Mar 14 2026
Why physical AI is becoming manufacturing’s next advantage

For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough.

Today’s manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intelligence that can operate reliably in the physical world.

This is where physical AI—intelligence that can sense, reason, and act in the real world—marks a decisive shift. And it is why Microsoft and NVIDIA are working together to help manufacturers move from experimentation to production at industrial scale.

The industrial frontier: Intelligence and trust, not just automation

Most early AI adoption focused on narrow optimization: automating tasks, improving utilization, and cutting costs. While valuable, that phase often created new friction, including skills gaps, governance concerns, and uncertainty about long‑term impact. Furthermore, the use cases were plentiful but not as strategic.

The industrial frontier represents a different approach. Rather than asking how much work machines can replace, frontier manufacturers ask how AI can expand human capability, accelerate innovation, and unlock new forms of value while remaining trustworthy and controllable.

Across industries, companies that successfully move into this frontier phase share two non‑negotiables:

  • Intelligence: AI systems must understand how the business actually handles its data, workflows, and institutional knowledge.
  • Trust: As AI begins to act in high‑stakes environments, organizations must retain security, governance, and observability at every layer.

Without intelligence, AI becomes generic. Without trust, adoption stalls.

Why manufacturing is the proving ground for physical AI

Manufacturing is uniquely positioned at the center of this shift.

AI is no longer confined to planning or analytics. It is moving into physical execution: coordinating machines, adapting to real‑world variability, and working alongside people on the factory floor. Robotics, autonomous systems, and AI agents must now perceive, reason, and act in dynamic environments.

This transition exposes a critical gap. Traditional automation excels at repetition but struggles with adaptability. Human workers bring judgment and context but are constrained by scale. Physical AI closes that gap by enabling human‑led, AI‑operated systems, where people set intent and intelligent systems execute, learn, and improve over time. Humans are essential for scaled success.

Microsoft and NVIDIA: Accelerating physical AI at scale

Physical AI cannot be delivered through point solutions. It requires agentic-driven, enterprise-grade development, deployment, and operations toolchains and workflows that connect simulation, data, AI models, robotics, and governance into a coherent system.

NVIDIA is building the AI infrastructure that makes physical AI possible, including accelerated computing, open models, simulation libraries, and robotics frameworks and blueprints that enable the ecosystem to build autonomous robotics systems that can perceive, reason, plan, and take action in the physical world. Microsoft complements this with a cloud and data platform designed to operate physical AI securely, at scale, and across the enterprise.

Together, Microsoft and NVIDIA are enabling manufacturers to move beyond pilots toward production‑ready physical AI systems that can be developed, tested, deployed, and continuously improved across heterogeneous environments spanning the product lifecycle, factory operations, and supply chain.

From intelligence to action: Human-agent teams in the factory

At the industrial frontier, AI is not a standalone system, but a digital teammate.

When AI agents are grounded in the proper operational data, embedded in human workflows, and governed end to end, they can assist with tasks such as:

  • Optimizing production lines in real time
  • Coordinating maintenance and quality decisions
  • Adapting operations to supply or demand disruptions
  • Accelerating engineering and product lifecycle decisions

For example, manufacturers are beginning to use simulation‑grounded AI agents to evaluate production changes virtually before deploying them on the factory floor, reducing risk while accelerating decision‑making.

Crucially, frontier manufacturers design these systems so humans remain in control. AI executes, monitors, and recommends, while people provide intent, oversight, and judgment. This balance allows organizations to move faster without losing confidence or control.

The role of trust in scaling physical AI

As physical AI systems scale, trust becomes the limiting factor.

Manufacturers must ensure that AI systems are secure, observable, and operating within policy, especially when they influence safety‑critical or mission‑critical processes. Governance cannot be an afterthought; It must be engineered into the platform itself.

This is why frontier manufacturers treat trust as a first‑class requirement, pairing innovation with visibility, compliance, and accountability. Only then can physical AI move from promising demonstrations to enterprise‑wide deployment.

Why this moment matters—and what’s next

The convergence of AI agents, robotics, simulation, and real‑time data marks an inflection point for manufacturing. What was once experimental is becoming operational. What was once siloed is becoming connected.

At NVIDIA GTC 2026, Microsoft and NVIDIA will demonstrate how this collaboration supports physical AI systems that manufacturers can deploy today and scale responsibly tomorrow. From simulation‑driven development to real‑world execution, the focus is on helping manufacturers cross the industrial frontier with confidence.

For manufacturing leaders, the question is no longer whether physical AI will reshape operations, but how quickly they can adopt it responsibly, at scale, and with trust built in from the start.

Discover more with Microsoft at NVIDIA GTC 2026.

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

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Mar 14 2026
How Vessi Sells Waterproof Shoes

Ray Hua is the director of ecommerce at Vessi, a Canada-based direct-to-consumer seller of waterproof sneakers. The brand launched in 2017 after its founders developed and patented breathable fabric that repels water. Ray joined the company in 2021.

In our recent conversation, he shared the challenges of targeting the right audience, cross-border selling, diversifying, and more.

Our entire audio is embedded below. The transcript is edited for length and clarity.

Eric Bandholz: Give us a quick rundown of who you are and what you do.

Ray Hua: I’m the director of ecommerce at Vessi, a direct-to-consumer waterproof sneaker brand. I oversee strategies for site experiences, performance, merchandising, and lifecycle marketing. It’s been with the company for about five years.

