2026-03-12 21:02:48
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.
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
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
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.
2026-03-12 21:00:00
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.
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.
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.
2026-03-12 18:00:00
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.
2026-03-11 20:46:21
Feng Qingyang had always hoped to launch his own company, but he never thought this would be how—or that the day would come this fast.
Feng, a 27-year-old software engineer based in Beijing, started tinkering with OpenClaw, a popular new open-source AI tool that can take over a device and autonomously complete tasks for a user, in January. He was immediately hooked, and before long he was helping other curious tech workers with less technical proficiency install the AI agent.
Feng soon realized this could be a lucrative opportunity. By the end of January, he had set up a page on Xianyu, a secondhand shopping site, advertising “OpenClaw installation support.” “No need to know coding or complex terms. Fully remote,” reads the posting. “Anyone can quickly own an AI assistant, available within 30 minutes.”
At the same time, the broader Chinese public was beginning to catch on—and the tool, which had begun as a niche interest among tech workers, started to evolve into a popular sensation.
Feng quickly became inundated with requests, and he started chatting with customers and managing orders late into the night. At the end of February, he quit his job. His side gig has now grown into a full-fledged professional operation with over 100 employees. So far, the store has handled 7,000 orders, each worth about 248 RMB or approximately $34.
“Opportunities are always fleeting,” says Feng. “As programmers, we are the first to feel the winds shift.”
Feng is among a small cohort of savvy early adopters turning China’s OpenClaw craze into cash. As users with little technical background want in, a cottage industry of people offering installation services and preconfigured hardware has sprung up to meet them. The sudden rise of these tinkerers and impromptu consultants shows just how eager the general public in China is to adopt cutting-edge AI—even when there are huge security risks.
“Have you raised a lobster yet?”
Xie Manrui, a 36-year-old software engineer in Shenzhen, says he has heard this question nonstop over the past month. “Lobster” is the nickname Chinese users have given to OpenClaw—a reference to its logo.
Xie, like Feng, has been experimenting with OpenClaw since January. He’s built new open-source tools on top of the ecosystem, including one that visualizes the agent’s progress as an animated little desktop worker and another that lets users voice-chat with it.
“I’ve met so many new people through ‘lobster raising,’” says Xie. “Many are lawyers or doctors, with little technical background, but all dedicated to learning new things.”
Lobsters are indeed popping up everywhere in China right now—on and offline. In February, for instance, the entrepreneur and tech influencer Fu Sheng hosted a livestream showing off OpenClaw’s capabilities that got 20,000 views. And just last weekend, Xie attended three different OpenClaw events in Shenzhen, each drawing more than 500 people. These self-organized, unofficial gatherings feature power users, influencers, and sometimes venture capitalists as speakers. The biggest event Xie attended, on March 7, drew more than 1,000 people; in the packed venue, he says, people were shoulder to shoulder, with many attendees unable to even get a seat.
Now China’s AI giants are starting to piggyback on the trend too, promoting their models, APIs, and cloud services (which can be used with OpenClaw), as well as their own OpenClaw-like agents. Earlier this month, Tencent held a public event offering free installation support for OpenClaw, drawing long lines of people waiting for help, including elderly users and children.
This sudden burst in popularity has even prompted local governments to get involved. Earlier this month the government of Longgang, a district in Shenzhen, released several policies to support OpenClaw-related ventures, including free computing credits and cash rewards for standout projects. Other cities, including Wuxi, have begun rolling out similar measures.
These policies only catalyze what’s already in the air. “It was not until my father, who is 77, asked me to help install a ‘lobster’ for him that I realized this thing is truly viral,” says Henry Li, a software engineer based in Beijing.
What’s making this moment particularly lucrative for people with technical skills, like Feng, is that so many people want OpenClaw, but not nearly as many have the capabilities to access it. Setting it up requires a level of technical knowledge most people do not possess, from typing commands into a black terminal window to navigating unfamiliar developer platforms. On the hardware side, an older or budget laptop may struggle to run it smoothly. And if the tool is not installed on a device separate from someone’s everyday computer, or if the data accessible to OpenClaw is not properly partitioned, the user’s privacy could be at risk—opening the door to data leaks and even malicious attacks.
