RFK Jr. adviser Calley Means and Kennedy’s son Finn attended the Enhanced Games, the pro-doping athletic competition and biohacking extravaganza that took place over the weekend in Las Vegas, according to The Washington Post. Send news tips and personal bests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
Ripple effects
For weeks, Republicans have been preoccupied with an immigration funding bill that they’re pushing through Congress, without support from Democrats. I’ve no
RFK Jr. adviser Calley Means and Kennedy’s son Finn attended the Enhanced Games, the pro-doping athletic competition and biohacking extravaganza that took place over the weekend in Las Vegas, according to The Washington Post. Send news tips and personal bests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
Ripple effects
For weeks, Republicans have been preoccupied with an immigration funding bill that they’re pushing through Congress, without support from Democrats. I’ve not been writing about that bill because it doesn’t include health care policies. But it’s now becoming relevant to health care, albeit indirectly.
Early last week, Republicans were expected to pass that budget reconciliation bill without much friction. By the end of the week, Senate Republicans adjourned for a week-long recess without voting on it due to an impasse over a new $1.8 billion settlement fund for Trump’s allies. They’d also butted heads with the president over his demands for $1 billion for a White House complex and ballroom.
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Hello, diligent HCI readers! I hope everyone enjoyed their Memorial Day weekends. We’ve got a lot of numbers in today’s edition. Get out your abacus. And tell me if you want more or less math in here: bob.herman@statnews.com.
$775 billion
Centers for Medicare and Medicaid Services
Republicans’ recent tax law targets supplemental Medicaid funds that have increasingly propped up hospitals.
This is the online version of STAT’s weekly email newsletter Health Care Inc. Sign up here.
Hello, diligent HCI readers! I hope everyone enjoyed their Memorial Day weekends. We’ve got a lot of numbers in today’s edition. Get out your abacus. And tell me if you want more or less math in here: bob.herman@statnews.com.
$775 billion
Centers for Medicare and Medicaid Services
Republicans’ recent tax law targets supplemental Medicaid funds that have increasingly propped up hospitals. The cuts are expected to be even bigger than originally forecast, which almost assuredly will provoke an opposition campaign from hospitals.
The move to block the acquisition of the cloud company that hosts the Dutch digital ID service comes as Europe continues to reduce its reliance on U.S. technology.
The move to block the acquisition of the cloud company that hosts the Dutch digital ID service comes as Europe continues to reduce its reliance on U.S. technology.
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, propert
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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WASHINGTON — People in the food world didn’t know what to expect when the Trump administration appointed a little-known Florida attorney as the FDA’s top food official in 2025.
They knew Kyle Diamantas worked at Jones Day representing food, beverage, and tobacco-industry clients. They saw the picture of him and Donald Trump Jr. holding giant, dead wild turkeys after a hunt. He had no experience in public health, in medicine or science, or in government.
The credentials didn’t scream quali
WASHINGTON — People in the food world didn’t know what to expect when the Trump administration appointed a little-known Florida attorney as the FDA’s top food official in 2025.
They knew Kyle Diamantas worked at Jones Day representing food, beverage, and tobacco-industry clients. They saw the picture of him and Donald Trump Jr. holding giant, dead wild turkeys after a hunt. He had no experience in public health, in medicine or science, or in government.
The credentials didn’t scream qualified. And Diamantas was stepping into a center rocked by DOGE layoffs and a defiant resignation by former leader Jim Jones.
On Sunday, the World Health Organization (WHO) declared an Ebola outbreak in the Democratic Republic of the Congo and Uganda to be a public health emergency. This outbreak is deadly, with hundreds of cases across at least two countries, including, by report, one American who was working in the area.
At the same time, a cluster of hantavirus cases linked to a Dutch cruise ship in the South Atlantic has killed three and exposed hundreds more.Read the rest…
On Sunday, the World Health Organization (WHO) declared an Ebola outbreak in the Democratic Republic of the Congo and Uganda to be a public health emergency. This outbreak is deadly, with hundreds of cases across at least two countries, including, by report, one American who was working in the area.
At the same time, a cluster of hantavirus cases linked to a Dutch cruise ship in the South Atlantic has killed three and exposed hundreds more.
