CloudNC has secured $20 million in new capital to scale its AI precision machining technology across global supply chain networks.
The investment round was led by US venture investor Nimble Ventures, with participation from Calculus Venture Capital, Entrepreneur First, and LM Ventures, the venture capital fund of Lockheed Martin.
Founded in 2015, CloudNC operates from headquarters in London and an active production facility in Chelmsford. The company previously drew backing from Atomico and Episode 1 Ventures, alongside strategic partnerships with Autodesk and Lockheed Martin.
Precision component suppliers face pressures to balance tight engineering tolerances with compressed delivery schedules. CloudNC designs its lead software product, CAM Assist, to automate computer numerical control (CNC) programming—generating machining strategies and toolpaths from computer-aided manufacturing models to accelerate production runs.
Automating CNC programming for supplier networks
The software shortens the transition phase between technical part design and factory production, allowing machinists to increase physical component output.
CloudNC reports that CAM Assist is now active across more than 1,000 machine shops globally, including several hundred facilities in the US. Confirmed commercial users include Lockheed Martin and Major Tool and Machine.
Theo Saville, CEO and co-founder of CloudNC, said: “Machine shops everywhere are under pressure to quote and program faster, and deliver more with the people and machines they already have.
John Burbank, founder of Nimble Ventures, added that automated CNC workflows will support “massive increases in onshoring of manufacturing and global production” for precision industrial supply bases.
CloudNC says it will direct the capital injection into go-to-market operations, technical support infrastructure, and partner activity across international regions.
AI quoting targets procurement turnaround times
CloudNC is expanding its software line with Quote Agent, an AI-assisted estimating tool scheduled for release later in 2026.
Preparing job estimates represents a major operational drag for manufacturing suppliers. Evaluating incoming technical drawings, calculating cycle times, and establishing part pricing remains heavily manual, exposing supply shops to administrative delays or miscalculated margins once components enter physical production.
Quote Agent applies AI to early-stage costing, enabling suppliers to return customer bids rapidly while standardising cost estimations.
“Quote Agent is a natural next step for CloudNC as we seek to accelerate global machining with AI,” says Saville. “CAM Assist already helps machinists get parts onto machines faster; Quote Agent will help shops assess new work, prepare quotes more efficiently and respond to customers with greater confidence.”
“We believe our AI can make quoting faster, more consistent and more scalable, helping manufacturers win more of the right work while keeping expert judgement firmly in control,” Saville added.
Knox Systems partnership advances FedRAMP authorisation
CloudNC is collaborating with Knox Systems to achieve FedRAMP certification for CAM Assist.
The compliance roadmap aims to clear CAM Assist for deployment by US government departments, defence contractors, and aerospace manufacturers operating under federal data governance rules.
Authorisation, if granted, will permit public-sector and defence suppliers to deploy automated toolpath generation across regulated production workloads.
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Samsung has partnered with Mistral AI to deploy on-premises models across its semiconductor manufacturing and engineering operations.
The agreement was announced during the bilateral state summit held in Paris between South Korea and France. Samsung will integrate Mistral’s software suite – including its flagship Mistral Large model – into internal semiconductor facilities to build customised models for intelligence-driven factory infrastructure.
On-premises AI models for semiconductor fab infrastructure
The deployment relies on private enterprise installations to process sensitive engineering and operational records within Samsung’s computing perimeter. This architecture keeps proprietary technical data contained within company infrastructure, avoiding external cloud exposure while maintaining control over operational assets.
“Increasing complexities involved in AI chip design and manufacturing requires continuous innovation in semiconductor technologies,” says Young Hyun Jun, Vice Chairman and CEO of the Device Solutions (DS) Division at Samsung Electronics.
Mistral will provide Samsung with a specialised stack of software tools to assist in how processors are designed and produced.
“AI is reshaping how we build complex technologies, from silicon to software,” says Arthur Mensch, co-founder and CEO of Mistral.
“We are proud to support Samsung Electronics with our expertise in electronics and semiconductors, helping to improve how chips are designed and manufactured, and to accelerate technical progress across the global semiconductor and AI value chain.”
Defect detection and yield stabilisation
Samsung plans to deploy the targeted models directly to automated defect detection and fab machinery tuning. As semiconductor production processes advance, rapid data analysis inside the fab becomes necessary to maintain factory throughput.
The company expects targeted AI models to accelerate development cycles, improve manufacturing precision, and stabilise production yields across advanced memory and logic chips. The operational scope covers Samsung’s memory division, logic design units, and contract foundry business.
Samsung also led Mistral AI’s Series D funding round, securing a strategic equity stake to support long-term technical cooperation.
The lead investment expands cross-industry collaboration between silicon manufacturers and AI developers across advanced memory, logic, and foundry operations.
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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.
Learn more about physical AI during thePhysical AI Expoheld in Amsterdam, London, and North America.
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Coca-Cola is using AI to recommend which products Malaysian retailers should order and in what quantities through its Coke Buddy platform.
The Perfect Basket feature uses Coca-Cola’s Central Recommendation Engine to analyse previous orders, ordering frequency, seasonality, weather, and purchasing patterns among similar businesses.
Coca-Cola said Coke Buddy currently supports about 39,000 retail outlets across Malaysia. The company describes Coke Buddy as a self-ordering platform that allows retailers to buy products through its app, website, or WhatsApp, with personalised order suggestions and order tracking also available.
Perfect Basket builds on those existing ordering functions by recommending both products and quantities before a retailer completes an order. Retailers can review the recommendations and retain control over what they purchase.
How Perfect Basket guides retailer orders
Coke Buddy already uses previous purchase history to suggest products a retailer is likely to order again. Perfect Basket adds other signals, including seasonality, weather, ordering frequency, and purchasing trends among comparable businesses.
Perfect Basket recommends products and quantities before retailers submit their orders through Coke Buddy. Fulfilment is handled separately by Coca-Cola Refreshments Malaysia or its suppliers under existing sales and distribution arrangements.
Coca-Cola said its sales teams remain involved with retailers alongside the digital ordering system, while retailers retain control over the final purchasing decision.
Coca-Cola recently disclosed usage figures for Perfect Basket following its Perfect Basket, Perfect Ride campaign, which ran from January to April 2026. The campaign encouraged retailers to use the recommendation feature when placing orders and received more than 4,500 entries from over 4,000 retailers in Malaysia.
During the campaign, 83% of participating outlets adopted Perfect Basket recommendations, according to Coca-Cola. The figure applies only to retailers taking part in the campaign, not the full network of about 39,000 outlets supported by Coke Buddy.
Coca-Cola also said participating outlets that followed the recommendations recorded higher sales revenue growth than comparable retail outlets. The company did not disclose the size of the difference or provide detailed performance data showing how individual recommendations affected sales or inventory levels.
The available Malaysian campaign data does not provide figures for forecast accuracy, stock availability, inventory levels, or logistics costs.
Suggested orders extend beyond Malaysia
Coca-Cola has deployed similar suggested-order capabilities elsewhere in its bottling network. In its first-quarter 2024 results, the company said it and its bottling partners had connected nearly eight million customers to B2B platforms globally, while AI-enabled suggested-order capabilities had reached more than three million outlets in Latin America.
