Seller Central AI Remakes Data Analysis

Amazon announced this week a new artificial intelligence feature in Seller Central that helps merchants explore performance data through visual workspaces rather than static reports.

Described as “a dynamic canvas experience,” the feature hints at a broader shift in reporting software toward what might be called conversational business intelligence.

Canvas Experience

To use this canvas experience, a seller could ask the Amazon AI assistant how advertising campaigns affected product sales, or request a sales comparison between two periods, as examples.

The AI assistant will then generate charts and graphs that display the requested metrics. The system becomes a text- or chat-based interface for Amazon’s vast marketplace datasets.

Sellers can arrange these visual elements within a custom workspace. Amazon describes the tool as a way to experiment with data rather than merely view reports.

Screenshot of a Seller Central canvas

Amazon’s AI-driven “canvas experience” suggests a larger trend toward conversational business intelligence tools. Click image to enlarge.

Workspace Trend

Given the rapid improvements and applications of AI, the Seller Central canvas experience is part of a broader trend in business analysis software.

It suggests a future in which folks rely less on spreadsheets, manual reporting, and even business intelligence tools, and more on AI systems that interpret signals, inform, and make decisions.

Performance analysis changes from someone digging through data or building reports to a conversation.

Tools such as the Seller Central AI canvas suggest future ecommerce analytics may look less like traditional dashboards and more like an ongoing dialogue. The seller asks questions. The system surfaces insights. Decisions follow.

There’s evidence of this trend beyond Amazon.

For example, Shopify’s Winter ’26 platform update introduced more than 150 AI-related enhancements, including updates to Sidekick, its AI assistant. The improved tool, including Sidekick Pulse, helps merchants analyze data, generate tasks, and automate workflows. Merchants can query Sidekick about sales trends, inventory, or marketing performance, much like the Amazon assistant.

Conversational BI

That concept — asking AI about business data — is not necessarily new. Variations of conversational business intelligence are already appearing in analytics software.

Tools such as Power BI, Looker, and Qlik allow users to ask questions in natural language — “Why did our conversion rate drop yesterday?” — and receive charts and summaries.

Implications for Merchants

Online sellers already have access to more data than they can realistically analyze. Amazon Seller Central alone provides reports covering traffic, conversions, advertising performance, and inventory levels. Understanding how those metrics interact often requires exporting data, building spreadsheets, or using external analytics tools.

Conversational business intelligence could reduce that complexity.

Instead of searching reports, a merchant might ask questions about performance and receive charts, summaries, and explanations within seconds. As they mature, the tools could change how merchants interact with ecommerce data in several ways.

  • Lowering the analytics barrier. Businesses gain access to insights that once required advanced reporting tools or technical expertise.
  • Faster decision-making. Merchants could receive performance data in near real-time.
  • More experimentation. AI-driven workspaces facilitate broader testing and analysis.
  • Better visibility across systems. Over time, the tools could connect disparate sources of ecommerce data, such as advertising platforms, analytics services, and marketplaces.

Still, conversational business analysis is unlikely to replace traditional reporting entirely. Merchants will still need reliable data models, clear metrics, and an understanding of how their businesses operate.

Decision Makers

As AI technology improves, the systems may move beyond answering queries to proactively recommend actions or even execute them automatically.

Within parameters, an AI assistant might increase the spend for a profitable advertising campaign, pause a poorly performing keyword group, or alert a merchant that inventory is running low — all on its own.

Thus conversational business intelligence may foretell a more automated environment wherein software not only explains the data but also helps run the business.

For now, tools such as Amazon’s Seller Central canvas may only respond to questions. But as AI evolves inside ecommerce platforms, the distance between insight and action should quickly shrink.

Bridging the operational AI gap

The transformational potential of AI is already well established. Enterprise use cases are building momentum and organizations are transitioning from pilot projects to AI in production. Companies are no longer just talking about AI; they are redirecting budgets and resources to make it happen. Many are already experimenting with agentic AI, which promises new levels of automation. Yet, the road to full operational success is still uncertain for many. And, while AI experimentation is everywhere, enterprise-wide adoption remains elusive.

Without integrated data and systems, stable automated workflows, and governance models, AI initiatives can get stuck in pilots and struggle to move into production. The rise of agentic AI and increasing model autonomy make a holistic approach to integrating data, applications, and systems more important than ever. Without it, enterprise AI initiatives may fail. Gartner predicts over 40% of agentic AI projects will be cancelled by 2027 due to cost, inaccuracy, and governance challenges. The real issue is not the AI itself, but the missing operational foundation.

To understand how organizations are structuring their AI operations and how they are deploying successful AI projects, MIT Technology Review Insights surveyed 500 senior IT leaders at mid- to large-size companies in the US, all of which are pursuing AI in some way.

The results of the survey, along with a series of expert interviews, all conducted in December 2025, show that a strong integration foundation aligns with more advanced AI implementations, conducive to enterprise-wide initiatives. As AI technologies and applications evolve and proliferate, an integration platform can help organizations avoid duplication and silos, and have clear oversight as they navigate the growing autonomy of workflows.

Key findings from the report include the following:

Some organizations are making progress with AI. In recent years, study after study has exposed a lack of tangible AI success. Yet, our research finds three in four (76%) surveyed companies have at least one department with an AI workflow fully in production.

AI succeeds most frequently with well-defined, established processes. Nearly half (43%) of organizations are finding success with AI implementations applied to well-defined and automated processes. A quarter are succeeding with new processes. And one-third (32%) are applying AI to various processes.

Two-thirds of organizations lack dedicated AI teams. Only one in three (34%) organizations have a team specifically for maintaining AI workflows. One in five (21%) say central IT is responsible for ongoing AI maintenance, and 25% say the responsibility lies with departmental operations. For 19% of organizations, the responsibility is spread out.

Enterprise-wide integration platforms lead to more robust implementation of AI. Companies with enterprise-wide integration platforms are five times more likely to use more diverse data sources in AI workflows. Six in 10 (59%) employ five or more data sources, compared to only 11% of organizations using integration for specific workflows, or 0% of those not using an integration platform. Organizations using integration platforms also have more multi-departmental implementation of AI, more autonomy in AI workflows, and more confidence in assigning autonomy in the future.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

New Ecommerce Tools: March 4, 2026

Our rundown this week of new services for ecommerce merchants includes updates on product photography, lead generation tools, seller assistants, retail media, shipping, cross-border ecommerce, cryptocurrencies, and AI-powered customer experiences.

Got an ecommerce product release? Email updates@practicalecommerce.com.

New Tools for Merchants

DHL Group and JD.com partner to drive growth for German brands in China and Europe. DHL Group and JD.com, China’s largest retailer, have partnered to support German brands’ growth in China and throughout European markets through JD.com’s retail platform, Joybuy. Also, by engaging JD.com’s cross-border Jingdong Logistics, German brands can sell directly to more than 700 million Chinese consumers on JD.com.

Home page of Joybuy

JD.com’s Joybuy

Amazon introduces an AI-powered canvas experience for sellers. Amazon has introduced a dynamic “canvas” in Seller Central to generate real-time personalized visual workspaces. The canvas uses the same agentic AI architecture as Seller Assistant, powered by Amazon Bedrock and leveraging Amazon Nova and Anthropic’s Claude. Sellers can query the Assistant or select from suggested prompts. Seller Assistant then assembles a personalized canvas with the data, insights, and actions.

VisibleFirst launches a free WordPress plugin for AI search. VisibleFirst, a generative-AI optimization platform, has launched a free WordPress plugin to help businesses get discovered by AI-powered search platforms, including ChatGPT, Claude, and Gemini. The product includes a free Visibility Score that analyzes how AI platforms currently see a given business. The plugin is available for free download on WordPress.org.