Sen. John Cornyn: How many patents do you [have?]AbbVie CEO Richard Gonzalez: … A hundred and thirty-six patents.Cornyn: A hundred and thirty-six patents on one drug?Gonzalez: But, well, remember, Humira is like nine different drugs, or 10 different drugs. So —Cornyn: I thought you said to Sen. [Debbie] Stabenow it was the samemolecule.Gonzalez: It is the same molecule, but it treats different conditions. And if you look at that patent portfolio —Cornyn: So you use the same molecule to treat dif
Sen. John Cornyn: How many patents do you [have?] AbbVie CEO Richard Gonzalez: … A hundred and thirty-six patents. Cornyn: A hundred and thirty-six patents on one drug? Gonzalez: But, well, remember, Humira is like nine different drugs, or 10 different drugs. So — Cornyn: I thought you said to Sen. [Debbie] Stabenow it was the same molecule. Gonzalez: It is the same molecule, but it treats different conditions. And if you look at that patent portfolio — Cornyn: So you use the same molecule to treat different conditions and you can get a patent on that treatment? Gonzalez: Certainly.
The above exchange comes from a 2019 congressional hearing. Sen. John Cornyn, a Republican from Texas, was asking AbbVie’s CEO, Richard Gonzalez, to explain to the Senate Committee on Finance how his company had amassed so many patents on this single drug called Humira. Gonzalez, who was no stranger to controversy, chose to respond by likening it to multiple drugs. After AbbVie had received the first regulatory approval for Humira to treat rheumatoid arthritis, a condition that causes inflammation of the joints, it thought the drug might also work on inflammatory bowel disease. In fact, AbbVie would eventually test, obtain patents for, and get FDA approval of the drug for several inflammation-related conditions. For Gonzalez, the 136 patents AbbVie had accumulated up until that point were justified. They were “innovations they had created,” he said. This would mean another 18 years of patent protection beyond the expiry of Humira’s original patent in 2016.
Hospitals and other providers are bracing for an end to the extra money they’ve gotten for treating Medicaid patients, one of the many cuts contained in Republicans’ sweeping 2025 tax law.
But the Trump administration disclosed this week that it plans to take the cuts to state directed payments even further, setting up what’s likely to be a showdown with provider groups.
Since 2024, some hospitals, doctors, nursing homes, and other types of providers have been reimbursed for Medicaid serv
Hospitals and other providers are bracing for an end to the extra money they’ve gotten for treating Medicaid patients, one of the many cuts contained in Republicans’ sweeping 2025 tax law.
But the Trump administration disclosed this week that it plans to take the cuts to state directed payments even further, setting up what’s likely to be a showdown with provider groups.
Since 2024, some hospitals, doctors, nursing homes, and other types of providers have been reimbursed for Medicaid services at much higher commercial rates, thanks to a Biden-era change. The One Big Beautiful Bill Act, passed in July, directs the Centers for Medicare and Medicaid Services to gradually trim those payments beginning in 2028 until they’re close to or on par with Medicare rates.
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 w
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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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
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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President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip exports. Nothing did. Not a single Nvidia H200 has shipped to China since Trump first authorised the sales in December 2025, and US Trade Representative Jamieson Greer told Bloomberg that semiconductor controls were not even on the bilateral agenda.
The summit theatre obscured a more interesting development underneath it. The H200 is
President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip exports. Nothing did. Not a single Nvidia H200 has shipped to China since Trump first authorised the sales in December 2025, and US Trade Representative Jamieson Greer told Bloomberg that semiconductor controls were not even on the bilateral agenda.
The summit theatre obscured a more interesting development underneath it. The H200 isn’t stuck because Washington won’t allow it. Washington already has allowed it. Roughly 10 Chinese firms, including Alibaba, Tencent, ByteDance, and JD.com, hold approved US export licences for up to 75,000 units each, with Lenovo and Foxconn authorised as distributors. The chips aren’t moving because Beijing won’t let its own companies take delivery.
Two frameworks, one deadlock
The mechanics of the stalemate are worth understanding clearly. US rules require that all H200 chips ordered by Chinese clients be used only in China. Beijing, meanwhile, has instructed Chinese tech companies to limit their use of Nvidia chips to overseas operations while supporting domestic manufacturing. The two requirements are mutually exclusive.
