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Mar 26 2026

The AI Hype Index: AI goes to war

AI is at war. Anthropic and the Pentagon feuded over how to weaponize Anthropic’s AI model Claude; then OpenAI swept the Pentagon off its feet with an “opportunistic and sloppy” deal. Users quit ChatGPT in droves. People marched through London in the biggest protest against AI to date. If you’re keeping score, Anthropic—the company founded to be ethical—is now turbocharging US strikes on Iran.

On the lighter side, AI agents are now going viral online. OpenAI hired the creator of OpenClaw, a popular AI agent. Meta snapped up Moltbook, where AI agents seem to ponder their own existence and invent new religions like Crustafarianism. And on RentAHuman, bots are hiring people to deliver CBD gummies. The future isn’t AI taking your job. It’s AI becoming your boss and finding God.

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Mar 26 2026

Agentic commerce runs on truth and context

Imagine telling a digital agent, “Use my points and book a family trip to Italy. Keep it within budget, pick hotels we’ve liked before, and handle the details.” Instead of returning a list of links, the agent assembles an itinerary and executes the purchase.

That shift, from assistance to execution, is what makes agentic AI different. It also changes the operating speed of commerce. Payment transactions are already clear in milliseconds. The new acceleration is everything before the payment: discovery, comparison, decisioning, authorization, and follow-through across many systems. As humans step out of routine decisions, “good enough” data stops being good enough. In an agent-driven economy, the constraint isn’t speed; it’s trust at machine speed and scale.

Automated markets already work because identity, authority, and accountability are built in. As agents transact across businesses, that same clarity is required. Master data management (MDM)—the discipline of creating a single master record—becomes the exchange layer: tracking who an agent represents, what it can do, and where responsibility sits when value moves. Markets don’t fail from automation; they fail from ambiguous ownership. MDM turns autonomous action into legitimate, scalable trust.

To make agentic commerce safe and scalable, organizations will need more than better models. They will need a modern data architecture and an authoritative system of context that can instantly recognize, resolve, and distinguish entities. It is the difference between automation that scales and automation that needs constant human correction.

The agent is a new participant

Digital commerce has long been built on two primary sides: buyers and suppliers/merchants. Agentic commerce adds a third participant that must be treated as a first-class entity: the agent acting on the buyer’s behalf.

That sounds simple until you ask the questions every enterprise will face:

Who is the individual, across channels and devices, with enough certainty for automation?
Who is the agent, and what permissions and limits define what it can do?
Who is the merchant or supplier, and are we sure we mean the right one?
Who holds liability if the agent acts with permission, but against user intent?

The practical risk is confusion. Humans, for example, can infer that “Delta” means the airline when they are booking a flight, not the faucet company. An agent needs deterministic signals. If the system guesses wrong, it either breaks trust or forces a human confirmation step that defeats the promise of speed.

Why ‘good enough’ data breaks at machine speed

Most organizations have learned to live with imperfect data. Duplicate customer records are tolerable. Incomplete product attributes are annoying. Merchant identities can be reconciled later.

Agentic workflows change that tolerance. When an agent takes action without a human checking the output, it needs data that is close to perfect, because it cannot reliably notice when data is ambiguous or wrong the way a person can.

The failure modes are predictable, and they show up in places that matter most:

Product truth: If the catalog is inconsistent, an agent’s choices will look arbitrary (“the wrong shirt,” “the wrong size,” “the wrong material”), and trust collapses quickly.
Payee truth: Agentic commerce expands beyond cards to account-to-account and open-banking-connected experiences, broadening the universe of payees and the need to recognize them accurately in real time.
Identity truth: People operate in multiple contexts (work versus personal). Devices shift. A system that cannot distinguish amongst these contexts will either block legitimate activity or approve risky activity, both of which damage adoption.

This is why unified enterprise data and entity resolution move from nice to have to operationally required. The more autonomy you want, the more you must invest in modern data foundations that ensure it is safe.

Context intelligence: The missing layer

When leaders talk about agentic AI, they often focus on model capability: planning, tool use, and reasoning. Those are necessary, but they are not sufficient.

Agentic commerce also requires a layer that provides authoritative context at runtime. Think of it as a real-time system of context that can answer instantly and consistently:

• Is this the right person?
• Is this the right agent, acting within the right permissions?
• Is this the right merchant or payee?
• What constraints apply right now (budget, policy, risk, loyalty rules, preferred suppliers)?

Two design principles matter.

First, entity truth must be deterministic enough for automation. Large language models are probabilistic by nature. That is helpful for creating options for writing and drawing. It is risky for deciding where money goes, especially in B2B and finance workflows, where “probably correct” is not acceptable.

Second, context must travel at the speed of interaction and remain portable across the entire connected network value chain. Mastercard’s experience optimizing payment flows is instructive: the more services you layer onto a transaction, the more you risk slowing it down. The pattern that scales pre-resolves, curates, and packages the signal so that execution is lightweight.

This is also where tokenization is heading. Initiatives like Mastercard’s Agent Pay and Verifiable Intent signal a future in which consumer credentials, agent identities, permissions, and provable user intent are encoded as cryptographically secure artifacts — enabling merchants, issuers and platforms to deterministically verify authorization and execution at machine speed.

What leaders should do in the next 12 to 24 months

Adoption will not be uniform. Early traction will often depend less on industry and more on the sophistication of an organization’s systems and data discipline.

That makes the next two years a window for practical preparation. Five moves stand out.

Treat agents as governed identities, not features. Define how agents are onboarded, authenticated, permissioned, monitored, and retired.
Prioritize entity resolution where the cost of being wrong is highest. Start with payees, suppliers, employee-versus-personal identity, and high-volume product categories.
Build a reusable context service that every workflow and agent can call. Do not force each system to reconstruct identity and relationships from scratch.
Precompute and compress signals. Resolve and curate context upstream so that runtime decisioning stays fast and predictable.
Expand autonomy only as trust is earned. Build a governance framework to address disputes, keep humans in the loop for higher-risk actions, measure accuracy, and expand automation as outcomes prove reliable.

