2026-04-16 21:00:00
There’s a fault line running through enterprise AI, and it’s not the one getting the most attention. The public conversation still tracks foundation models and benchmarks — GPT versus Gemini, reasoning scores, and marginal capability gains. But in practice, the more durable advantage is structural: who owns the operating layer where intelligence is applied, governed, and improved. One model treats AI as an on-demand utility; the other embeds it as an operating layer—the combination of workflow software, data capture, feedback loops and governance that sits between models and real work— that compounds with use.
Model providers like OpenAI and Anthropic sell intelligence as a service: you have a problem, you call an API, you get an answer. That intelligence is general-purpose, largely stateless, and only loosely connected to the day-to-day workflow where decisions are made. It’s highly capable and increasingly interchangeable. The distinction that matters is whether intelligence resets on every prompt or accumulates over time.
Incumbent organizations, by contrast, can treat AI as an operating layer: instrumentation across workflows, feedback loops from human decisions, and governance that turns individual tasks into reusable policy. In that setup, every exception, correction, and approval becomes a chance to learn—and intelligence can improve as the platform absorbs more of the organization’s work. The organizations most likely to shape the enterprise AI era are those that can embed intelligence directly into operational platforms and instrument those platforms so work generates usable signals.
The prevailing narrative says nimble startups will out-innovate incumbents by building AI-native from scratch. If AI is primarily a model problem, that story holds. But in many enterprise domains, AI is a systems problem — integrations, permissions, evaluation, and change management — where advantage accrues to whomever already sits inside high-volume, high-stakes workflows and converts that position into learning and automation.
Traditional services organizations are built on a simple architecture: humans use software to do expert work. Operators log into systems, navigate workflows, make decisions, and process cases. Technology is the medium. Human judgment is the product.
An AI-native platform inverts this. It ingests a problem, applies accumulated domain knowledge, executes autonomously what it can with high confidence, and routes targeted sub-tasks to human experts when the situation demands judgment that the system can’t yet reliably provide.
But inverting human-AI interaction isn’t just a UI redesign — it requires raw material. It’s only possible when the platform is built on a foundation of domain expertise, behavioral data, and operational knowledge accumulated over years.
AI-native startups begin with a clean architectural slate and can move quickly. What they can’t easily manufacture is the raw material that makes domain AI defensible at scale:
Services companies already have all three. But these ingredients aren’t moats on their own. They become an advantage only when a company can systematically convert messy operations into AI-ready signals and institutional knowledge — then feed the results back into the workflow so the system keeps improving.
In most services organizations, expertise is tacit and perishable. The best operators know things they cannot easily articulate: heuristics developed over the years, edge-case intuitions, and pattern recognition that operate below the level of conscious reasoning.
At Ensemble, the strategy for addressing this challenge is knowledge distillation. The systematic conversion of expert judgment and operational decisions into machine-readable training signals.
In health-care revenue cycle management, for example, systems can be seeded with explicit domain knowledge and then deepen their coverage through structured daily interaction with operators. In Ensemble’s implementation, the system identifies gaps, formulates targeted questions, and cross-checks answers across multiple experts to capture both consensus and edge-case nuance. It then synthesizes these inputs into a living knowledge base that reflects the situational reasoning behind expert-level performance.
Once a system is constrained enough to be trusted, the next question is how it gets better without waiting for annual model upgrades. Every time a skilled operator makes a decision, they generate more than a completed task. They generate a potential labeled example—context paired with an expert action (and sometimes an outcome). At scale, across thousands of operators and millions of decisions, that stream can power supervised learning, evaluation, and targeted forms of reinforcement—teaching systems to behave more like experts in real conditions.
For example, if an organization processes 50,000 cases a week and captures just three high-quality decision points per case, that’s 150,000 labeled examples every week without creating a separate data-collection program.
A more advanced human-in-the-loop design places experts inside the decision process, so systems learn not just what the right answer was, but how ambiguity gets resolved. Practically, humans intervene at branch points—selecting from AI-generated options, correcting assumptions, and redirecting the workflow. Each intervention becomes a high-value training signal. When the platform detects an edge case or a deviation from the expected process, it can prompt for a brief, structured rationale, capturing decision factors without requiring lengthy free-form reasoning logs.