Veho expands shipping hubs in U.S. Veho, an ecommerce delivery provider, has expanded its network to 66 U.S. markets. Veho opened two new regional hubs — in Phoenix and Ontario, California — spanning more than 150,000 square feet and located minutes from major air, rail, and port terminals, including Los Angeles and Long Beach. Veho can now enable next-day delivery across much of the Southwest, with coast-to-coast delivery as fast as two days by air or four days by ground.

Home page of Veho

Veho

OpenAI and Amazon announce strategic partnership. OpenAI and Amazon Web Services will create an environment powered by OpenAI models for AWS customers to build, deploy, and manage generative AI applications and agents. OpenAI and Amazon will develop models to power Amazon’s customer-facing applications. Amazon will also invest $50 billion in OpenAI.

Kevel launches Adobe Experience Platform integration for real-time retail media. Kevel, a retail media technology provider, has launched its native Adobe Experience Platform Destination, enabling retailers, marketplaces, and commerce media platforms to activate first-party audience data in real-time for retail media campaigns. Kevel states that the integration expands its Retail Media Cloud and ensures that customer insights are actionable at the moment of ad decisioning.

Klaviyo and Google partner to power autonomous customer experiences. Klaviyo has partnered with Google to help brands deliver autonomous AI-driven customer experiences. The partnership combines Google’s capabilities in search, advertising, AI, and messaging with Klaviyo’s real-time customer data and decisioning, enabling brands to move beyond static campaigns toward experiences that adapt automatically to customer intent and behavior. Customer intent signals captured across Google surfaces can now inform personalized actions within Klaviyo, with every interaction flowing back into a customer profile.

Home page of Klaviyo

Klaviyo

Ordoro and ShipperHQ partner on smarter shipping. Ordoro, a developer of multichannel ecommerce operations software, has partnered with ShipperHQ to support merchants navigating complex shipping and fulfillment decisions. This collaboration focuses on education and visibility rather than product integration. Through co-marketing and shared content, Ordoro and ShipperHQ will spotlight common ecommerce pain points and offer guidance on how merchants can overcome them, highlighting smarter shipping strategies that connect front-end checkout experience with back-end operational success.

Amazon India reduces seller referral fees. Amazon will no longer referral fees to sellers in India for products under 1,000 rupees ($10.90), as it attracts merchants to its marketplace. The move expands on Amazon’s zero-referral fee policy launched last year, which covered roughly 12 million products priced below 300 rupees. Effective March 16, the new structure ⁠covers more than 125 million products. Amazon is also reducing some shipping charges.

Infobip launches AgentOS for autonomous AI-driven customer journeys. Infobip is launching AgentOS, a platform that builds on Infobip’s recently launched AI Agents for autonomous customer communications. AgentOS combines Infobip’s Conversational Customer Data Platform with real-time journey orchestration to deliver one-way and two-way contextual engagement across all natively integrated channels. According to Infobip, the platform unites marketing, sales, and support to connect every customer touchpoint into a seamless journey.

Home page of Infobip

Infobip

LeadQuizzes relaunches lead generation platform with AI builder and scoring. LeadQuizzes, a lead qualification and generation platform, has relaunched with an AI-powered builder for lead generation funnels, enhanced lead scoring, and native integrations with HubSpot, Salesforce, ActiveCampaign, and Zapier. Per LeadQuizzes, the platform enables quizzes from a single text prompt. Every response feeds a real-time scoring engine that automatically qualifies, segments, and tags leads. Users get real-time reporting on which questions predict high-value outcomes, where prospects drop off, and how scores track to conversion.

Instant launches Studio for ecommerce product photography. Instant, a Shopify store-building app, has released Studio to generate product photography, lifestyle images, and reusable AI avatars. According to Instant, Studio’s AI product shots provide visuals with customizable product angles and backgrounds, while lifestyle images provide contextual scenes featuring customizable AI avatars. Users can choose from curated styles, scene presets, or build-your-own, and adjust aspect ratios, image quality, and AI model settings.

2328.io launches crypto payment infrastructure for online businesses. 2328.io, a developer of cryptocurrency and financial automation systems, has launched a cryptocurrency payment platform for businesses operating in cross-border and digital-native markets. The system enables the acceptance of cryptocurrency and stablecoin payments across websites, Telegram bots, Discord bots, mobile and desktop applications, and point-of-sale software environments. Integration is available via hosted checkout or API-based implementation.

Yottaa expands Web Performance Cloud. Yottaa, a platform to improve download speeds, has updated its Web Performance Cloud, powered by its Hybrid Real User Monitoring. According to Yottaa, the update strengthens websites’ Core Web Vitals and third-party application diagnostics, combining analytics that help ecommerce and marketing teams understand what’s happening and take action on complex performance data. Yottaa has also relaunched YoBot, its automated performance assistant, with generative AI capabilities.

Home page of Yottaa

Yottaa

Scaling the agentic web with NLWeb

Imagine a web ecosystem where not just humans but AI agents communicate with websites, going beyond traditional browsing. Unlike conventional web experiences, where people click, scroll, and search, AI agents can navigate, interpret, and even perform tasks autonomously on your site. This is not a futuristic concept. It is already unfolding. This is the emergence of the agentic web.

Table of contents

Key takeaways

  • The agentic web enables AI agents to autonomously navigate and interact with websites, shifting user responsibilities from manual navigation to decision-making
  • Protocols are crucial for communication among AI agents; they must rely on structured, machine-readable data for effective coordination
  • SEO professionals must adapt to the agentic web by optimizing websites as endpoints for AI queries, ensuring structured data and clarity
  • NLWeb facilitates interaction between agents and websites by exposing structured data and allowing for natural language queries without traditional interface limitations
  • Yoast’s collaboration with NLWeb helps WordPress users prepare for the agentic web by organizing content and making it easier to integrate structured data

The big shift: From web for users to a web for users and agents

For years, the web followed a simple pattern. Humans searched, clicked, compared, and completed tasks manually. Even as search engines evolved, the interaction model stayed the same: search and click.

That model is changing.

The agentic web represents a shift from a web designed only for human users to one designed for both people and AI assistants. Instead of manually researching products, comparing services, filling out forms, and completing transactions, users will increasingly delegate those tasks to intelligent assistants that can search, interpret information, and act on their behalf. The user’s role shifts from active navigator to decision-maker.

From searching to delegating.

This is not about smarter chat interfaces. It is about autonomous agents that can interpret the search intent, compare options, and execute actions on behalf of users. Websites are no longer just pages to be visited. They are endpoints to be queried.

For that to work at scale, intelligence cannot reside in a single assistant or on a closed platform. It has to be distributed. Systems must be able to communicate with other systems without friction. That requires a web that is machine-readable, interoperable, and built for agent-to-agent interaction.

The agentic web is not a prediction. It is an architectural shift already underway!

Protocol thinking and the infrastructure of agentic web communication

If the agentic web is about intelligent systems interacting with websites, then the real question becomes simple: how do these systems understand each other?

The answer is not design. It is infrastructure.

The web has always depended on shared communication rules. HTTP allows browsers to request pages. RSS distributes updates. Structured data helps search engines interpret meaning. These are not features. They are protocols. They are agreements that enable large-scale coordination.

Now the same logic applies to AI agents.

In the agentic web, agents will not click buttons or visually scan pages. They will send requests, interpret structured responses, compare options, and complete tasks. For that to work across millions of websites, communication cannot be improvised. It must be standardized.

This is where protocol thinking becomes essential.

Protocol thinking means designing websites so they are predictable for machines. Instead of building custom integrations for every assistant or platform, websites expose a consistent interaction layer. Agents do not need to learn every interface. They rely on shared rules.