A tsunami effect across industries

Agentic AI will not be confined to shopping carts. It will touch procurement, travel, claims, customer service, and finance operations. It will compress decision cycles and remove manual steps, but only for organizations that can supply agents with clean identity, precise entity truth, and reliable context.

The winners will treat entity truth and context as core infrastructure for automation, not as a back-office cleanup project. In commerce at machine speed, trust is not a brand attribute; it is an architectural decision encoded in identity, context, and control.

This content was produced by Reltio. It was not written by MIT Technology Review’s editorial staff.

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Mar 26 2026

The Download: reawakening frozen brains, and the AI Hype Index returns

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 scientist rewarmed and studied pieces of his friend’s cryopreserved brain

L. Stephen Coles’s brain sits in a vat at a storage facility in Arizona. It has been held there at a temperature of around −146 degrees °C for over a decade, largely undisturbed. Before he died in 2014, Coles had the brain frozen with an ambitious goal in mind: reanimation.

His friend, cryobiologist Greg Fahy, believes it could be revived one day. But other experts are less optimistic.

Still, Fahy’s research could lead to new ways to study the brain. And using cryopreservation for organ transplantation is becoming a viable reality.

Read the full story to find out what the future holds for the technology.

—Jessica Hamzelou

The AI Hype Index

Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition.

MIT Technology Review Narrated: how Pokémon Go is giving delivery robots an inch-perfect view of the world

Pokémon Go was the world’s first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. “500 million people installed that app in 60 days,” says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year.

Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model—a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely.

—Will Douglas Heaven

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The next era of space exploration

Our footprint in the solar system is rapidly expanding. Programs to build permanent Moon bases and find life on Mars have transitioned from science fiction to active space agency missions. The scientists behind them will not only shed new light on the cosmos, but also reveal where humanity is headed.

To examine what the future holds in store, MIT Technology Review features editor Amanda Silverman will sit down today with award-winning science journalist and author Robin George Andrews for an exclusive subscriber-only Roundtable conversation about “The Next Era of Space Exploration.” Register here to join the session at 16:00 GMT / 12:00 PM ET / 9:00 AM PT.

The must-reads

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

1 OpenAI is shutting down AI video generator Sora
The app attracted at least as much controversy as acclaim. (CNBC)
+ Closing it means saying goodbye to $1 billion from Disney. (BBC)
+ OpenAI is cutting back on side projects ahead of an expected IPO. (WSJ $)
+ But it’s focusing its efforts on building a fully automated researcher. (MIT Technology Review)

2 A judge suspects the Pentagon is illegally punishing Anthropic
She labelled the DoD’s ban “troubling.” (Bloomberg)
+ Anthropic and the Pentagon are facing off in court. (Guardian)
+ The DoD wants AI companies to train on classified data. (MIT Technology Review)

3 Meta has been ordered to pay $375 million for endangering children online
Prosecutors said the company knew it put children at risk. (Engadget)
+ Meta is offering its top talent stock options as incentives for its AI push. (CNBC)

4 Arm will sell its own computer chips for the first time
It’s aimed at data centers that run AI tasks. (NYT $)
+ Arm stock jumped 13% on the news. (CNBC)

5 Manus’s founders have been barred from leaving China following Meta’s takeover
Beijing is reviewing the $2 billion acquisition of the AI startup. (FT $)

6 Baltimore has sued xAI over Grok’s fake nude images
The chatbot allegedly violated consumer protections. (Guardian)
+ There’s a big market for pornographic deepfakes of real women. (MIT Technology Review)

7 NASA plans to send a nuclear-powered spacecraft to Mars in 2028
It’ll take a payload of Ingenuity-class helicopters to the Red Planet. (NYT $)
+ NASA also wants to put a $20 billion base on the Moon. (The Verge)

8 A company is secretly turning Zoom meetings into AI-generated podcasts
WebinarTV turns the calls into content without telling anyone. (404 Media)

9 Iranian volunteers have built their own missile warning map
It fills the gap left by Iran’s lack of a public emergency alert tool. (Wired $)
+ Here’s where OpenAI’s tech could show up in Iran. (MIT Technology Review)

10 A nonprofit is sending basic income payments to AI-impacted workers
It’s starting by giving 25-50 people $1,000 per month. (Gizmodo)

Quote of the day

“I am first and foremost a scientist. My goal is to understand nature. But doing science is, sort of, like reading the mind of God.”

—DeepMind CEO Demis Hassabis shares his approach to AI strategy with the FT.

One More Thing

many ui windows framing different views of an asteroid on the way to Earth

Inside the hunt for the most dangerous asteroid ever

As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed.

This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever found—all under the tightest of timelines and with the highest of stakes. Find out how they did it.

—Robin George Andrews

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.)

+ Soothe subscription fatigue with this simple cancellation tool.
+ Takashi Murakami’s reimagined Monets are pop-art magic.
+ Jump into a rabbit hole with this app that visualizes links between Wikipedia pages.
+ This playful lynx that snatched the top prize in a photo competition is a delight.

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App Artificial intelligence

Mar 26 2026

This startup wants to change how mathematicians do math

Axiom Math, a startup based in Palo Alto, California, has released a free new AI tool for mathematicians, designed to discover mathematical patterns that could unlock solutions to long-standing problems.

The tool, called Axplorer, is a redesign of an existing one called PatternBoost that François Charton, now a research scientist at Axiom, co-developed in 2024 when he was at Meta. PatternBoost ran on a supercomputer; Axplorer runs on a Mac Pro.