Coca-Cola has said these systems combine customer data, external information, and AI to generate predictive order recommendations. Then-chief executive James Quincey said in 2024 that digital ordering also allows retailers to adjust deliveries without waiting for a salesperson to visit.
Coca-Cola has also linked suggested orders to changes in its sales process. Quincey said AI-generated orders allow pre-sales staff to spend less time taking routine orders and more time on account development, while retailers continue to make the final purchasing decision.
Coca-Cola has reported results from earlier pilots using similar recommendation systems. In its second-quarter 2024 earnings call, the company said retailers receiving AI-generated product recommendations based on previous orders and market data were more than 30% more likely to purchase the recommended SKUs in initial pilots. These results did not relate specifically to Perfect Basket in Malaysia.
In a separate demand-prediction project, Coca-Cola combined historical sales data with weather and geolocation information to generate replenishment recommendations. CIO Neeraj Tolmare told Fortune in 2025 that a three-country pilot recorded sales 7% to 8% higher than outlets that were not using the AI algorithm.
Perfect Basket remains available after the campaign. Coca-Cola said it plans to continue developing Coke Buddy and the recommendation feature using retailer feedback and data, while retailers will continue to have access to the company’s sales representatives alongside the digital ordering system.
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Google’s newest AI weather forecasting model predicts wind speed at 100 metres above the ground, roughly the height of a modern wind turbine. It also forecasts cloud cover and how much sunlight reaches the surface, and it updates every hour. Energy traders, grid operators and wind and solar developers already pay other companies for that data. The introduction of WeatherNext 3 now puts Google in their market.
Google DeepMind and Google Research released the model on September 3. It produces a global forecast every hour at up to five-kilometre resolution for surface variables such as temperature and moisture. The previous version, WeatherNext 2, worked on a 25-kilometre grid and refreshed every six hours. Google says the new energy variables are meant to help grid operators and developers predict how much power their wind and solar assets will generate, then match that against demand.
The consumer side of the launch has had most of the attention. WeatherNext 3 now powers weather results in Google Search, the Gemini app, Google Maps and the Google Maps Platform Weather API. Behind it sits an enterprise layer that matters more commercially. The same forecast data can be queried in BigQuery and Earth Engine or downloaded in bulk from Google Cloud Storage, with no model setup required by the customer.
Why the energy sector is buying AI weather forecasting
Grid operators are running a system that has become harder to predict at both ends. On the generation side, renewables now account for most new capacity. S&P Global Market Intelligence’s US Grid Outlook 2026 projects solar and energy storage as the primary sources of new capacity this year, at 51.2GW and 25.7GW respectively out of more than 90GW of planned additions.
Solar and wind generate according to the weather rather than demand, so each gigawatt added makes a short-term forecast more accurate.
On the consumption side, the new load is coming from AI. S&P Global identifies the spread of data centres across North America as a primary driver of the recent surge in electricity demand, forcing utilities to revise their load forecasts upward. Deloitte’s 2026 Power and Utilities Industry Outlook projects peak demand growing by roughly 26% by 2035, with data centre demand alone potentially reaching 176GW, five times its 2024 level.
The cost of getting a forecast wrong is straightforward. If an operator underestimates how much wind power will arrive, it has to buy replacement electricity at short notice, usually from gas plants kept on expensive standby. If it overestimates, wind and solar farms end up being paid to switch off because the grid cannot absorb what they are producing. Both outcomes are expensive, and both are forecasting failures.
The market Google is entering
Selling weather forecasts to the energy sector is an established business. Vaisala, Solcast, DNV’s WindGEMINI and IBM’s HyperWatch all compete in it. So does Jua, a Swiss firm that claims its EPT-2 model beats Microsoft Aurora and DeepMind’s earlier GraphCast on accuracy while updating 24 times a day, against what it describes as a typical four updates a day among competitors.
Google’s advantage is reach. The same forecast appears as a table in BigQuery, a layer in Earth Engine, an API in Google Maps Platform and the default answer in Google Search. No specialist vendor has that spread, and the hourly refresh closes the update-frequency gap those vendors have used to differentiate themselves.
The incumbents have one technical argument left. Jua’s published position is that physics-based models such as ECMWF’s HRES still outperform purely data-driven AI models during record-breaking extreme weather, because physics models encode rules about how energy and mass move through the atmosphere, while AI models learn patterns from past data.
Jua sells a physics-constrained product, so the claim serves its own interests. It also describes the conditions grid operators worry about most, when a storm falls outside anything the model has seen in training.
What is new, and what is being oversold
WeatherNext 3 system architecture showing satellite mosaic and analysis inputs producing gridded forecasts, station data and cyclone tracks. Photo from Google’s blog
The architectural claim behind WeatherNext 3 is that it learns from real observations instead of from simulations. Most AI weather models, WeatherNext 2 included, are trained on output from numerical weather prediction models, which are supercomputer-driven physics simulations that carry a six-hour data lag. That lag can introduce bias in fast-changing variables such as rain and surface temperature. WeatherNext 3 ingests live geostationary satellite imagery and trains directly on readings from individual weather stations.
The shift is real, though narrower than much of the coverage has suggested. Google’s own system diagram shows the model taking in one-hour satellite mosaics alongside traditional historical analysis. DeepMind senior research scientist Ilan Price told Bloomberg the gain comes from not waiting for the next analysis and using the most recent information available instead.
Reporting puts the remaining data lag at three to four hours, down from about seven. Dependence on numerical weather prediction has been reduced, not removed.
The accuracy figures need similar care. Google reports improvements of up to 60% against NASA’s IMERG satellite product, 30% against MRMS radar and 10% against rain gauge readings at early lead times, measured using a standard scoring method for probability forecasts. Those are three separate baselines, and the percentages do not add together. The widely repeated claim of 50% better precipitation forecasting applies specifically to forecasts a day or more ahead. Every figure carries an “up to” qualifier, which makes each one a best case rather than a typical result.
Google published no independent third-party validation alongside the launch. It points instead to live evaluations by Brightband, whose leaderboard it cites in claiming WeatherNext 3 is the most accurate global weather model to date. A utility considering a switch away from a paid specialist will care more about performance in its own service territory, on its own assets, than about a global leaderboard position.
Google’s own stake in the problem
Google is selling forecasting tools into a grid problem its own industry helped create. The data centre build-out driving the load growth utilities are struggling to serve is led by the hyperscalers, Google among them, and Google has signed multi-gigawatt renewable procurement agreements to supply its own facilities.
Accurate prediction of wind and solar output is directly useful to a company matching large volumes of clean energy against a load that is both growing and variable. That is commercial logic, and it goes some way to explaining why the energy variables shipped in this release.
Google has not published pricing for enterprise access to WeatherNext 3, or said whether the BigQuery and Earth Engine data carries standard Cloud query charges or a separate licence. Utilities weighing a move away from a paid specialist will want that figure before they weigh any accuracy claim.
2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.
The bank also said AI-based transaction screening allowed it to review more than twice the previous transaction volume while reducing manual operator checks by half.
Bank of America is using a generative AI-enabled system called EricaAssist with more than 18,000 customer service employees. The tool summarises why a customer is calling, retrieves relevant information, and recommends possible next steps while keeping the employee responsible for the interaction.
Bank of America said in July 2026 that EricaAssist can deliver contextual guidance in under three seconds and has reduced average call times by nearly one minute. The bank plans to extend the system to additional servicing scenarios and business lines later in 2026.
