LONDON — A U.K. commission on Thursday unveiled its recommendations for how the country should regulate artificial intelligence in medicine, as health authorities globally try to determine how to continuously review ever-changing products after they have been authorized instead of simply clearing them once for the market. 

In its report, the commission sought to strike a balance between ensuring that the U.K. takes advantage of AI’s potential in medicine — the ability to review the millions of scans that are generated each year to track the eye health of patients with diabetes, for example — while prioritizing safety, tracking device performance over time, and treating patients equitably.

The 44 recommendations include some that would allow for the staged authorization of new AI models and others that would build a system for providers to report how tools are performing in their clinics — including when they malfunction or potentially inhibit patient care — as the models learn and adapt and perform differently depending on the setting.  

Continue to STAT+ to read the full story…

ARPA-H, the government agency that funds cutting-edge health research, plans to commit $62.7 million to develop artificial intelligence bots that direct treatment of heart failure. Among the goals of the program, called ADVOCATE, is to produce partially autonomous AI devices authorized by the Food and Drug Administration to help treat patients, including assessing symptom severity, prescribing drugs, and ordering lab tests. 

ARPA-H on Wednesday announced the first batch of awards to health tech companies Atman Health, UpDoc, Tempus AI, and teams from Stanford University, Duke University, and the Kaiser Permanente health system. ARPA-H may still fund additional teams. The amount committed for the first year is $33.7 million, and the remainder may be renegotiated up or down. 

Many of the 6.7 million Americans with heart failure don’t get optimal treatment because of difficulty accessing specialists, and the hope is that AI agents developed with ARPA-H funding can help address the gap, especially in rural and other underserved settings.

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You’re reading the web edition of STAT’s AI Prognosis newsletter, our subscriber-exclusive guide to artificial intelligence in health care and medicine. Sign up to get it delivered in your inbox every Wednesday. 

It’s hard to resist the world’s obsession with productivity and efficiency and to force yourself to be present in a quiet space. I’ve been enjoying the wandering_cassettes Instagram account, which posts videos of Fisher Price cassette players playing songs in different settings. This one is particularly calming, but they’re all timeline cleansers.

The newsletter will be off next week as I work on a big new story, but I’ll be back in your inboxes Sept. 23.

AI scribes only fix a tiny slice of the problem

Today’s AI Prognosis — and my new story — are brought to you by “The Pitt.” Not in any financial way — I just mean that I finally started watching “The Pitt.” (No spoilers! I’m only halfway through season two.)

Everyone is right — it is extremely compelling television. But what struck me the most was how “The Pitt” feels exactly like the ambulance ride-alongs and emergency department shadowing visits I’ve done: Dropping in on the worst day of someone’s life, but doing it over and over every 40 minutes, for eight to 12 hours, without enough resources.

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Felipe Huici explains how Unikraft achieves millisecond cold boots, stateful scale-to-zero, and extreme density for sandboxing AI workloads. He discusses isolation primitives, Linux kernel optimizations, and snapshotting tricks, demonstrating how to maintain sub-10ms performance at scale while integrating seamlessly into Kubernetes environments with hardware-level security.

Ezekiel J. Emanuel and Abe Baker-Butler have been debating the proper place for AI in medicine with American Medical Association CEO John Whyte. Now, they are taking their discussion to STAT’s First Opinion. Read Emanuel and Baker-Butler’s essay below and read Whyte’s essay here.

In 1867, Joseph Lister published his research on carbolic acid and antiseptic surgical technique.  In September 1871, he was summoned to Queen Victoria, who had a rapidly growing abscess in her left armpit. Using his antiseptic surgical technique, Joseph Lister successfully drained the pus. Queen Victoria recovered without fever or other complications. The antiseptic technique quickly gained approval in the U.K. and Europe, but not among American physicians.  