Vessie launched nine years ago. Our founders developed and patented a lightweight, waterproof, and breathable fabric called Dyma-tex. People assume waterproof means it is not breathable. But our product is comfortable and looks like a regular sneaker.

During the pandemic, we gifted our product to healthcare workers. We received a lot of positive feedback from other communities, so we collaborated with niche networks to offer our products at a discount.

We’ve hired a lot of paid influencers in categories where folks are on their feet all day. We have tiers of influencers. Some have dedicated landing pages; others are for getting our name out.

We invest heavily in Meta for customer acquisition. We’re looking to diversify into Google and TikTok Shop. We’ve advertised on TikTok and even Reddit. Both drove a lot of traffic, but the quality was not very high. We couldn’t easily attribute revenue coming from those channels.

Bandholz: Vessi now sells apparel.

Hua: It’s more of an experiment in response to feedback in our customer surveys. Many mentioned expanding into apparel, socks, and accessories. They like our technology and want items that are fashionable and functional.

So we’re testing those categories for additional revenue. It hasn’t been smooth. We developed apparel that performed poorly and diverted resources from our footwear line.

Still, it was a good experiment and demonstrated the steep learning curve for a category we are not familiar with.

Bandholz: Vessi has warehouses in Canada and the U.S. Do you market differently to consumers in those countries?

Hua: Yes, we use different ads for each market. People in Canada know our brand. Our messaging to them is typically announcements about dropping new colors or limited editions.

We’re not as prominent in the U.S. Our ads there introduce the brand and explain the product’s benefits. Seattle is probably our best region in the U.S. It’s close to Vancouver and gets a lot of rain. We’re also strong in Florida, however, which is both sunny and rainy.

Bandholz: Does AI influence your marketing efforts?

Hua: We’re using AI tools mostly for operations. For example, we use AI to identify influencers aligned with our interests.

We’ve dabbled in AI to produce ad copy. We haven’t gone into AI-generated images or videos, mainly because we have strict brand guidelines.

Bandholz: Where can people find you, support you, buy your products?

Hua: Check out our products at Vessi.com. I’m on LinkedIn.

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Mar 13 2026
Rethinking SEO in the age of AI

For years, SEO followed a fairly predictable playbook: create valuable content, optimize it for search engines, and compete for rankings on Google. But the way people discover information online is changing quickly. Tools like ChatGPT, Perplexity, and Gemini are introducing a new layer between users and search engines, where answers are generated and synthesized rather than simply retrieved.

In a recent episode of the Get Discovered podcast, Joe Walsh, CEO of Prerender.io, sat down with Yoast’s Principal Architect Alain Schlesser to discuss what this shift means for SEO and online discoverability. Their conversation explores how AI answer engines are reshaping the search landscape and why many traditional SEO assumptions no longer fully apply.

Alain shares insights on:

  • How AI systems retrieve and surface information
  • Why brands must rethink their online positioning, and
  • What businesses should start preparing for as AI-driven discovery evolves over the next 12–18 months?

Watch the full conversation between Joe Walsh and Yoast’s Principal Architect, Alain Schlesser, in the Get Discovered podcast below.

Table of contents

The new discovery layer: AI is becoming the gatekeeper

“There’s now a layer in front of search that acts as a gatekeeper before you even hit those search engines.”

AI adds a new layer to the information discovery process for the searchers

That’s how Alain describes one of the biggest structural shifts happening in online discovery today. For years, the flow of search was straightforward: a user typed a search term into a search engine, the engine returned a list of results, and the user decided which link to click.

But AI-powered systems have added a new layer to that process.

From search queries to conversational discovery

Today, many users begin their search journey by asking questions in tools like ChatGPT, Perplexity, or Gemini instead of typing traditional keyword queries. The AI system then determines whether it needs external information and may generate multiple search queries behind the scenes to retrieve relevant sources.

The discovery flow now looks something like this:

The traditional vs the new agentic search

Previously:

User → Search engine → Website

Now:

User → AI model → Search engine → Website → AI synthesis → User

Instead of presenting a list of links, the AI model interprets and combines information before generating an answer. Alain explains this process in more detail in the podcast, highlighting how AI systems now act as a filtering layer between users and the web.

Search is fragmenting beyond Google

“We were in a rather comfortable position where we were only dealing with a monopoly search.”

For much of the past two decades, SEO largely meant optimizing for one ecosystem: Google. Even though other search engines existed, Google dominated how people discovered information online.

But that environment is changing.

As Alain explains, AI systems are introducing a new layer of fragmentation in discovery. Different AI platforms rely on different combinations of search engines, indexes, and training data, which means results can vary widely between them.

In practice, that means a brand might appear prominently in one AI system while barely showing up in another. For SEO teams, this marks a shift toward thinking about visibility across multiple AI-driven environments rather than just one search engine.

Do checkout: Why does having insights across multiple LLMs matter for brand visibility?

What hasn’t changed: The fundamentals of SEO

Despite technological changes, Alain emphasizes that the core principles of good SEO remain intact.

“You shouldn’t try to game the search engine. You need to create valuable content that humans actually want to read, and structure it so search engines can understand it.”

At its core, search still aims to deliver the best possible answers to users. Whether the request comes from a person typing a query or an AI model generating one behind the scenes, the goal remains the same: surface useful, reliable information.

That means SEO teams should continue focusing on fundamentals such as:

AI systems may change how information is surfaced, but they still rely on the same underlying signals of quality and relevance.

The “top results or nothing” reality

As the discovery landscape evolves, another important shift emerges in how AI systems interact with search results.