The goal is to permanently embed the accumulated expertise of thousands of domain experts—their knowledge, decisions, and reasoning—into an AI platform that amplifies what every operator can accomplish. Done well, this produces a quality of execution that neither humans nor AI achieve independently: higher consistency, improved throughput, and measurable operational gains. Operators can focus on more consequential work, supported by an AI that has already completed the analytical groundwork across thousands of analogous prior cases.
The broader implication for enterprise leaders is straightforward. Advantages in AI won’t be determined by access to general-purpose models alone. It will come from an organization’s ability to capture, refine, and compound what it knows, its data, decisions, and operational judgment, while building the controls required for high-stakes environments. As AI shifts from experimentation to infrastructure, the most durable edge may belong to the companies that understand the work well enough to instrument it and can turn that understanding into systems that improve with use.
This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.
2026-04-16 21:00:00
The AI boom has hit across industries, and public sector organizations are facing pressure to accelerate adoption. At the same time, government institutions face distinct constraints around security, governance, and operations that set them apart from their business counterparts. For this reason, purpose-built small language models (SLMs) offer a promising path to operationalize AI in these environments.
A Capgemini study found that 79 percent of public sector executives globally are wary about AI’s data security, an understandable figure given the heightened sensitivity of government data and the legal obligations surrounding its use. As Han Xiao, vice president of AI at Elastic, says, “Government agencies must be very restricted about what kind of data they send to the network. This sets a lot of boundaries on how they think about and manage their data.”
The fundamental need for control over sensitive information is one of many factors complicating AI deployment, particularly when compared against the private sector’s standard operational assumptions.
Unique operational challenges
When private-sector entities expand AI, they typically assume certain conditions will be in place, including continuous connectivity to the cloud, reliance on centralized infrastructure, acceptance of incomplete model transparency, and limited restrictions on data movement. For many state institutions, however, accepting these conditions could be anything from dangerous to impossible.
Government agencies must ensure that their data stays under their control, that information can be checked and verified, and that operational disruptions are kept to an absolute minimum. At the same time, they often have to run their systems in environments where internet connectivity is limited, unreliable, or unavailable. These complexities prevent many promising public sector AI pilots from moving beyond experimentation. “Many people undervalue the operating challenge of AI,” Xiao says. “The public sector needs AI to perform reliably on all kinds of data, and then to be able to grow without breaking. Continuity of operations is often underestimated.” An Elastic survey of public sector leaders found that 65 percent struggle to use data continuously in real time and at scale.
Infrastructure constraints compound the problem. Government organizations may also struggle to obtain the graphics processing units (GPUs) used to train and access complex AI models. As Xiao points out, “Government doesn’t often purchase GPUs, unlike the private sector—they’re not used to managing GPU infrastructure. So accessing a GPU to run the model is a bottleneck for much of the public sector.”
A smaller, more practical model
The many nonnegotiable requirements in the public sector make large language models (LLMs) untenable. But SLMs can be housed locally, offering greater security and control. SLMs are specialized AI models that typically use billions rather than hundreds of billions of parameters, making them far less computationally demanding than the largest LLMs.
The public sector does not need to build ever-larger models housed in offsite, centralized locations. An empirical study found that SLMs performed as well or better than LLMs. SLMs allow sensitive information to be used effectively and efficiently while avoiding the operational complexity of maintaining large models. Xiao puts it this way: “It is easy to use ChatGPT to do proofreading. It’s very difficult to run your own large language models just as smoothly in an environment with no network access.”
SLMs are purpose-built for the needs of the department or agency that will use them. The data is stored securely outside the model, and is only accessed when queried. Carefully engineered prompts ensure that only the most relevant information is retrieved, providing more accurate responses. Using methods such as smart retrieval, vector search, and verifiable source grounding, AI systems can be built that cater to public sector needs.
Thus, the next phase of AI adoption in the public sector may be to bring the AI tool to the data, rather than sending the data out into the cloud. Gartner predicts that by 2027, small, specialized AI models will be used three times more than LLMs.
Superior search capabilities
“When people in the public sector hear AI, they probably think about ChatGPT. But we can be much more ambitious,” says Xiao. “AI can revolutionize how the government searches and manages the large amounts of data they have.”