The aim is to put the power of PatternBoost, which was used to crack a hard math puzzle known as the Turán four-cycles problem, in the hands of anyone who can install Axplorer on their own computer.

Last year, the US Defense Advanced Research Projects Agency set up a new initiative called expMath—short for Exponentiating Mathematics—to encourage mathematicians to develop and use AI tools. Axiom sees itself as part of that drive.

Breakthroughs in math have enormous knock-on effects across technology, says Charton. In particular, new math is crucial for advances in computer science, from building next-generation AI to improving internet security.

Most of the successes with AI tools have involved finding solutions to existing problems. But finding solutions is not all that mathematicians do, says Axiom Math founder and CEO Carina Hong. Math is exploratory and experimental, she says.

MIT Technology Review met with Charton and Hong last week for an exclusive video chat about their new tool and how AI in general could change mathematics.

Math by chatbot

In the last few months, a number of mathematicians have used LLMs, such as OpenAI’s GPT-5, to find solutions to unsolved problems, especially ones set by the 20th-century mathematician Paul Erdős, who left behind hundreds of puzzles when he died.

But Charton is dismissive of those successes. “There are tons of problems that are open because nobody looked at them, and it’s easy to find a few gems you can solve,” he says. He’s set his sights on tougher challenges—“the big problems that have been very, very well studied and famous people have worked on them.” Last year, Axiom Math used another of its tools, called AxiomProver, to find solutions to four such problems in mathematics.

The Turán four-cycles problem that PatternBoost cracked is another big problem, says Charton. (The problem is an important one in graph theory, a branch of math that’s used to analyze complex networks such as social media connections, supply chains, and search engine rankings. Imagine a page covered in dots. The puzzle involves figuring out how to draw lines between as many of the dots as possible without creating loops that connect four dots in a row.)

“LLMs are extremely good if what you want to do is derivative of something that has already been done,” says Charton. “This is not surprising—LLMs are pretrained on all the data that there is. But you could say that LLMs are conservative. They try to reuse things that exist.”

However, there are lots of problems in math that require new ideas, insights that nobody has ever had. Sometimes those insights come from spotting patterns that hadn’t been spotted before. Such discoveries can open up whole new branches of mathematics.

PatternBoost was designed to help mathematicians find new patterns. Give the tool an example and it generates others like it. You select the ones that seem interesting and feed them back in. The tool then generates more like those, and so on.

It’s a similar idea to Google DeepMind’s AlphaEvolve, a system that uses an LLM to come up with novel solutions to a problem. AlphaEvolve keeps the best suggestions and asks the LLM to improve on them.

Special access

Researchers have already used both AlphaEvolve and PatternBoost to discover new solutions to long-standing math problems. The trouble is that those tools run on large clusters of GPUs and are not available to most mathematicians.

Mathematicians are excited about AlphaEvolve, says Charton. “But it’s closed—you need to have access to it. You have to go and ask the DeepMind guy to type in your problem for you.”

And when Charton solved the Turán problem with PatternBoost, he was still at Meta. “I had literally thousands, sometimes tens of thousands, of machines I could run it on,” he says. “It ran for three weeks. It was embarrassing brute force.”

Axplorer is far faster and far more efficient, according to the team at Axiom Math. Charton says it took Axplorer just 2.5 hours to match PatternBoost’s Turán result. And it runs on a single machine.

Geordie Williamson, a mathematician at the University of Sydney, who worked on PatternBoost with Charton, has not yet tried Axplorer. But he is curious to see what mathematicians do with it. (Williamson still occasionally collaborates with Charton on academic projects but says he is not otherwise connected to Axiom Math.)

Williamson says Axiom Math has made several improvements to PatternBoost that (in theory) make Axplorer applicable to a wider range of mathematical problems. “It remains to be seen how significant these improvements are,” he says.

“We are in a strange time at the moment, where lots of companies have tools that they’d like us to use,” Williamson adds. “I would say mathematicians are somewhat overwhelmed by the possibilities. It is unclear to me what impact having another such tool will be.”

Hong admits that there are a lot of AI tools being pitched at mathematicians right now. Some also require mathematicians to train their own neural networks. That’s a turnoff, says Hong, who is a mathematician herself. Instead, Axplorer will walk you through what you want to do step by step, she says.

The code for Axplorer is open source and available via GitHub. Hong hopes that students and researchers will use the tool to generate sample solutions and counterexamples to problems they’re working on, speeding up mathematical discovery.

Williamson welcomes new tools and says he uses LLMs a lot. But he doesn’t think mathematicians should throw out the whiteboards just yet. “In my biased opinion, PatternBoost is a lovely idea, but it is certainly not a panacea,” he says. “I’d love us not to forget more down-to-earth approaches.”

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Mar 26 2026

Why this battery company is pivoting to AI

Qichao Hu doesn’t mince words about how he sees the state of the battery industry. “Almost every Western battery company has either died or is going to die. It’s kind of the reality,” he says.

Hu is the CEO of SES AI, a Massachusetts-based battery company. It once had aims of making huge amounts of advanced lithium metal batteries for major industries like electric vehicles—but now the company is placing its bets on AI materials discovery.

Hu sees the pivot as an essential one. “It’s just not possible for a Western company to build a sustainable business,” he says. The company is still making some batteries, but only for smaller markets like drones rather than those that would require higher volumes, like EVs. The new focus is the company’s battery materials discovery platform—which it can either license to other battery companies or use to develop materials to sell.

Some leading US EV battery companies have folded in recent months, and others, like SES AI, are making dramatic changes in strategy. This shift in who’s building batteries and where they’re doing it could shape the future geopolitics of energy.

The work that would eventually evolve into SES AI began at MIT, where Hu completed his graduate research. His battery work was aimed at applications in oil and gas exploration. The industry uses sensors that go deep underground, where temperatures can top 120 °C (about 250 °F). The team hoped to develop a battery that could withstand those high temperatures and last longer on a single charge.

