systems can involve several parties across development, manufacturing, and deployment. These include AI developers, robotics manufacturers, semiconductor suppliers, and infrastructure operators.
MLex also noted that responsibility can be harder to assign when systems continue adapting after deployment through software updates, telemetry, and operational data.
IMDA says organisations and humans remain accountable for agent actions, even when agents operate autonomously. The framework calls for clear responsibility across the agentic AI value chain, from model and platform providers to deployers, tooling providers, and end users.
Applied Materials said large-scale robotics deployment is also tied to semiconductor economics and systems integration. Om Nalamasu, the company’s chief technology officer, said robotics systems will depend on better sensors, energy efficiency, advanced packaging, and computing architectures.
Nalamasu said robotics systems would require purpose-built designs adapted to specific industrial ecosystems rather than a single solution for all environments.
Zhao Yuli, chief strategy officer of Chinese robotics startup Galbot, said Beijing is prioritising deployment scale and industrial commercialisation through government-backed testbeds, industrial partnerships, and long-term funding initiatives.
Galbot has deployed humanoid robotics systems in retail, warehouse, and pharmaceutical operations in China. These include autonomous stores that operate around the clock. Zhao said semi-structured industrial environments are likely to become an early commercialisation path because they offer more controllable operating conditions.
Japan is placing more focus on standards-setting, robotics datasets, and safety governance. Professor Yutaka Matsuo of the University of Tokyo’s Graduate School of Engineering pointed to an “AI Association” project aimed at collecting 100,000 hours of robotics data to support robotic foundation models.
Matsuo also referred to Japan’s AI Safety Institute and the Hiroshima AI Process as part of broader efforts to develop governance standards for embodied AI systems with Singapore and other Asian countries.
Singapore sets out agent controls
Singapore’s framework sets out four governance areas for agentic AI. These cover upfront risk assessment, human accountability, technical controls, and end-user responsibility. The framework describes them as an iterative process rather than a one-time assessment.
The framework says human oversight has to be adapted for agentic systems because continuous review of all workflows becomes impractical at scale. It recommends human approval at significant checkpoints, including high-stakes actions, irreversible actions, and outlier behaviour.
IMDA also identifies automation bias and alert fatigue as risks when humans supervise capable agents. It recommends auditing oversight through indicators such as human override rates and response times, and using automated real-time monitoring to flag unexpected behaviour.
The framework says users should be told what actions an agent can take, what data it can access, and what responsibilities remain with the user. It also recommends employee training on human-agent interaction, oversight, and the professional skills needed to assess agent outputs.
Companies test AI in regulated workflows
JPMorgan is implementing AI tools across its global investment banking business, Paul Uren, the bank’s Asia Pacific head of investment banking, told Reuters. The bank said the tools help bankers access more information and synthesise it with internal systems. They are also being used to prepare content and support client engagement.
JPMorgan CEO Jamie Dimon told Bloomberg News that the bank would hire more AI specialists and fewer traditional bankers. Reuters reported that global banks are increasing AI investment, reshaping workforces, and changing job roles.
The bank is also among selected organisations permitted by Anthropic to use its Mythos cybersecurity model under a controlled initiative known as Project Glasswing. According to Anthropic, Mythos can detect old vulnerabilities in browsers, infrastructure, and software.
Reuters reported that Goldman Sachs, Citigroup, Bank of America, and Morgan Stanley also have access to, or are testing, Mythos, citing sources and company executives.
IMDA’s framework includes a case study from OCBC Bank of Singapore on source-of-wealth analysis. The system parses income-related documents and drafts a source-of-wealth memo. It does not make credit, onboarding, or risk decisions autonomously.
In that case, the workflow is limited to task-level autonomy and operates only when triggered by predefined workflows. Human review is required at critical decision points, and final validation remains with designated reviewers.
Robots move into industrial use
In Japan, one-third of companies are already using or considering AI-powered robots, according to a Reuters survey conducted by Nikkei Research from May 1 to May 15. The survey contacted 492 companies, with 220 responding on the condition of anonymity.
About 4% of respondents said they already use AI robots, 5% plan to deploy them, and 25% are considering doing so. The remaining 66% said they had no such plans.
Transportation equipment manufacturers were the most active group in the survey, with 80% already using AI robots or considering deployment. By comparison, 94% of wholesale sector respondents said they had no plans to deploy AI robots.
Among companies using, planning to use, or considering AI robots, 71% selected manufacturing as a use case. Another 19% selected dangerous tasks, while 11% selected customer-facing services.