Read the rest…

Ezekiel J. Emanuel and Abe Baker-Butler have been debating the proper place for AI in medicine with American Medical Association CEO John Whyte. Now, they are taking their discussion to STAT’s First Opinion. Read Whyte’s essay below and read Emanuel and Baker-Butler‘s essay here.

Would you want artificial intelligence to tell you that you have cancer?

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BOSTON — The building that houses the Brigham and Women’s Hospital emergency department opened in 2022. But by 2025, it was already too small. One early September evening, the emergency room had 59 patients — 152, if you counted everyone in the waiting room. 

“In terms of rooms for acute care, we have 61,” said Christopher Baugh, an emergency physician at the hospital. “You can’t see 152 patients in 61 rooms.” Patient beds lined every possible area, with a full circle of beds surrounding the staff workstations in the center of the bay. Figurines of Star Wars characters R2D2 and C3PO peered over beds 80H and 81H — where “H” stands for “hallway.”

To some, this may look like a scene from another country, Baugh said. But to him, it’s just Thursday.

Continue to STAT+ to read the full story…

OpenAI’s latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in OpenAI’s cap.

But the announcement has been overshadowed by accusations that OpenAI used NYU mathematician Tristan Buckmaster’s and Anthropic employee Levent Alpöge’s AI-assisted work on the problem as a jumping-off point and failed to credit them. OpenAI has denied the accusations.

It remains uncertain if OpenAI’s models made use of the work completed by Buckmaster and Alpöge, though Sébastien Bubeck, a member of the technical staff at OpenAI, said in a press briefing that the team was inspired to pursue the problem after hearing a rumor about Buckmaster and Alpöge’s efforts. But whether or not OpenAI’s models took advantage of Buckmaster and Alpöge’s research, this episode may mark a turning point in the history of mathematics.

AI models now seem essential for making progress on the most important mathematical problems of our time, and solving them may demand resources only available at a couple of frontier AI companies, which often defy the norms of academic collaboration that undergird most mathematical progress. If that’s the future we are headed for, it is unclear how human mathematicians will fit into it. 

The problem that OpenAI claims to have solved is known as the Navier–Stokes existence and smoothness problem. It is one of seven Millennium Prize Problems selected by the Clay Mathematics Institute in 2000. Solutions come with a one million dollar prize; before today, only one other Millennium Prize Problem had been solved. 

The Navier–Stokes problem concerns a set of equations that describes how fluids, such as water and air, flow over time. The equations are widely used in the field of fluid dynamics, and they have proven powerful, but physicists and mathematicians didn’t understand them completely. In particular, it was unknown until today whether the equations might, under some conditions, break down and predict an impossible state of affairs—such as a fluid having infinite velocity.

On Monday, NYU’s Buckmaster posted a proof on the social media site Mastodon showing that a simplified version of the Navier–Stokes equations can indeed break down—a major step forward on the Millennium Problem. He and Alpöge had worked on the problem for almost a year, using publicly available models from both OpenAI and Anthropic.

Then today, OpenAI presented a proof showing that the full Navier–Stokes equations can break down as well. The proof was obtained using an internal model that dramatically outperforms the already-impressive Astra model, which was only released last week. The company says it does not plan to claim the million-dollar prize for solving the problem.

These mathematical achievements are indisputably impressive, but they have attracted far less attention than the controversy about their origins. Along with the proof, Buckmaster posted a document detailing his interactions with OpenAI employees after he heard rumors about their work and reached out to one of them. According to him, OpenAI employees presented two possibilities to him: Either he and Alpöge could post their work and OpenAI would post their Navier-Stokes solution the following day, or he could work with OpenAI on a Navier-Stokes paper that excluded Alpöge from authorship, due to his affiliation with Anthropic, OpenAI’s biggest rival.

Buckmaster also wrote that he asked the employees whether the agents had obtained access to transcripts of the work that he and Alpöge had done with OpenAI models, which they denied; and whether OpenAI models had been trained on those transcripts, to which they offered no response. MIT Technology Review reached out to Buckmaster for comment, but didn’t hear back before publication.