The chosen technology was a solid polymer lithium metal battery. These cells use lithium metal for their anode and a polymer for their electrolyte (the material that ions move through in a battery cell). Together, these components can increase the energy density of a cell significantly, relative to the lithium-ion batteries that are common in personal devices and EVs today. (Lithium-ion batteries generally use a graphite material for their anode and a liquid for the electrolyte.)

That solid-state battery technology became the foundation of Solid Energy, a startup Hu founded that spun out from MIT in 2012 and raised its first private investment in 2013.

The team eventually realized that underground oil exploration was a small market, so after several years of operation they began to focus on electric vehicles, which were starting to come into the mainstream. After the team tweaked the chemistry to work better at lower temperatures, the company built its first pilot facility in Massachusetts and eventually another facility in Shanghai.

By 2021, the battery industry was booming, Hu recalls, and EVs were the hottest industry to be in. There was a ton of interest in next-generation battery technology from major automakers at the time, and Solid Energy started developing technology with GM, Hyundai, and Honda.

Larger vehicles, like SUVs and trucks, seemed like a good fit for next-generation batteries, Hu says. Massive vehicles like the ones Americans like to drive would need lighter batteries so they could have a reasonable range without being prohibitively heavy.

The company also shifted its chemistry focus, and in 2022 it announced a battery with a silicon anode rather than a lithium metal one. That shift could help make the battery easier to manufacture.

Since then, growth in the EV market has slowed, at least in the US, partly because of major pullbacks in funding from the Trump administration. EV tax credits for drivers, a key piece of support pushing Americans toward electric options, ended in late 2025. With the market for large electric cars in trouble, Hu says, “now we have to look at every market.”

The AI materials discovery platform on which it’s pinning many of its hopes is called Molecular Universe. The company seeks not only to provide its software to other battery companies but also to identify new battery materials and either license them or sell them to those companies.

vials of electrolytes inside a machine at the synthesis foundry

The platform has already identified six new electrolyte materials, according to the company. Hu says one is an additive that could help improve the lifetime of batteries with silicon anodes.

One of the challenges with silicon anodes is that they tend to swell a lot during use, which can cause physical damage and prevent efficient charging and discharging. To address the problem, the industry typically uses a material called fluoroethylene carbonate (FEC), which can help form an elastic film on the anode so the battery can still charge effectively. That additive can degrade at high temperatures, though, producing gases that can harm a battery’s lifetime. The SES platform identified a compound that works like FEC but doesn’t release those gases.

The company’s long history and deep battery knowledge could help make its platform a useful tool, Hu says. He sees the actual model as less crucial than SES’s domain expertise and data from years of making and testing batteries.

“By not actually making the physical battery, we’re actually able to scale and then generate revenue faster,” he says.

But some experts are skeptical about the near-term prospects for AI materials discovery to revive the industry. “New materials development, as much as we thought that was what people wanted (and, frankly, it should be what the cell makers want)—I don’t know that that seems to be the real linchpin of the battery industry’s progress,” says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on the energy storage industry.

Investors are pulling back, and a slowdown in public support is making things difficult for some parts of the battery industry, she adds: “I don’t know that the ability to discover any new material is going to unlock anything new for the battery industry at this point in time.”

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Roundtables Space Subscriber-Only Stories

Mar 26 2026

Roundtables: The Next Era of Space Exploration

Listen to the session or watch below

Whether it’s the race to find life on Mars, the campaign to outsmart killer asteroids, or the quest to make the moon a permanent home to astronauts, scientists’ efforts in space can tell us more about where humanity is headed. This subscriber-only discussion examines the progress and possibilities ahead.

Speakers: Amanda Silverman, features & investigations editor, and Robin George Andrews, award-winning science journalist and author

Recorded on March 25, 2026

Related Stories:

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Mar 26 2026

New Ecommerce Tools: March 25, 2026

Our rundown this week of new services for ecommerce merchants includes updates on predictive intelligence, same-day shipping, automated marketing, agentic commerce, installment payments, product videos, and dropshipping.

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

New Tools for Merchants

ShipStation leverages predictive intelligence on fulfillment, delivery, and returns. ShipStation, a shipping and logistics platform, has launched Intelligence, a predictive AI tool to help merchants reduce operational inefficiencies and expenses. According to ShipStation, Intelligence enables merchants to automatically select the best carrier and service level for every shipment, identify delivery risks and delays, automate fulfillment workflows, and optimize international shipping decisions, including customs and duty management.

ShipStation

FedEx to offer same-day delivery options. FedEx has announced the rollout of SameDay Local in collaboration with OneRail, a last-mile delivery company. SameDay Local lets shoppers choose two-hour or end-of-day delivery directly at checkout. The service will connect FedEx customers to a U.S. network of roughly 1,000 delivery providers, coordinated through intelligent orchestration that matches orders with vehicles and drivers, tracked with live updates from pickup to delivery.

Reddit introduces new tools for shopping. Reddit is launching shopping tools to enhance its Dynamic Product Ads experience. Collection Ads allow businesses to lead with a lifestyle hero image paired with shoppable product tiles in a single carousel. Community overlays indicate products that are resonating on Reddit, while Deal overlays automatically call out discounts and sale pricing. Also, Reddit says its new Shopify integration simplifies catalog and pixel setup, enabling frictionless onboarding for new advertisers.

Klaviyo expands autonomous marketing with Composer. Klaviyo, a marketing automation platform, has launched Composer, an agentic experience that generates, optimizes, and recommends marketing campaigns and flows from a single prompt. With Composer, marketers describe what they want in plain language, and Composer builds a launch-ready campaign, including audience segments and messaging optimized for each channel. Klaviyo has also released new retail tools for its Customer Agent, including order tracking, returns and exchanges, subscription editing, and loyalty lookup.