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YouTube was the most frequently cited website in Google AI Overviews across a panel of vitamin and supplement searches, according to new research. The video platform appeared in 186 of 350 AI-generated answers, or 53.1%, making it the only website cited in more than half of the answers collected.
Searcherries followed 50 searches daily from August 30 to September 5, 2026. The questions came from Google Autocomplete and covered topics including hair growth, energy, sleep, anxiety and weight loss. Every search produced an AI Overview, the AI-generated summary that Google displays alongside its search results.
GoodRx was the second most frequently cited website, appearing in 48.3% of answers. Healthline followed at 28.6%, Cleveland Clinic at 28.0%, and the National Institutes of Health domain, nih.gov, at 25.1%. Websites were ranked by the number of answers citing them, with each website counted only once per answer.
YouTube’s presence extended across 44 of the 50 queries. It was particularly prominent in the group beginning with “vitamins for,” appearing in 78.6% of those answers. Across the full panel, YouTube appeared almost twice as often as Healthline.
The research also found that AI Overviews frequently cited pages outside the recorded organic results. Only 32.4% of cited pages appeared in the organic listings collected for the same search; 67.6% did not. Matching by website instead of exact page raised the overlap to 52.5%, showing that some AI citations pointed to a different page on a website already present in organic search.
Individual answers were less consistent than the overall leaderboard. For “supplements for weight loss,” nih.gov appeared every day and YouTube appeared on six of seven days, while other websites moved in and out. By contrast, “supplements to help with glp-1 side effects” returned the same cited pages throughout the week.
About the research
Searcherries monitored a fixed set of 50 Google Autocomplete queries once daily using US English desktop results configured for New York. The analysis covered 350 AI Overviews and 5,036 citation occurrences across 572 distinct pages and 218 websites. Organic overlap was calculated within each search, counting repeated citations to the same page once; the recorded organic lists contained seven to ten results.
MG Ship has introduced an AI route optimisation and carrier selection module as logistics deployments demonstrate rapid cost and time returns.
The technical module targets global retailers and commercial shippers, pairing automated routing algorithms with carrier recommendation systems across international trade corridors. The deployment arrives as enterprise supply chain operators report measurable operational returns from machine learning tools, moving capital allocations away from speculative trials toward production deployments.
Measurable returns from deploying AI for logistics
Suki Cheung, CEO of MG Ship, will present deployment metrics during a panel discussion at the upcoming WMX Asia conference. Cheung will join executives from Pos Malaysia, Omniva, and OnyX Space for the session, titled AI Beyond the Hype: Measurable Results in Logistics Today.
“Too many AI conversations in logistics remain focused on future possibilities,” said Cheung. “The reality is that AI is already delivering measurable business outcomes today. Leading organisations are reducing transportation costs, improving forecast accuracy, increasing warehouse productivity, and achieving payback within months rather than years.”
Industry operational data indicates that initial investment returns are concentrating across three primary workflows:
Dynamic route planning has reduced enterprise fuel consumption by 15–20 percent, improved transit speeds by 15–25 percent, and lowered overall transportation costs by 12–22 percent, with capital payback reached within three to six months.
Predictive demand forecasting has reduced projection errors by 20–40 percent, improved planning accuracy by up to 35 percent, and decreased excess inventory by 20–30 percent within six to 12 months.
Automated freight documentation processing has cut manual task duration by up to 85 percent, recovering initial expenditure inside three to six months.
Over five-year deployment cycles, enterprise adopters have recorded average operational expense reductions between 10–25 percent, accompanied by warehouse productivity gains of 25–35 percent.
Routing algorithms and carrier scoring
MG Ship built the new routing capability directly into its visibility and supply chain intelligence platform, which serves retailers, manufacturers, and freight operators across multiple international markets. The base system synthesises live cargo telemetry with trade intelligence, risk monitoring, and predictive analytics to support operational planning and trade financing.
The route optimisation engine processes live and historical lane transit logs, weather patterns, air and ocean port congestion indicators, customs risk alerts, and transit reliability data. Shippers receive automated recommendations identifying low-cost, low-risk transit paths.
Carrier evaluation features rank transport providers per lane and service tier. Rather than selecting capacity purely on spot freight pricing, the system scores carriers against historical on-time metrics, transit consistency, exception occurrences, claims rates, available volume, and total cost-to-serve.
Logistics teams can also execute scenario simulations prior to peak shipping quarters. The software models lead times, service levels, freight spend, and risk exposures under alternative carrier allocation rules.
Early enterprise implementations demonstrate lower lead-time variance, reduced expedited freight expenditure, and improved on-time-in-full delivery rates.
Cheung stated that the platform “does not simply tell businesses where their cargo is”, adding that “it recommends the best route, the right carrier, and the lowest-risk option based on real-time conditions, helping organisations make faster and more profitable decisions.”
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M&T Bank has deployed AI copilots to more than 15,000 employees as the US regional bank applies AI to internal operations, customer service, software development, and risk management.
The bank uses AI to analyse call-centre conversations, draft reports, generate code, identify customer needs, and flag portfolio risks, according to Fast Company. M&T is also examining agentic AI applications in cybersecurity and fraud detection.
American Banker reported in September 2025 that 16,000 of M&T’s roughly 22,000 employees were already using Microsoft Copilot for tasks including drafting emails and reports and summarising call-centre conversations.
Before the wider rollout, M&T initially restricted employee access to public large language models. Chief data officer Andrew Foster told American Banker that the bank blocked the tools because employees could potentially enter sensitive company information into public-facing services.
M&T later evaluated enterprise providers and selected Microsoft Copilot, starting with a pilot involving about 800 employees before expanding access across the organisation.
Foster said using generative AI to summarise call-centre conversations saves about six minutes per call. Software developers at the bank also use GitLab tools to generate code, while employees remain responsible for reviewing AI-generated work.
M&T’s human-review requirement is also reflected in its 2026 Code of Business Conduct and Ethics. The policy requires employees to use approved AI tools and prohibits confidential, proprietary, customer, employee, or regulated information from being entered into unapproved systems. Employees remain responsible for the accuracy and appropriateness of AI-assisted work.
Building the technology and data foundation
M&T’s AI deployment follows a technology overhaul that began in 2018. The bank said more than half of its technology specialists were external workers at the time, compared with an 80% in-house technology workforce today.
M&T now has about 2,000 technologists working across more than 300 agile teams and has hired more than 1,000 technology specialists during the programme.
The bank has also replaced dozens of older platforms. M&T said technology outages have fallen by more than 80% since 2018, while the number of system upgrades completed annually has increased by 300%.
Technology spending exceeded $1.2 billion in 2025, nearly three times its 2017 level. Wisler told Forbes in August 2026 that annual technology releases increased from about 15,000 in 2018 to 65,000 in 2025.
Wisler joined M&T as chief information officer in 2018 before becoming senior executive vice-president for technology and operations in 2025. His current remit covers both technology and operational functions across the bank.
M&T’s data programme developed alongside the broader technology overhaul. Foster, who joined the bank in 2023, began building a data-lineage programme to track where information originates, how it is used, and how it moves between systems.
Foster told American Banker that the data-lineage work was not created in response to generative AI. He described it as a core capability for understanding M&T’s data estate.