“They don’t see the full search result page. What the LLM typically sees is just the five topmost elements per search query.”

Unlike human users, AI systems typically work with a very small set of retrieved sources before generating an answer. That means if your content doesn’t appear among those top results, it may never reach the AI system at all.

In a world where AI answers rely on the summarization of modern content, only the sources that make it into that small retrieval window influence the final response.

This makes strong search visibility more important than ever. Ranking well isn’t just about earning clicks anymore. It determines whether your content is even considered when AI systems construct an answer.

Why “safe” content strategies are no longer enough

Even if your content reaches those top results, there’s another layer of filtering happening inside the AI model itself.

Large language models compress enormous amounts of information during training. As Alain explains:

What the model keeps are the dominant signal and the outliers. Everything in between is often compressed away as statistical noise.

In the podcast, Alain uses this idea to explain why brands that try to be broadly acceptable or “safe” may struggle to stand out in AI-driven discovery.

The takeaway is clear: in a world where AI systems summarize and compress information, having a clear and distinctive perspective becomes increasingly important.

Why Yoast launched AI visibility tracking

As AI systems reshape how information is discovered and summarized, a new challenge emerges for businesses: understanding how their brand appears in AI-generated answers. That’s the problem Yoast set out to address with Yoast SEO AI +, a feature designed to help businesses monitor how their brand shows up across major AI platforms.

Earlier in this article, we explored how AI systems now sit between users and search engines, retrieve only a small set of results, and synthesize answers through the summarization of modern content. Together, these changes create a new discovery layer that is far less transparent than traditional search.

As Alain explains in the podcast:

“We need more visibility and observability into that AI-based layer to figure out what is going on there. Right now, it’s mostly a black box.”

Unlike traditional search engines, AI systems don’t provide clear rankings, impressions, or click data that explain why a source was selected. Instead, answers are generated from a mix of retrieved content, training data, and model reasoning. For businesses, that makes it much harder to understand whether their brand is visible in AI-driven discovery.

This is where AI visibility tracking becomes valuable. Rather than focusing only on search rankings, teams also need insight into how their brand is represented inside AI responses.

Yoast SEO AI + helps surface that layer by allowing teams to observe how their brand appears across AI systems, such as ChatGPT, Perplexity, and Gemini.

Must read: What is ChatGPT Search (and how does it use Bing data)?

The goal is not simply to track another metric. It’s to help businesses understand how AI systems interpret and represent their brand.

As Alain notes, visibility in AI systems can vary significantly depending on the platform, because each one relies on different combinations of:

  • search engines
  • indexes
  • training datasets

This means a brand might appear frequently in one AI system while barely showing up in another. Without visibility into those differences, it becomes difficult for teams to understand how their content performs in the new discovery landscape.

In that sense, tools like Yoast SEO AI + are less about selling a new SEO feature and more about helping businesses observe a rapidly changing ecosystem where discoverability no longer happens only in search results.

The next evolution: AI agents making decisions

“What we will increasingly see is automated transactions where AI agents navigate websites and initiate actions on behalf of users.”

So far, much of the discussion around AI and search has focused on how answers are generated. But according to Alain, the next phase of this evolution may go further.

Over the next 12–18 months, AI systems may begin moving beyond answering questions and start performing tasks on behalf of users. Instead of guiding someone toward a website to make a decision, AI agents could increasingly compare options, interact with websites, and complete actions automatically.

If that shift happens, the traditional customer journey could change significantly. Alain shares a fascinating perspective on what this might mean for businesses in the coming years in the full podcast conversation.

SEO matters more than ever

AI isn’t replacing SEO. If anything, it’s reinforcing why good SEO matters in the first place. What’s changing is the path between users and content. Instead of navigating search results themselves, users increasingly receive answers that AI systems retrieve, interpret, and synthesize.

That makes strong fundamentals more important than ever. Businesses still need to focus on:

  • valuable content
  • clear structure
  • discoverable and indexable pages
  • a distinctive brand identity

But the central question for SEO is evolving. It’s no longer just:

“Can Google find my website?”

It’s now:

“Does the AI have a reason to remember my brand?”

For more insights from Alain Schlesser on how AI is reshaping SEO, watch the full Get Discovered podcast episode.

Avatar of Ahad Qureshi


Ahad Qureshi

I’m a Computer Science grad who accidentally stumbled into writing—and stayed because I fell in love with it. Over the past six years, I’ve been deep in the world of SEO and tech content, turning jargon into stories that actually make sense. When I’m not writing, you’ll probably find me lifting weights to balance my love for food (because yes, gym and biryani can coexist) or catching up with friends over a good cup of chai.

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Mar 13 2026
Building a strong data infrastructure for AI agent success

In the race to adopt and show value from AI, enterprises are moving faster than ever to deploy agentic AI as copilots, assistants, and autonomous task-runners. In late 2025, nearly two-thirds of companies were experimenting with AI agents, while 88% were using AI in at least one business function, up from 78% in 2024, according to McKinsey’s annual AI report. Yet, while early pilots often succeed, only one in 10 companies actually scaled their AI agents.

One major issue: AI agents are only as effective as the data foundation supporting them. Experts argue that most companies are seeing delays in implementing AI, not because of shortcomings in the models, but because they lack data architectures that deliver business context to be reliably used by humans and agents.

Companies need to be ready with the right data architecture, and the next few months — years, at most — will be critical, says Irfan Khan, president and chief product officer of SAP Data & Analytics.