Klaviyo

Alibaba launches Accio Work AI agent for global merchants. Alibaba International has unveiled Accio Work, a plug-and-play enterprise AI agent. The platform requires no setup, per Alibaba International, and deploys specialized agents to execute complex, long-horizon operations. Accio Work enables businesses (online or physical) to manage real-time VAT filings, tax refunds, and customs documentation across more than 100 markets. Businesses can issue requests for quotation and conduct multi-round negotiations with suppliers through tools such as Telegram and WhatsApp.

Algolia enhances search for Shopify merchants. Algolia, an AI search and retrieval platform, has announced enhancements to its Shopify integration. The release introduces (i) Commerce Pipeline, an indexing foundation that improves speed and reliability, and (ii) Click-to-Activate Pixel Analytics, which captures shopper behavior. Algolia has expanded customization within Shopify App Blocks, and Algolia now indexes Shopify Metaobjects and Shopify’s Standard Product Taxonomy, enabling richer search content and more precise merchandising control.

Google enhances shopping for AI agents. Google has announced updates to the Universal Commerce Protocol for AI-driven commerce. UCP now offers an option that lets agents add multiple items to a shopping cart at once from a single store. UCP adopters can access a capability that lets agents retrieve real-time product details from a retailer’s catalog. UCP also supports identity linking, allowing shoppers on UCP-integrated platforms to receive loyalty or member benefits, such as pricing or free shipping.

Constructor unveils AI agent for product discovery. Constructor, an AI-powered search and discovery platform for ecommerce companies, has released Merchant Intelligence Agent, which brings conversational intelligence to merchandising teams to improve how shoppers discover products. Teams can ask the new AI agent natural-language questions, investigate campaign performance, request recommendations to accomplish merchandising goals, and more.

Constructor Merchant Intelligence Agent

Coveo launches search-native conversational AI for product discovery. Coveo, a platform for AI-powered relevance for digital experiences, has launched Conversational Product Discovery, a capability that allows shoppers to discover products through natural language conversations within the search experience. The discovery agent coordinates multiple AI functions to interpret shopper intent, retrieve relevant products from a retailer’s catalog, and assemble responses grounded in catalog data and merchandising rules. Users maintain control through defined layouts, content guardrails, and merchandising directives.

Splitit launches Go to extend installments for field sales. Splitit, an installment payments platform, has launched Go, a mobile tool that brings credit-card-linked installments into face-to-face environments. Splitit Go allows customers to use their existing credit cards, while merchants can generate installment offers from a smartphone, tablet, or laptop. Customers receive the offer via QR code, text, or email, review the terms on their own device, and complete the purchase using available credit on their card.

Claude, ChatGPT, and Cursor can now take direct action on WordPress.com. WordPress.com has launched new write capabilities for its Model Context Protocol server. The update enables AI agents, including Claude, ChatGPT, and Cursor, to create, edit, and manage content on WordPress.com sites directly through natural conversation. Users can instruct their AI agent to draft and publish blog posts and pages, edit and update existing content, and create new pages and manage site content — all through natural conversation.

WizCommerce launches AI Video Generator for brands. WizCommerce, an ecommerce platform, has launched its AI Video Generator, a module inside WizStudio that transforms a single product image into a publish-ready video in minutes. Lifestyle Product Videos places products in on-brand environments with cinematic motion, optimized for social media and storefronts. Product Closeups highlight materials, textures, and details. 360° Spin Videos allow shoppers to explore products from every angle.

WizCommerce

Shoplazza adopts agentic commerce architecture. Shoplazza, a D2C commerce platform, has launched an agentic commerce architecture, with AI agents to execute ecommerce operational tasks. According to Shoplazza, merchants can describe the business outcome, and the platform coordinates actions across operations, payments, marketing, and customer-management systems. The platform can automatically execute tasks such as creating campaigns, updating storefront merchandising, and monitoring performance.

Mliveo launches AI-powered livestream cross-border ecommerce solution. Mliveo, a provider of cross-border ecommerce tools, has launched its upgraded AI Livestream Commerce Suite. The new suite integrates AI hosts, intelligent product sourcing, and a global dropshipping network to provide a unified B2B growth for cross-border sellers and brands. With Mliveo, sellers need only to upload product SKUs and define their profit margins. The platform then automates product analysis, livestream distribution, and order conversion.

Zonos launches AI products for cross-border shipments. Zonos, a cross-border technology company, has launched AI-powered products that infer and validate customs data. Classify determines the correct commodity code. Country of Origin provides a ranked list of probable locales. Customs Value returns a recommended value alongside a range of prices. Customs Description transforms product names and retail descriptions into concise customs descriptions. Vision extracts item information from a photograph. Greenlight is an API for validating export compliance.

Netguru launches design system for marketplaces and commerce products. Netguru, an ecommerce consultancy, has launched Silk, a system to help product teams design and ship marketplaces and commerce applications. Silk provides a toolkit of reusable UI components, design tokens, and documented patterns that enable teams to build digital marketplaces, ecommerce platforms, and B2B commerce products without having to produce core interface elements from scratch. Silk is available as a free design system for Figma, including component libraries, documentation, and example product screens.

Netguru

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Mar 25 2026

Google Analytics Launches Scenario Planner and Projections via @sejournal, @brookeosmundson

Google Analytics has launched Scenario Planner and Projections, two new features designed to help advertisers plan and monitor paid media budgets across channels.

The rollout is part of Google Analytics’ cross-channel budgeting feature, which is still in beta and not yet available to every Google Analytics property.

Read on to learn more about the tools, who’s eligible, and how advertisers can use them.

Introducing Scenario Planner and Projections

The rollout includes two separate tools built for different stages of campaign planning.