The bank also established a Data Academy focused on data governance and data skills, with around 2,000 employees participating in the programme.
M&T has created an internal repository called Edison containing authoritative documents and information on bank policies. The bank also uses data-lineage software from Solidatus and Monte Carlo to trace information as it passes through databases, applications, and business-intelligence systems.
The lineage work gives M&T visibility into the source, meaning, quality, and governance of individual data elements, according to Foster. He said one application for that governed data is the bank’s use of Copilot.
M&T also uses retrieval-augmented generation with internal, governed data, according to American Banker.
Scaling AI into daily banking operations
Wisler told Forbes that M&T is pursuing generative AI through three routes: general employee use, AI capabilities embedded in existing applications, and proprietary systems built around the bank’s own data and processes.
M&T operates more than 1,800 applications, many supplied by third-party vendors. Wisler said one of the bank’s AI pathways is identifying useful AI capabilities already embedded within those applications.
M&T’s third pathway involves proprietary AI development around the bank’s own data and processes. Forbes reported that early applications include repetitive operational work, software development, fraud prevention, and cyber defence.
Fast Company’s September report also said M&T continues to assess both internally developed AI systems and external tools, including general enterprise software and technology designed specifically for banks.
Earlier workforce use cases centred on drafting, summarisation, call-centre work, and software development. Fast Company reported that newer applications include identifying customer needs and flagging portfolio risks.
Other large US banks have also expanded generative AI across employee workflows.
JPMorganChase launched its internal LLM Suite platform to more than 200,000 employees in 2024. By 2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.
The bank also said AI-based transaction screening allowed it to review more than twice the previous transaction volume while reducing manual operator checks by half.
Bank of America is using a generative AI-enabled system called EricaAssist with more than 18,000 customer service employees. The tool summarises why a customer is calling, retrieves relevant information, and recommends possible next steps while keeping the employee responsible for the interaction.
Bank of America said in July 2026 that EricaAssist can deliver contextual guidance in under three seconds and has reduced average call times by nearly one minute. The bank plans to extend the system to additional servicing scenarios and business lines later in 2026.
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Just over half of American adults, 50.5%, say a partner’s romantic or sexual relationship with an AI can count as cheating.
The figure comes from an AI romance survey of 2,150 U.S. adults, run by AI Girlfriend Coach through SurveyMonkey Audience on 26 and 27 August 2026 and analysed on the 1,709 responses that remained after quality screening removed 441. Respondents were asked how they would regard a partner’s romantic or sexual relationship with an AI, what they think draws people to these products, and whether they could develop genuine feelings for one themselves.
The question is not as strange as it sounds. AI companions flirt, remember what they were told last week and raise it again unprompted, sustain a relationship across months, and simulate intimacy convincingly enough that users describe grief when a model is retired. Unlike pornography or a fictional character, they respond to the specific person using them, and the relationship develops through interaction rather than sitting fixed and identical for everyone who opens it.
AI romance does not fit any existing definition of cheating
Infidelity norms assume a third person who can be confronted, resented, or left over, and AI romance removes that person while keeping most of what surrounds them. There can still be secrecy, emotional attachment, sexual content, and a substantial share of someone’s attention going somewhere their partner cannot see.
Relationship researchers have long treated emotional infidelity as damaging in its own right rather than as a lesser version of the sexual kind, and AI companions land in that first category while generating chat logs and images that resemble the second. Neither of the categories couples usually argue from quite covers it.
Regulators have run into the same gap from another direction. China’s rules on anthropomorphic AI services took effect on 15 July 2026, while California already has companion-chatbot legislation on the books and New York lawmakers passed additional AI-companion legislation in 2026, with much of the regulatory emphasis falling on minors, disclosure and crisis handling. What these products do inside adult partnerships has been left to households to work out.
The survey also undercuts the assumption that this mainly concerns single people. Engaged and married respondents were nearly twice as likely as single ones to have used an AI girlfriend or boyfriend app, 30.7% against 16.3%.
The discomfort runs wider than the cheating label
A further fifth (21.2%) said they would not reach for the word cheating but would still be uncomfortable with a partner doing it. That takes the share applying some kind of limit to 71.7%, against fewer than three in ten (28.3%) who dismissed the question on the basis that an AI is not a real person.
The disagreement is therefore not really about definitions, since a partner does not need to win an argument over what technically constitutes infidelity for the behaviour to cause damage. Roughly seven in ten are placing some kind of limit around AI intimacy whatever noun ends up attached to it.
Within the half who did use the word, a third (33.4%) said any romantic or sexual interaction with an AI already crosses the line, while 17.1% said it becomes cheating only once emotional attachment develops, placing the boundary at the feeling rather than at the software.
The people open to AI feelings judge AI romance more harshly
Among respondents open to developing genuine romantic feelings for an AI, 73.0% said AI romance can count as cheating, against 41.2% of those who said they never could. The gap of nearly thirty-two points runs opposite to the obvious assumption, which is that sceptics would be strictest and anyone open to AI attachment would wave the question through.
Experience produces the same pattern rather than the reverse, with current AI companion users calling it cheating at 80.6%, former users at 60.2%, and people who had never tried one at 44.0%. Familiarity with these products is associated with a harsher judgement rather than a softer one.
Once someone accepts that feelings directed at an AI can be genuine, the distinction between a real and an artificial relationship stops doing much work in deciding whether a boundary has been crossed. The object of the attachment being synthetic stops functioning as a defence, which leaves the people with firsthand experience of these products applying the firmest rules to them.
AI companionship is not primarily about sex
Asked what would be the main reason for using an AI companion, 56.6% chose having someone to talk to or feeling less alone, against 10.3% who chose affection, romance or sex, and loneliness was the leading answer in every subgroup measured.
That makes the cheating discussion more complicated rather than less. Had AI companionship turned out to be primarily sexual it would map onto existing norms about pornography reasonably well, since most couples have some working arrangement there. An ongoing emotional relationship competing for attention, disclosure and intimacy is harder to file, resembling an undisclosed close friendship more than it resembles adult content.
Respondents largely see these products as a response to loneliness rather than to sexual demand, whatever they are being sold as, which places a heavier responsibility on the category than its marketing usually implies.
Couples will have to define these boundaries themselves
Every earlier version of this question was settled privately in the end. Couples negotiate their own terms around pornography, contact with ex-partners, dating apps left installed, how much of a friendship belongs on a private thread, and how close is too close with a colleague. None of those has a universal rule either, but each arrived with decades of accumulated convention to argue from.
AI companions did not. The survey finds opinion split almost exactly down the middle on the word itself, a clear majority saying the behaviour is at least worth discussing, and no established norm for anyone to fall back on. Couples are being asked to legislate for themselves, on a technology most of them have not tried, during the period when the category is growing fastest.
Explicit AI clauses will probably become as ordinary in relationship conversations as deleting the apps became, though not before a good deal of avoidable argument. For anyone using these products in the meantime, the working assumption the data supports is that roughly seven in ten people would place some kind of boundary around it.
AI Girlfriend Coach tests AI companion platforms firsthand and publishes original research on the industry and the people using it. Full methodology, the questionnaire and the excluded responses are published with the survey.
OneRail has launched an AI-powered delivery platform that uses Nvidia technology to help retailers, wholesalers, and distributors decide how individual orders should be delivered.