Chris Zhao, known as “Qi Shifu” online, organizes OpenClaw social media groups and events in Beijing. On apps like Rednote and Jike, Zhao routinely shares his thoughts on AI, and he asks other interested users to leave their WeChat ID so he can invite them to a semi-private group chat. The proof required to join is a screenshot that shows your “lobster” up and running. Zhao says that even in group chats for experienced users, hardware and cloud setup remain a constant topic of discussion.
The relatively high bar for setting up OpenClaw has generated a sense of exclusivity, creating a natural opening for a service industry to start unfolding around it. On Chinese e-commerce platforms like Taobao and JD, a simple search for “OpenClaw” now returns hundreds of listings, most of them installation guides and technical support packages aimed at nontechnical users, priced anywhere from 100 to 700 RMB (approximately $15 to $100). At the higher end, many vendors offer to come to help you in person.
Like Feng, most providers of these services are early adopters with some technical ability who are looking for a side gig. But as demand has surged, some have found themselves overwhelmed. Xie, the developer in Shenzhen who created tools to layer on OpenClaw, was asked by a friend who runs one such business to help out over the weekend; the friend had a customer who worked in e-commerce and had little technical experience, so Xie had to show up in person to get it done. He walked away with 600 RMB ($87) for the afternoon.
The growing demand has also pushed vendors like Feng to expand quickly. He has now standardized his operation into tiers: a basic installation, a custom package where users can make specific requests like configuring a preferred chat app, and an ongoing tutoring service for those who want a hand to hold as they find their footing with the technology.
Other vendors in China are making money combining OpenClaw with hardware. Li Gong, a Shenzhen-based seller of refurbished Mac computers, was among the first online sellers to do this—offering Mac minis and MacBooks with OpenClaw preinstalled. Because OpenClaw is designed to operate with deep access to a hard drive and can run continuously in the background unattended, many users prefer to install it on a separate device rather than on the one they use every day. This would help prevent bad actors from infiltrating the program and immediately gaining access to a wide swathe of someone’s personal information. Many turn to secondhand or refurbished options to keep the cost down. Li says that in the last two weeks, orders have increased eightfold.
Though OpenClaw itself is a new technology, the general practice of buying software bundles, downloading third-party packages, and seeking out modified devices is nothing new for many Chinese internet users, says Tianyu Fang, a PhD candidate studying the history of technology at Harvard University. Many users pay for one-off IT support services for tasks from installing Adobe software to jailbreaking a Kindle.
Still, not everyone is getting swept up. Jiang Yunhui, a tech worker based in Ningbo, worries that ordinary users who struggle with setup may not be the right audience for a technology that is still effectively in testing.
“The hype in first-tier cities can be a little overblown,” he says. “The agent is still a proof of concept, and I doubt it would be of any life-changing use to the average person for now.” He argues that using it safely and getting anything meaningful out of it requires a level of technical fluency and independent judgment that most new users simply don’t have yet.
He’s not alone in his concerns. On March 10, the Chinese cybersecurity regulator CNCERT issued a warning about the security and data risks tied to OpenClaw, saying it heightens users’ exposure to data breaches.
Despite the potential pitfalls, though, China’s enthusiasm for OpenClaw doesn’t seem to be slowing.
Feng, now flush with the earnings from his operation, wants to use the momentum—and the capital—to keep building out his own venture with AI tools at the center of it.
“With OpenClaw and other AI agents, I want to see if I can run a one-person company,” he says. “I’m giving myself one year.”
2026-03-11 20:38:00
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.
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
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
—Bret Schafer, an expert on information manipulation, tells the Washington Post how pro-Iran networks are gaining traction with posts about Epstein.
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.
2026-03-10 22:00:00
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.
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.
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.
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.
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.