“The only prediction anybody can reliably make is that we don’t know what’s going to happen in the years, months — or even weeks — ahead with AI,” he says. “To be able to get quick wins right now, you need to adopt an AI mindset and … ground your AI models with reliable data.”

While data has always been important for business, it will be even more so in the age of AI. The capabilities of agentic AI will be set more by the soundness of enterprise data architecture and governance, and less by the evolution of the models. To scale the technology, businesses need to adopt a modern data infrastructure that delivers context along with the data.

More business context, not necessarily more data

Traditional views often conflate structured data with high value, and unstructured data with less value. However, AI complicates that distinction. High-value data for agents is defined less by format and more by business context. Data for critical business functions — such as supply-chain operations and financial planning — is context dependent. While fine-grained, high-volume data, such as IoT, logs, and telemetry, can yield value, but only when delivered with business context.

For that reason, the real risk for agentic AI is not lack of data, but lack of grounding, says Khan.

“Anything that is business contextual will, by definition, give you greater value and greater levels of reliability of the business outcome,” he says. “It’s not as simple as saying high-value data is structured data and low-value data is where you have lots of repetition — both can have huge value in the right hands, and that’s what’s different about AI.”

Context can be derived through integration with software, on-site analysis and enrichment, or through the governance pipeline. Data lacking those qualities will likely be untrusted — one reason why two-thirds of business leaders do not fully trust their data, according to the Institute for Data and Enterprise AI (IDEA). The resulting “trust debt” has held back businesses in their quest for AI readiness. Overcoming that lack of trust requires shared definitions, semantic consistency, and reliable operational context to align data with business meaning.

Data sprawl demands a semantic, business-aware layer

Over the past decade, the most important shift in enterprise data architecture has been the separation of compute and storage, cloud-scale flexibility, says Khan. Yet, that separation and move to cloud also created sprawl, with data housed in multiple clouds, data lakes, warehouses, and a multitude of SaaS applications.

As companies move to AI, that sprawl does not go away. In fact, the problem is growing with more than two-thirds of companies citing data siloes as a top challenge in adopting AI, with more than half of enterprises struggling with 1,000 data sources or more. While the last era was about laying the foundation on which to build software-as-a-service — separating compute and storage and building lakes — the next era is about delivering the right data to autonomous AI agents tasked with various business functions.

“Probably the biggest innovation that occurred in data management was the separation of compute and store,” Khan says. “But what’s really making a distinction now is the way that we harmonize the data and harvest the value of the data across multiple sources of content.”

To do that requires a semantic or knowledge layer that supports multiple platforms, encodes business rules and relationships, provides a business-contextual and governed view of data, and allows humans and agents to access the data in the appropriate ways. But legacy data architectures cannot power the autonomous AI systems of the future, consultancy Deloitte stated in its State of AI in the Enterprise report. Only four in 10 companies believe their data management process is ready for AI, and that’s down from 43% the previous year, suggesting that as companies explore AI deployment, they are realizing their infrastructure’s shortcomings.

Agentic AI does not replace SaaS

Some investors and technologists speculate that AI agents will make SaaS applications obsolete. Khan strongly disagrees. Over the past 15 years, value has steadily moved up the stack, from on-premises infrastructure to infrastructure as a service (IaaS) to platform as a service (PaaS) to SaaS. Agentic AI is simply the next layer. Agentic AI will have its own layer to access the data and interact with the business logic. The value rises up the stack, but nothing below disappears, he says.

“SaaS doesn’t go away,” he says. “It just means SaaS and these agents will cooperate with one another. Companies are not going to throw away their entire general ledger and replace it with an agent. What’s the agent going to do? It doesn’t know anything without business context and business processing.”

In this emerging model, the software stack is being reshaped so that applications and data provide governed context within which AI can act effectively. SaaS applications remain the systems of record, while the semantic layer becomes the business-context source of truth. AI agents become a new engagement layer, orchestrating across systems, and both humans and agents become “first-class citizens” in how they access business logic, he says.

Critically, agents cannot directly connect to every operational system. “If we’re saying agents are going to take over the world … you can’t have an agent talking to every operational backend system,” Khan warns. “It just doesn’t work that way.”

This further elevates the importance of a semantic or business-fabric layer.

Where to start

Most enterprises need to begin where their data already lives — in platforms like Snowflake, Databricks, Google BigQuery, or an existing SAP environment. Khan says that’s normal, but warns against rebuilding old patterns of vendor lock-in.

He suggests that companies prioritize the data that matters most by focusing on preserving and providing business context to operational and application data. Companies should also invest early in governance and semantics by defining shared policies, access rules, and semantic models before scaling pilots. Finally, businesses should prioritize openness and fabric-style interoperability rather than forcing all data into one stack.

Khan cautions against aiming for full automation too early. “There is a new brave opportunity to really engage in the agentic and AI world,” Khan says, “Fully automating [critical business processes] is maybe a stretch, because there’s going to be a lot of extra oversight necessary.” Early wins will likely come from less-critical processes and from agents that work off fresh, stateful data rather than stale dashboards, he adds. As AI begins to deliver value and adoption increases, leaders must decide how to reinvest those gains to drive top-line efficiency or enter new markets.

Register for “The Fabric of Data & AI” virtual event on March 24, 2026. Hear insights from executives and thought leaders who are shaping the future of data and AI.

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.

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Mar 13 2026
Brutal times for the US battery industry

Just a few years ago, the battery industry was hot, hot, hot. There was a seemingly infinite number of companies popping up, with shiny new chemistries and massive fundraising rounds. My biggest problem was sifting through the pile to pick the most exciting news to cover.

