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

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Every major economy is staring at the same problem right now. Artificial intelligence is consuming electricity at a pace that grids were never designed to handle. In the US, capacity market prices in PJM, the country’s largest grid operator, have risen more than tenfold in two years, with data-centre growth identified as a primary driver. In Europe, utilities are scrambling to upgrade transmission infrastructure fast enough to keep pace with hyperscalers’ demand.

The International Energy Agency (IEA) projects global data-centre electricity consumption could approach 1,000 TWh by the end of this decade. Renewable energy is largely there, but the ability to coordinate it, through AI energy grid mapping at national scales, is what most countries still lack. But China just built it.

A study published in Nature this week by researchers from Peking University and Alibaba Group’s DAMO Academy has produced something that no country has managed before: a complete, high-resolution, AI-generated inventory of an entire nation’s wind and solar infrastructure, with the analytical framework to coordinate it as a unified system.

Using a deep-learning model trained on sub-metre satellite imagery, the team identified China’s 319,972 solar photovoltaic facilities and 91,609 wind turbines, processing 7.56 terabytes of imagery to do so.

AI energy grid mapping

Prior research into solar-wind complementarity – the idea that two sources can offset each other’s variability in time and geography – has largely relied on hypothetical or modelled deployment scenarios. How complementarity manifests under real-world infrastructure, and how it shapes system-level integration outcomes, has until now remained unclear.

The researchers show that solar-wind complementarity substantially reduces generation variability, with effectiveness increasing as the geographic scope of pairing expands.

In practical terms, the further apart the facilities being coordinated are, the more reliably they achieve balance. A cloud that covers solar farms in Gansu does not darken wind corridors in Inner Mongolia, for example. The study’s findings point to a structural inefficiency in how China currently manages its grid: coordination happens at a provincial rather than national level.

Transitioning to a unified national scale, the researchers argue, would make it easier to pair complementary energy sources, stabilise the grid, and avoid curtailment – the wasting of generated renewable power that has long been one of China’s most costly clean-energy problems.

Liu Yu, a professor at Peking University’s School of Earth and Space Sciences, described the inventory as allowing China to see its new-energy landscape from a “God’s-eye view,” a phrase that carries more operational weight than it might first suggest. Grid operators cannot optimise what they are not aware of – until now.

China is in the middle of an AI-driven electricity demand surge that is straining its grid. The rapid proliferation of data services and massive computing facilities have pushed the sector’s power consumption up 44% year-on-year in the first quarter of 2026, reaching 22.9 billion kilowatt-hours, according to the China Electricity Council.

That is an extraordinary rate of growth for a sector whose demand was already great. This has accelerated data-centre expansion in China’s northern and western provinces, where land is cheaper, wind and solar resources are more available, with commensurately lower electricity prices. The provinces being targeted for new data centres are the same regions with the highest solar-wind complementarity.

Behind the model

The technical achievement behind this is worth understanding in its own right. DAMO’s deep-learning model was trained to identify solar photovoltaic facilities and wind turbines from sub-metre resolution satellite imagery, a task complicated by the sheer diversity of installation types, terrain conditions, and image quality.

The resulting dataset covers installations in 1,915 Chinese counties, spanning everything from rooftop panels in coastal cities to utility-scale wind farms on the Mongolian plateau. Processing 7.56 terabytes of imagery to produce a nationally consistent, county-level inventory is a demonstration of what large-scale geospatial AI can do when applied to infrastructure problems, and a template that other countries could, in principle, replicate.

China’s clean energy sector generated an estimated 15.4 trillion yuan (US$2.26 trillion) in economic output last year, equivalent to Brazil’s entire GDP, according to the Finland-based Centre for Research on Energy and Clean Air. Managing an asset base of that scale without a national-level visibility tool was always going to be a limiting factor, a limit that’s now gone.

The study’s dataset and code have been made publicly available via Zenodo.

(Photo by Luo Lei)

See also: Inside China’s push to apply AI in its energy system

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The ceremony was scheduled. The CEOs were on the guest list. And then it wasn’t happening.

On Thursday, US President Donald Trump scrapped a planned AI executive order, which had already been delayed multiple times, citing concerns that it might erode America’s competitive edge over China.

“We’re leading China, we’re leading everybody, and I don’t want to do anything that’s going to get in the way of that lead,” Trump told reporters in the Oval Office. What he did not say was that the order had been effectively killed by the very industry it was meant to oversee.

