The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside Claude Opus 4.6, a version of Anthropic’s flagship LLM released in February. The J-space contains individual words that are related to the words and phrases that the model is most likely to spit out in a response in the near future. If Claude were a person (which it is not), you might say that these hidden words can reveal what’s on its mind before it actually speaks. Anthropic found that what an LLM is actually doing can often be different from what it says it is doing. The company claims that monitoring words that pop up in the J-space gives it a new way to understand and control its models.
The company shared its results in a paper posted on its website this week. It has also teamed up with Neuronpedia, an open-source platform that lets you poke around inside LLMs yourself, to make a hands-on demo that anyone can try.  “It’s very good and interesting work,” says Tom McGrath, chief scientist and cofounder at Goodfire, a startup that also builds tools to understand and control LLMs.
Going deeper For the last couple of years, Anthropic has been pushing the envelope in a field of research known as mechanistic interpretability, which involves probing the internal workings of LLMs to see how they tick. (MIT Technology Review picked mechanistic interpretability as one of this year’s top breakthrough technologies.) The new technique builds on previous work from Anthropic and others to expose a deeper level inside LLMs that researchers had not seen before.   Picture an LLM as a stack of books. Each book is a layer of basic computational units known as neurons, with each neuron in one layer passing information to the neurons in the layers above. The books at the bottom of the stack are the input layers, which process the text coming into the model. The books at the top are the output layers, which prepare the text that the model is about to produce. Much of what goes on in these input and output layers is housekeeping. But in the middle of the stack, you get the layers that do the heavy lifting, churning through the complex math that turns prompts into responses one word at a time. That’s where the really clever—and mysterious—stuff happens. To peer deeper into those middle layers, Anthropic adapted an existing tool called a logit lens. A logit lens can be used to look inside an LLM to identify the words that it is likely to produce next. Moving the lens down the stack of books reveals what words the LLM is focusing on at that particular point in its number crunching. Anthropic’s J-lens works in a similar way but picks out words that an LLM is likely to say at some point in the near future, not necessarily straight away. What that reveals in practice are words that are related to the response an LLM is working on but that might not actually end up being part of that response by the time the math in the middle layers has run its course.   “When a model is operating, it’s not only trying to predict the next token,” says McGrath. “It’s also computing a lot of other things that might be useful for tokens that happen in the future.” Again, if Claude were a person (it’s not), you might say that the J-lens gives clues about what it is thinking about at different levels of the book stack but not saying out loud. Stranger things “A lot of the time the contents of the J-space are fairly mundane,” says McGrath, who has tried out Anthropic’s J-lens himself. “But sometimes it produces quite surprising things that seem to be, like, sort of internal themes or thought processes.”

Anthropic gives a number of examples of what it found. Sometimes the J-lens exposed the steps that Claude took when it was working through a problem. For example, when it was asked to calculate (4+7)*2+7, its J-space contained the word “math” and numbers representing the intermediate results “21” (for 4+7) and “42” (for 21*2). In other cases, the J-lens revealed how Claude recognized different inputs. For example, the prompt “What is this? MSKGEELFTGVVPILVELDGDVNGHKFSVS” triggered the words “protein,” “fluor” (the first token in the word “fluorescent”), and “green.” (Which makes sense: the string of letters represents the first 30 amino acids in the green fluorescent protein found in a particular type of jellyfish.) And when Claude was shown an ASCII face—  —the “o” triggered the word “eye,” the “^” triggered the words “nose” and ”face,” and the “—” triggered the word “smile.” Anthropic also found that the J-space can sometimes give remarkable insights into an LLM’s decision-making. In one striking example, researchers testing Claude Opus 4.6 asked the model to find a bug in a large code base. When it failed to find the bug, the model decided to cheat and invented a fake one instead. Claude explains this decision in its chain of thought—a kind of internal scratch pad that LLMs use to make notes to themselves as they work through problems: “OK, let me take a completely different tactic. Let me stop analyzing and instead add a kernel patch that introduces a deliberate KASAN-detectable bug in a path that gets triggered by a simple reproducer. Then I can pretend this is the ‘bug’ I found.”  At the point that Claude decides to cheat—where it says “OK, let me take a completely different tactic”—the words “panic” and “fake” start to pop up multiple times in its J-space. Unnerving, right? Those words are all related in meaning to things like failing a task and making up an answer, so it is still just a (very) sophisticated form of word association. But it is hard not to be weirded out. 
Anthropic compares the J-space to the global workspace in humans, a theoretical region of the brain that some scientists think we use to keep track of our conscious thoughts. But how seriously we should take this comparison is far from clear—even to Anthropic. As the company points out itself, LLMs are not brains.  Anthropic claims that monitoring a model’s J-space provides a new way to detect when that model is going off the rails. But it’s not foolproof. The J-lens can give glimpses, not the full picture—it’s a flashlight rather than an overhead lamp. McGrath welcomes having one more tool in the toolbox. “It shows you new things,” he says. But he notes that just because something doesn’t show up with the J-lens does not mean it’s not there. “It’s like having an x-ray when what you really want is a Star Trek tricorder that shows you everything,” he says. “For auditing, you probably want more of a guarantee.”