The Japanese government expects AI robots to help address the country’s chronic labour shortage and support its position in industrial robotics. Japan is home to robotics companies including Fanuc, Yaskawa Electric, and Kawasaki Heavy Industries, but faces competition from China and the United States in AI-enabled robotics.
Retail agents expand beyond search
Walmart has outlined plans to use agentic AI across shopping, employee, supplier, and developer workflows.
In July 2025, the retailer announced plans for four AI-powered “super agents.” They are designed for shoppers, store employees, suppliers and sellers, and software developers. Walmart said these agents would become the main entry point for AI interactions across those groups.
One of the tools, Sparky, is already available in Walmart’s app as a generative AI-powered shopping assistant. Hari Vasudev, Walmart’s US chief technology officer, said its expanded version would be able to reorder items and plan events. It would also use computer vision to suggest recipes based on the contents of a shopper’s fridge.
Walmart is also developing an Associate super agent for store workers and corporate staff. A separate Marty agent is being built for sellers, suppliers, and advertisers. The retailer is also working on a Developer super agent for testing, building, and launching future AI tools.
The company declined to say whether the agents would replace jobs. Dave Glick, senior vice president of enterprise business systems, said the tools would create new jobs, without giving further details.
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Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perform well in pilot, but don’t seem to cut it in the real world. Despite the presence of what might be a negative term, multiple speakers and sessions addressed ways in which the forward-thinking business might not ever have to experience the technological cemetery.
The different show tracks of the second day of this event dived deeper into the pervasive issues that may be affecting AI deployments. Sessions in the Enterprise AI Implementation, ROI and Adoption tracks took stalled pilots as a starting point, and tried to ascertain the reasons behind faltering projects. There was a good deal of sound advice for organisations, with sessions on focusing agentic AI on specific business areas, building agent-ready data foundations (planning for success under the hood), and the realities of token-based AI charging on the business’s finances.
At an infra level, there were deeper discussions too on whether companies should buy or build physical infrastructure for their AI projects, and the best ways to create durable ROI on data and AI projects when all the many effecting factors are given due consideration..
In projects where AI roll-outs get stuck, the core issue could be epitomised by the concept of the ‘personal copilot’. This works well on a single worker’s desk and for their individual workflows, but doesn’t really scale to a whole department – never mind a whole business. Many companies report having the budget to start such AI experiments at the level of the single user, and there are usually great results. When said user is a C-suite executive, a personally-achieved efficiency tends to increase the levels of excitement around the company, which has to be considered a positive. But transitioning from this point to meaningful change across the business is where many organisations find their individual struggles and roadblocks. Here was the meat and gravy of day two’s activities on the show floor and the numerous stages at the San Jose McEnery Convention Center.
Cyber issues
Despite the use of terms like ‘stalled’ and ‘difficult to scale’, in the Cyber Security and Cloud Expo stage, speakers cited the the speed at which businesses and organisations adopt agentic AI systems as a cause of a ‘velocity gap’. Where AI deployments are successful, they gain traction fast! But security and governance issues crop up when business units adopt generative AI faster than the security team can govern and ensure the enterprise’s safety.
Like the proverbial double-edged sword, AI can be considered as a force that changes and can improve both attack and defence in the cybersecurity space. There are the issues created internally by unbounded agents and large language models, plus the addition to attackers’ arsenals of AI scanning tools that can identify potential exploits.
Also prevalent among the round-table discussions and keynote speeches was the older theme of shadow IT, now presenting in its new guise as shadow AI. If staff place sensitive material into unsanctioned tools for example, or if approved AI systems are poorly bounded and managed, then the attack surface can expand without the cybersecurity team even being aware of it happening. Therefore, data governance and system oversight are becoming more intertwined than before – this was the message from both cybersecurity strands of the show, and the Cloud and Big Data elements too.
For pure-play cybersecurity functions, zero trust was presented as one answer to the runaway adoption of AI outside the auspices of cybersecurity teams – the adoption the ‘denial by default’ position for humans and machines alike. Proof of identity and privilege levels need also to apply to services and agents; that way, automated workflows are subject to the same permission models as every other element in the IT stack.
The second day of TechEx North America was certainly not a rejection of decision-makers’ AI ambitions – the role of AI and even agents were things of accepted fact among speakers, thought-leaders, and delegates at the event. But there were details and considerations presented by representatives from different industries and business functions, each with positive and insightful things to contribute. Each placed their concerns and their enthusiasms on the table, adding to the discussions around AI implementation in 2026.