That tide has turned, and in 2026, what seems to be in unlimited supply isn’t battery success stories but stumbles or straight-up implosions. Companies are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore. On Monday, Steve Levine at The Information (paywalled link) reported that 24M Technologies, a battery company founded in 2010, was shutting down and would auction off its property.

The company itself has been silent, but this is the latest in a string of bad signs, and it’s a big one—at one point 24M was worth over $1 billion, and the company’s innovations could have worked with existing technology. So where does that leave the battery industry?

Many buzzy battery startups in recent years have been trying to sell some new, innovative chemistry to compete with lithium-ion batteries, the status quo that powers phones, laptops, electric vehicles, and even grid storage arrays today. Think sodium-ion batteries and solid-state cells.

24M wasn’t trying to sell a departure from lithium-ion but improvements that could work with the tech. One of the company’s major innovations was its manufacturing process, which involved essentially smearing materials onto sheets of metal to form the electrodes, a simpler and potentially cheaper technique than the standard one. 

The layers in the company’s batteries were thicker, which cut down on some of the inactive materials in cells and improved the energy density. That allows more energy to be stored in a smaller package, boosting the range of EVs—the company famously had a goal of a 1,000-mile battery (about 1,600 kilometers).

We’re still thin on details of what exactly went down at 24M and what comes next for its tech. The company didn’t get back to my questions sent to the official press email, and nobody picked up the phone when I called. 24M cofounder and MIT professor Yet-Ming Chiang declined to speak on the record.

For those who have been closely following the battery industry, more bad news isn’t too surprising. It feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. “It just feels like there’s not a lot of appetite for innovation,” says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on the energy storage industry.

Natron Energy, one of the leading sodium-ion startups in the US, shut down operations in September last year. Ample, an EV battery-swapping company, filed for bankruptcy in December 2025.  

There were always going to be failures from the recent battery boom. Money was flowing to all sorts of companies, some pitching truly wild ideas. But what recent months have made clear is that the battery market is turning brutal, even for the relatively safe bets.

Because 24M’s technology was designed to work into existing lithium-ion chemistry, it could have been an attractive candidate for existing battery companies to license or even acquire. “It’s a great example of something that should have been easier,” Rodby says.  

The gutting of major components of the Inflation Reduction Act, key legislation in the US that provided funding and incentives for batteries and EVs, certainly hasn’t helped. The EV market in the US is cooling off, with automakers canceling EV models and slashing factory plans.

There are bright spots. China’s battery industry is thriving, and its battery and EV giants are looking ever more dominant. The market for stationary energy storage is also still seeing positive signs of growth, even in the US. 

But overall, it’s not looking great. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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Mar 13 2026
Pragmatic by design: Engineering AI for the real world

The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our worlds.

The use of AI in product engineering follows a disciplined and pragmatic trajectory. A significant majority of engineering organizations are increasing their AI investment, according to our survey, but they are doing so in a measured way. This approach reflects the priorities typical of product engineers. Errors have concrete consequences beyond abstract fears, ranging from structural failures to safety recalls and even potentially putting lives at risk. The central challenge is realizing AI’s value without compromising product integrity.

DOWNLOAD THE REPORT

Drawing on data from a survey of 300 respondents and in-depth interviews with senior technology executives and other experts, this report examines how product engineering teams are scaling AI, what is limiting broader adoption, and which specific capabilities are shaping adoption today and, in the future, with actual or potential measurable outcomes.

Key findings from the research include:

Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the risk high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are fixed at release, product failures can lead to real-world risks that cannot be rolled back. Product engineers are therefore adopting layered AI systems with distinct trust thresholds instead of general-purpose deployments.

Predictive analytics and AI-powered simulation and validation are the top near-term investment priorities for product engineering leaders. These capabilities—selected by a majority of survey respondents—offer clear feedback loops, allowing companies to audit performance, attain regulatory approval, and prove return on investment (ROI). Building gradual trust in AI tools is imperative.

Nine in ten product engineering leaders plan to increase investment in AI in the next one to two years, but the growth is modest. The highest proportion of respondents (45%) plan to increase investment by up to 25%, while nearly a third favor a 26% to 50% boost. And just 15% plan a bigger step change—between 51% and 100%. The focus for product engineers is on optimization over innovation, with scalable proof points and near-term ROI the dominant approach to AI adoption, as opposed to multi-year transformation.

Sustainability and product quality are top measurable outcomes for AI in product engineering. These outcomes, visible to customers, regulators, and investors, are prioritized over competitive metrics like time to-market and innovation—rated of medium importance—and internal operational gains like cost reduction and workforce satisfaction, at the bottom. What matters most are real-world signals like defect rates and emissions profiles rather than internal engineering dashboards.

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.

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Mar 13 2026
The Download: Early adopters cash in on China’s OpenClaw craze, and US batteries slump

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.

Hustlers are cashing in on China’s OpenClaw AI craze 

In January, Beijing-based software engineer Feng Qingyang started tinkering with OpenClaw, a new AI tool that can take over a device and autonomously complete tasks. Within weeks, he was advertising “OpenClaw installation support” on a second-hand shopping site. Today, his side gig is a fully-fledged business with over 100 employees and 7,000 completed orders. 