Looking beyond chatbots reveals one of AI’s most immediate opportunities: dramatically improved search. Like many organizations, the public sector has mountains of unstructured data—including technical reports, procurement documents, minutes, and invoices. Today’s AI, however, can deliver results sourced from mixed media, like readable PDFs, scans, images, spreadsheets, and recordings, and in multiple languages. All of this can be indexed by SLM-powered systems to provide tailored responses and to draft complex texts in any language, while ensuring outputs are legally compliant. “The public sector has a lot of data, and they don’t always know how to use this data. They don’t know what the possibilities are,” says Xiao.
Even more powerful, AI can help government employees interpret the data they access. “Today’s AI can provide you with a completely new view of how to harness that data,” says Xiao. A well-trained SLM can interpret legal norms, extract insights from public consultations, support data-driven executive decision-making, and improve public access to services and administrative information. This can contribute to dramatic improvements in how the public sector conducts its operations.
The small-language promise
Focusing on SLMs shifts the conversation from how comprehensive the model can be to how efficient it is. LLMs incur significant performance and computational costs and require specialized hardware that many public entities cannot afford. Despite requiring some capital expenses, SLMs are less resource-intensive than LLMs, so they tend to be cheaper and reduce environmental impact.
Public sector agencies often face stringent audit requirements, and SLM algorithms can be documented and certified as transparent. Some countries, particularly in Europe, also have privacy regulations such as GDPR that SLMs can be designed to meet.
Tailored training data produces more targeted results, reducing errors, bias, and hallucinations that AI is prone to. As Xiao puts it, “Large language models generate text based on what they were trained on, so there is a cut-off date when they were trained. If you ask about anything after that, it will hallucinate. We can solve this by forcing the model to work from verified sources.”
Risks are also minimized by keeping data on local servers, or even on a specific device. This isn’t about isolation but about strategic autonomy to enable trust, resilience, and relevance.
By prioritizing task-specific models designed for environments that process data locally, and by continuously monitoring performance and impact, public sector organizations can build lasting AI capabilities that support real-world decisions. “Do not start with a chatbot; start with search,” Xiao advises. “Much of what we think of as AI intelligence is really about finding the right information.”
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.
2026-04-16 20:10:00
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.
Inside a money-laundering center in Cambodia, an employee opens a banking app on his phone. It asks for a photo linked to the account, so he uploads a picture of a 30-something Asian man.
The app then requests a video “liveness” check. The scammer holds up a static image of a woman who doesn’t match the account. After 90 seconds, he’s in.
The exploit relies on illicit hacking services sold on Telegram that break “Know Your Customer” (KYC) facial scans. MIT Technology Review found 22 channels and groups advertising these services. This is what we discovered.
—Fiona Kelliher
—Casey Crownhart
Last week, news emerged that Microsoft was pausing carbon removal purchases. It was a bombshell—Microsoft effectively is the carbon removal market, single-handedly purchasing around 80% of all contracted carbon removal.
The report sparked fear across the industry, raising questions about the future of carbon removal and the role of Big Tech. Read the full story.
This story is from The Spark, our weekly newsletter exploring the technology that could combat the climate crisis. Sign up to receive it in your inbox every Wednesday.
—Emma Marris
Humans have done some destructive things to the ecosystems around us. But conservationists are learning that we can also be a force for good.
To understand how we work best with nature, a group of scientists, authors, and philosophers have developed new measurements of human-nonhuman relationships. Now, a team in the United Nations is continuing the work. Find out why—and what they hope to achieve.