Lobbied out in one night

According to Semafor, which first reported the backstory, the White House’s plans were halted after Elon Musk of xAI, Meta CEO Mark Zuckerberg, and venture capitalist David Sacks, who, until recently, was Trump’s AI and cryptocurrency tsar, all spoke directly with Trump between Wednesday night and Thursday morning.

The argument that landed, according to US media, citing sources, was an appeal to the “accelerationist” faction in the administration, including officials at the National Economic Council and staffers in the Vice President’s office.

The order itself was not a sweeping regulatory framework. It would have established a voluntary mechanism for AI developers to engage with federal agencies and submit advanced models for security review up to 90 days before their public release. No licensing regime. No mandatory hold periods. Voluntary.

That was apparently still too much. Trump said he postponed it “because I didn’t like certain aspects of it,” declining to specify which ones. He added that he worried it “could have been a blocker,” a telling phrase from a president who has otherwise positioned AI as a jobs and national security priority.

A vacuum with consequences

The US has yet to pass comprehensive AI legislation. What governance architecture exists has been assembled piecemeal, through executive orders, agency guidance, and voluntary agreements. Earlier this month, the federal Centre for AI Standards and Innovation announced evaluation agreements with Google DeepMind, Microsoft, and xAI, allowing the government to assess models before public availability. That programme continues regardless of Thursday’s non-signing.

But the broader picture is one of regulatory drift. In early March, the Trump administration released a National AI Legislative Framework urging Congress to preempt state-level AI laws that “impose undue burdens,” arguing for a single national standard over what it called “fifty discordant ones.” Congress has not acted on it.

The contrast with China is sharp and increasingly difficult to ignore. Beijing’s State Council issued a 2026 legislative work plan in May outlining plans to accelerate comprehensive AI legislation, deploying language on AI governance in formal planning documents for the first time. The National People’s Congress has listed AI legislation for review for the third consecutive year.

In April, Beijing issued new rules requiring AI companies to establish internal ethics review committees. China is writing rules. Washington is cancelling ceremonies.

Who shapes US AI policy

Thursday’s episode clarified something implicit for months: in the current administration, the effective veto on AI regulation sits with a small group of industry principals who have direct access to the president.

Musk, whose xAI is a direct competitor to OpenAI and Anthropic, has a structural interest in keeping the regulatory field open. Zuckerberg’s Meta has similarly positioned itself as a champion of open-source AI development. Sacks, despite having formally left his White House advisory role in March, evidently retains enough influence to shape executive action.

Separately, Semafor reports that OpenAI has secured White House backing for a parallel effort to push AI regulations at the state level, an interesting manoeuvre given that Trump’s earlier executive order threatened states that enacted AI laws the administration disliked. That the administration appears to be simultaneously discouraging state regulation and endorsing OpenAI’s state-level strategy suggests the policy coherence problem runs deeper than one postponed signing.

The China frame does real work, but in both directions

Trump’s stated reason for pulling back, protecting the US lead over China, is the same logic that has driven every major AI policy decision since he returned to office, from the H200 export licence framework to the Stargate infrastructure programme. It is also the logic that China is watching closely.

At the Trump-Xi summit in Beijing earlier this month, the two leaders agreed to launch an intergovernmental dialogue on AI, according to the Chinese Foreign Ministry. Beijing will have noted that Washington’s internal debate about even voluntary AI oversight was resolved not by policymakers, but by the companies that stand to profit most from the absence of guardrails.

In a report by the South China Morning Post, Lizzi C. Lee, a fellow at the Asia Society Policy Institute’s Centre for China Analysis, noted that both the US and China are grappling with the same underlying question: where should the regulatory frontier sit for frontier AI, particularly as models become more capable of autonomous action and more relevant to cybersecurity.

“I think a separate, potentially more important race is on governance and safety: not about who has the most advanced models, but who can govern powerful AI without choking off innovation,” she said.

The same report highlighted what Kyle Chan at the Brookings Institution put it more simply: “AI safety and regulation can be done in a way that doesn’t compromise innovation.”

Neither argument was enough on Thursday. Whether it becomes enough next time, assuming there is a next time, remains unclear.