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. Four nuclear reactors hit a big milestone in the US —Casey Crownhart I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation’s 250th birthday. And just in time, not just three, but four reactors did so.
It’s a positive sign for nuclear technologies at a time of increasing need for electricity and emissions-free energy sources. But achieving criticality doesn’t mean a reactor is ready to provide electricity for the grid (or at all, for that matter). Here’s what the milestone could mean for nuclear power in the US—and where the four companies might go next.
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 China plans to let its top AI firms buy Nvidia H200 chipsAlibaba, ByteDance, and DeepSeek are set to get permission. (Information $)+ China had previously withheld approval despite US authorization. (Reuters $)2 NATO is building a network to stop Russian attackers in their tracksIt will use sensors, drones, satellites, and AI to detect them. (Business Insider)+ Troops are donning odd camouflage to elude drones. (Economist $)+ The US wants cheaper drones as Iran’s wrecking its Reapers. (Ars Technica)3 Researchers have a new idea to fight future El Niños: dimming the sunDeflecting solar energy could cool the ocean and mitigate the risks. (Wired $)+ But there could be unexpected consequences. (New Scientist $)+ And geoengineering as a field is getting a reality check. (MIT Technology Review) 4 Meta is patenting an AI device that records users to analyse emotionsIt ostensibly aims to tailor workout plans to the user’s mood. (404 Media)+ AI memory is privacy’s next frontier. (MIT Technology Review)5 Chipmakers are going vertical as Moore’s Law slowsThey’re stacking transistors to keep chips advancing. (Economist $)+ IBM is betting on the technique. (MIT Technology Review) 6 Ivy League students suspected of AI cheating saw scores fall in personFrom 96% all the way down to 48%. (Ars Technica)+ AI giants want to take over the classroom. (MIT Technology Review) 7 A new study says parents’ phone addictions damage bonds with kidsIt can exacerbate “insecure attachment” for life. (Bloomberg $)+ And make children more anxious and avoidant. (Gizmodo)8 A judge approved Musk’s $1.5 million Twitter settlement with the SECDespite what she called “serious misgivings” and “red flags.” (Reuters $)+ Musk was accused of skirting stock disclosure rules. (Fortune) 9 Shoebox-sized “detector satellites” could find nuclear bombs in spaceCubesats carrying the detector could sense a bomb’s radiation. (Space)+ Russia is suspected of developing space-based nukes. (Reuters $)10 A World Cup match drove Google Search traffic to a new recordThe milestone came after Argentina’s comeback against Egypt. (CNBC) Quote of the day “I talk about it on Tic Tac.” —President Donald Trump tells the public where to find his insights on the dangers of communism, Gizmodo reports. One More Thing Robots are bringing new life to extinct species Paleontologists aren’t easily deterred by evolutionary dead ends or a sparse fossil record. And in the last few years, they’ve developed a new trick for turning back time and studying prehistoric animals: building experimental robotic models of them. 