As emphasized in discussions of distributed intelligence, the goal is not to let a single chatbot control everything. The intelligence must be distributed. Systems need a simplified way to communicate without having to understand the technical details of every tool they connect to.

That only works when there is common ground.

In practical terms, this means:

  • Websites must expose structured, machine-readable data
  • Agents must know what they can ask
  • Responses must follow predictable formats
  • Communication must scale beyond one platform

Protocols create that shared language.

What does this mean for SEO professionals?

As the web evolves to support AI agents, SEO professionals are starting to ask a new question: how do you stay visible when answers are generated instead of ranked?

A clear example of this surfaced during Microsoft’s Ignite event. In a Q&A session, a consultant described a client who sells products like mayonnaise and wanted their brand to appear when someone asks an AI assistant about mayonnaise. The question was simple, but it revealed something deeper. If AI systems generate answers instead of listing search results, what does optimization look like?

This is where the shift becomes real.

The agentic web does not replace the open web. It adds another layer on top of it. Search engines still index pages. Rankings still matter. But intelligent systems can now query websites directly, compare information across sources, and generate synthesized responses.

For SEOs, this changes the website’s role.

It is no longer enough to think in terms of pages to be visited. Websites must be treated as endpoints to be queried.

This means structured data, clean information architecture, and machine-readable content are not just enhancements for rich results. They are the foundation that allows AI systems to interpret and select your content in the first place.

Watch the full event here!

Key takeaway for SEOs

The agentic web is an additional layer on the open web, not a replacement for it. To stay visible, SEO professionals must ensure their websites are structured, accessible, and ready to be queried by intelligent systems.

Visibility in this new layer depends on clarity, interoperability, and infrastructure.

Must read: Why does having insights across multiple LLMs matter for brand visibility?

Introducing NLWeb

NLWeb was first introduced by Microsoft in May 2025 as an open project designed to make it simple for websites to offer rich natural language interfaces using their own data and model of choice. Later, in November at Microsoft Ignite, Microsoft presented NLWeb again alongside its first enterprise offering through Microsoft Foundry.

At its core, NLWeb aims to make it easy for a website to function like an AI app. Instead of navigating pages manually, users and agents can query a site’s content directly using natural language.

But NLWeb is more than just a conversational layer.

Every NLWeb instance is also a Model Context Protocol, or MCP, server. This means that when a website enables NLWeb, it becomes inherently discoverable and accessible to agents operating within the MCP ecosystem. In simple terms, agents do not need custom integrations for every site. If a website supports NLWeb, agents can recognize it and interact with it in a standardized way.

NLWeb is a conversational layer that interacts with a website and retrieves information

NLWeb builds on formats that websites already use, such as Schema.org and RSS. It combines that structured data with large language models to generate natural language responses. This allows websites to expose their content in a way that both humans and AI agents can understand.

Importantly, NLWeb is technology agnostic. Site owners can choose their preferred infrastructure, models, and databases. The goal is interoperability, not platform lock-in.

In many ways, NLWeb is positioned to play a role in the agentic web similar to what HTML did for the early web. It provides a shared communication layer that allows agents to query websites directly, without relying only on traditional crawling or visual interfaces.

How is NLWeb different from standard LLM citations?

With standard LLM citations, the model generates an answer first, then adds sources. The response is still probabilistic, which can introduce inaccuracies or hallucinations.

NLWeb works differently.

It treats the language model as a smart retrieval layer. Instead of inventing answers, it pulls verified objects directly from the website’s structured data and presents them in natural language.

That distinction matters. It means responses are grounded in the publisher’s own data from the start, reducing the risk of hallucination and giving site owners greater control over how their content is represented.

What NLWeb means for the agentic web

The agentic web depends on systems being able to communicate at scale. Agents cannot manually interpret every interface or navigate every page visually. They need structured, machine-readable access.

NLWeb helps enable that.

Instead of requiring custom integrations for every assistant or platform, a website can expose an NLWeb-enabled endpoint. Agents only need to know that a site supports NLWeb. The protocol handles how requests are made and how responses are structured.

This supports a more distributed ecosystem. The goal is not to let one chatbot control everything. Intelligence must be distributed across the web.

Generative interfaces do not replace content. They depend on well-structured, accessible content. When an AI system summarizes results or compares options, it is still drawing from the information that websites provide. NLWeb simply creates a clearer path for that interaction.

Yoast’s collaboration with NLweb and what it means for WordPress users

As part of the NLWeb announcement, Microsoft highlighted Yoast as a partner helping bring agentic search capabilities to WordPress. You can read more about this collaboration in our official press announcement on Yoast and Microsoft’s NLWeb integration.

For many WordPress site owners, concepts like infrastructure, endpoints, and protocols can feel abstract. That is exactly where preparation matters.

While Yoast does not automatically deploy NLWeb for users, the schema aggregation feature in Yoast SEO, Yoast SEO Premium, Yoast WooCommerce SEO, and Yoast SEO AI+ organizes and structures content, making it significantly easier to build NLWeb. When site owners enable the relevant Yoast feature, nothing changes visually on the front end. What changes is the underlying structure.

In short, we map and organize structured data to reduce the technical effort required to build NLWeb on top of it. In other words, we help publishers complete much of the groundwork.

The agentic web is not about chasing a trend. It is about ensuring your content remains discoverable, understandable, and usable in a world where intelligent systems increasingly act on behalf of users.

This startup claims it can stop lightning and prevent catastrophic wildfires

On June 1, 2023, as a sweltering heat wave baked Quebec, thousands of lightning strikes flashed across the province, setting off more than 120 wildfires.

The blazes ripped through parched forests and withered grasslands, burned for weeks, and compounded what was rapidly turning into Canada’s worst fire year on record. In the end, nearly 7,000 fires scorched tens of millions of acres across the country, generated nearly 500 millions tons of carbon emissions, and forced hundreds of thousands of people to flee their homes.

Lightning sparked almost 60% of the wildfires—and those blazes accounted for 93% of the total area burned.

Now a Vancouver-based weather modification startup, Skyward Wildfire, says it can prevent such catastrophic fires in the future—by stopping the lightning strikes that ignite them. It just raised millions of dollars in a funding round that it plans to use to accelerate its product development and expand its operations.

Until last week the company, which highlights the role lightning played in the 2023 infernos, stated on its website that it has demonstrated technology capable of preventing “up to 100% of lightning strikes.”

It was an eye-catching claim that went well beyond the confidence level of researchers who have studied the potential for humans to suppress lightning—and the company took it down following inquiries from MIT Technology Review.

“While the statement reflected an observed result under specific conditions, it was not intended to suggest uniform outcomes and has been removed,” Nicholas Harterre, who oversees government partnerships at Skyward, said in an email. “In complex atmospheric systems, consistent 100% outcomes are not realistic, as the experts you spoke to rightly pointed out.” 

The company now states it demonstrated that it “can prevent the majority of cloud-to-ground lightning strikes in targeted storm cells.” So far, Skyward hasn’t publicly revealed how it does so, and in response to our questions Harterre said only that the materials are “inert and selected in accordance with regulatory standards.” 

But online documents suggest the company is relying on an approach that US government agencies began evaluating in the early 1960s: seeding clouds with metallic chaff, or narrow fiberglass strands coated with aluminum. 

The military uses the material to disrupt radar signals; fighter jets, for example, deploy it during dogfights to throw off guided missile systems. Field trials conducted decades ago by US agencies suggest it could help reduce lightning strikes, at least to some degree and under certain conditions.

If Skyward could employ it reliably on significant scales, it might offer a powerful tool for countering rising fire risks as climate change drives up temperatures, dries out forests, and likely increases the frequency of lightning strikes.