(Photo by White House)

See also: The US-China AI gap closes amid responsible AI concerns

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The Nvidia Vera chip is rarely the headline when earnings beat estimates, but it should be. When Nvidia reported Q1 revenue of US$81.62 billion on Wednesday, beating analyst estimates of US$78.86 billion, and guided Q2 at US$91 billion–well above Wall Street’s US$86.84 billion forecast–the numbers did what Nvidia numbers always do: dominate the room. 

But buried in CEO Jensen Huang’s conference call with analysts was something more strategically interesting than another quarterly beat. Huang told analysts that Nvidia’s new Vera central processors unlock access to a US$200 billion market, one that sits entirely outside the US$1 trillion the company has already forecast from its Blackwell and Rubin AI GPU lineup between 2025 and 2027. 

He expects Vera chip revenue to hit US$20 billion by the end of this fiscal year. “I expect (Vera) to be the second largest” sales contributor, Huang said during the call.

That’s not a footnote. That’s a second front.

The Vera chip and the inference pivot

The reason Nvidia needs a second front is straightforward: its biggest customers are building their own. Google, Amazon, and Microsoft–collectively expected to pour more than US$700 billion into AI infrastructure this year, up sharply from around US$400 billion in 2025, are simultaneously pouring funds into custom silicon to run AI models. Intel and AMD are also touting CPUs as a credible play for inference workloads. 

The narrative in the chip industry has shifted from who can train the biggest model to who can serve it cheapest and fastest. Inference is where Nvidia’s GPU dominance is most exposed. Training large models is still firmly Nvidia territory, but inference, generating answers at scale, in real time, is increasingly where custom chips from Google’s TPU line, Amazon’s Trainium and others are making their case.

Nvidia’s answer is Vera. The chip, developed in part using technology from Groq, a startup specialising in inference that Nvidia licensed in a deal reportedly worth around US$17 billion, targets exactly this workload. The full Vera Rubin platform, which combines the Vera CPU with Rubin GPUs, is set to launch later this year.

Supply is already the constraint

Huang was candid about one problem: supply. “My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he said on the call. It’s a telling admission for a product Nvidia is positioning as a major growth pillar. To get ahead of disruptions, Nvidia is spending heavily on the supply chain. The company disclosed that its supply commitments rose to US$119 billion in Q1, up from US$95.2 billion the previous quarter, a significant jump that reflects both confidence in demand and anxiety about a global memory chip crunch.

Nvidia also announced a US$80 billion share repurchase programme and raised its quarterly cash dividend to 25 cents per share, from 1 cent, moves that signal financial confidence even as Huang warned of tightening supply.

The question investors are asking

Despite the beats, Nvidia shares fell 1.6% in extended trading after the results. eMarketer analyst Jacob Bourne captured the mood: “Nvidia delivered another beat, but at this point that’s essentially priced in as it keeps beating quarter after quarter. The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel.”

Huang pushed back with numbers of his own. He pointed to a growing sub-segment of AI-specific cloud customers whose spend is now roughly equal to the hyperscalers, but growing faster quarter-over-quarter. “We should be growing faster than hyperscale capex,” he said.

The Vera chip is central to that argument. Whether the supply chain cooperates is a different question entirely.

(Image source: Nvidia’s Newsroom)

See Also: The Nvidia H200 China deal survived the Trump-Xi summit–just not in the way anyone expected

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Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language model, signalling that the company is building an integrated AI stack rather than just filling a gap left by US export controls.

The Zhenwu M890, developed by Alibaba’s semiconductor subsidiary T-Head, delivers three times the performance of its predecessor, the Zhenwu 810E, according to the company, as per Reuters report. But the performance jump is less notable than the architectural intent behind the chip: the M890 is purpose-built for AI agents, where software systems must retain long stretches of context, coordinate with other models in real time, and execute complex multi-step tasks with limited human intervention. 

Those demands, heavy on memory bandwidth and inter-model communication, are meaningfully different from what standard inference chips are optimised for. The difference matters because it tells you something about where Alibaba thinks AI compute is heading. The company isn’t designing around today’s dominant use case; it’s building for the workload profile it expects to define enterprise AI over the next several years.

Built for AI agents, not just inference

More significant than the chip itself is the roadmap Alibaba put alongside it. The M890 will be followed by the V900 in the third quarter of 2027, expected to deliver another roughly threefold performance gain, followed by the J900 in the third quarter of 2028. That’s a deliberate, sustained cadence of in-house silicon upgrades that mirrors the kind of tick-tock product cycles Nvidia has used to maintain its lead in AI accelerators.

