Meta describes how an AI agent can be designed to capture the logic and expertise of domain experts, rather than simply storing documents or retrieving relevant information. The system, dubbed an "organizational second brain", was built for a specialized compliance domain, but Meta argues the architecture generalizes to areas like security, finance, engineering, and procurement.

Google’s newest AI weather forecasting model predicts wind speed at 100 metres above the ground, roughly the height of a modern wind turbine. It also forecasts cloud cover and how much sunlight reaches the surface, and it updates every hour. Energy traders, grid operators and wind and solar developers already pay other companies for that data. The introduction of WeatherNext 3 now puts Google in their market.

Google DeepMind and Google Research released the model on September 3. It produces a global forecast every hour at up to five-kilometre resolution for surface variables such as temperature and moisture. The previous version, WeatherNext 2, worked on a 25-kilometre grid and refreshed every six hours. Google says the new energy variables are meant to help grid operators and developers predict how much power their wind and solar assets will generate, then match that against demand.

The consumer side of the launch has had most of the attention. WeatherNext 3 now powers weather results in Google Search, the Gemini app, Google Maps and the Google Maps Platform Weather API. Behind it sits an enterprise layer that matters more commercially. The same forecast data can be queried in BigQuery and Earth Engine or downloaded in bulk from Google Cloud Storage, with no model setup required by the customer.

Why the energy sector is buying AI weather forecasting

Grid operators are running a system that has become harder to predict at both ends. On the generation side, renewables now account for most new capacity. S&P Global Market Intelligence’s US Grid Outlook 2026 projects solar and energy storage as the primary sources of new capacity this year, at 51.2GW and 25.7GW respectively out of more than 90GW of planned additions. 

Solar and wind generate according to the weather rather than demand, so each gigawatt added makes a short-term forecast more accurate.

On the consumption side, the new load is coming from AI. S&P Global identifies the spread of data centres across North America as a primary driver of the recent surge in electricity demand, forcing utilities to revise their load forecasts upward. Deloitte’s 2026 Power and Utilities Industry Outlook projects peak demand growing by roughly 26% by 2035, with data centre demand alone potentially reaching 176GW, five times its 2024 level.

The cost of getting a forecast wrong is straightforward. If an operator underestimates how much wind power will arrive, it has to buy replacement electricity at short notice, usually from gas plants kept on expensive standby. If it overestimates, wind and solar farms end up being paid to switch off because the grid cannot absorb what they are producing. Both outcomes are expensive, and both are forecasting failures.

The market Google is entering

Selling weather forecasts to the energy sector is an established business. Vaisala, Solcast, DNV’s WindGEMINI and IBM’s HyperWatch all compete in it. So does Jua, a Swiss firm that claims its EPT-2 model beats Microsoft Aurora and DeepMind’s earlier GraphCast on accuracy while updating 24 times a day, against what it describes as a typical four updates a day among competitors.

Google’s advantage is reach. The same forecast appears as a table in BigQuery, a layer in Earth Engine, an API in Google Maps Platform and the default answer in Google Search. No specialist vendor has that spread, and the hourly refresh closes the update-frequency gap those vendors have used to differentiate themselves.

The incumbents have one technical argument left. Jua’s published position is that physics-based models such as ECMWF’s HRES still outperform purely data-driven AI models during record-breaking extreme weather, because physics models encode rules about how energy and mass move through the atmosphere, while AI models learn patterns from past data. 

Jua sells a physics-constrained product, so the claim serves its own interests. It also describes the conditions grid operators worry about most, when a storm falls outside anything the model has seen in training.

What is new, and what is being oversold

The architectural claim behind WeatherNext 3 is that it learns from real observations instead of from simulations. Most AI weather models, WeatherNext 2 included, are trained on output from numerical weather prediction models, which are supercomputer-driven physics simulations that carry a six-hour data lag. That lag can introduce bias in fast-changing variables such as rain and surface temperature. WeatherNext 3 ingests live geostationary satellite imagery and trains directly on readings from individual weather stations.

The shift is real, though narrower than much of the coverage has suggested. Google’s own system diagram shows the model taking in one-hour satellite mosaics alongside traditional historical analysis. DeepMind senior research scientist Ilan Price told Bloomberg the gain comes from not waiting for the next analysis and using the most recent information available instead. 

Reporting puts the remaining data lag at three to four hours, down from about seven. Dependence on numerical weather prediction has been reduced, not removed.

The accuracy figures need similar care. Google reports improvements of up to 60% against NASA’s IMERG satellite product, 30% against MRMS radar and 10% against rain gauge readings at early lead times, measured using a standard scoring method for probability forecasts. Those are three separate baselines, and the percentages do not add together. The widely repeated claim of 50% better precipitation forecasting applies specifically to forecasts a day or more ahead. Every figure carries an “up to” qualifier, which makes each one a best case rather than a typical result.

Google published no independent third-party validation alongside the launch. It points instead to live evaluations by Brightband, whose leaderboard it cites in claiming WeatherNext 3 is the most accurate global weather model to date. A utility considering a switch away from a paid specialist will care more about performance in its own service territory, on its own assets, than about a global leaderboard position.

Google’s own stake in the problem

Google is selling forecasting tools into a grid problem its own industry helped create. The data centre build-out driving the load growth utilities are struggling to serve is led by the hyperscalers, Google among them, and Google has signed multi-gigawatt renewable procurement agreements to supply its own facilities.

Accurate prediction of wind and solar output is directly useful to a company matching large volumes of clean energy against a load that is both growing and variable. That is commercial logic, and it goes some way to explaining why the energy variables shipped in this release.

Google has not published pricing for enterprise access to WeatherNext 3, or said whether the BigQuery and Earth Engine data carries standard Cloud query charges or a separate licence. Utilities weighing a move away from a paid specialist will want that figure before they weigh any accuracy claim.

2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.

The bank also said AI-based transaction screening allowed it to review more than twice the previous transaction volume while reducing manual operator checks by half.

Bank of America is using a generative AI-enabled system called EricaAssist with more than 18,000 customer service employees. The tool summarises why a customer is calling, retrieves relevant information, and recommends possible next steps while keeping the employee responsible for the interaction.

Bank of America said in July 2026 that EricaAssist can deliver contextual guidance in under three seconds and has reduced average call times by nearly one minute. The bank plans to extend the system to additional servicing scenarios and business lines later in 2026.

(Photo by Google)

See also: MIT AI forecasts extreme weather without historical data

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

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InfoQ expands its online certification portfolio with new AI Engineering and Organizational Architecture cohorts, giving senior practitioners a confidential peer group to pressure-test production AI, platform, team design, and architecture decisions.

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.

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

The post Alibaba is designing AI chips around agents, and that changes what the race is actually about appeared first on AI News.

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.

See also: Why companies like Apple are building AI agents with limits

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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