“Preventing lightning on high-risk days saves lives, billions in wildfire costs, and is one of the highest-leverage and most immediate climate solutions available,” Sam Goldman, Skyward’s founder and chief executive, said in a statement posted on LinkedIn last year.

But researchers and environmental observers say there are plenty of remaining uncertainties, including how well the seeding may work under varying weather and climate conditions, how much material would need to be released, how frequently it would have to be done, and what sorts of secondary environmental impacts might result from lighting suppression on commercial scales.

Some observers are also concerned that the company appears to have moved ahead with weather modification field trials in parts of Canada without providing wide public notice or openly discussing what materials it’s putting into the clouds.

Given the escalating fire dangers, it’s “reasonable” to evaluate the potential for new technologies to mitigate them, says Keith Brooks, programs director at Environmental Defence, a Canadian advocacy organization.

“But we should be doing so cautiously and really transparently, with a robust scientific methodology that’s open to scrutiny,” he says.

Seeding the clouds

Skyward’s website offers few technical details, but the company says it worked with Canadian wildfire agencies in 2024 and 2025 to demonstrate its technology. The company also says it has developed AI tools to predict lightning strikes that could set off fires.

Skyward announced last month that it raised $7.9 million in Canadian dollars ($5.7 million), in an extension of a seed round initially closed early last year. Investors included Climate Innovation Capital, Active Impact Investments, and Diagram Ventures.

“Our first season demonstrated that prevention is possible at scale,” Goldman said in a statement. “This funding allows us to expand into new regions and support partners who need reliable, operational tools to reduce wildfire risk before emergencies begin.”

The company doesn’t use the term “cloud seeding” on its site or in its recent announcements. But a press release highlighting its selection as a finalist last year in a conservation group’s Fire Grand Challenge states that it suppresses lightning “by cloud seeding with safe, non-toxic materials to neutralize storm charges,” as The Narwhal previously reported.

In addition, Unorthodox Philanthropy, a foundation that provided a grant to support Skyward’s efforts “to test and deploy” the technology, offered more detail in an awardee write-up about Goldman.

It states: “The Skyward team … settled on an inert substance consisting of aluminum covered glass fibers, which is regularly used in military operations to intercept and confuse enemy radar and can also dis-charge clouds.”

Additional details were disclosed in a document marked “Proprietary and Confidential,” which the World Bank nonetheless released within a package of materials from companies developing means of addressing fire risks.

Skyward’s diagrams show planes dropping particles into clouds to prevent cloud-to-ground lightning strikes in “high risk areas.” The company also notes in the document that it uses artificial intelligence for a number of purposes, including forecasting lightning storms, prioritizing treatments, targeting storm cells, and optimizing flight paths.  

Harterre stressed that the company would deploy the technology judiciously and reserve it for storm events with elevated wildfire risk, adding that such storms account for less than 0.1% of lightning activity in a given area.

“Our objective is to reduce the probability of ignition on the limited number of extreme-risk days when fires threaten lives, critical infrastructure, and ecosystems, and when suppression costs and impacts can escalate rapidly,” he said.

The document posted by the World Bank states that Skyward partnered with Alberta Wildfire in August of 2024 to “prove suppression by plane and drone,” and that its process produced a “60-100% reduction” in lightning compared with “control cells” (which likely means storm cells that weren’t seeded). 

The document added that the company would be carrying out additional field trials in the summer of 2025 with the wildfire agencies in British Columbia and Alberta to “provide landscape level solutions with more advanced aircraft, sensors and forecasting.”

“BC Wildfire Service is aware that Skyward is developing technology that aims to reduce instances of lightning in targeted situations,” the British Columbia agency acknowledged in a statement provided to MIT Technology Review. “Last year, preliminary trials were conducted by Skyward to gain a better understand [sic] of the technology and its applicability in B.C. Should a project/technology like this move forward in B.C., we would engage with the project team in an effort to learn and ensure we’re using every tool available to us to respond to wildfire in B.C.”

The BC agency declined to make anyone available for an interview and didn’t respond to questions about what materials were used, where the tests were carried out, or whether it provided public disclosures or required the company to. Alberta Wildfire didn’t respond to similar questions from MIT Technology Review.

Rising lightning risks

Clouds are just water in various forms—vapor, droplets, and ice crystals, condensed enough to form the floating Rorschach tests we see in the sky. Within them, snowflakes and tiny ice pellets known as graupel rub together, causing atoms to trade electrons. This process creates highly reactive ions with negative and positive charges. 

Updrafts separate the light snowflakes from the graupel, building up larger differences in the charges across the electrical field until … crack! An electrostatic discharge occurs in the form of a lightning strike.

The 2023 fire season wasn’t a particularly big year for lightning strikes in Canada—but then it didn’t have to be. It was so hot and dry that every bolt that struck the surface had a better than usual chance of igniting a fire, says Piyush Jain, a research scientist at the Canadian Forest Service and lead author of a study published in Nature Communications that analyzed the year’s fires.  

aerial image of 2023 wildfire in Quebec
A fire burns in Mistissini, Québec, on June 12, 2023.
CPL MARC-ANDRé LECLERC/CANADIAN ARMED FORCES

Climate change is, however, likely to produce more lightning strikes, if it hasn’t started to already. Warmer air holds more moisture and adds more convective energy to the atmosphere, which drives the vertical movement of air that forms clouds and stirs up lightning storms. 

“So the conditions are there, and the conditions are likely to increase,” Jain says.

Different models arrive at different lightning forecasts for some regions of the world. But a clearer trend is already emerging in the northernmost latitudes, where the planet is warming fastest. Studies show that lightning-ignited fires have substantially increased in the Arctic boreal region, and predict that they will continue to rise

This combines with other growing risks like longer fire seasons, warmer temperatures, and drier vegetation, together raising the odds of more severe fires and more greenhouse-gas emissions, says Brendan Rogers, a senior scientist at the Woodwell Climate Research Center who studies the effect of fires on permafrost thaw.

In fact, Canada’s emissions from the 2023 fires were more than four times its emissions from fossil fuels.

Midcentury field trials

Scientists have conducted a variety of experiments exploring the possibility of preventing lightning, but most of it happened in the later half of the last century. 

Amid the cultural optimism and booming economy of the postwar period, US research agencies and corporations went on a tear of cloud seeding experiments aimed at conquering nature—or at least moderating its dangers. Research teams launched or dropped materials like dry ice and silver iodide into clouds in attempts to boost rainfall, reduce hail, dissipate fog, and redirect hurricanes.

“Cloud seeding activity was so intensive that at its peak in the early 1950s, approximately 10% of the US land area was under some kind of weather modification program,” wrote MIT’s Phillip Stepanian and Earle Williams in a 2024 history of lightning suppression efforts in the Bulletin of the American Meteorological Society. (MIT Technology Review is owned by MIT but is editorially independent.) 

Harry Gisborne, then chief of the division of fire research at the US Forest Service, wondered if the technique could be used to trigger downpours that might extinguish hard-to-reach wildfires on public lands. But when he put the question to Vincent Schaefer of General Electric, who had done pioneering research in cloud seeding, Schaefer thought they could perhaps do one better: prevent the lighting that sparked the fires in the first place.

The conversations kicked off what would become Project Skyfire, a multiagency private-public research program that carried out a series of experiments through the 1950s and 1960s. Research teams seeded clouds over the San Francisco Peaks of Arizona, the Bitterroot Mountains at the edge of Idaho, and the Deerlodge National Forest in Montana, among other places.

After comparing treated and untreated storm clouds, the researchers concluded that seeding decreased cloud-to-ground lightning by more than half. But as MIT’s Stepanian and Williams noted, the sample sizes were small, and questions remained about the statistical significance of the findings.