The parallel to Huawei is worth noting. Huawei laid out a similar chip roadmap for its Ascend line last year, and both announcements reflect the same underlying reality: Chinese technology companies have concluded that depending on foreign silicon, even in scenarios where export restrictions might ease, is a structural risk they cannot accept. The response has been to treat semiconductor development as a long-term capability-building exercise rather than a procurement problem.

Alibaba’s commitment to that exercise is not shallow. The company pledged more than 380 billion yuan, roughly US$53 billion, on cloud and AI infrastructure over three years last year, its largest-ever investment commitment to the sector. The M890 and its successors are downstream of that spending.

Traction that predates the announcement

T-Head said it has shipped more than 560,000 Zhenwu units to date, with over 400 external customers across 20 industries deploying the chips, including automakers and financial services firms. That is a material production footprint, not lab hardware, and it provides Alibaba with real-world deployment data at scale ahead of the M890’s rollout.

The new chip will be available to Chinese enterprise customers through Alibaba Cloud’s domestic model platform, Bailian, packaged inside the Panjiu AL128, a server system that stacks 128 M890 accelerators into a single rack.

The software side of the stack

Alongside the hardware, Alibaba announced Qwen 3.7-Max, the latest version of its flagship large language model, described as engineered for advanced coding and long-running agent tasks. The company said the model can operate continuously for up to 35 hours without performance degradation, a capability specification that only makes sense if you are designing for extended autonomous operation.

The timing is deliberate. Releasing a chip and a model optimised for the same workload class on the same day is a platform play. Alibaba is building a closed loop: its own silicon in T-Head, its own model in Qwen, its own cloud delivery in Bailian. Each component reinforces the others, and the combined stack is designed to reduce enterprise customers’ dependence on any external vendor.

More than half a million chips have been shipped. A successor is arriving in 2027, with another planned for 2028. T-Head is not hedging. At some point, building around US export controls stops being a workaround and starts being a strategy. Alibaba appears to have crossed that line.

(Image source: The White House)

See Also: Alibaba Qwen is challenging proprietary AI model economics

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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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.

(Image source: The White House)

See Also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

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The open-source AI movement has never lacked for options. Mistral, Falcon, and a growing field of open-weight models have been available to developers for years. But when Meta threw its weight behind Llama, something shifted. A company with three billion users, vast compute resources, and the credibility of a tech giant was now building openly, and the developer community responded.

By early 2026, the Llama ecosystem had reached 1.2 billion downloads, averaging about 1 million per day. That is the context for what happened on April 8, 2026. Meta launched Muse Spark, its first major new Meta AI model in a year, and the first product from its newly formed Meta Superintelligence Labs.

It is capable in ways Llama 4 never was, benchmarks well against the current frontier, and is completely proprietary. No free download. No open weights. No building on it unless Meta decides you can.

The companyspentUS$14.3 billion, brought in Alexandr Wang from Scale AI to lead its AI rebuild, then spent nine months tearing down its entire AI stack and starting over. Muse Spark is what came out the other side. The developer community that made Llama what it was is now being asked to wait for a future open-source version that may or may not arrive on any predictable timeline.

What is Muse Spark?

Muse Spark is a natively multimodal reasoning model with tool-use, visual chain of thought, and multi-agent orchestration built in. It now powers Meta AI, which reaches over three billion users in Meta’s apps. Meta rebuilt its technology infrastructure from scratch, letting the company create a model that is as capable as its older midsize Llama 4 variant for an order of magnitude less compute.

That efficiency number is worth noting. At the scale Meta operates, compute costs compound fast, and running a frontier-class Meta AI model at a fraction of the cost of its predecessors changes the economics of deploying it in billions of interactions daily.

On benchmarks, the picture is genuinely mixed. Muse Spark scores 52 on the Artificial Intelligence Index v4.0, placing it fourth overall behind Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Meta has not claimed to have built the best model in the world, which is itself a departure from the over-claiming that damaged Llama 4’s credibility.

Where Muse Spark leads is health. On HealthBench Hard – open-ended health queries – it scores 42.8, substantially ahead of Gemini 3.1 Pro at 20.6, GPT-5.4 at 40.1, and Grok 4.2 at 20.3. Health is a stated priority for Meta; the company says it worked with over 1,000 physicians to curate training data for the model.