Called OmniSTAR, the system evaluates options including owned fleets, couriers, parcel carriers, and other delivery modes, then selects the lowest-cost option that meets the required service level, according to OneRail.
The platform combines Nvidia’s cuOpt decision optimisation engine and cuDF data processing software with OneRail’s delivery pricing and performance data. Nvidia accelerated computing infrastructure is used to process the routing and delivery-mode calculations.
OneRail said the system can reduce computation times by as much as 10 times. A calculation that previously took 20 minutes can be completed in under two minutes, while a calculation taking a week can be reduced to about two days, according to the company.
OneRail said the shorter processing time allows the optimisation to run within live delivery operations, where multiple fulfilment options can be evaluated before an order is assigned.
“If you don’t have the ability to make lightning-fast decisions, you’re giving up margin,” Catania said in an interview with CNBC. “Last-mile fulfilment is expensive.”
From prediction to delivery decisions
OneRail’s broader AI systems use prediction and optimisation for different parts of the delivery process. The company said its machine-learning models estimate factors including service time, lateness risk, the probability of first-attempt delivery success, and expected price ranges.
OneRail said those predictions feed into optimisation systems that determine how an order should be executed. Separately, the company said OmniSTAR compares different fulfilment modes before selecting an option based on cost and service requirements.
Research on dynamic vehicle routing makes a similar distinction between predicting changing conditions and recalculating operational decisions as new information becomes available. A 2024 review in the European Journal of Operational Research identified travel-time prediction and real-time re-optimisation as separate areas of time-dependent routing research.
Nvidia cuOpt handles route optimisation
Nvidia describes cuOpt as an open-source, GPU-accelerated optimisation library for vehicle routing and other mathematical optimisation problems.
Nvidia’s documentation shows that cuOpt can account for vehicle costs, capacities, travel times, operating windows, starting locations, and other restrictions when calculating routes. Its cost models can also use distance, time, monetary cost, or a weighted combination of those measures.
OmniSTAR applies cuOpt to both routing and delivery-mode selection. OneRail said this allows the system to compare available fulfilment options for an order and identify the lowest-cost option that still meets its service requirements.
OneRail said many retailers still rely on static rules or manual planning when making these decisions, and that OmniSTAR is designed to evaluate more delivery combinations within shorter operational timeframes.
Nvidia said cuOpt does not exhaustively test every possible route. Instead, the solver generates candidate solutions and iteratively improves them using GPU-accelerated heuristics to produce high-quality results within a set computation time.
The platform also uses Nvidia cuDF, a GPU-accelerated library for tabular data processing, including filtering, joining, and aggregating datasets.
OneRail combines those capabilities with its own delivery data and operational models. Its dataset is based on millions of deliveries across a network that the company said includes more than 12 million drivers and over 1,000 logistics partners.
The data covers pricing and delivery performance across different transportation modes. OneRail said OmniSTAR can use the information to identify delivery rules that increase costs and assess how delivery choices affect item-level profitability.
The architecture disclosed for OmniSTAR centres on GPU-accelerated data processing and mathematical optimisation. Nvidia describes cuOpt as the optimisation component used for problems including vehicle routing.
Because cuOpt is stateless, changes in operating conditions require the optimisation problem to be modelled and submitted again. Nvidia cites vehicle breakdowns, driver absences, road blockages, traffic, and new high-priority orders as examples of changes that can prompt this type of dynamic reoptimisation.
OneRail said OmniSTAR can rerun delivery scenarios as variables including fuel costs, weather, and shipping conditions change. The company has separately said its use of cuOpt allows it to evaluate more routing scenarios and recalculate routes faster than its previous approach.
OmniSTAR moves into live operations
OmniSTAR is already deployed with selected enterprise customers.
At US Foods, OneRail said the system identified delivery configurations that were reducing margins, including low-margin products being transported long distances using higher-cost equipment. US Foods subsequently used the findings to adjust pricing and restructure some delivery patterns, according to OneRail.
OneRail also told CNBC that an unnamed large tire distributor using the platform achieved $40 million in run-rate savings over three years. The customer was not identified, and the savings figure was provided by OneRail. The company also told CNBC that it expects OmniSTAR to exceed $6 billion in gross merchandise volume during the fourth quarter of 2026.
CNBC reported that OneRail and Nvidia had worked on the project for three years before its launch. OneRail said the collaboration included direct engagement with Nvidia’s cuOpt engineering team on last-mile delivery and large-scale logistics optimisation, alongside its participation in the Nvidia Inception programme.
In March this year, FedEx launched FedEx SameDay Local in collaboration with OneRail, connecting customers to a national network of more than 1,000 delivery providers.
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NVIDIA has agreed to acquire Hugging Face for $12.93 billion to scale the open-source model repository’s platform and infrastructure.
The transaction targets platform growth and infrastructure investment, aiming to expand AI access for enterprise developers, software engineers, and research institutions globally.
Built over the past decade by Clem Delangue, Julien Chaumond, Thomas Wolf, and their engineering team, Hugging Face serves as the primary home for the open model developer community.
Platform metrics show more than 18 million developers, researchers, and creators share more than three million models, 500,000 datasets, and one million applications. Commercial adoption includes more than 200,000 companies using the environment to discover, evaluate, customise, and deploy AI models.
Hardware neutrality and multi-cloud commitments
NVIDIA stated that Hugging Face will remain an open platform for the entire AI sector. Developers will retain full control over their selection of models, software frameworks, cloud providers, inference services, and computing hardware.
NVIDIA hardware will not be mandatory to build on or deploy software through the platform. The service will maintain operational support for alternative accelerators, multi-cloud architectures, and open-weight models from all third-party builders.
Jensen Huang, Founder and CEO of NVIDIA, said: “Hugging Face will remain an open platform for the entire AI ecosystem. Developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want, and the computing platforms they want.
“NVIDIA compute will not be required to build on or deploy through Hugging Face.”
Open-weight models and distributed development
Huang noted a recent open letter he co-authored regarding the role of open weights in the AI economy. The position argued that open weights broaden AI access and ensure technical leadership remains distributed across companies, academic institutions, and developer communities.
Under this operational model, commercial businesses, startups, universities, and public bodies can build on advanced capabilities without the expense of training baseline models from scratch.
The approach allows organisations to match specific models to operational tasks across factories, hospitals, farms, classrooms, and commercial businesses, while addressing cybersecurity and data sovereignty requirements.
Julien Chaumond, Co-Founder and CEO of Hugging Face, commented: “AI is at an inflection point. Open-source AI can become less relevant in the coming years if the big closed labs run away with it, or it can become the foundational fabric of the next phase of human civilisation.
“Those are vastly different outcomes, and we need the critical mass to ensure we give our collective best shot to the second outcome. Given Jensen Huang’s stance on open source AI and how he stepped up to defend it when it was under threat earlier in the summer, NVIDIA was the only partner we truly considered.”
Infrastructure expansion and brand preservation
NVIDIA stands as the largest contributor of open models and data to Hugging Face, with a portfolio of more than 500 open models and more than 250 open datasets. The company builds its libraries, tools, and models openly to allow external engineers to inspect, modify, and build atop the software.
Technical integration will focus on applying NVIDIA infrastructure and engineering resources to improve repository reliability, safety controls, model evaluation tooling, inference execution, and deployment pipelines.