(Soviet scientists also carried out some field experiments on lightning suppression in the 1950s, as well as some related research that involved using rockets to launch lead iodide into thunderstorms in the 1970s, but it’s difficult to find further details about those programs.)

A near tragedy reignited US government interest in the possibility of lightning suppression in 1969, when lightning struck the Apollo 12 space shuttle twice within seconds of launch. The astronauts were able to reset their systems and successfully complete their mission to the moon, but it was a very close call.

In the aftermath, NASA and NOAA teamed up on what became known as Project Thunderbolt, which relied on the metallic chaff normally used in military countermeasures.

Researchers at the US Army Electronics Laboratory had previously proposed the possibility of suppressing lightning by deploying this material, which a handful of defense contractors manufacture. The idea is that chaff acts as a conductor in a forming electrical field, stripping electrons from some oxygen and nitrogen molecules and adding them to others. The mismatched electrons already collecting in cloud water molecules, thanks to all that rubbing between snowflakes and graupel, can then leap over to those newly charged atoms. That, in turn, should reduce the buildup of static electricity that otherwise results in lightning.

“By continuously redistributing—and thereby neutralizing—charges within the storm in a weak electric field, the strong electric fields required to produce lightning would never develop,” Stepanian and Williams wrote.

NASA and NOAA carried out a series of experiments seeding clouds with chaff from the early to mid 1970s, over Boulder, Colorado, and later at the Kennedy Space Center. Here, too, the experiments showed “generally promising field results.” But NASA eventually grew concerned about the possibility that chaff could affect radio communications and shuttered the program.

“Lightning suppression research was once again abandoned, and the responsibility for mitigating lightning hazards reverted to weather forecasters,” Stepanian and Williams concluded.

‘Hard to draw conclusions’

So what does all this tell us about our ability to prevent lightning?

“In my opinion, it’s unambiguously true that this technique can be used to reduce lightning strikes in a storm,” says Stepanian, a technical staff member at MIT Lincoln Laboratory’s air traffic control and weather systems group. “With some major caveats.”

For example, it’s not clear how much material you would need to release, how long it would persist, and how the effectiveness might change under different climate and weather conditions.

(Stepanian consulted with Skyward in its early stages, and he declined to discuss the startup.)

His coauthor on the history of lightning suppression seems a tad more skeptical. In an email, Williams, a research scientist at MIT who studies physical meteorology and atmospheric electricity, said there’s unmistakable evidence that chaff “has an impact on the electrification of thunderstorms.” But in email responses, he said its effectiveness in reducing or eliminating lighting activity “remains controversial” and requires further testing. (Williams says he did not consult for Skyward.) 

In his own written reviews, he’s highlighted a number of potential shortcomings with earlier research, including unaccounted-for differences in cloud heights between treated and untreated storms. In addition, he’s noted that some studies used detection systems that pick up only cloud-to-ground strikes, not intracloud lightning, which is far more common. 

He also points to the results of a more recent study that he and Stepanian collaborated on with researchers at New Mexico Tech. They relied upon data from weather radars in Tampa and Melbourne, Florida, located on opposite sides of the state, to detect the presence of chaff released over the central part of the state during military training and testing exercises. 

They compared 35 storms during which chaff was clearly detected in clouds with 35 instances when it wasn’t.

According to an abstract of the paper—which hasn’t been peer-reviewed or published but was presented at the American Geophysical Union conference in December—storms that occurred when chaff was present were generally “smaller and shorter-lived.” 

But the number of total flashes—which includes ground strikes as well as lightning within and between clouds and the air—was actually significantly higher in clouds carrying chaff: 62,250 versus 24,492.

“In summary, so far, it is hard to draw any conclusion about lightning suppression using chaff,” the authors wrote.

Williams says their results and other studies suggest that large chaff concentrations may be needed to suppress lightning. That could be because there’s a strong tendency for the ions released from the chaff fibers to be captured by cloud droplets before they reach the charged particles that would need to be neutralized.

But that may also present a significant deployment challenge, since chaff quickly becomes dilute once it’s released into the midst of turbulent storm clouds, Williams adds. 

Skyward’s Harterre said he couldn’t comment on the results of the Florida study but noted that storms in the state are very different from those that occur in the Canadian provinces where his company operates.

“Our work to date has focused on regions where operational feasibility has been evaluated and wildfire risk is highest,” he wrote.

‘Unintended consequences’

The possibility of releasing more chaff into the air also raises the questions of what else it could do in the atmosphere, and what will happen once it lands. 

The US military has produced a number of studies exploring the environmental and health effects of chaff and found that it disperses widely, breaks down in the environment, and is “generally nontoxic.”

For instance, a Naval Health Research Center report assessing environmental impacts from decades of training exercises near Chesapeake Bay concluded that “current and estimated use of aluminized chaff by American forces worldwide” will not raise total aluminum levels above the Environmental Protection Agency’s established limits. 

But a US Government Accountability Office report in 1998 raised a few other flags, noting that chaff can also affect civilian air traffic control radar and weather forecasts. It also highlighted a “potential but remote chance of collecting in reservoirs and causing chemical changes that may affect water and the species that use it.”

Stepanian says that if lightning suppression efforts require more chaff than the military currently releases, further studies may be needed to properly evaluate the environmental effects. 

Brooks of Environmental Defence Canada says he wants to know more about what materials Skyward is using, where they’re sourced from, what the effort leaves behind in the environment, and what the impacts on animals could be. He is also wary of the possible secondary effects of intervening in storms.

“I just think there’s the potential for unintended consequences if we start to mess with a complex system, like weather,” Brooks says, adding: “It makes me nervous to think there are pilots going on without people knowing about them.”

Harterre said that the company abides by any applicable regulations, and that it conducts its field activities “in coordination with relevant authorities and with appropriate authorization.”

He added that it releases seeding materials at lower volumes and concentrations than those associated with defense use and that deployments “are limited to defined high-wildfire-risk storm conditions.”

Remaining doubts

It’s not clear whether or to what degree Skyward has meaningfully advanced the science of lightning suppression or cleared up the questions that have lingered since the studies from the last century. 

The company hasn’t released data from its field trials, published any papers in peer-reviewed literature, or disclosed how its tests were performed, as far as MIT Technology Review was able to determine. 

Without such information it’s impossible to assess its claims, Williams says. He and two of his New Mexico Tech coauthors—associate professor Adonis Leal and master’s student Jhonys Moura—had all expressed skepticism about the company’s previous claim of “up to 100%” lightning prevention.

Harterre said Skyward intends to release more technical information as its programs mature.

“We look forward to the opportunity to share more detailed information,” he wrote.

In the meantime, Skyward’s investors have high hopes for the company and see “tremendous opportunity” in its potential ability to counteract fire dangers.

“Mitigating the exponentially increasing risk of wildfires can only happen if we shift from reactive suppression to proactive prevention,” Kevin Kimsa, managing partner of Climate Innovation Capital, said in a statement when the company’s recent funding was announced.

Rogers, of the Woodwell Climate Research Center, has spoken with Skyward several times but hasn’t worked with them. He also stressed that it’s crucial to understand potential environmental impacts from lightning suppression and to consult with citizens in affected areas, including Indigenous communities.

But he says he’s “optimistic” about the role that lighting suppression could play, if it works effectively and without major downsides.

That’s because preventing wildfires is far cheaper than putting them out, and it avoids risks to firefighters, ecosystems, infrastructure and local communities.

“If you’re able to go after fires before they’ve even ignited, you remove a lot of that from the equation,” he says.