Muse Spark also offers three modes of interaction: Instant mode for quick answers, Thinking mode for multi-step reasoning tasks, and Contemplating mode, which orchestrates multiple agents’ reasoning in parallel to compete with the most demanding reasoning modes from Gemini Deep Think and GPT Pro.

The open-source retreat

This is the part of the Muse Spark story that the benchmark tables do not capture. Unlike Meta’s previous models, which were released as open-weight models – meaning anyone could download and run them on their own equipment – Muse Spark is entirely proprietary. The company said it will offer the model in a private preview to select partners through an API, making Muse Spark even more proprietary than the paid models offered by Meta’s rivals.

Wang addressed the change directly, stating: “Nine months ago, we rebuilt our AI stack from scratch. New infrastructure, new architecture, new data pipelines. This is step one. Bigger models are already in development with plans to open-source future versions.”

The developer community’s response has been sceptical. Some see this as a necessary pivot after Llama 4 failed to gain expected traction. Others view it as Meta closing the gates once it has something worth protecting. That is the community now being asked to wait while competitors without that open-source legacy continue shipping freely available weights.

Distribution over benchmarks

Meanwhile, Meta is not waiting for the developer community to come around. Muse Spark will debut in the coming weeks inside Facebook, Instagram, WhatsApp, and Messenger, as well as in Meta’s Ray-Ban AI glasses. That rollout path is arguably more consequential than any benchmark result. OpenAI and Anthropic sell to developers and enterprises. Meta deploys directly to over three billion people already inside its apps daily.

Meta’s push into health does raise privacy questions worth watching. Muse Spark users will need to log in with an existing Meta account to use it, and while Meta does not explicitly say personal account information will be used by the AI, the company has generally trained on public user data and has positioned Muse Spark as a personal superintelligence product.

Meta stock rose more than 9% on the day of the launch, a signal that investors read the Muse Spark release as proof that the US$14.3 billion bet on Wang and the nine-month rebuild produced something real. Whether the promised open-source versions actually materialise is a question the developer community will press every quarter. The answer will define how this chapter of Meta’s AI story is remembered.

See Also: The Meta-Manus review: What enterprise AI buyers need to know about cross-border compliance risk

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running.

That model is Claude Mythos Preview, and the initiative is called Project Glasswing.

The launch partners include Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks. 

Beyond that core group, Anthropic has extended access to over 40 additional organisations that build or maintain critical software infrastructure. Anthropic is committing up to US$100 million in usage credits for Mythos Preview across the effort, along with US$4 million in direct donations to open-source security organisations. 

A model that outgrew its own benchmarks

Mythos Preview was not specifically trained for cybersecurity work. Anthropic said the capabilities “emerged as a downstream consequence of general improvements in code, reasoning, and autonomy”, and that the same improvements making the model better at patching vulnerabilities also make it better at exploiting them. 

That last part matters. Mythos Preview has improved to the extent that it mostly saturates existing security benchmarks, forcing Anthropic to shift its focus to novel real-world tasks–specifically, zero-day vulnerabilities. These flaws were previously unknown to the software’s developers. 

Among the findings: a 27-year-old bug in OpenBSD, an operating system known for its strong security posture. In another case, the model fully autonomously identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD–CVE-2026-4747–that allows an unauthenticated user anywhere on the internet to obtain complete control of a server running NFS. No human was involved in the discovery or exploitation after the initial prompt to find the bug. 

Nicholas Carlini from Anthropic’s research team described the model’s ability to chain together vulnerabilities: “This model can create exploits out of three, four, or sometimes five vulnerabilities that in sequence give you some kind of very sophisticated end outcome. I’ve found more bugs in the last couple of weeks than I found in the rest of my life combined.” 

Why is it not being released?

“We do not plan to make Claude Mythos Preview generally available due to its cybersecurity capabilities,” Newton Cheng, Frontier Red Team Cyber Lead at Anthropic, said. “Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout–for economies, public safety, and national security–could be severe.” 

This is not hypothetical. Anthropic had previously disclosed what it described as the first documented case of a cyberattack largely executed by AI–a Chinese state-sponsored group that used AI agents to autonomously infiltrate roughly 30 global targets, with AI handling the majority of tactical operations independently. 

The company has also privately briefed senior US government officials on Mythos Preview’s full capabilities. The intelligence community is now actively weighing how the model could reshape both offensive and defensive hacking operations. 