The march of the robots
And there was a great deal of excitement, still, in many areas of the conference floor. The humanoid robots on show were a source of much enthusiasm (everyone seems to love a lovable android!), but more pragmatically, the new Physical AI track drew some of the show’s biggest audiences. Multiple delegates away from the track cited software coding as the place that has first yielded positive results from the use of large language models in professional settings. And from many places too came the opinion that automated physical systems will be the next industry segment set to benefit from concerted work around new models and their practical harnesses.
The AI models at the heart of next-gen physical AI are unlikely to be LLMs (although these will be useful if the devices are designed to interact with humans), and as such models develop and emerge from their research stages, it’s the TechEx Events series that will be the first to showcase and present these, and how they can work viably in business contexts.
New learning strands to the event
This year’s event saw a welcome injection of pragmatic coding, with hands-on learning sessions that took attendees through spinning up their own AI agentic models, with lessons in how agents can improve themselves, right from interactive Google Colab instances. The TechEx Learning Hub also featured workshops from Nvidia and the ever-popular Google Hackathon, with learners ranging in abilities from those that needed introducing to an IDE through to those that came with software skills already well-tuned. Putting learnings into practice is what this event is all about, whether it’s C-suite decision makers taking on lessons on best strategic practices, or developers turning creative ideas into reality.
TechEx takes the cutting edge, and distils it through the business lens; pragmatic yet future facing. Catch the next leg of TechEx in Amsterdam this September – who knows how far we may have progressed in the space of four short months?
(Image source: TechEx Events)
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Exciting times are ahead in the world of enterprise perimeter security with a new partnership between Thrive Logic, an AI agent-driven security and operational intelligence platform, and Asylon, a security robotics company. Together, the companies are to introduce physical AI into the network edge security arena, combining “autonomous perimeter patrols with agentic AI analytics and automated incident workflows.” The goal is to reduce response friction and let security leaders report with confidence in high-security exterior zones.
Physical AI understands real-world situations and is capable of responding actively via a continuous, mobile security presence. This is in comparison to merely recording events as and when they take place, for actions to happen later.
Using Asylon’s robotic patrols and Thrive Logic’s AI agent, the integration will monitor perimeter areas and analyse any incidents that may occur. Security teams might therefore relax a little and let AI detect issues in real time. In this arena, it could soon be ‘AI – 1, Bad Actors – 0.’
24/7 robotic patrol oversight
With pressure rising on security leaders in perimeter-intensive environments (labour volatility and unreliable patrol executions are two examples that spring to mind), Asylon’s Robotic Security Operations Centre (RSOC) helps combat challenges with audit-read security outcomes. Alongside Thrive Logic’s integration, robotic patrols won’t just collect video streams, but will produce alerts and step-by-step response processes. Therefore, security teams can respond more effectively, proving humans and AI can work in harmony.
How it works
Video captured by Asylon’s robotic patrols is securely sent to Thrive Logic’s platform. From here, the Thrive Logic AI agent continues to track connected streams, triggering alerts to relevant staff and stakeholders, and generating automated incident workflows aligned to SOP if or when these are required.
The system allows enterprise security organisations to reduces operational friction, and see improvements in response consistency. The system will generate audit-ready, time-stamped incident records for all sites where the technology operates.
Damon Henry, CEO of Asylon Robotics, said: “Security leaders don’t need more dashboards – they need reliable coverage, consistent response, and defensible reporting. Robotic systems that extend perimeter presence, paired with AI that turns what’s observed into clear actions and documented outcomes. By integrating Asylon’s RSOC-managed robotic patrols with Thrive Logic’s agentic AI analytics and incident workflow automation, we’re giving enterprise teams a practical, scalable way to reduce response friction and elevate operational maturity across sites.”
Nate Green, CEO of Thrive Logic, also emphasised the importance of physical AI. “Physical AI is where security becomes truly operational – persistent real-world visibility paired with intelligence that drives action,” he said. “Asylon’s robotic patrols create a high-value mobile layer across large perimeters. When connected to Thrive Logic’s AI agent and workflow automation, that visibility becomes actionable alerts, guided response, and audit-ready documentation.”
You may have to wait your turn to experience the Asylon-Thrive Logic Physical AI integration as it’s currently only available for enterprise security teams managing high-activity exterior environments, but the companies are hoping for greater availability to all business sizes in the near future.
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