“I am honored that Clem came to me as he considered the next chapter of Hugging Face and believed NVIDIA would be a great home for the company, its community and the future of open models,” Huang stated.
Hugging Face will retain its independent brand identity following the completion of the transaction, with the existing team continuing operations across multi-cloud and multi-accelerator environments.
“This gives fuel to our long-term vision and mission of unlocking the community’s progress to ensure that AI, which is the greatest breakthrough of our lifetime, is accessible to as many people as possible,” Chaumond concludes.
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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.
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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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OpenAI will open its first Applied AI Lab outside the US in Singapore. The lab is part of a new partnership with the Ministry of Digital Development and Information.
The initiative, called OpenAI for Singapore, was announced at the ATx Summit and is backed by a commitment of more than S$300 million.
The lab will create more than 200 Singapore-based technical roles over the next few years. OpenAI said Singapore will also become one of its global hubs for forward-deployed engineers who will work with organisations on AI deployment. OpenAI said the lab’s work will be aligned with Singapore’s AI Mission priorities which include public service, finance, and digital infrastructure.
Focus on deployment and talent
The company will work with government agencies and local partners on education and workforce programmes within the Ministry of Education and GovTech. OpenAI also plans to support educators through a Singapore chapter of the OpenAI Academy, participate in the National AI Impact Programme, and run Codex for Teachers hackathons.
The partnership includes plans to work with local partners on accelerator programmes for AI-native startups in the form of workshops for micro-entrepreneurs and small businesses, covering how founders and SMEs can use AI in operations and customer service.
Chng Kai Fong, Permanent Secretary for Digital Development and Information, said Singapore’s response to AI includes growing new sectors, anchoring global frontier companies, and equipping workers with relevant skills.
Singapore updates agentic AI framework
Singapore has also updated its governance framework for agentic AI, which was launched by the Infocomm Media Development Authority at the World Economic Forum in January 2026. The framework builds on Singapore’s earlier Model AI Governance Framework for AI, introduced in 2020, and gives organisations guidance on the responsible deployment of AI agents, including measures to reduce the risks inherent in agentic AI.
IMDA has now updated the framework after seeking feedback and case studies from the industry, with the revised version following input from more than 60 organisations, including AWS, DBS, Google, and Salesforce.
The update adds guidance on risks linked to multi-agent systems, third-party agents, automation bias, and human accountability. The framework now includes more than ten case studies showing how organisations have applied its recommendations.
The case studies were contributed by Singaporean and international organisations, including Ant International, City Developments Limited, Cyber Sierra, Dayos, Google, Knovel, OCBC, PwC, Stability Solutions, Tencent, Terminal 3, Workday, X0PA, and GovTech Singapore.
Case studies show governance controls
One case study focuses on Dayos, a Singapore-headquartered enterprise AI automation company with operations in the US. Dayos built an AI-powered ticketing agent that handles internal IT requests. The agent can resolve some requests automatically and route requests to a human when needed.
Dayos used tiered risk levels to determine what actions the agent could take. Low-risk and reversible actions, like password resets, could be automated and audited biweekly, while moderate-risk actions required human approval before execution. Higher-risk actions, like permission changes with limited reversibility, were excluded from the agent’s authority.
Tencent contributed a case study on CodeBuddy, an agentic AI coding system developed by Tencent Cloud. CodeBuddy can plan, write, and deploy code through natural language instructions and can access filesystems, terminal commands, external APIs, and MCP tools.
CodeBuddy uses preset defaults and configurable permissions. Human approval is required for actions like editing files, running shell commands, making network requests, or using external tools.
The system explains complex commands in plain language before users approve them. Suspicious commands still require human approval, even if similar commands had been pre-approved.
GovTech Singapore’s case study covers the rollout of agentic coding assistants in government. The first phase was limited to GovTech employees, did not allow external tools, and was restricted to low-risk systems. GovTech developed central logging and a framework for connecting approved external tools. The agency also tested the system against potential attacks.
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Every major economy is staring at the same problem right now. Artificial intelligence is consuming electricity at a pace that grids were never designed to handle. In the US, capacity market prices in PJM, the country’s largest grid operator, have risen more than tenfold in two years, with data-centre growth identified as a primary driver. In Europe, utilities are scrambling to upgrade transmission infrastructure fast enough to keep pace with hyperscalers’ demand.
The International Energy Agency (IEA) projects global data-centre electricity consumption could approach 1,000 TWh by the end of this decade. Renewable energy is largely there, but the ability to coordinate it, through AI energy grid mapping at national scales, is what most countries still lack. But China just built it.
A study published in Nature this week by researchers from Peking University and Alibaba Group’s DAMO Academy has produced something that no country has managed before: a complete, high-resolution, AI-generated inventory of an entire nation’s wind and solar infrastructure, with the analytical framework to coordinate it as a unified system.
Using a deep-learning model trained on sub-metre satellite imagery, the team identified China’s 319,972 solar photovoltaic facilities and 91,609 wind turbines, processing 7.56 terabytes of imagery to do so.
AI energy grid mapping
Prior research into solar-wind complementarity – the idea that two sources can offset each other’s variability in time and geography – has largely relied on hypothetical or modelled deployment scenarios. How complementarity manifests under real-world infrastructure, and how it shapes system-level integration outcomes, has until now remained unclear.
The researchers show that solar-wind complementarity substantially reduces generation variability, with effectiveness increasing as the geographic scope of pairing expands.
In practical terms, the further apart the facilities being coordinated are, the more reliably they achieve balance. A cloud that covers solar farms in Gansu does not darken wind corridors in Inner Mongolia, for example. The study’s findings point to a structural inefficiency in how China currently manages its grid: coordination happens at a provincial rather than national level.
Transitioning to a unified national scale, the researchers argue, would make it easier to pair complementary energy sources, stabilise the grid, and avoid curtailment – the wasting of generated renewable power that has long been one of China’s most costly clean-energy problems.
Liu Yu, a professor at Peking University’s School of Earth and Space Sciences, described the inventory as allowing China to see its new-energy landscape from a “God’s-eye view,” a phrase that carries more operational weight than it might first suggest. Grid operators cannot optimise what they are not aware of – until now.
China is in the middle of an AI-driven electricity demand surge that is straining its grid. The rapid proliferation of data services and massive computing facilities have pushed the sector’s power consumption up 44% year-on-year in the first quarter of 2026, reaching 22.9 billion kilowatt-hours, according to the China Electricity Council.
That is an extraordinary rate of growth for a sector whose demand was already great. This has accelerated data-centre expansion in China’s northern and western provinces, where land is cheaper, wind and solar resources are more available, with commensurately lower electricity prices. The provinces being targeted for new data centres are the same regions with the highest solar-wind complementarity.
Behind the model
The technical achievement behind this is worth understanding in its own right. DAMO’s deep-learning model was trained to identify solar photovoltaic facilities and wind turbines from sub-metre resolution satellite imagery, a task complicated by the sheer diversity of installation types, terrain conditions, and image quality.
The resulting dataset covers installations in 1,915 Chinese counties, spanning everything from rooftop panels in coastal cities to utility-scale wind farms on the Mongolian plateau. Processing 7.56 terabytes of imagery to produce a nationally consistent, county-level inventory is a demonstration of what large-scale geospatial AI can do when applied to infrastructure problems, and a template that other countries could, in principle, replicate.