Scenario Planner is designed for future planning. It allows advertisers to model different budget allocations across channels and estimate how those changes may impact conversions, revenue, or return on investment. The tool is intended for building media plans ahead of campaign launches or defined planning periods.

Projections is designed for active campaigns. It helps advertisers evaluate whether current spend is pacing toward selected goals and where adjustments may be needed before the reporting period ends. This includes visibility into projected budget delivery, conversions, and revenue by channel.

Google says the tools are meant to be used together. Scenario Planner can be used to build a forward-looking budget plan, while Projections can be used to monitor how campaigns are tracking against that plan once they are live.

The feature is not limited to Google Ads data. Advertisers can incorporate campaign data from both Google and non-Google paid channels, provided cost data and integrations are properly configured.

There are, however, some requirements that may limit access. According to Google, eligibility requirements include:

At least one year of conversion data
Channels with cost are required and must be data compatible with Primary Channel Grouping
At least one year of campaign data from at least two channels (Google and non-Google)

Google also notes that both tools rely on modeled estimates based on historical performance, meaning outputs are directional rather than guaranteed.

Cross-channel budgeting is currently labeled as a beta feature, and Google notes that it may not yet be available to all Google Analytics properties, but is working on expanding to more accounts.

Why This Matters For Advertisers

For many teams, budget planning and performance analysis still happen in separate places.

Planning often lives in spreadsheets or internal forecasts, while performance is measured inside ad platforms and Google Analytics after the fact. That separation can make it harder to evaluate whether budget decisions are working in real time.

These tools bring some of that planning workflow into Google Analytics.

Advertisers now have a way to model budget allocation before campaigns begin and check pacing while campaigns are still running, using the same data source they rely on for performance reporting.

That could be useful for teams managing spend across multiple paid channels, particularly when trying to compare performance beyond a single platform’s recommendations.

At the same time, the usefulness of the feature will depend on data quality and setup. Advertisers with incomplete cost imports, limited historical data, or inconsistent conversion tracking may not be able to fully use the tools or may see less reliable projections.

What Comes Next

For advertisers already using Google Analytics as a central reporting tool, Scenario Planner and Projections may offer a more practical way to pressure-test budget decisions before and during campaign execution.

How useful the tools become in day-to-day planning will likely depend on how many advertisers qualify for access and how reliable the forecasting proves to be over time.

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Mar 25 2026

Half Your Traffic Left. The SEO Industry Sent Thoughts and Frameworks

Before AI Overviews launched in May 2024, Define Media Group’s portfolio of major U.S. publishers averaged 1.7 billion organic search clicks per quarter. Steady. Predictable. The kind of number you build a business model on and then stop thinking about, because why would you?

After the launch, traffic dropped 16% and never recovered. When Google expanded AI Overviews in May 2025, the decline accelerated. By Q4 2025, organic search traffic across that portfolio was down 42% from the pre-AIO baseline.

Nearly half the organic traffic, gone, from a portfolio large enough to be directional for the entire publishing industry.

The traffic bargain (you produce content, Google sends clicks, advertising revenue funds the next round of production) has been the economic engine of the open web for 20 years. That engine is seizing up in plain sight, and the industry’s response has been to argue about which dashboard to stare at while it happens.

New Interface, Same Delusion

The first camp did what the SEO industry always does when the ground shifts: they built new tools to measure the shaking.

Prompt tracking. LLM visibility dashboards. Share-of-answer metrics. In under 18 months, an entire vendor category materialized to sell you a number that tells you how often your brand appears in AI-generated responses. It’s Search Console for the chatbot era, and it comes with the same comforting implication: If the number goes up, you’re winning. If it goes down, buy more of the thing that makes it go up.

I’ve written about this before, and I’ll be blunt again: These tools are selling you bullshit with a confidence interval drawn on it in crayon. When a dashboard tells you your brand “appeared in 73% of relevant AI responses,” what it actually measured is: We fired some prompts at an API, got some outputs, and counted mentions. That’s not a ranking. That’s a lottery ticket.

The engineers who built these models cannot fully explain why a specific output appeared. But sure, a SaaS tool perched atop Mount Dunning-Kruger with a trend line has it all figured out.

The industry keeps buying because the alternative is admitting we’re flying blind. Questioning the data means telling the room that the “directional” charts in the client deck are noise dressed up as insight. Nobody wants to be that person. So the vendors keep selling, the dashboards keep flickering, and the number doesn’t need to correlate with revenue. It just needs to fluctuate enough to sustain a subscription.

Jono Alderson made the broader version of this argument in a recent piece, Clicks Don’t Count (and They Never Did). His point: SEO has always measured the interface rather than the forces underneath it. Rankings, traffic, visibility scores. None of these were measures of competitiveness. They were measurements of a presentation layer. We spent two decades optimizing what we could see and calling it strategy.

He’s right. And prompt tracking is the newest iteration of the same mistake. Old retrieval visibility in a trench coat, pretending to be two disciplines.

The second camp is more intellectually serious. Jono’s piece is the best version of this argument, and I agree with more of it than I’m about to make it sound like.

His framework: stop measuring the interface, start measuring competitiveness. Six structural dimensions drawn from marketing science validated for decades: experience integrity, physical availability, mental availability, distinctiveness, reputation, commercial proof. AI systems aggregate signals about brands across the web, not pages in isolation. The entities that are genuinely competitive get recommended and surfaced. Visibility is the output, not the input.

I think this is broadly correct. I also think it has a timing problem the size of a crater.

Those six dimensions operate on timescales of years. Building mental availability is a sustained brand investment. Earning reputation signals is the compound interest of consistently not being terrible. Strengthening distinctive assets requires buy-in from people who’ve never heard of Ehrenberg-Bass and aren’t going to read a blog post to find out.

The traffic collapse is happening in quarters.