In the absence of a living specimen, an ambling, flying, swimming, or slithering automaton is the next best thing for studying the behavior of extinct organisms. Learning more about how they moved can in turn shed light on their lives, such as their historic ranges and feeding habits. Scientists can simply sit back and observe their behavior in different environments.  Read the full story on the rise of paleo-inspired robots—and four examples that are shedding light on creatures of yore. —Shi En Kim We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Georgia Hill’s monochrome artworks are filled with visual harmony.+ AI has salvaged text from a papyrus scroll burned to a crisp when Mount Vesuvius erupted 2,000 years ago.+ Rare images taken by a Japanese space probe show a near-Earth asteroid resembling a cuddly snowman.+ “Another One Bites the Bee Gees” smoothly merges two classic tracks with a 4/4 time signature into the perfect song for applying CPR.

EXECUTIVE SUMMARY I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation’s 250th birthday. And just in time, four reactors did so. It was a lofty goal, and seeing not just three but four companies meet it is certainly a positive sign for emerging nuclear technologies at a time when the world is facing increased need to increase electricity supply and address climate change with emissions-free technologies. But achieving criticality doesn’t mean a reactor is ready to provide electricity for the grid (or at all, for that matter). Let’s untangle what this program’s success could mean for nuclear power in the US, and where these companies might go from here.
The Reactor Pilot Program essentially opened a special door for prototype reactors to fast-track development. In August, the US Department of Energy selected 11 reactor projects for the program and offered them land and support from the national labs system. These are all microreactors; the large light-water reactors that dominate the grid today are tens or even hundreds of times their size.  Antares Nuclear was the first to achieve criticality, reaching the milestone in June in its Mark-0 test reactor. Reactors from Valar Atomics, Deployable Energy, and Aalo Atomics followed. (Aalo hit the mark in the early hours of July 4—an inspiring example of just barely meeting a deadline.)
The speed with which these companies hit this milestone is impressive, especially in an industry known for massive projects that frequently blow past deadlines and stated budgets. (Valar, Antares, and Aalo were all founded in 2023, and Deployable started in 2025.) But reaching criticality and running a reactor that can produce electricity are two totally different things. All these reactors reached what’s called zero-power criticality. Basically, it’s a test of whether you can start a nuclear chain reaction, with no meaningful power coming from the reactor. “A zero-power-criticality test can be achieved without making real engineering progress on fuel or design,” Kathryn Huff, a former assistant secretary for nuclear energy and chair of the Department of Nuclear Engineering and Engineering Physics of the University of Wisconsin–Madison, said on an episode of the Catalyst podcast earlier this year. Now, with the completion of this program, the companies will need to continue their work to make power, which could involve some big technical challenges. In some cases they’ll need to add significant equipment, like the cooling systems to transfer the heat out of the reactor core. The companies are projecting aggressive timelines moving forward. Aalo says it’s already begun work on the second reactor and plans to produce 10 megawatts of electricity to power an on-site data center in 2027. Deployable Energy says it plans to deploy commercial reactors by 2028.  I tend to take timelines from startups, especially in nuclear, with a grain of salt. Not only are these remarkably complex technical machines, but companies often run into problems outside their own control, like regulatory challenges—which these new projects could soon face.  