Chips cleared for export cannot legally be deployed where Beijing wants to deploy them, and Beijing won’t authorise the domestic use the US licences require, according to Implicator.
Commerce Secretary Howard Lutnick stated at a Senate hearing last month that Chinese firms are trying to keep their investment focused on domestic suppliers, including Huawei. Beijing’s State Council has also ordered a supply-chain security review aimed at cutting dependence on US semiconductors.
The policy contradiction is not accidental. That is the point.
What Huawei gained while diplomats talked
The days around the summit produced several data points that matter more for the long term than Trump’s parting comment. DeepSeek confirmed its latest model had been optimised to run on Huawei processors. Tencent’s chief strategy officer said Chinese GPU supply would increase progressively through 2026, and an Alibaba executive said its T-Head proprietary GPUs had achieved scaled mass production.
This follows the April launch of DeepSeek V4, which adapted the model for Huawei’s Ascend chips – the first major Chinese frontier model to do so in training, not just inference. What the summit week confirmed is that the shift is no longer experimental. It is now a supply-chain policy. Nvidia’s China revenue has fallen to roughly 5% in recent quarters, down from above 20% before export controls tightened. The company’s own guidance for the current quarter assumes zero revenue from China.
Huang’s last-minute inclusion in the delegation – Trump called him directly after seeing media coverage that he had not been invited – suggested urgency. The outcome suggested the limits of what CEO diplomacy can achieve when the obstruction is structural, not procedural.
The read for the AI industry
The stalemate matters beyond bilateral optics. Chinese AI platforms are now operating under a domestic mandate to build on Huawei’s compute stack. The question of which AI hardware architecture becomes dominant in the world’s second-largest AI market is being answered not by technical benchmarks but by government directive.
Beijing steering platforms toward Huawei Ascend chips rather than Nvidia H200S is not just a trade posture. It is a structural bet that the performance gap will close fast enough that being locked into the domestic stack is manageable. DeepSeek V4’s results suggest it may be right, at least for inference workloads.
Trump said something could happen. Greer said the decision is sovereign for China. Both are true, and neither changes the current position: the H200 deal is approved, licensed, and frozen, with Huawei filling the space it leaves behind.
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Some lawmakers are calling for widespread reforms following years of surveillance scandals and abuses across successive U.S. administrations. But even if the spy law known as Section 702 expires in April, the government's spy powers will not automatically lapse.
Some lawmakers are calling for widespread reforms following years of surveillance scandals and abuses across successive U.S. administrations. But even if the spy law known as Section 702 expires in April, the government's spy powers will not automatically lapse.
The Commodity Futures Trading Commission announced Friday that it has won a temporary restraining order preventing Arizona from pursuing its criminal case against Kalshi.
The Commodity Futures Trading Commission announced Friday that it has won a temporary restraining order preventing Arizona from pursuing its criminal case against Kalshi.
To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure.
When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a platform to foundational infrastructure, altering the governing rules entirely.
At the initial product stage, exert
To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure.
When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a platform to foundational infrastructure, altering the governing rules entirely.
At the initial product stage, exerting tight corporate control often feels highly advantageous. Closed development environments iterate quickly and tightly manage the end-user experience. They capture and concentrate financial value within a single corporate entity, an approach that functions adequately during early product development cycles.
However, IBM’s analysis highlights that expectations change entirely when a technology solidifies into a foundational layer. Once other institutional frameworks, external markets, and broad operational systems rely on the software, the prevailing standards adapt to a new reality. At infrastructure scale, embracing openness ceases to be an ideological stance and becomes a highly practical necessity.
AI is currently crossing this threshold within the enterprise architecture stack. Models are increasingly embedded directly into the ways organisations secure their networks, author source code, execute automated decisions, and generate commercial value. AI functions less as an experimental utility and more as core operational infrastructure.
The recent limited preview of Anthropic’s Claude Mythos model brings this reality into sharper focus for enterprise executives managing risk. Anthropic reports that this specific model can discover and exploit software vulnerabilities at a level matching few human experts.
In response to this power, Anthropic launched Project Glasswing, a gated initiative designed to place these advanced capabilities directly into the hands of network defenders first. From IBM’s perspective, this development forces technology officers to confront immediate structural vulnerabilities. If autonomous models possess the capability to write exploits and shape the overall security environment, Thomas notes that concentrating the understanding of these systems within a small number of technology vendors invites severe operational exposure.