Tell a publisher who just lost 42% of their search traffic to “strengthen structural competitiveness” and watch their face. It’s like telling someone whose house is flooding to invest in better drainage. You’re not wrong. You’re just not helping.

Jono knows this, to his credit. When someone in his comments asked how to operationalize the framework, his answer was honest: Redefine SEO to own those areas, or navigate the organizational politics of working with the teams that do. “Lots of organizational politics, either way.” That’s the kind of understatement that only someone who’s actually tried it would make.

What Actually Broke

The measurement debate is a sideshow. The traffic bargain wasn’t a metric. It was the economic foundation of content production on the open web.

Google needed content to crawl. Publishers needed distribution to monetize. Produce something worth indexing, Google sends traffic, you convert it into revenue, that revenue funds more content. The loop ran for 20 years. Everyone pretended it was a partnership rather than a dependency, and the pretence held because the numbers worked.

AI Overviews break the loop. Google synthesizes the answer from your content and serves it directly. The user gets what they need. Your content gets consumed on Google’s surface, with Google’s ads, generating Google’s engagement metrics. You get a citation link that roughly nobody clicks and a warm feeling about “brand visibility.”

Google’s own VP of Product for Search, Robby Stein, recently described how they had to “teach the model how to link out.” Linking to publishers wasn’t the default behavior. It had to be engineered back in. The system’s natural state is to absorb your content and answer the question. Sending traffic your way is the afterthought they bolted on, so the extraction doesn’t look like what it actually is: taking your stuff and serving it as theirs.

The breakage isn’t uniform. Define’s data shows breaking news traffic up 103% across all Google surfaces, while evergreen content dropped 40%. The Top Stories carousel has been largely shielded from AI Overview incursion. Evergreen content has not. The how-to guides, the explainers, the reference material, the content categories that built the SEO industry, are exactly the categories AI Overviews were designed to absorb and replace.

Google is selecting which content survives the transition. Time-sensitive content still drives clicks because you can’t summarize something that’s still developing. Everything else is increasingly raw material for the answer machine, and the machine doesn’t pay for raw materials.

If “competitiveness” replaces traffic as the operating metric, SEO’s scope has to change. Jono’s six dimensions are mostly owned by product, brand, and marketing. Experience integrity is product and UX. Mental availability is brand investment. Reputation is years of not cutting corners. Commercial proof is a function of whether the thing you sell is actually good. SEO teams control technical discoverability, content strategy, and site architecture. That’s one layer of the competitiveness framework, not the whole building.

So the discipline either expands into a cross-functional strategic role (good luck explaining to the CMO that SEO now owns brand strategy because the retrieval models changed) or it contracts honestly and positions itself as the technical infrastructure that makes competitiveness legible to machines. Either option beats “we’ll get you more organic traffic,” which is a promise that ages worse every quarter.

Clicks may not have been the right metric. Jono makes a persuasive case. We measured the interface and called it the system.

But clicks paid the bills. They funded editorial teams, justified content investment, and sustained the publishing ecosystem that both search engines and AI systems depend on for training data and retrieval sources. Without content to crawl, there’s nothing to index. Without content to train on, there’s nothing to synthesise. The irony is apparently lost on the company deploying AI Overviews.

Nobody’s building a transition strategy. The prompt-tracking vendors are selling the new dashboard. The strategists are selling the long view. Google won’t help. They broke the bargain, and their Discover push suggests they’d rather build a distribution surface they fully control than repair the one that shared value with publishers. The AI companies need content to exist, but haven’t worked out how to fund its production.

Everyone’s got a framework. Nobody’s got an answer.

The clicks didn’t count. But something needs to. Soon.

More Resources:

This post was originally published on The Inference.

Featured Image: Accogliente Design/Shutterstock

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Mar 25 2026

How Zero-Party & First-Party Data Can Fuel Your Intent-Based SEO Strategy via @sejournal, @rio_seo

There’s an interesting paradox currently occurring in the realm of marketing. Marketers have more tools and data at their fingertips, yet despite this influx of information, marketing leaders also somehow have less clarity than ever before.

Over the past decade, Google’s algorithms and privacy regulations have significantly shifted traditional SEO best practices. SEO has evolved from a precise science to more of a trust discipline, where marketers must infuse credibility and authority into their content to improve visibility.

The new opportunity at hand isn’t scraping more consumer behavior but rather listening to it in a new manner. By diving deeper into zero-party data, information customers willingly share, and first-party data, behavior observed directly on your own channels, chief marketing officers can shape their SEO strategies around real human intent.

Search success will be contingent on whether brands understand their audience well enough to create relevant, authentic, and trustworthy content at every step of the customer journey, not just when an algorithm prompts them to.

The Connection Between Zero-Party Data And SEO

Zero-party data is marketing’s cleanest and clearest source of truth. It uncovers the information customers want you to have. It unveils their preferences, motivations, and needs through methods like surveys, quizzes, chatbots, and more.

First-party data shows what users do. Zero-party data shows you why they did what they did. When paired together, both forms of data bridge the gap between analytics and empathy.

For example, a retail brand might ask site visitors in a post-purchase survey, “What is most likely to motivate you to make a purchase?” The choices the site visitor can choose between are price, sustainability, or convenience. Now, consider if nearly half of those respondents chose “sustainability.”

This insight shouldn’t fall into a void, but rather should be acted upon quickly. It’s not a trend but rather a clear signal. The content and SEO teams can now focus on creating content around “eco-friendly shopping” and other relevant sustainability topics, while communications teams can align messaging around the same topic. In turn, seamless collaboration and alignment take place.

Moving Beyond Keywords To Conversations

Traditional SEO honed in on what people typed into the search bar. Zero-party data reveals what people mean when they’re searching for a business, product, or service. Algorithms are increasingly rewarding intent satisfaction when evaluating content. When your content addresses and is built on declared motivations, like why someone is looking for your specific solution, you’re aligned with the future of search.