The Download: The startup that says it can stop lightning, and inside OpenAI’s Pentagon deal

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

This startup claims it can stop lightning and prevent catastrophic wildfires

Startup Skyward Wildfire says it can prevent catastrophic fires by stopping the lightning strikes that ignite them. So far, it hasn’t publicly revealed how it does so, but online documents suggest the company is relying on an approach the US government began evaluating in the early 1960s: seeding clouds with metallic chaff, or narrow fiberglass strands coated with aluminum. 

It just raised millions of dollars to accelerate its product development and expand its operations. But researchers and environmental observers say uncertainties remain, including how well the seeding may work under varying conditions, how much material would need to be released, how frequently it would have to be done, and what sorts of secondary environmental impacts might result. Read the full story. 

—James Temple

OpenAI’s “compromise” with the Pentagon is what Anthropic feared

OpenAI has reached a deal that will allow the US military to use its technologies in classified settings. CEO Sam Altman said the negotiations, which the company began pursuing only after the Pentagon’s public reprimand of Anthropic, were “definitely rushed.”

OpenAI has taken great pains to say that it has not caved to allow the Pentagon to do whatever it wants with its technology. The company published a blog post explaining that its agreement protected against use for autonomous weapons and mass domestic surveillance, and Altman said the company did not simply accept the same terms that Anthropic refused. 

But it’s not yet clear if OpenAI can build in the safety precautions it promises as the military rushes out a politicized AI strategy during strikes on Iran, or if the deal will be seen as good enough by employees who wanted the company to take a harder line. Walking that tightrope will be tricky. Read the full story.

—James O’Donnell

The story is from The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Gulf states are racing against time to intercept Iran’s drone attacks

They could run out of interceptors very soon. (WSJ $)

  • Amazon says it lost three data centers in the strikes. (Business Insider $)
  • There has been a spike in GPS attacks too, affecting nearby shipping. (Wired)
  • Crypto stocks are tumbling in response. (Bloomberg)

2 Apple is considering using Google’s Gemini AI to power Siri

It’s also set to deepen its reliance on Google’s cloud infrastructure. (The Information $)

3 A database shows which topics fall foul of the Trump administration

National parks are being forced to erase any exhibits that display “partisan ideology”. (WP $)

  • The transatlantic battle over free speech is coming. (FT $)
  • What it’s like to be banned in the US for fighting online hate. (MIT Technology Review)

4 Can AI actually enhance jobs, not just destroy them?

Three economists take the optimistic view (New Yorker)

5 Are “bossware” apps tracking you? 

Tools to watch what workers are doing are getting more and more sophisticated. (NYT)

6 RFK Jr says he is about to unleash 14 banned peptides

By reversing a Biden-era FDA ban on their production. (Gizmodo)

7 Meta is testing an AI shopping research tool

It hopes to rival Gemini and ChatGPT. (Bloomberg)

8 Maybe data centers in space aren’t as crazy as they sound? 

They could be cheaper, with the right tech. (Economist

9 Why climate change is making turbulence worse

Buckle up, people. (New Yorker)

10 6G is on its way!

And the hype cycle is doing its thing again. (The Verge $)

Quote of the day

“We don’t list markets directly tied to death. When there are markets where potential outcomes involve death, we design the rules to prevent people from profiting from death.”

—Tarek Mansour, CEO and founder of prediction market company Kalshi, tries to justify the $54 million bet on “Ali Khamenei out as Supreme Leader?” on his platform, 404 Media reports.

One More Thing

surveillance and control concept

EDEL RODRIGUEZ

South Africa’s private surveillance machine is fueling a digital apartheid

Johannesburg is birthing a uniquely South African surveillance model. Over the past decade, the city has become host to a centralized, coordinated, entirely privatized mass surveillance operation. These tools have been enthusiastically adopted by the local security industry, grappling with the pressures of a high-crime environment.

Civil rights activists worry the new surveillance is fueling a digital apartheid and unraveling people’s democratic liberties, but a growing chorus of experts say the stakes are even higher. 

They argue that the impact of artificial intelligence is repeating the patterns of colonial history, and here in South Africa, where colonial legacies abound, the unfettered deployment of AI surveillance offers just one case study in how a technology that promised to bring societies into the future is threatening to send them back to the past. Read the full story.

—Karen Hao and Heidi Swart

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ These influencers are on a mission to save the UK’s pubs. 

+ Here’s what a map of America solely made up of its rivers would look like.

+ The winner of the Underwater Photographer of the Year awards is incredibly cute.
+ Pokémon may have turned 30 years old, but the franchise is more popular than ever.

Payment Friction Wins in Africa

The ideal ecommerce checkout is frictionless and linear: enter one’s address and payment details and then await product delivery.

In Africa, providing digital payment info is a leap of faith. The checkout process is often conversational and skeptical.

Consumers may click “Buy,” but they aren’t reaching for their payment details. They first need proof of the product and company. They may ask via WhatsApp for real-time product photos and delivery timelines. They might demand a voice note to ensure a human is on the other side of the screen. It’s a do-it-yourself verification system.

“Cautious consumers” is McKinsey & Company’s term for Africa and Middle East-based ecommerce shoppers in its 2020 report (PDF).

Conversational Commerce

It is a mistake to view this reliance on WhatsApp as a workaround. For consumers in Africa, a WhatsApp chat is akin to looking a seller in the eye.

Consider the January 2026 partnership in Nigeria between PayPal and Paga, the mobile payment platform. After two decades of restrictions, Nigerians could finally receive international funds from PayPal into their Paga wallets.

The reception, however, was not great. Freelancers flooded Nigerian X with vitriol and skepticism stemming from a long memory of frozen PayPal funds.

This collective memory creates a psychological barrier that the partnership may struggle to overcome.

Trust

Paystack’s instant bank transfer settles transactions in one day.

Local payment platforms such as Flutterwave and Stripe-owned Paystack have succeeded because they understood consumers’ memories of money restrictions and failed transactions. The infrastructure of both reflects how people actually move capital.

Bank transfers. In Nigeria, merchants need settlement within one day of the transaction to keep their businesses running. For the customer, the transfer is final and verifiable.

 M-Pesa. In Kenya, STK Push is a consumer-controlled security protocol enabling money transfers on mobile devices. Africa accounts for roughly 70% of global mobile money payments; ignoring STK Push is costly.

Kiosks. In Egypt, consumers often demand physical confirmation before payment. Fawry’s cash-at-kiosk model allows shoppers to order online but pay at one of thousands of physical kiosks.

Success

Foreign ecommerce merchants cannot buy their way into Africa with tech alone. Success comes from leaning into the friction consumers require.

  • Use social media to consummate transactions. In Africa, an abandoned cart could mean that a shopper is waiting for the merchant on WhatsApp to prove it’s real.
  • Localize the rails. Don’t force a Kenyan to use a Visa card or a Nigerian to rely on an international gateway that might flag the transaction as high risk. Use recognizable payment methods such as instant transfers, mobile payments, and in-person dialogue.
  • Invest in the boring stuff. Don’t invest excessively in technology while ignoring operations. Logistics and customer support are where trust is either cemented or broken.
New: Futureproof your website for the agentic web with Yoast SEO Schema Aggregation 

In November 2025, Yoast announced a collaboration with NLWeb, an open web protocol developed by Microsoft designed to simplify building conversational interfaces for the web.

Today, we are proud to introduce the first major result of that work: Yoast SEO Schema Aggregation. This is an opt in feature that brings your website’s structured data together in a clearer and more consistent way. By choosing to enable it, you can help search engines and intelligent agents better understand and use your content.

If you want to see which schema types are available for your WordPress setup, our schema overview explains what is included across different product plans.

Bridging the gap: from discovery to conversation

Yoast has a history of helping WordPress websites be represented fairly and responsibly in the open web.