With models achieving infrastructure status, IBM argues the primary issue is no longer exclusively what these machine learning applications can execute. The priority becomes how these systems are constructed, governed, inspected, and actively improved over extended periods.
As underlying frameworks grow in complexity and corporate importance, maintaining closed development pipelines becomes exceedingly difficult to defend. No single vendor can successfully anticipate every operational requirement, adversarial attack vector, or system failure mode.
Implementing opaque AI structures introduces heavy friction across existing network architecture. Connecting closed proprietary models with established enterprise vector databases or highly sensitive internal data lakes frequently creates massive troubleshooting bottlenecks. When anomalous outputs occur or hallucination rates spike, teams lack the internal visibility required to diagnose whether the error originated in the retrieval-augmented generation pipeline or the base model weights.
Integrating legacy on-premises architecture with highly gated cloud models also introduces severe latency into daily operations. When enterprise data governance protocols strictly prohibit sending sensitive customer information to external servers, technology teams are left attempting to strip and anonymise datasets before processing. This constant data sanitisation creates enormous operational drag.
Furthermore, the spiralling compute costs associated with continuous API calls to locked models erode the exact profit margins these autonomous systems are supposed to enhance. The opacity prevents network engineers from accurately sizing hardware deployments, forcing companies into expensive over-provisioning agreements to maintain baseline functionality.
Why open-source AI is essential for operational resilience
Restricting access to powerful applications is an understandable human instinct that closely resembles caution. Yet, as Thomas points out, at massive infrastructure scale, security typically improves through rigorous external scrutiny rather than through strict concealment.
This represents the enduring lesson of open-source software development. Open-source code does not eliminate enterprise risk. Instead, IBM maintains it actively changes how organisations manage that risk. An open foundation allows a wider base of researchers, corporate developers, and security defenders to examine the architecture, surface underlying weaknesses, test foundational assumptions, and harden the software under real-world conditions.
Within cybersecurity operations, broad visibility is rarely the enemy of operational resilience. In fact, visibility frequently serves as a strict prerequisite for achieving that resilience. Technologies deemed highly important tend to remain safer when larger populations can challenge them, inspect their logic, and contribute to their continuous improvement.
Thomas addresses one of the oldest misconceptions regarding open-source technology: the belief that it inevitably commoditises corporate innovation. In practical application, open infrastructure typically pushes market competition higher up the technology stack. Open systems transfer financial value rather than destroying it.
As common digital foundations mature, the commercial value relocates toward complex implementation, system orchestration, continuous reliability, trust mechanics, and specific domain expertise. IBM’s position asserts that the long-term commercial winners are not those who own the base technological layer, but rather the organisations that understand how to apply it most effectively.
We have witnessed this identical pattern play out across previous generations of enterprise tooling, cloud infrastructure, and operating systems. Open foundations historically expanded developer participation, accelerated iterative improvement, and birthed entirely new, larger markets built on top of those base layers. Enterprise leaders increasingly view open-source as highly important for infrastructure modernisation and emerging AI capabilities. IBM predicts that AI is highly likely to follow this exact historical trajectory.
Looking across the broader vendor ecosystem, leading hyperscalers are adjusting their business postures to accommodate this reality. Rather than engaging in a pure arms race to build the largest proprietary black boxes, highly profitable integrators are focusing heavily on orchestration tooling that allows enterprises to swap out underlying open-source models based on specific workload demands. Highlighting its ongoing leadership in this space, IBM is a key sponsor of this year’s AI & Big Data Expo North America, where these evolving strategies for open enterprise infrastructure will be a primary focus.
This approach completely sidesteps restrictive vendor lock-in and allows companies to route less demanding internal queries to smaller and highly efficient open models, preserving expensive compute resources for complex customer-facing autonomous logic. By decoupling the application layer from the specific foundation model, technology officers can maintain operational agility and protect their bottom line.
The future of enterprise AI demands transparent governance
Another pragmatic reason for embracing open models revolves around product development influence. IBM emphasises that narrow access to underlying code naturally leads to narrow operational perspectives. In contrast, who gets to participate directly shapes what applications are eventually built.