Feng is among a small cohort of savvy early adopters making serious cash from China’s OpenClaw craze. As users with little technical background want in, a cottage industry of installation services and preconfigured hardware has sprung up. The rise of these tinkerers shows just how eager the general public in China is to adopt cutting-edge AI—despite huge security risks. Read the full story. 

—Caiwei Chen 

Brutal times for the US battery industry 

Another battery business has fallen: 24M Technologies, once worth over $1 billion, is reportedly shutting down. 

Just a few years ago, the industry was hot, hot, hot. Countless companies were popping up, with shiny new chemistries and huge funding rounds. But now, the tide has turned. Businesses are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore.  

There are bright spots. China’s battery industry is thriving, and US stationary storage remains resilient. But it feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. 

This story is from The Spark, our weekly climate newsletter. Sign up to receive it in your inbox every Wednesday. 

—Casey Crownhart 

The must-reads 

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

1 Iran has put US tech giants on a list of potential targets 
The companies include Google, Microsoft, Palantir, IBM, Nvidia, and Oracle. (Al Jazeera)  
+ Pro-Iran hackers have launched their first major strike on a US firm during the war. (CNN) 
+ AI is warping perceptions of the conflict. (MIT Technology Review)  
 
2 Grammarly is being sued for turning real people into AI-generated experts 
A journalist has filed a lawsuit over her inclusion as a writing analyst. (Wired $) 
+ Grammarly has now disabled the ‘Expert Review’ feature. (Engadget)  
+ Here’s what’s next for AI copyright lawsuits. (MIT Technology Review) 
 
3 Professors are losing the fight to protect critical thinking from AI 
They describe the tech as an “existential threat.”(The Guardian) 
+ Silicon Valley’s dream of an AI classroom faces a skeptical reality. (MIT Technology Review) 
 
4 Big tech is backing Anthropic in its fight against the Trump administration  
Google, Amazon, Apple, and Microsoft are publicly supporting its legal action. (BBC) 
+ Is this an Oppenheimer moment for Anthropic? (The Atlantic $) 

5 A Cybertruck owner has sued Tesla over a self-driving crash  
He called the company “negligent” for retaining Elon Musk as CEO. (Electrek)  
+ Tech has sparked a new wave of theft in the luxury car industry. (MIT Technology Review) 
 
6 Is “AI-washing” providing cover for massive corporate layoffs? 
The tech isn’t ready to replace workers, but the layoffs are happening anyway. (The Atlantic)  
+ Software giant Atlassian is slashing 10% of its workforce ahead of an AI push. (The Guardian) 
+ At least lawyers’ jobs look safer than first feared. (MIT Technology Review) 
 
7 Software giants claim they’re not worried that AI will destroy them 
Oracle and Salesforce CEOs have dismissed fears of an “SaaS-pocalypse.” (Reuters) 
 
8 Lab-grown brains have started solving engineering problems 
Scientists trained the organoid to decode an engineering task. (Popular Mechanics) 
+ Other organoids are being impregnated with human embryos. (MIT Technology Review) 
 
9 English-language music is losing its grip on Spotify 
The variety of languages in its top 50 songs has doubled since 2020. (BBC) 
 
10 AI is redrawing the boundaries of physics 
It’s blurring the boundaries between a machine and a researcher. (The Economist $)  

Quote of the day 

“Elon Musk is an aggressive and irresponsible salesman, who has a long history of making dangerous design choices and over-promising the features of his products.”

—A lawsuit over Tesla’s Full Self-Driving mode takes aim at the company’s CEO, Gizmodo reports.

One More Thing

This town’s mining battle reveals the contentious path to a cleaner future 

a view from the median line of an empty Main Street, Tamarack MN after a recent rain shower

ACKERMAN + GRUBER

In a tiny Minnesota town, an exploratory mining company called Talon plans to dig up as much as 725,000 metric tons of raw ore per year. 

It says the site will help power a greener future for the US by producing the nickel needed for EV batteries. But many local citizens aren’t eager for major mining operations near their towns.  

The tensions have created a test case for conflicts between local environmental concerns and global climate goals. Read the full story. 

—James Temple 

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.)

+ Mario is finally getting a LEGO minifigure.  
+ This new social platform boldly aims to burst filter bubbles. 
+ NASA is backing DSLR cameras by taking a trusty old Nikon D5 to the moon. 
+ This nuclear escalation simulator helped me learn to stop worrying and love the bomb. 

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Mar 13 2026
A defense official reveals how AI chatbots could be used for targeting decisions

The US military might use generative AI systems to rank lists of targets and make recommendations—which would be vetted by humans—about which to strike first, according to a Defense Department official with knowledge of the matter. The disclosure about how the military may use AI chatbots comes as the Pentagon faces scrutiny over a strike on an Iranian school, which it is still investigating.  

A list of possible targets might be fed into a generative AI system that the Pentagon is fielding for classified settings. Then, said the official, who requested to speak on background with MIT Technology Review to discuss sensitive topics, humans might ask the system to analyze the information and prioritize the targets while accounting for factors like where aircraft are currently located. Humans would then be responsible for checking and evaluating the results and recommendations. OpenAI’s ChatGPT and xAI’s Grok could, in theory, be the models used for this type of scenario in the future, as both companies recently reached agreements for their models to be used by the Pentagon in classified settings.

The official described this as an example of how things might work but would not confirm or deny whether it represents how AI systems are currently being used.

Other outlets have reported that Anthropic’s Claude has been integrated into existing military AI systems and used in operations in Iran and Venezuela, but the official’s comments add insight into the specific role chatbots may play, particularly in accelerating the search for targets. They also shed light on the way the military is deploying two different AI technologies, each with distinct limitations.