2019: Yoast introduced the first of its kind schema graph and API, helping search engines better understand your content as they moved beyond keywords and evolved into discovery engines.

Today: we are taking the next step. As the agentic web becomes more important, we are helping your WordPress site move from being discovered to being understood and engaged with through conversation.

Starting today, the new Schema Aggregation feature in Yoast SEO is here. It establishes a standardized connection between your website’s structured data and the systems that power AI-driven discovery and interaction. These include large language models, agents, and conversational assistants such as Copilot. It helps ensure your published content can be understood correctly by AI. This matters as AI becomes part of how people find and use information online.

The NLWeb + Yoast integration is built in collaboration with the NLWeb team, including R.V. Guha, co-founder of Schema.org. Together, we are extending the open web standards you already rely on, so your WordPress website can participate confidently in the emerging agentic web in a responsible and future ready way.

Benefits of the Schema Aggregation feature

Questions about AI often come down to one thing: who can access your data. This feature is built with a privacy first approach from the start.

  • Complete: All indexable content included
  • Clean: No duplicate entities, no navigation clutter
  • Connected: Relationships between entities preserved (author → articles)
  • Compliant: Respects exisiting privacy settings
  • Fast: Sub-100ms cached responses, pagination for large sites

For developers and technical users who want more control, we have developer documentation on schema markup. It explains how to inspect and extend your schema graph. This gives you maximum personalization, while retaining standardization at scale.

“You can’t stop the AI wave, but you can direct it. Our integration with NLWeb puts you back in charge. It allows you to manage server load efficiently and ensures that when AIs do access your content, they get the rich, semantic understanding necessary to represent you correctly.” Alain Schlesser – Principal Architect, Yoast.

What’s new

The next time you log in and open Yoast SEO (updated to 27.1), you’ll see a short guided walkthrough. It introduces the new Schema Aggregation feature. It also shows how to enable it using a simple toggle.

We have added a new endpoint to Yoast SEO (free), making the Schema Aggregation feature available to all customers who choose to enable it. The endpoint exposes your site’s full structured data graph in a proposed new standard called a schemamap.

That means, instead of an AI system crawling hundreds of pages individually (or however many pages you have on your website), it can now retrieve your site’s schema, including articles, authors, products, and organizational data, in one optimized request.

Before and after: from pages to a connected site

Below is an example of the structured data Yoast already outputs on an individual page. This page level schema helps search engines understand what that specific page is about, including its content type, author, and relationships.

An example of Yoast schema markup at the individual page level, the example shown is yoast.com

With Schema Aggregation enabled, Yoast provides a site-level view. Instead of looking at pages in isolation, your entire website’s structured data is connected. It consolidates into a single output called a schemamap. This can appear quite overwhelming to look at. It makes it easier for AI systems to understand your content. They can see how your articles, authors, products, and organisation relate to each other across the site.

Nothing about your existing schema changes. The same data is reused, simply organized in a way that reflects how your website works as a whole. Here is a schema map example from Yoast.com, displayed with the Yoast SEO Schema Visualizer.

How it works: Standardized, connected, and deduplicated

The Schema Aggregation feature doesn’t just share data; it organizes it for AI consumption:

  • Eliminates data mess: It merges duplicate mentions of authors, products, or articles into one scalable, connected record.
  • Integrates automatically: If you use one of our Schema API partners like The Events Calendar or WP Recipe Maker, those schema types are included in the graph automatically.

Developers can also explore our Schema Integrations page to see how Schema API partners connect to and extend the Yoast SEO Schema Framework (the graph).

Collaborative innovation

When working at scale across tens of millions of websites, careful testing is essential to ensure a safe and reliable launch. This feature was developed with agencies and advanced users in mind, and tested in controlled environments.

We collaborated closely with Syde, our Innovation Partner, to test the new feature across a diverse range of real-world client scenarios. The approach for this release was tested in controlled environments to confirm scalability and consistent output quality before deployment.

Syde’s feedback has been instrumental in refining the schema aggregation logic. We look forward to continuing this partnership, working together to help clients remain visible and accurately represented as AI driven systems evolve.

Be visible, understood, and represented

The rules of discovery are shifting, but your site doesn’t have to be left behind. With NLWeb and Yoast, your website stays at the center of the conversation.

Ready to see it in action? Update to the latest version of Yoast SEO and enable the NLWeb integration in your Yoast SEO settings today. For more information about how to enable Schema Aggregation, visit this help article.

I checked out one of the biggest anti-AI protests yet

Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple of hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta, and Google DeepMind, chanting slogans and waving signs. The march was organized by two separate activist groups, Pause AI and Pull the Plug, which billed it as the largest protest of its kind yet.

The range of concerns on show covered everything from online slop and abusive images to killer robots and human extinction. One woman wore a large homemade billboard on her head that read “WHO WILL BE WHOSE TOOL?” (with the Os in “TOOL” cut out as eye holes). There were signs that said “Pause before there’s cause” and “EXTINCTION=BAD” and “Demis the Menace” (referring to Demis Hassabis, the CEO of Google DeepMind). Another simply stated: “Stop using AI.”

An older man wearing a sandwich board that read “AI? Over my dead body” told me he was concerned about the negative impact of AI on society: “It’s about the dangers of unemployment,” he said. “The devil finds work for idle hands.”

This is all familiar stuff. Researchers have long called out the harms, both real and hypothetical, caused by generative AI—especially models such as OpenAI’s ChatGPT and Google DeepMind’s Gemini. What’s changed is that those concerns are now being taken up by protest movements that can rally significant crowds of people to take to the streets and shout about them.  

The first time I ran into anti-AI protesters was in May 2023, outside a London lecture hall where Sam Altman was speaking. Two or three people stood heckling an audience of hundreds. In June last year Pause AI, a small but international organization set up in 2023 and funded by private donors, drew a crowd of a few dozen people for a protest outside Google DeepMind’s London office. This felt like a significant escalation.

“We want people to know Pause AI exists,” Joseph Miller, who heads its UK branch and co-organized Saturday’s march, told me on a call the day before the protest: “We’ve been growing very rapidly. In fact, we also appear to be on a somewhat exponential path, matching the progress of AI itself.”

Miller is a PhD student at Oxford University, where he studies mechanistic interpretability, a new field of research that involves trying to understand exactly what goes on inside LLMs when they carry out a task. His work has led him to believe that the technology may forever be beyond our control and that this could have catastrophic consequences.

It doesn’t have to be a rogue superintelligence, he said. You just needed someone to put AI in charge of nuclear weapons. “The more silly decisions that humanity makes, the less powerful the AI has to be before things go bad,” he said.

After a week in which the US government tried to force Anthropic to let it use its LLM Claude for any “legal” military purposes, such fears seem a little less far-fetched. Anthropic stood its ground, but OpenAI signed a deal with the DOD instead. (OpenAI declined an invitation to comment on Saturday’s protest.)

For Matilda da Rui, a member of Pause AI and co-organizer of the protest, AI is the last problem that humans will face. She thinks that either the technology will allow us to solve—once and for all—every other problem that we have, or it will wipe us out and there will be nobody left to have problems anymore. “It’s a mystery to me that anyone would really focus on anything else if they actually understood the problem,” she told me.

And yet despite that urgency, the atmosphere at the march was pleasant, even fun. There was no sense of anger and little sense that lives—let alone the survival of our species—were at stake. That could be down to the broad range of interests and demands that protesters brought with them.