Providing broad access enables governments, diverse institutions, startups, and varied researchers to actively influence how the technology evolves and where it is commercially applied. This inclusive approach drives functional innovation while simultaneously building structural adaptability and necessary public legitimacy.
As Thomas argues, once autonomous AI assumes the role of core enterprise infrastructure, relying on opacity can no longer serve as the organising principle for system safety. The most reliable blueprint for secure software has paired open foundations with broad external scrutiny, active code maintenance, and serious internal governance.
As AI permanently enters its infrastructure phase, IBM contends that identical logic increasingly applies directly to the foundation models themselves. The stronger the corporate reliance on a technology, the stronger the corresponding case for demanding openness.
If these autonomous workflows are truly becoming foundational to global commerce, then transparency ceases to be a subject of casual debate. According to IBM, it is an absolute, non-negotiable design requirement for any modern enterprise architecture.
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.
Next-generation AI assistants being developed in the Apple ecosystem and by chipmakers like Qualcomm, but early reports suggest they are being designed with limits in place.
Tom’s Guide has described early versions of these assistants as capable of navigating apps, carrying out bookings, and managing tasks in services. For instance a private beta agentic system completed tasks like booking services or posting content in apps. In one test, it moved through an app workflow and reached a payment sc
Next-generation AI assistants being developed in the Apple ecosystem and by chipmakers like Qualcomm, but early reports suggest they are being designed with limits in place.
Tom’s Guide has described early versions of these assistants as capable of navigating apps, carrying out bookings, and managing tasks in services. For instance a private beta agentic system completed tasks like booking services or posting content in apps. In one test, it moved through an app workflow and reached a payment screen before asking the user for confirmation.
AI agents are being built with approval checkpoints. Sensitive actions, especially those tied to payments or account changes, require user confirmation before they are completed. The “human-in-the-loop” model lets the system prepare an action, but leaves approval to the user. Research linked to Apple’s AI work has explored ways to ensure systems pause before taking actions users did not explicitly request.
Banking apps already require confirmation for transfers. The same idea is now being applied to AI-driven actions in multiple services.
Limits and control
A control layer comes from restricting what the AI can access. Rather than providing the system full access to apps and data, businesses are establishing limits, such as which apps the AI can interact with and when actions can be triggered.
In practice, this means the AI may be able to draft a purchase or prepare a booking, but not finalise it without approval. It also means the system cannot move freely in all services unless it has been granted permission.
According to Tom’s Guide, the facility is for privacy. If data remains on the device, it eliminates the need to send sensitive information to external servers.
In areas like payments, AI systems are expected to work with partners that already have strict rules in place. In one reported example, payment providers’ services are being integrated to provide secure authentication before transactions are completed, though such safeguards are still under development. The existing systems act as an additional layer of oversight. They can set transaction limits or require extra verification.
Much of the discussion around AI governance has focused on enterprise use. That includes areas like cybersecurity and large-scale automation. The consumer side introduces a different challenge and companies must design controls that work for everyday users. That means clear approval steps and built-in privacy protections.
Autonomy with boundaries
As AI gains the ability to carry out actions, the risks become greater as errors can lead to financial loss or data exposure.
By placing controls at multiple points, including approval and infrastructure, companies are trying to manage those risks.
The approach may shape how agentic AI develops in the near term. Rather than aiming for full independence, companies appear focused on controlled environments where the risks can be managed.
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.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
Zac Jiwa, a federal Medicare official, delivered a eulogy of sorts at a Thursday Medicare event highlighting the successes of the Health Tech Ecosystem initiative.
The eulogy’s subject? The clipboard.
For the past eight months, hundreds of health tech companies have been working to meet goals set out by the federal government to make patient records more portable, create systems that import patients’ data into providers’ electronic health records systems, and stand up various patient a
Zac Jiwa, a federal Medicare official, delivered a eulogy of sorts at a Thursday Medicare event highlighting the successes of the Health Tech Ecosystem initiative.
The eulogy’s subject? The clipboard.
For the past eight months, hundreds of health tech companies have been working to meet goals set out by the federal government to make patient records more portable, create systems that import patients’ data into providers’ electronic health records systems, and stand up various patient apps. The idea is to make filling out a stack of paperwork at every doctor’s visit, on that ubiquitous clipboard, a thing of the past.
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