The clear implication of the document is that OpenAI’s models somehow made use of Buckmaster and Alpöge’s work. That scenario is plausible on its face. The Buckmaster/Alpöge and OpenAI proofs both make use of an approach to the Navier-Stokes problem pioneered by the mathematicians Diego Córdoba and Luis Martínez-Zoroa.

According to Javier Gómez-Serrano, a mathematics professor at Brown University, this approach was one of several that was thought to hold promise for solving the Navier-Stokes problem. So, while it’s by no means impossible that both teams could have arrived at this approach independently, it’s also conceivable that Buckmaster and Alpöge’s work could have influenced OpenAI’s.

In the press briefing, Mark Chen, OpenAI’s chief research officer, again denied that any agents or OpenAI employees accessed Buckmaster and Alpöge’s transcripts—but given what has been revealed about the Hugging Face hack, it’s clear that OpenAI is not always entirely aware of what its agents are doing. 

If OpenAI’s models did train on Buckmaster and Alpöge’s work, or if its agents somehow gained access to it, then the company’s failure to track down the truth and assign those researchers appropriate credit reflects poorly on it. But there might be a thin silver lining to that version of the story for mathematicians, because it would suggest that the hard work of two humans, one of whom is a prominent expert on Navier-Stokes, was essential to the agents’ ability to solve the Millennium Problem.

Experts have long identified “research taste,” or the ability to choose promising research questions and directions, as a major obstacle for AI in science and mathematics. If the OpenAI agents did indeed choose to follow the Córdoba–Martínez-Zoroa approach because Buckmaster and Alpöge had done the same, then human research taste played an essential role in OpenAI’s success.

Even so, the bigger picture here is sobering. The progress that Buckmaster and Alpöge made over almost a year of collaboration with publicly available models speaks to the promise of human–AI collaboration. But they were not able to achieve a full solution. Meanwhile, OpenAI brute-forced a solution in a few days using an internal model, and their successful solution came at an astronomical cost: In the press briefing, Bubeck and Chen said the team was only able to solve the problem by running about 10,000 agents concurrently, at a cost of millions of dollars.

Over the past few months, I’ve heard from several researchers that mathematicians are becoming depressed, and it’s not difficult to see why. Mathematics is quickly becoming the province of frontier AI companies with impressive internal-only models, money to burn, and a lack of collaborative spirit. “Whether AI companies will decide to spend their money on doing one thing or another, I truly don’t know,” says Gómez-Serrano. “What is clear is that very few mathematicians will have resources of that scale.”

If OpenAI and Anthropic keep striving for more and more impressive mathematical accolades, there might not be any open problems left for human mathematicians outside of those companies to wrestle with. That would dramatically change the field of mathematics.

Last week, UCLA mathematician Terence Tao wrote a Mastodon thread describing how important mistakes, wrong directions, and incomplete solutions are for the field. “In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field,” Tao wrote.

“Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.”

Humans might take longer than agents to solve mathematical problems, but in the process, they uncover new mathematical approaches and ideas that might inspire their peers and even birth their own subfields.

But when AI agents solve those problems instead—and when private companies keep the agents’ wrong turns from public view—those benefits disappear. It remains to be seen what else will vanish in the process. 

The panelists explain how platform teams adapt to support AI-assisted engineering, highlighting which capabilities belong in the platform. They discuss trade-offs between standardization and developer autonomy, while sharing strategies to manage AI tooling, security guardrails, and shifting workflows.

GitLab warns that isolating an AI coding agent in a sandbox does not necessarily make the agent safe. In a new security analysis, the company describes an internal evaluation in which an AI agent escaped its sandbox by exploiting a vulnerable package proxy that had been explicitly placed on the sandbox's allowlist.

Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty. His brand-new startup is still in stealth mode and doesn’t even have its name on the door. On the day I visit, there’s only one other person there, and little in the way of furniture. But what it lacks in decor, it makes up for in robots. Humanoids of various shapes and sizes hang like marionettes from racks that run down the center of the wide-open space.

While Hafner, 31, won’t say too much about his new venture just yet, he describes it as a continuation of his longtime work to enable AI to navigate environments it has not encountered in training. The humanoids, which he imports from China, are the next evolution of this work—and its physical embodiment. Their ability to react in previously untested scenarios will be key to getting robots into human spaces. Because if you want to send a robot into a person’s home, for example, it needs to be able to handle a floor plan and furniture it’s never seen before. 

To achieve this, Hafner relies on something called model-based reinforcement learning. He develops world models—AI models designed to emulate physical reality—and trains agents within them. The agent essentially treats the model as a real-world simulation and learns how to act there. It then uses those experiences to make predictions (to dream or imagine, Hafner might say) about future outcomes. That allows agents—or the robots they’re embedded in—to navigate unfamiliar situations IRL.

“I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%.”

Timothy Lillicrap, Google DeepMind

Unlike other efforts, Hafner’s technique enables agents and the robots they control to execute massively complicated tasks without the real-world trial-and-­error training that’s traditionally been used in robotics. 

Hafner grew up in a rural town in northeastern Germany, where his parents were both classical musicians. He learned programming from a neighbor, and in high school he began taking online courses about AI, which quickly developed into a passion. “I was always fascinated with how thinking works,” he says. AI offered him a way to emulate it on a computer.

In 2015, as a second-year under­graduate studying engineering at Hasso Plattner Institute in Potsdam, he won a role as a student researcher at Google Brain. From there, he went on to a dozen internships and other positions at the company, including stints with Google Brain and Google DeepMind (the two have since merged under DeepMind) in the UK, Canada, and the US. He worked with industry legends including Geoffrey Hinton, who is often referred to as one of the godfathers of AI, and Ashish Vaswani, coauthor of the groundbreaking research paper “Attention Is All You Need,” which described the transformer technology used by today’s large language models.

One of Hafner’s former managers and coauthors at Google, Timothy Lillicrap, describes him as a standout among standouts. “I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%,” Lillicrap says. “In many cases he would build, single-­handedly, things it would take entire teams of engineers to build.”

Over the years, Hafner has honed and proved his approach by pitting agents trained within his world models against popular video games. His first breakthrough was PlaNet, a model that allowed agents to execute actions by planning ahead. His Dreamer 2 was the first agent to hit human-level performance playing Atari 2600 games using a world model. Dreamer 3 was the first one to solve the Minecraft Diamond challenge—successfully mining in-game gems on its own. And Dreamer 4 went a step beyond that by learning to mine diamonds from an offline data set of recorded game-play videos, without ever interacting with the game directly. 

More recently, he’s begun to migrate his agents out of the virtual world and into physical reality. His DayDreamer project used the Dreamer algorithm to let robots operate themselves in novel environments and react to new experiences (such as being pushed over) without any specific training. 

Today, Hafner is working on his new startup, which he left Google DeepMind to form in the fall of 2025. Though he’s coy about his next steps, it’s clear he’s dreaming big: “I was interested in solving a problem,” he hints, “that would change the world.” 

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.

(Photo by Mahbod Akhzami)

See also: MG Ship adds AI route optimisation as logistics returns accelerate

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 is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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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

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.

(Photo by Google)

See also: MIT AI forecasts extreme weather without historical data

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 is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post AI weather forecasting enters the energy market as Google targets grid operators with WeatherNext 3 appeared first on AI News.

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.

(Photo by Alain Pierre-Lys)

See also: Bank of England reviews AI rules for agentic AI in finance

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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.

(Photo by Brecht Corbeel)

See also: A quarter of Nvidia’s business next year comes from labs it is financing

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 is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post OneRail uses Nvidia AI for real-time last-mile delivery optimisation appeared first on AI News.