This story is from the next issue of our print magazine, which is all about nature. Subscribe now to read it when it lands on Wednesday, April 22.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Ukraine says Russian troops have surrendered to robots
They claim a fully automated attack captured army positions for the first time in history. (404 Media)
+ Europe’s vision for future wars is full of drones. (MIT Technology Review)
2 Monkeys with BCIs are navigating virtual worlds using only their thoughts
The research could help people with paralysis. (New Scientist)
+ But these implants still face a critical test. (MIT Technology Review)
3 NASA wants to put nuclear reactors on the Moon
They could power lunar bases and extend spaceflight. (Wired $)
+ NASA is also building a nuclear-powered spacecraft. (MIT Technology Review)
4 Plans for online age verification in the US are raising red flags
Experts warn of compliance issues and potential data breaches. (NBC News)
+ In the EU, an age verification app is about to launch. (Reuters $)
5 An AI chip boom just pushed Taiwan’s stock market past the UK’s
It’s risen past $4 trillion to become the world’s seventh largest. (FT $)
+ Future AI chips could be built on glass. (MIT Technology Review)
6 The public backlash against data centers is intensifying in the US
Protests and litigation are blocking projects. (CNBC)
+ One potential solution? Putting them in space. (MIT Technology Review)
7 Five-minute EV charging is becoming a reality
China’s BYD has started rolling it out. (Gizmodo)
+ “Extended-range electric vehicles” are about to hit US streets. (Atlantic $)
8 Stealth signals are bypassing Iran’s internet blackout
Files hidden in satellite TV broadcasts keep information flowing. (IEEE)
9 Shoe brand Allbirds made a shock pivot to AI, sending stock up 700%
No bubble to see here, folks. (CNBC)
+ What even is the AI bubble? (MIT Technology Review)
10 The largest ever map of the universe is complete
It captures 47 million galaxies and quasars. (Space.com)
Quote of the day
—Sylvia Whitt, a 78-year-old retiree based in Virginia, tells the Washington Post why they’re protesting against data centers.
One More Thing
Some Republican lawmakers want to criminalize porn and arrest its creators. But what if porn is wholly created by an algorithm? In that case, whether it’s obscene, ethical, or safe becomes a secondary issue. The primary concern will be what it means for porn to be “real”—and what the answer demands from all of us.
Technological advances could even remove the “messy humanity” from sex itself. The rise of AI-generated porn may be a symptom of a new synthetic sexuality, not the cause. Read the full story.
—Leo Herrera
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.)
+ An animator turned his son’s drawings into epic anime characters.
+ Hundreds of baby green sea turtles made a spectacular first journey to the ocean.
+ You can now track rocket launches from take-off to orbit in real time.
+ These musical mistakes prove that even the classics aren’t perfect.
2026-04-16 20:00:00
The availability of artificial intelligence for use in warfare is at the center of a legal battle between Anthropic and the Pentagon. This debate has become urgent, with AI playing a bigger role than ever before in the current conflict with Iran. AI is no longer just helping humans analyze intelligence. It is now an active player—generating targets in real time, controlling and coordinating missile interceptions, and guiding lethal swarms of autonomous drones.
Most of the public conversation regarding the use of AI-driven autonomous lethal weapons centers on how much humans should remain “in the loop.” Under the Pentagon’s current guidelines, human oversight supposedly provides accountability, context, and nuance while reducing the risk of hacking.
But the debate over “humans in the loop” is a comforting distraction. The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually “thinking.” The Pentagon’s guidelines are fundamentally flawed because they rest on the dangerous assumption that humans understand how AI systems work.
Having studied intentions in the human brain for decades and in AI systems more recently, I can attest that state-of-the-art AI systems are essentially “black boxes.” We know the inputs and outputs, but the artificial “brain” processing them remains opaque. Even their creators cannot fully interpret them or understand how they work. And when AIs do provide reasons, they are not always trustworthy.
In the debate over human oversight, a fundamental question is going unasked: Can we understand what an AI system intends to do before it acts?
Imagine an autonomous drone tasked with destroying an enemy munitions factory. The automated command and control system determines that the optimal target is a munitions storage building. It reports a 92% probability of mission success because secondary explosions of the munitions in the building will thoroughly destroy the facility. A human operator reviews the legitimate military objective, sees the high success rate, and approves the strike.
But what the operator does not know is that the AI system’s calculation included a hidden factor: Beyond devastating the munitions factory, the secondary explosions would also severely damage a nearby children’s hospital. The emergency response would then focus on the hospital, ensuring the factory burns down. To the AI, maximizing disruption in this way meets its given objective. But to a human, it is potentially committing a war crime by violating the rules regarding civilian life.
Keeping a human in the loop may not provide the safeguard people imagine, because the human cannot know the AI’s intention before it acts. Advanced AI systems do not simply execute instructions; they interpret them. If operators fail to define their objectives carefully enough—a highly likely scenario in high-pressure situations—the “black box” system could be doing exactly what it was told and still not acting as humans intended.
This “intention gap” between AI systems and human operators is precisely why we hesitate to deploy frontier black-box AI in civilian health care or air traffic control, and why its integration into the workplace remains fraught—yet we are rushing to deploy it on the battlefield.