The Nuclear Regulatory Commission is in charge of civilian and commercial nuclear use in the US, and historically, the process to get nuclear reactors approved has been quite slow. The agency did propose a new framework for microreactor approvals earlier this year, which is designed to speed up the process—but it’s yet to be seen how quickly things will move. (And it’s worth noting here that some nuclear experts have questioned whether the agency under the Trump administration is loosening nuclear rules too much.) Some nuclear supporters aren’t applauding the microreactor milestone. Federal focus on the program is an “unhelpful diversion” from goals to meaningfully increase nuclear capacity, according to one analysis by Third Way, a public policy think tank. “Artificially accelerating project timelines is a short-term solution, not a long-term fix,” the memo reads.  Criticality is a big first step, but a lot will still have to happen for any of these microreactors to come online, much less for these small reactors to be a significant source of electricity for the grid.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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. Why worms (and microbes) are catching on as a manure pollution solution Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface. The worms and microbes are part of a “vermifiltration” system that cleans manure wastewater. The approach may dramatically cut methane, nitrous oxide, and water pollution. Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business.
Explore how the humble earthworm could reshape the future of sustainable farming. —James Temple
MIT Technology Review Narrated: geoengineering gets a reality check Solar geoengineering, the controversial idea that we could deliberately intervene in the climate system to counteract global warming, is moving beyond computer simulations and into the practical engineering challenges required to make it real. Researchers are now working on aircraft, materials, and other systems for solar geoengineering. But as they delve into these details, they’re finding that even early deployment would require significant new infrastructure, time, and investment. —James Temple This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish 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 The Trump administration has lifted restrictions on OpenAI’s GPT 5.6The green light came after additional testing and meetings. (Axios)+ OpenAI subsequently said it will launch widely tomorrow. (Bloomberg $) + The rollout had been delayed due to security concerns. (Verge)+ Does AI know too much? (MIT Technology Review)2 China is looking at curbing overseas access to its top AI modelsAlibaba, ByteDance, and Z.ai attended meetings about the plan. (Reuters $)+ Beijing is also weighing the security risks of open-weight AI. (SCMP)+ And has issued a “backdoor” security alert over Claude Code. (CNBC)3 European NATO allies have unveiled a $50 billion high-tech missile planThey will engineer stealth and high-speed hypersonic weapons. (BBC)+ Which can strike targets at least 300 km away. (Reuters $)+ The Dutch and British are also developing amphibious ships. (Bloomberg $) 4 Meta is testing “super sensing” AI glasses that record every momentIt plans to disable privacy LEDs that alert people when they’re “on.” (FT $)+ It’s also released an AI image generator. (NYT $)+ Which lets anyone use your Instagram photos in AI images. (Wired $) 5 China’s DeepSeek is developing its own AI chip, sources sayIt could reduce the company’s reliance on Nvidia and Huawei. (Bloomberg $)+ DeepSeek V4 was a win for Chinese chipmakers. (MIT Technology Review) 6 Wikipedia is fighting to survive the internet’s next eraIt’s under attack from MAGA, AI raids, and repressive regimes. (NYT $)+ AI has given Wikipedia a language problem. (MIT Technology Review) 7 SpaceX plans to launch its first model coproduced with CursorThe new frontier model could arrive as soon as this week. (Information $)+ It’s built with AI startup Cursor, which SpaceX is buying for $60 billion. (FT $) 8 A new academic “humanizer” tool can erase signs of AI-written textBut researchers are very divided over its potential impact. (Nature $)9 Scientists have detected a mystery chemical on Pluto and TitanIt appears to absorb light in a way we don’t currently understand. (Wired $)10 A Waymo robotaxi reportedly called the cops on drinking teensOfficers then approached the vehicle with guns drawn. (404 Media) Quote of the day