China’s clean energy sector generated an estimated 15.4 trillion yuan (US$2.26 trillion) in economic output last year, equivalent to Brazil’s entire GDP, according to the Finland-based Centre for Research on Energy and Clean Air. Managing an asset base of that scale without a national-level visibility tool was always going to be a limiting factor, a limit that’s now gone.
The study’s dataset and code have been made publicly available via Zenodo.
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The ceremony was scheduled. The CEOs were on the guest list. And then it wasn’t happening.
On Thursday, US President Donald Trump scrapped a planned AI executive order, which had already been delayed multiple times, citing concerns that it might erode America’s competitive edge over China.
“We’re leading China, we’re leading everybody, and I don’t want to do anything that’s going to get in the way of that lead,” Trump told reporters in the Oval Office. What he did not say was that the order had been effectively killed by the very industry it was meant to oversee.
Lobbied out in one night
According to Semafor, which first reported the backstory, the White House’s plans were halted after Elon Musk of xAI, Meta CEO Mark Zuckerberg, and venture capitalist David Sacks, who, until recently, was Trump’s AI and cryptocurrency tsar, all spoke directly with Trump between Wednesday night and Thursday morning.
The argument that landed, according to US media, citing sources, was an appeal to the “accelerationist” faction in the administration, including officials at the National Economic Council and staffers in the Vice President’s office.
The order itself was not a sweeping regulatory framework. It would have established a voluntary mechanism for AI developers to engage with federal agencies and submit advanced models for security review up to 90 days before their public release. No licensing regime. No mandatory hold periods. Voluntary.
That was apparently still too much. Trump said he postponed it “because I didn’t like certain aspects of it,” declining to specify which ones. He added that he worried it “could have been a blocker,” a telling phrase from a president who has otherwise positioned AI as a jobs and national security priority.
A vacuum with consequences
The US has yet to pass comprehensive AI legislation. What governance architecture exists has been assembled piecemeal, through executive orders, agency guidance, and voluntary agreements. Earlier this month, the federal Centre for AI Standards and Innovation announced evaluation agreements with Google DeepMind, Microsoft, and xAI, allowing the government to assess models before public availability. That programme continues regardless of Thursday’s non-signing.
But the broader picture is one of regulatory drift. In early March, the Trump administration released a National AI Legislative Framework urging Congress to preempt state-level AI laws that “impose undue burdens,” arguing for a single national standard over what it called “fifty discordant ones.” Congress has not acted on it.
The contrast with China is sharp and increasingly difficult to ignore. Beijing’s State Council issued a 2026 legislative work plan in May outlining plans to accelerate comprehensive AI legislation, deploying language on AI governance in formal planning documents for the first time. The National People’s Congress has listed AI legislation for review for the third consecutive year.
In April, Beijing issued new rules requiring AI companies to establish internal ethics review committees. China is writing rules. Washington is cancelling ceremonies.
Who shapes US AI policy
Thursday’s episode clarified something implicit for months: in the current administration, the effective veto on AI regulation sits with a small group of industry principals who have direct access to the president.
Musk, whose xAI is a direct competitor to OpenAI and Anthropic, has a structural interest in keeping the regulatory field open. Zuckerberg’s Meta has similarly positioned itself as a champion of open-source AI development. Sacks, despite having formally left his White House advisory role in March, evidently retains enough influence to shape executive action.
Separately, Semafor reports that OpenAI has secured White House backing for a parallel effort to push AI regulations at the state level, an interesting manoeuvre given that Trump’s earlier executive order threatened states that enacted AI laws the administration disliked. That the administration appears to be simultaneously discouraging state regulation and endorsing OpenAI’s state-level strategy suggests the policy coherence problem runs deeper than one postponed signing.
The China frame does real work, but in both directions
Trump’s stated reason for pulling back, protecting the US lead over China, is the same logic that has driven every major AI policy decision since he returned to office, from the H200 export licence framework to the Stargate infrastructure programme. It is also the logic that China is watching closely.
At the Trump-Xi summit in Beijing earlier this month, the two leaders agreed to launch an intergovernmental dialogue on AI, according to the Chinese Foreign Ministry. Beijing will have noted that Washington’s internal debate about even voluntary AI oversight was resolved not by policymakers, but by the companies that stand to profit most from the absence of guardrails.
In a report by the South China Morning Post, Lizzi C. Lee, a fellow at the Asia Society Policy Institute’s Centre for China Analysis, noted that both the US and China are grappling with the same underlying question: where should the regulatory frontier sit for frontier AI, particularly as models become more capable of autonomous action and more relevant to cybersecurity.
“I think a separate, potentially more important race is on governance and safety: not about who has the most advanced models, but who can govern powerful AI without choking off innovation,” she said.
The same report highlighted what Kyle Chan at the Brookings Institution put it more simply: “AI safety and regulation can be done in a way that doesn’t compromise innovation.”
Neither argument was enough on Thursday. Whether it becomes enough next time, assuming there is a next time, remains unclear.
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The Nvidia Vera chip is rarely the headline when earnings beat estimates, but it should be. When Nvidia reported Q1 revenue of US$81.62 billion on Wednesday, beating analyst estimates of US$78.86 billion, and guided Q2 at US$91 billion–well above Wall Street’s US$86.84 billion forecast–the numbers did what Nvidia numbers always do: dominate the room.
But buried in CEO Jensen Huang’s conference call with analysts was something more strategically interesting than another quarterly beat. Huang told analysts that Nvidia’s new Vera central processors unlock access to a US$200 billion market, one that sits entirely outside the US$1 trillion the company has already forecast from its Blackwell and Rubin AI GPU lineup between 2025 and 2027.
He expects Vera chip revenue to hit US$20 billion by the end of this fiscal year. “I expect (Vera) to be the second largest” sales contributor, Huang said during the call.
That’s not a footnote. That’s a second front.
The Vera chip and the inference pivot
The reason Nvidia needs a second front is straightforward: its biggest customers are building their own. Google, Amazon, and Microsoft–collectively expected to pour more than US$700 billion into AI infrastructure this year, up sharply from around US$400 billion in 2025, are simultaneously pouring funds into custom silicon to run AI models. Intel and AMD are also touting CPUs as a credible play for inference workloads.
The narrative in the chip industry has shifted from who can train the biggest model to who can serve it cheapest and fastest. Inference is where Nvidia’s GPU dominance is most exposed. Training large models is still firmly Nvidia territory, but inference, generating answers at scale, in real time, is increasingly where custom chips from Google’s TPU line, Amazon’s Trainium and others are making their case.
Nvidia’s answer is Vera. The chip, developed in part using technology from Groq, a startup specialising in inference that Nvidia licensed in a deal reportedly worth around US$17 billion, targets exactly this workload. The full Vera Rubin platform, which combines the Vera CPU with Rubin GPUs, is set to launch later this year.
Supply is already the constraint
Huang was candid about one problem: supply. “My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he said on the call. It’s a telling admission for a product Nvidia is positioning as a major growth pillar. To get ahead of disruptions, Nvidia is spending heavily on the supply chain. The company disclosed that its supply commitments rose to US$119 billion in Q1, up from US$95.2 billion the previous quarter, a significant jump that reflects both confidence in demand and anxiety about a global memory chip crunch.