Since at least 2017, the US military has been working on a “big data” initiative called Maven. It uses older types of AI, particularly computer vision, to analyze the oceans of data and imagery collected by the Pentagon. Maven might take thousands of hours of aerial drone footage, for example, and algorithmically identify targets. A 2024 report from Georgetown University showed soldiers using the system to select targets and vet them, which sped up the process to get approval for these targets. Soldiers interacted with Maven through an interface with a battlefield map and dashboard, which might highlight potential targets in one color and friendly forces in another.

The official’s comments suggest that generative AI is now being added as a conversational chatbot layer—one the military may use to find and analyze data more quickly as it makes decisions like which targets to prioritize. 

Generative AI systems, like those that underpin ChatGPT, Claude, and Grok, are a fundamentally different technology from the AI that has primarily powered Maven. Built on large language models, they are much less battle-tested. And while Maven’s interface forced users to directly inspect and interpret data on the map, the outputs produced by generative AI models are easier to access but harder to verify. 

The use of generative AI for such decisions is reducing the time required in the targeting process, added the official, who did not provide details when asked how much additional speed is possible if humans are required to spend time double-checking a model’s outputs.

The use of military AI systems is under increased public scrutiny following the recent strike on a girls’ school in Iran in which more than 100 children died. Multiple news outlets have reported that the strike was from a US missile, though the Pentagon has said it is still under investigation. And while the Washington Post has reported that Claude and Maven have been involved in targeting decisions in Iran, there is no evidence yet to explain what role generative AI systems played, if any. The New York Times reported on Wednesday that a preliminary investigation found outdated targeting data to be partly responsible for the strike. 

The Pentagon has been ramping up its use of AI across operations in recent months. It started offering nonclassified use of generative AI models, for tasks like analyzing contracts or writing presentations, to millions of service members back in December through an effort called GenAI.mil. But only a few generative AI models have been approved by the Pentagon for classified use. 

The first was Anthropic’s Claude, which in addition to its use in Iran was reportedly used in the operations to capture Venezuelan leader Nicolas Maduro in January. But following recent disagreements between the Pentagon and Anthropic over whether Anthropic could restrict the military’s use of its AI, the Defense Department designated the company a supply chain risk and President Trump demanded on social media that the government stop using its AI products within six months. Anthropic is fighting the designation in court. 

OpenAI announced an agreement on February 28 for the military to use its technologies in classified settings. Elon Musk’s company xAI has also reached a deal for the Pentagon to use its model Grok in such settings. OpenAI has said its agreement with the Pentagon came with limitations, though the practical effectiveness of those limitations is not clear. 

If you have information about the military’s use of AI, you can share it securely via Signal (username jamesodonnell.22).

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Mar 13 2026
Why OpenAI Acquired Promptfoo

If predictions are correct, AI agents can soon do “real” work, such as adjusting advertising budgets, updating product listings, and authorizing refunds.

But is there a security risk? Before it can delegate that level of control, a business must ensure the agent will behave predictably and safely.

That concern helps explain why OpenAI has announced plans to acquire Promptfoo, a startup that develops tools for testing and securing artificial intelligence applications.

Home page of Promptfoo

OpenAI’s plan to acquire Promptfoo may signal how enterprise AI systems test for prompt vulnerabilities.

Testing AI Systems

Promptfoo began as an open-source framework for developers to evaluate prompts and AI responses. The platform evolved into a testing environment, enabling engineers to run thousands of simulated AI interactions before releasing an application or agent.

Those tests can expose weaknesses, including:

  • Opportunities for prompt injection attacks,
  • Agents using tools in unsafe ways,
  • Unintended API calls,
  • Data leakage through responses.

Promptfoo is akin to an AI quality-assurance framework. Traditional software testing verifies code with known outcomes. Yet AI systems behave differently. Developers need tools that can probe many possible inputs and edge cases. Promptfoo automates that process.

AI Agents

The Promptfoo acquisition also implies a shift in how companies deploy AI agents and applications.

Enterprise deployments thus far have focused on chatbots and knowledge assistants. Many rely on retrieval-augmented generation, in which models answer questions by retrieving information from a database.

More recently, developers have begun building AI agents that can plan tasks, call external tools, and execute multi-step workflows. Examples include:

  • Analyze advertising performance and adjust campaign budgets,
  • Manage customer-service workflows,
  • Update product listings or pricing,
  • Run marketing or analytics queries.

The agents interact directly with CRMs, inventory databases, and ecommerce platforms. That capability expands what an AI agent can do. It also increases the risks.

Industry Shift

OpenAI’s acquisition is not the only signal that AI agents are increasingly prominent, or that businesses must focus on AI security.

Meta recently acquired Moltbook, a social network of sorts for autonomous AI agents. The company’s technology enables agents to communicate and coordinate through a shared system.

Home page of Moltbook

Moltbook is an early look at how AI agents communicate.

Taken together, the actions of OpenAI and Meta highlight different parts of the emerging agent ecosystem.

Meta’s acquisition focuses on enabling AI agents to interact with one another, while OpenAI’s addresses their behavior and safety.

The combination suggests that large tech companies anticipate software agents that interact with humans and other agents.

Security

An AI chatbot that produces an incorrect answer is typically an inconvenience‚ a hallucination.

An AI agent with system access can create real problems. From a prompt-injection attack, for example, an agent could:

  • Share sensitive customer information,
  • Trigger unauthorized or fraudulent refunds,
  • Modify pricing or inventory,
  • Expose proprietary data to other agents.