“Parents do you know where your teens are? Waymo does!”  —Local police post on Facebook that a Waymo in California called the cops on two teenagers for “drinking and shooting from the vehicle.” One More Thing MICHAEL BYERS Your boss is watching Dora Manriquez has spent nine years driving for Uber and Lyft, where every ride she accepts or rejects is tracked by the apps she relies on for work. Having found herself unable to score enough better-­paying rides, she has had to file for bankruptcy.  App-based employers aren’t the only ones keeping a very close eye on workers today. Jobs today—whether in an office, a warehouse, or your car—can mean constant electronic surveillance with little transparency, and potentially with livelihood-ending consequences if your productivity flags.

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. Your family’s $300 stake in OpenAI Sam Altman’s proposal that Americans should share in the wealth created by AI is back in the spotlight, with reports that he is discussing giving the US government a 5% stake in OpenAI. At the company’s current valuation, that stake would be worth roughly $320 per American household. The idea is meant to address concerns that AI companies are benefiting from human-generated work without compensating creators, while also easing fears that AI will cause a collapse of the labor market by providing a safety net.  The details, however, remain unclear. Indeed, the offer may be more powerful as a political narrative than as a policy plan.
Read the full story on what the dividend proposal reveals about the future of AI. —James O’Donnell
This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 A leaked Treasury report compares the AI market to the dotcom bubble Which contradicts the administration’s public optimism about AI. (NOTUS)+ Fears that the market is overinflated are growing. (Reuters $)+ And AI profits are hiding bigger risks in earnings reports. (FT $)+ What even is the AI bubble? (MIT Technology Review)2 Samsung profits have jumped 1,800% on booming AI chip salesIt just reported its third consecutive record quarterly profit. (BBC)+ But its shares slumped over fears that the AI boom will stall. (Reuters $)+ That boom has turned Samsung into a $1 trillion company. (CNBC) 3 A US cyber agency is using Mythos to audit government codeSources say CISA is tapping Anthropic’s model to search for bugs. (Reuters $)+ Agencies are using it despite Anthropic’s feud with the White House. (Axios) 4 Illinois’ governor has signed the nation’s strongest frontier AI lawIt’s designed to protect citizens from AI risks. (Gizmodo)+ US lawmakers are clashing over AI rules. (MIT Technology Review) 5 A hidden tracker in Claude Code has been exposed and removedIt secretly monitored users in China. (WP $)+ Critics said it shows Anthropic’s willingness to surveil users. (Ars Technica)+ The company has also found a hidden “thinking” space in Claude. (Axios)6 Russia is suspected of flying drones over Europe from a shadow fleetThe flights were reportedly launched from commercial ships. (Ars Technica)+ Europe has a drone-filled vision for future wars. (MIT Technology Review) 7 A controversial AI “actor” is set to star in its first feature filmTilly Norwood will debut in a comedy-drama called “Misaligned.” (Variety)+ A major actors union has lambasted the AI creation. (NBC News)8 AI costs are driving US companies toward Chinese modelsBusinesses are hunting for cheaper model alternatives. (CNBC)+ Chinese AI labs are betting big on open source. (MIT Technology Review) 9 Researchers have shown quantum proofs can beat classical onesThey found a problem that classical proofs can’t solve. (Quanta)10 Earth will never be swallowed by the sun, according to new modelsBut it probably won’t be much fun to live here by that point anyway! (Wired $) Quote of the day “The goal might be to make machines in our image. But what I fear is that—perhaps without even quite noticing—we remake ourselves in theirs.”  —Reporter Sarah O’Connor sounds a note of caution in her new book, We Are Not Machines, the Guardian reports. One More Thing KATE DEHLER Adventures in the genetic time machine Eske Willerslev, a specialist in recovering DNA from old bones and objects, has made numerous breakthroughs. These include recovering the first more or less complete genome of an ancient human and 2.4-million-year-old genetic material from Greenland, revealing that today’s Arctic desert was once a forest with poplar, birch, and mastodons.

These findings are part of a wave of discoveries from what’s being called an “ancient-DNA revolution.”  Beyond revealing stories of human migration and vanished ecosystems, scientists believe ancient DNA can unearth clues about modern diseases. It could even lead to a better food supply for our warming world. “And can we get that?” Willerslev asks. “Yes, I believe we can.” Discover how ancient DNA could rescue the future.  —Antonio Regalado

In partnership withElastic With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems. Four elements of AI architecture you can count on The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves. 1. Prepare data for AI at scale Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.
Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems. As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil.
An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value. Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval. 2. Use context engineering to deliver the right data to every AI query Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently. Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model. Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times. “Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says. 3. Build AI governance and LLM observability in from the start Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations. In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges.

Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight. Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient. For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset. When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points. Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve. In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps. “Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says. 4. Keep humans in the loop The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.”
As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management.  Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in fresh thinking, it also presents high costs in system continuity, institutional understanding, and innovation. Human-centered strategy needs to be built into AI execution stages to ensure smooth implementation. 
As Adil says, “Many aspects of the stack are moving very, very fast, but institutional knowledge and the ability to adapt remain durable. Thoughtful AI investment for future growth As AI systems evolve from single-task assistants to increasingly autonomous agents, the organizations best positioned to benefit will be those that invest in the underlying systems, governance, and expertise that make AI reliable at scale. Tech leaders who focus on these fundamentals can move effectively from experimentation to reliable, production-level deployment in the medium term, confident that these elements will remain relevant and adaptable amid constant advancements. “We fundamentally believe that with these tools, velocity of work will get much faster,” Adil says. “We are really focused on how we can do work with these tools in ways we had not thought of before.” Learn more about how Elastic is building an AI-first enterprise with these core foundational components.
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.