Nvidia also announced a US$80 billion share repurchase programme and raised its quarterly cash dividend to 25 cents per share, from 1 cent, moves that signal financial confidence even as Huang warned of tightening supply.
The question investors are asking
Despite the beats, Nvidia shares fell 1.6% in extended trading after the results. eMarketer analyst Jacob Bourne captured the mood: “Nvidia delivered another beat, but at this point that’s essentially priced in as it keeps beating quarter after quarter. The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel.”
Huang pushed back with numbers of his own. He pointed to a growing sub-segment of AI-specific cloud customers whose spend is now roughly equal to the hyperscalers, but growing faster quarter-over-quarter. “We should be growing faster than hyperscale capex,” he said.
The Vera chip is central to that argument. Whether the supply chain cooperates is a different question entirely.
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Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language model, signalling that the company is building an integrated AI stack rather than just filling a gap left by US export controls.
The Zhenwu M890, developed by Alibaba’s semiconductor subsidiary T-Head, delivers three times the performance of its predecessor, the Zhenwu 810E, according to the company, as per Reuters report. But the performance jump is less notable than the architectural intent behind the chip: the M890 is purpose-built for AI agents, where software systems must retain long stretches of context, coordinate with other models in real time, and execute complex multi-step tasks with limited human intervention.
Those demands, heavy on memory bandwidth and inter-model communication, are meaningfully different from what standard inference chips are optimised for. The difference matters because it tells you something about where Alibaba thinks AI compute is heading. The company isn’t designing around today’s dominant use case; it’s building for the workload profile it expects to define enterprise AI over the next several years.
Built for AI agents, not just inference
More significant than the chip itself is the roadmap Alibaba put alongside it. The M890 will be followed by the V900 in the third quarter of 2027, expected to deliver another roughly threefold performance gain, followed by the J900 in the third quarter of 2028. That’s a deliberate, sustained cadence of in-house silicon upgrades that mirrors the kind of tick-tock product cycles Nvidia has used to maintain its lead in AI accelerators.
The parallel to Huawei is worth noting. Huawei laid out a similar chip roadmap for its Ascend line last year, and both announcements reflect the same underlying reality: Chinese technology companies have concluded that depending on foreign silicon, even in scenarios where export restrictions might ease, is a structural risk they cannot accept. The response has been to treat semiconductor development as a long-term capability-building exercise rather than a procurement problem.
Alibaba’s commitment to that exercise is not shallow. The company pledged more than 380 billion yuan, roughly US$53 billion, on cloud and AI infrastructure over three years last year, its largest-ever investment commitment to the sector. The M890 and its successors are downstream of that spending.
Traction that predates the announcement
T-Head said it has shipped more than 560,000 Zhenwu units to date, with over 400 external customers across 20 industries deploying the chips, including automakers and financial services firms. That is a material production footprint, not lab hardware, and it provides Alibaba with real-world deployment data at scale ahead of the M890’s rollout.
The new chip will be available to Chinese enterprise customers through Alibaba Cloud’s domestic model platform, Bailian, packaged inside the Panjiu AL128, a server system that stacks 128 M890 accelerators into a single rack.
The software side of the stack
Alongside the hardware, Alibaba announced Qwen 3.7-Max, the latest version of its flagship large language model, described as engineered for advanced coding and long-running agent tasks. The company said the model can operate continuously for up to 35 hours without performance degradation, a capability specification that only makes sense if you are designing for extended autonomous operation.
The timing is deliberate. Releasing a chip and a model optimised for the same workload class on the same day is a platform play. Alibaba is building a closed loop: its own silicon in T-Head, its own model in Qwen, its own cloud delivery in Bailian. Each component reinforces the others, and the combined stack is designed to reduce enterprise customers’ dependence on any external vendor.
More than half a million chips have been shipped. A successor is arriving in 2027, with another planned for 2028. T-Head is not hedging. At some point, building around US export controls stops being a workaround and starts being a strategy. Alibaba appears to have crossed that line.
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The AI and Big Data programme on day two of TechEx North America referred at least once to the “AI graveyard,” meaning the large number of pilots that never become durable systems. That phrase set the tone. The question was proof.
The Enterprise AI Implementation, ROI and Adoption track dealt with the hard middle of AI work. Its sessions covered stalled pilots, agentic AI for business impact, the move from experimentation to impact, the decision to buy or build, and durable ROI and autonomous decisioning. A system has to be adopted, governed and measured before it deserved to be called successful.
The session on the AI graveyard was useful because it named the failure pattern. Many companies have enough budget to start AI experiments and enough executive attention to publicise them. Fewer have the data quality, process design, operating authority, and risk control to keep them going.
A day-two session on moving beyond copilots towards agentic AI framed the issue as business impact not novelty. Copilots have been useful as individual productivity tools, but their value is often hard to measure. Agents promise a closer connection to business process, yet they also increase the need for boundaries. An agent that can act in systems has to be evaluated by the quality of the action.
That point linked directly with the Future of AI track. Its opening theme, trust as a competitive advantage, was a useful counterweight to speed. The programme dealt with transparency, governance, regulation, banking analytics, and risk. It also included material from Hex on data agent, with evaluation and governance built in. Agentic AI will not mature in enterprise settings if evaluation remains informal.
Governance appeared in several forms. There was cross-functional governance, which reflects the reality that AI risk does not belong to legal, security or engineering. There was governance in the data layer, where trust depends on lineage and quality. There was governance around agent personas and risk stacks, where companies need to understand what an AI agent is permitted to know and do. The banking session gave the theme a sectoral focus, since financial services have less room for undefinedassurances about automation.
Digital Transformation Week carried the same day-two pressure into business delivery. The programme was built around real use cases, business impact, ROI, AI agents built on APIs, change readiness, government service transformation, city innovation and the conversion of data into financial value. The change-readiness material was especially important. AI fails because staff do not change routines, managers do not alter incentives, or the data needed for daily use never appears in the right place.
Sessions involving the DMV and the City of San Jose placed AI and transformation inside government service. In government, the measure of quality includes reliability, access, explainability and public trust. The Dow material on turning data into dollars sat at the commercial end of the same argument. In both cases, value depends on connecting data work with an accountable outcome.
The Cyber Security and Cloud Expo day-two programme expanded on risk. Its cloud-first enterprise track dealt with AI-led threats, cloud security, the “GenAI velocity gap,” threat intelligence, identity security and AI governance. The cyber programme treated AI as a force that changes attack and defence alike. It can help automate defensive work, but it can also accelerate misuse, widen leakage routes, and increase the strain on existing controls.
The phrase “velocity gap” was used several times during day two. Business units are adopting generative AI faster than many security teams can oversee it: the tools arrive first, policy and monitoring arrive later. The sessions on jailbreaking and data leaks made the point more concretely. If staff place sensitive material into unsanctioned tools, or if approved AI systems are poorly bounded, cloud security and data governance become one and the same.
Zero trust was presented as one answer, with a stronger interpretation of zero trust must now include AI systems, agents, and the data around them. Identity is not limited to human users, but services, agents and automated workflows require permission models as well. The cloud-first enterprise is therefore becoming a place where identity, data classification, AI governance, and threat detection are part of the same control mechanisms.
(Image source: TechEx/TechForge)
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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)
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.
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