How To Turn Customer Data Into Search Strategy

The issue isn’t that CMOs aren’t collecting data; it’s the struggle with turning it into action that drives meaningful change.

An intent-based SEO strategy has three phases, which we will discuss next (capture, interpret, and activate).

Phase 1: Capture

Customers aren’t going to hand over information if they don’t see a clear value in doing so. To encourage this, marketers must highlight a mutual benefit in the information exchange. A few methods include:

Gated research studies.
Short post-purchase surveys.
Interactive quizzes or calculators.
Preference centers so customers only receive communication around specified topics that matter most to them.
Incentives such as coupons and exclusive promotions for newsletter subscribers.

Each of the aforementioned information exchanges becomes a declared-intent breadcrumb. Users have granted your business permission to act on their feedback and are much more actionable than cookie trails alone.

Phase 2: Interpret

Collecting information from myriad channels can make it difficult to determine where they should focus their attention first. To dissect and pull out the insights that matter most from unstructured and structured feedback, CMOs should invest in qualitative analysis tools. Tools like text analytics, for example, can make it easy for CMOs and CX teams alike to mine for common themes.

Customer Data Platforms (CDPs), can also help you create audiences and segments to deliver more personalized content that resonates with customers. This might look like a retail marketing manager only receiving newsletters, ebooks, or blogs that are related to the retail industry and trends.

These types of thematic content pillars can help inform supporting search queries, schema markup, content priorities, and more.

Phase 3: Activate

In this phase, you’ll set your plans into action. First, connect declared intent to keyword intent. For example, if customers talk about “security peace of mind,” this gives you clear insight into what they’re interested in learning more about and how your company can help. You could create content that explicitly speaks to “how we secure your personal data.”

On the other hand, if they’re talking about “easy to implement,” it may be beneficial for you to provide explainer-type content, such as a short video or an FAQ page (with FAQ schema), to address “how to integrate [product name]” searches.

Zero-party data helps move the needle with SEO efforts; from a guessing game to an action engine, producing content that doesn’t just satisfy search algorithms, but also the people behind the search, too.

Leadership Enablement: Aligning Teams, Culture, And Technology

To build an insight-to-action culture, CMOs should encourage teams to share qualitative learnings regularly, whether through a cadence of weekly meetings, via email, or a combination of the two. Customer experience teams should make Voice of Customer insights loud and clear to help inform SEO and content briefs.

It’s also important to highlight and reward cross-functional wins to showcase how working together helps drive growth. This might look like an SEO strategy that was informed by CX feedback or a case study that solves a pressing challenge clients typically face, informed by online reputation feedback.

Operationalize The Feedback Loop

CMOs can install a regular “intent feedback loop” to operationalize the data your company receives and act upon that data. This might look like:

Gather declared data (surveys, chatbot transcripts, online reviews, call center logs).
Identify what motivates consumers most (customers often talk about time savings, value for money, trust issues, emotions).
Update content briefs and keyword maps (primary and secondary keywords, content requirements, search intent to ensure you’re staying up to speed).
Measure whether your content is landing with your intended audience on an emotional and intellectual level. Engagement, recall, and action are key determinants of content success, not just how it ranks.

This type of feedback framework helps organizations embed customers’ preferences and desires directly into the content published, helping your business create the content that actually connects with your target audience.

The Metrics To Add

Measuring what matters most is integral to assess the impact of zero-party data analysis efforts. Alongside other SEO metrics, the following can gain a holistic view of your SEO performance:

Resonance Metrics

Engagement quality is a true testament of attention. Meanwhile, volume, while great to have, is somewhat meaningless if you have an abundance of unqualified leads. Instead, look at:

Average engagement time: How long people stick around to view your content.
Return visits: People who come back to consume more of your content.
Scroll depth: Visitors should scroll down to read the entirety of your content because they find it to be that interesting.

Relevance Metrics

Marketers must track growth in high-intent and branded queries, as these are most often the terms that someone who is on the verge of buying will use when searching for your business. If you’re showing up for phrases customers typically use when at the decision-making stage, such as “State Farm compared vs. Geico car insurance,” this indicates deeper resonance.

Relationship Metrics

Loyalty metrics, while not a metric SEOs track, can correlate with how well your SEO program is working. Reframing SEO performance as a reflection of customer understanding helps CMOs dig a layer deeper, past solely tactics, and understand deeper-rooted customer emotions that could be preventing your business from scaling. Look at:

Zero-party response rate: The percentage of users who are willing to share their personal information and experiences.
Repeat engagement: Consumers who continue to engage with your business and see value in doing so.
Customer lifetime value: How valuable a customer is to your business over time (how much they purchase, do they churn quickly)
Retention rate: Customers who continue to do business with you that you’ve worked hard to acquire and keep.

The Future Belongs To Human-Declared Intent

We may be in the age of AI, but the future is human. Yes, AI can generate a keyword-optimized blog in a matter of seconds, but human touch is where the real value is. And human-informed data will be your business’s ultimate differentiator.

Zero- and first-party data reveal pertinent insights that elevate organizations when this data is acted upon. It unlocks insights into why people search and not just what they search for. It also uncovers where in the sales journey customers are getting stuck and blockers for purchasing.

Moving forward, to fuel your SEO efforts:

Ask customers what matters most to them.
Listen to what they have to say.
Create content that addresses those asks.
Optimize it for human needs, not just engagement and clicks.
Measure customer experience metrics, not just SEO.

When marketing leaders take consumer feedback to heart, they bridge the gap between traffic and trust, building stronger relationships that lead to more purchases, repeat customers, and improved brand experiences.

More Resources:

Featured Image: Anton Vierietin/Shutterstock

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