Businesses, therefore, need guardrails that prevent manipulation and unpredictability.

Promptfoo appears to provide that capability. By integrating testing tools directly into its enterprise AI platform, OpenAI can help developers identify vulnerabilities before deploying agents in production environments.

Fraud

Security extends beyond internal systems to include fraud prevention.

Jeff Otto, chief marketing officer at Riskified, a fraud-prevention platform, said the rise of AI agents could create software systems that interact with one another (similar to Moltbook).

“Meta’s decision to house a social network for AI agents within Superintelligence Labs is a strong signal that agentic commerce is moving from theory to reality,” Otto said. “Moltbook’s agents were built on the OpenClaw framework, which enables autonomous agents to interact, coordinate, and potentially transact on behalf of human users.”

If that vision develops, Otto said, ecommerce fraud detection will need to evolve as well.

“That shift sets the stage for a high-stakes machine-versus-machine environment,” he said. “For retailers, the traditional rules-based fraud playbook is no longer sufficient. When bots are the ones clicking ‘buy,’ merchants need a defense layer that can distinguish between a legitimate AI assistant and a malicious agent in milliseconds.”

Agentic Commerce

With their agent-related acquisitions, OpenAI and Meta are presumably planning for what’s next.

If that future includes agentic commerce, merchants must consider an environment in which software agents — not just humans — do the shopping.

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Mar 12 2026
The Download: Pokémon Go to train world models, and the US-China race to find aliens

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.

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. Read the full story. 

—Will Douglas Heaven 

MIT Technology Review Narrated: America was winning the race to find Martian life. Then China jumped in. 

In July 2024, after more than three years on Mars, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or sedimentary layers, this one had spots. Those specks were the best hint yet of alien life.  

NASA began a new mission to bring the rocks back to Earth to study. But now, just over a year and a half later, the project is on life support. As a result, those oh-so-promising rocks may be stuck out there forever. 

This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. The superpower is moving full steam ahead with its own version of NASA’s mission.  

—Robin George Andrews 

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 must-reads 

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

1 Viral AI fakes of the Iran war are flooding X 
And Grok is failing to flag them. (Wired $) 
+ The conflict could wreak havoc on data centers and electricity costs. (The Verge)  
+ Pro-Iran bots are weaponizing posts about Epstein. (Gizmodo)  
+ AI is turning the Iran conflict into a show. (MIT Technology Review) 

2 Anthropic fears the loss of billions due to the Pentagon’s blacklisting  
That’s what the company has told a judge as it seeks to block its designation as a supply-chain risk. (Bloomberg $) 
+ Microsoft has backed the company in its legal fight with the Pentagon. (FT $) 
+ OpenAI’s “compromise” with the DoD dealt a big blow to Anthropic. (MIT Technology Review) 
 
3 Meta has bought a social network that’s exclusively for bots 
Moltbook is a Reddit-like site where AI agents interact with each other. (NYT $) 
+ The platform is  AI theater. (MIT Technology Review)  
 
4 Ukraine is eagerly offering the US its expertise and tech to counter Iranian drones 
Kyiv has sent drones and UAV specialists to military bases in Jordan. (WSJ $) 
+ A radio-obsessed civilian is shaping Ukraine’s drone defense. (MIT Technology Review) 
 
5 OnlyFans “chatters” are earning $2 per hour to impersonate models 
A worker in the Philippines described the job as “heartbreaking” and “icky.” (BBC) 
 
6 The DHS has removed officials who objected to “illegal” orders about surveillance tech 
The officers had refused to mislabel records about the technologies in order to block their release. (Wired) 

7 This startup is building data centers run on brain cells  
The “biological data centers” are coming to Melbourne and Singapore. (New Scientist $) 

8 Anduril is expanding into space defense 
The company is buying ExoAnalytic, which specializes in missile defense tracking. (Reuters) 
+ We saw a demo of an AI system powering Anduril’s vision for war. (MIT Technology Review) 
 
9 Big tech has a new big idea: AI compute as compensation 
Silicon Valley is pitching it as a job perk. (Business Insider) 
 
10 Wordle’s creator is back with a new game 
It’s inspired by cryptic crosswords. (The New Yorker $)  

Quote of the day 

“You come for the Epstein content, and you stay for the propaganda.” 

—Bret Schafer, an expert on information manipulation, tells the Washington Post how pro-Iran networks are gaining traction with posts about Epstein. 

One More Thing 

white line drawing of crops drawn over an image with a Mars rover

MEREDITH MIOTKE | PHOTO: NASA/JPL-CALTECH/MSSS

The quest to figure out farming on Mars  

If ever a blade of grass grew on Mars, those days are over. But could they begin again? What would it take to grow plants to feed future astronauts on Mars?  

To grow food there, we can’t just drop seeds in the ground and add water. We will need to create a layer of soil that can support life. And to do that, we first have to get rid of the red planet’s toxic salts.  

Researchers recently discovered a potential solution—and the early signs are promising. Read the full story.

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) 

+ Finally, a rebellion arises against mint’s tyranny over our teeth: Peanut Butter Cup toothpaste. 
+ DIY decorators rejoice! The humble paint tray has received an ingeniously simple renovation. 
+ Saudi surgeons have successfully separated two conjoined twins. 
+ If you’re looking for real innovation, check out British Pie Week’s beef rendang, jerk chicken, and double-size pasties. 

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