The open-source problem

One dimension of Project Glasswing that goes beyond the headline coalition: open-source software. Jim Zemlin, CEO of the Linux Foundation, put it plainly: “In the past, security expertise has been a luxury reserved for organisations with large security teams. Open-source maintainers, whose software underpins much of the world’s critical infrastructure, have historically been left to figure out security on their own.”

Anthropic has donated US$2.5 million to Alpha-Omega and OpenSSF through the Linux Foundation, and US$1.5 million to the Apache Software Foundation–giving maintainers of critical open-source codebases access to AI cybersecurity vulnerability scanning at a scale that was previously out of reach.

What comes next

Anthropic says its eventual goal is to deploy Mythos-class models at scale, but only when new safeguards are in place. The company plans to launch new safeguards with an upcoming Claude Opus model first, allowing it to refine them with a model that does not pose the same level of risk as Mythos Preview. 

The competitive picture is already shifting around it. When OpenAI released GPT-5.3-Codex in February, the company called it the first model it had classified as high-capability for cybersecurity tasks under its Preparedness Framework. Anthropic’s move with Glasswing signals that the frontier labs see controlled deployment–not open release–as the emerging standard for models at this capability level.

Whether that standard holds as these capabilities spread further is, at this point, an open question that no single initiative can answer.

See Also: Anthropic’s refusal to arm AI is exactly why the UK wants it

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.

The post Anthropic keeps new AI model private after it finds thousands of external vulnerabilities appeared first on AI News.

The failure mode for enterprise AI in 2026 is not what most people expected. It is not that the models are wrong, or that agents cannot reason, or that the technology is overhyped. The failure mode is that the data feeding those systems is fragmented, inconsistently labelled, and spread across dozens of applications that were never designed to share context. 

Boomi calls this the agentic AI data activation problem, and after tracking 75,000 AI agents running in production across its customer base, the company says solving it comes before everything else. That figure comes from February, when Boomi reported its strongest momentum to date: more than 30,000 customers globally, 75,000 AI agents in production, and a customer base that includes over a quarter of the Fortune 500. 

Yet the consistent pattern across those deployments, according to Steve Lucas, chairman and CEO of Boomi, is that AI value only materialises once the data problem is resolved. “AI only delivers value when data is properly activated, trusted and governed first,” Lucas said when the company announced its latest platform capabilities on March 9.

The fragmentation problem

Enterprise data is not missing; it exists in abundance, distributed across ERP systems, CRMs, data lakes, SaaS platforms, and legacy applications that have accumulated over decades. What is missing is the shared context that allows an AI agent to treat data from one system as reliably compatible with data from another. 

An agent drawing customer records from a CRM and pricing data from an ERP may be working from conflicting definitions of what a customer or a product actually is. The outputs it produces are only as coherent as the data standards beneath them.

Boomi’s answer is Meta Hub, a central system of record announced in its March 9 platform update, designed to standardise business definitions across the enterprise and extend that context to every AI agent operating within it. The goal is to ensure agents reason from a consistent understanding of business logic rather than generating outputs based on fragmented interpretations pulled from disconnected systems.

The same release introduced real-time SAP data extraction via change data capture, addressing one of the most common integration bottlenecks in large enterprises, where SAP data is often inaccessible due to slow, manual export processes that render it effectively unavailable to AI workflows in real-time. 

New governance capabilities for Snowflake Cortex agents within Boomi’s Agent Control Tower added audit trails and session logs, addressing a concern that has moved steadily up enterprise priority lists: AI agents operating as a black box, taking actions with no visible reasoning chain.

What the analyst’s recognition signals

Two independent assessments in March gave Boomi external validation of its positioning. On March 16, Gartner named Boomi a Leader in its 2026 Magic Quadrant for Integration Platform as a Service–the twelfth consecutive time–and positioned it highest for Ability to Execute. 

On March 31, the IDC MarketScape for Worldwide API Management named Boomi a Leader, specifically noting its AI-centric strategy that treats APIs as both the fuel and the control plane for AI workloads. The Gartner framing is pointed. 

The report stated that AI-ready integration is a strategic capability that aligns architecture, integration, and governance to enable AI agents to effectively access enterprise data and operate within business processes. That framing validates the problem Boomi is addressing and signals that iPaaS platforms are now being evaluated on AI readiness rather than traditional integration capabilities alone.

The broader pattern

By now, we are aware that the shift from pilot to production in enterprise AI is stalling in a predictable place. Organisations have models. They have agents. What many do not have is the data infrastructure that makes those agents reliable enough to trust with real business processes.