A chemistry researcher I met ticked off a litany of complaints, which ranged from the conspiracy-adjacent (that data centers emit infrasound below the threshold of human hearing, inducing paranoia in people who live near them) to the reasonable (that the spread of AI slop online is making it hard to find reliable academic sources). The researcher’s solution was to make it illegal for companies to profit from the technology: “If you couldn’t make money from AI, it wouldn’t be such a problem.”

Most people I spoke to agreed that technology companies probably wouldn’t take any notice of this kind of protest. “I don’t think that the pressure on companies will ever work,” Maxime Fournes, the global head of Pause AI, told me when I bumped into him at the march. “They are optimized to just not care about this problem.”

But Fournes, who worked in the AI industry for 12 years before joining Pause AI, thinks he can make it harder for those companies. “We can slow down the race by creating protection for whistleblowers or showing the public that working in AI is not a sexy job, that actually it’s a terrible job—you can dry up the talent pipeline.”

In general, most protesters hoped to make as many people as possible aware of the issues and to use that publicity to push for government regulation. The organizers had pitched the march as a social event, encouraging anyone curious about the cause to come along.

It seemed to have worked. I met a man who worked in finance who had tagged along with his roommate. I asked why he was there. “Sometimes you don’t have that much to do on a Saturday anyway,” he said. “If you can see the logic of the argument, if it sort of makes sense to you, then it’s like ‘Yeah, sure, I’ll come along.’”

He thought raising concerns around AI was hard for anyone to fully oppose. It’s not like a pro-Palestine protest, he said, where you’d have people who might disagree with the cause. “With this, I feel like it’s very hard for someone to totally oppose what you’re marching for.”

After winding its way through King’s Cross, the march ended in a church hall in Bloomsbury, where tables and chairs had been set up in rows. The protesters wrote their names on stickers, stuck them to their chests, and made awkward introductions to their neighbors. They were here to figure out how to save the world. But I had a train to catch, and I left them to it. 

The Download: protesting AI, and what’s floating in space

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

I checked out one of the biggest anti-AI protests ever

Pull the plug! Pull the plug! Stop the slop! Stop the slop! For a few hours this Saturday, February 28, I watched as a couple hundred anti-AI protesters marched through London’s King’s Cross tech hub, home to the UK headquarters of OpenAI, Meta and Google DeepMind, chanting slogans and waving signs. The march was organized by a coalition of two separate activist groups, Pause AI and Pull the Plug, who billed it as the largest protest of its kind yet.

This is all familiar stuff. Researchers have been calling out the harms, both real and hypothetical, caused by generative AI— especially models such as OpenAI’s ChatGPT and Google DeepMind’s Gemini—for years. What’s changed is that those concerns are now being taken up by protest movements that can rally significant crowds of people to take to the streets and shout about it. Read the full story.

—Will Douglas Heaven

We’re putting more stuff into space than ever. Here’s what’s up there.

Earth’s a medium-size rock with some water on top, enveloped by gases that keep everything that lives here alive. Just at the edge of that envelope begins a thin but dense layer of human-built, high-tech stuff.

People started putting gear up there in 1957, and now it’s a real habit. Telescopes look up and out at the wild universe. Humans live in an orbiting metal bubble. In the last five years, the number of active satellites in space has increased from barely 3,000 to about 14,000—and climbing. And then there’s the garbage. Here’s a closer look at Earth’s ever-thickening shell of human-made matter—the anthroposphere.

—Jonathan O’Callaghan

This story is from the latest print issue of MIT Technology Review magazine. If you haven’t already, subscribe now to receive future issues once they land. 

MIT Technology Review is a 2026 ASME finalist in reporting

The American Society of Magazine Editors has named MIT Technology Review as a finalist for a 2026 National Magazine Award in the reporting category. 

The shortlisted story—“We did the math on AI’s energy footprint. Here’s the story you haven’t heard”—is part of our Power Hungry package on AI’s energy burden. 

In a rigorous investigation, senior AI reporter James O’Donnell and senior climate reporter Casey Crownhart spent six months digging through hundreds of pages of reports, interviewing experts, and crunching the numbers. Read more about what they found out.

What comes after the LLMs?

The AI industry is organized around LLMs: tools, products, and business models. Yet many researchers believe the next breakthroughs may not look like language models at all. Join us for a LinkedIn Live discussion at 12.30pm ET on Tuesday March 3 to dive into the emerging directions that could define AI’s next era. Register here!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Pentagon wanted Anthropic to analyze bulk data collected from Americans 
It proved the sticking point in talks as OpenAI swooped in to ink a new deal. (The Atlantic $)+ Anthropic has vowed to legally challenge its “security risk” label. (FT $)
+ Here’s a blow-by-blow look at how negotiations fell apart. (NYT $)
+ Downloads of Claude are on the up. (TechCrunch)

2 Iranian apps and websites were hacked in the wake of the US-Israeli strikes
News sites and a religious app were co-opted to display anti-military messages. (Reuters)
+ They urged personnel to abandon the regime and to liberate the country. (WSJ $)
+ Unsurprisingly, X is rife with disinformation about the attacks. (Wired $)
+ The campaign has disrupted online delivery orders across the Middle East. (Bloomberg $)

3 DeepSeek is poised to release a new AI model this week
The multimodal V4 is being released ahead of China’s annual parliamentary meetings. (FT $)

4 The UK is trialing a social media ban for under-16s
Hundreds of teens will test overnight digital curfews and screen time limits. (The Guardian)
+ What it’s like to attend a phone addiction meeting. (Boston Globe $)

5 Celebrities are winning huge sums playing on this major crypto casino’s slots
In fact, their lucky wins appear to spike while they’re livestreaming. (Bloomberg $)

6 America is desperate to steal China’s critical mineral lead
The victor essentially controls global computing, aerospace and defense. (Economist $)
+ This rare earth metal shows us the future of our planet’s resources. (MIT Technology Review)

7 How lasers became the military’s weapon of choice
From Ukraine to the US, soldiers are deploying laser guns. But why? (The Atlantic $)
+ They’re a key part of America’s arsenal in manning the southern border. (New Yorker $)
+ This giant microwave may change the future of war. (MIT Technology Review)

8 How quantum entanglement became big business
It promises unhackable communication—but is it too good to be true? (New Scientist $)
+ Useful quantum computing is inevitable—and increasingly imminent. (MIT Technology Review)

9 The iPod is proving a hit among Gen Z
Even though Apple discontinued the music player four years ago. (NYT $)

10 Chinese parents are joining matchmaking apps in their droves
In a bid to marry off their adult children as soon as humanly possible. (Nikkei Asia)

Quote of the day

“Day to day it just feels untenable…Some managers know this is the case, but executives just keep pointing to some bigger AI picture.”

—An anonymous Amazon employee describes the stresses of trying to increase productivity amid the company’s commitment to reducing headcount to the Financial Times.

One more thing

The iPad was meant to revolutionize accessibility. What happened?

On April 3, 2010, Steve Jobs debuted the iPad. What for most people was basically a more convenient form factor was something far more consequential for non-speakers: a life-­changing revolution in access to a portable, powerful communication device for just a few hundred dollars.

But a piece of hardware, however impressively designed and engineered, is only as valuable as what a person can do with it. After the iPad’s release, the flood of new, easy-to-use augmentative and alternative communication apps that users were in desperate need of never came.

Today, there are only around half a dozen apps, each retailing for $200 to $300, that ask users to select from menus of crudely drawn icons to produce text and synthesized speech. It’s a depressingly slow pace of development for such an essential human function. Read the full story.

—Julie Kim

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Neanderthal by name, not by nature—these prehistoric men were surprisingly romantic, thank you very much.
+ If you’re lucky enough to live in Boston, make sure you swing by these beautiful bars.
+ Hmm, this sticky hoisin sausage traybake sounds intriguing.
+ George Takei, you are an absolute maverick.