Data activation–moving data from static storage into live, governed, context-rich flows that agents can actually reason from–is one articulation of what that missing layer needs to look like. Whether that framing becomes the industry standard or gets absorbed into a broader category is a question 2026 will start to answer. 

What is not in question is that the enterprises finding ROI from agentic AI are the ones that sorted the data layer first.

Boomi will be exhibiting at the AI & Big Data Expo at TechEx North America, taking place 18–19 May 2026 at the San Jose McEnery Convention Centre.

(Photo by Boomi)

See also: Autonomous AI systems depend on data governance

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.

The post Boomi calls it “data activation” and says it’s the missing step in every AI deployment appeared first on AI News.

The Anthropic UK expansion story is less about diplomatic courtship and more about what happens when a government punishes a company for having principles. In late February, US Defence Secretary Pete Hegseth gave Anthropic CEO Dario Amodei a stark ultimatum: remove guardrails preventing Claude from being used for fully autonomous weapons and domestic mass surveillance, or face consequences. 

Amodei didn’t budge. He wrote that Anthropic could not “in good conscience” grant the Pentagon’s request, arguing that some uses of AI “can undermine rather than defend democratic values.” Washington’s response was swift. 

Trump directed every federal agency to immediately cease all use of Anthropic’s technology, and the Pentagon designated the company a supply chain risk, a label ordinarily reserved for adversarial foreign entities like Huawei. The US$200 million Pentagon contract was pulled. 

Defence tech companies instructed employees to stop using Claude and switch to alternatives. London, watching all of this unfold, saw something different.

The UK’s pitch

Staff at the UK’s Department for Science, Innovation and Technology (DSIT) have drawn up proposals for the US$380 billion company, ranging from a dual stock listing on the London Stock Exchange to an office expansion in the capital, according to multiple people with knowledge of the plans. Prime Minister Keir Starmer’s office has backed the effort, which will be put to Amodei when he visits in late May. 

Anthropic already has around 200 employees in Britain and appointed former prime minister Rishi Sunak as a senior adviser last year. The infrastructure for a meaningful UK presence is already there. What the British government is now offering is an explicit signal that Anthropic’s approach to AI–built on embedded ethical constraints–is an asset, not an obstacle.

A dual listing in London, if it materialised, would give Anthropic access to European institutional investors at a moment when its domestic regulatory standing remains under active legal challenge. The Pentagon’s appeal of the court-ordered injunction blocking the supply chain designation is still before the Ninth Circuit, and the outcome remains uncertain.

Ethics as a competitive advantage

The dispute has been framed largely as a legal and political fight. But its implications for global AI governance run deeper. Anthropic’s lawyers argued in court filings that Claude was not developed to be used for lethal autonomous weapons without human oversight, nor deployed to spy on US citizens, and that using the tools in these ways would represent an abuse of its technology. 

US District Judge Rita Lin, who granted a preliminary injunction blocking the blacklist in March, found the government’s actions “troubling” and concluded they likely violated the law. That judicial finding matters in the UK context. Britain is positioning itself as a regulatory environment sitting between Washington’s current posture, which demands unrestricted military access, and Brussels, where the EU AI Act imposes its own constraints. 

The UK government presents itself as offering a less constrained environment for AI companies than either the US or the European Union. Crucially, that pitch doesn’t ask Anthropic to abandon the guardrails it went to court to defend.

The courtship also sits alongside broader UK efforts to build domestic AI capability, including a recently announced £40 million state-backed research lab, after officials acknowledged the absence of a homegrown competitor to the leading US frontier labs.

Competition in London

The UK’s play for Anthropic is not happening in a vacuum. OpenAI has already committed to making London its biggest research hub outside the US. Google has anchored itself in King’s Cross since acquiring DeepMind in 2014. The race to secure frontier AI in London is already competitive, and Anthropic’s current circumstances make it the most consequential target yet.

Anthropic has been expanding internationally regardless of its domestic legal battles, including opening a Sydney office as its fourth Asia-Pacific location. The global growth strategy is already in motion. What remains to be seen is how much of it London gets to claim.

The company Washington blacklisted for having an AI ethics policy is now being actively courted by another G7 government that wants exactly that. The late May meetings with Amodei will be telling.

See Also: Anthropic selected to build government AI assistant pilot

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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