To make matters worse, if one side in a conflict deploys fully autonomous weapons, which operate at machine speed and scale, the pressure to remain competitive would push the other side to rely on such weapons too. This means the use of increasingly autonomous—and opaque—AI decision-making in war is only likely to grow.
The science of AI must comprise both building highly capable AI technology and understanding how this technology works. Huge advances have been made in developing and building more capable models, driven by record investments—forecast by Gartner to grow to around $2.5 trillion in 2026 alone. In contrast, the investment in understanding how the technology works has been minuscule.
We need a massive paradigm shift. Engineers are building increasingly capable systems. But understanding how these systems work is not just an engineering problem—it requires an interdisciplinary effort. We must build the tools to characterize, measure, and intervene in the intentions of AI agents before they act. We need to map the internal pathways of the neural networks that drive these agents so that we can build a true causal understanding of their decision-making, moving beyond merely observing inputs and outputs.
A promising way forward is to combine techniques from mechanistic interpretability (breaking neural networks down into human-understandable components) with insights, tools, and models from the neuroscience of intentions. Another idea is to develop transparent, interpretable “auditor” AIs designed to monitor the behavior and emergent goals of more capable black-box systems in real time.
Developing a better understanding of how AI functions will enable us to rely on AI systems for mission-critical applications. It will also make it easier to build more efficient, more capable, and safer systems.
Colleagues and I are exploring how ideas from neuroscience, cognitive science, and philosophy—fields that study how intentions arise in human decision-making—might help us understand the intentions of artificial systems. We must prioritize these kinds of interdisciplinary efforts, including collaborations between academia, government, and industry.
However, we need more than just academic exploration. The tech industry—and the philanthropists funding AI alignment, which strives to encode human values and goals into these models—must direct substantial investments toward interdisciplinary interpretability research. Furthermore, as the Pentagon pursues increasingly autonomous systems, Congress must mandate rigorous testing of AI systems’ intentions, not just their performance.
Until we achieve that, human oversight over AI may be more illusion than safeguard.
Uri Maoz is a cognitive and computational neuroscientist specializing in how the brain transforms intentions into actions. A professor at Chapman University with appointments at UCLA and Caltech, he leads an interdisciplinary initiative focused on understanding and measuring intentions in artificial intelligence systems (ai-intentions.org).
2026-04-16 18:00:00
Last week, news outlets reported that Microsoft was pausing carbon removal purchases. It was something of a bombshell.
The thing is, Microsoft is the carbon removal market. The company has single-handedly purchased something like 80% of all contracted carbon removal. If you’re looking for someone to pay you to suck carbon dioxide out of the atmosphere, Microsoft is probably who you’re after.
The company has said that it is not permanently ending its carbon removal purchases (though it didn’t directly answer further questions about this apparent pause). But with this flurry of news, there’s a lot of fear in the industry—so, it’s worth talking about the state of carbon removal, and where Big Tech companies fit in.
Carbon removal aims to reliably pull carbon dioxide out of the atmosphere and permanently store it. There’s a wide range of technologies in this space, including direct air capture (DAC) plants, which usually use some kind of sorbent or solvent to pull carbon dioxide from the air. Another important method is bioenergy with carbon capture and storage (BECCS), in which biomass like trees or waste-derived biofuels are burned for energy, and scrubbing equipment captures the greenhouse gases.
There was a huge boom of interest in carbon removal technologies in the first half of this decade. One UN climate report in 2022 found that nations may need to remove up to 11 billion metric tons of carbon dioxide every year by 2050 to keep warming to 2 °C above preindustrial levels.
One nagging problem is that the economics here have always been tricky. There’s a major potential public good to pulling carbon pollution out of the atmosphere. The question is, Who will pay for it?
So far, the answer has been Microsoft. The company is by far the largest buyer of carbon removal contracts, and it’s the only purchaser that has made megatonne-scale purchases, says Robert Höglund, cofounder of CDR.fyi, a public-benefit corporation that analyzes the carbon removal sector. “Microsoft has had a huge importance, especially for getting large-scale projects off the ground and showing there is demand for large deals,” Höglund said via email.
Microsoft has pledged to become carbon-negative by 2030 and to remove the equivalent of its historic emissions by 2050. Progress on actually cutting emissions has been tough to achieve though—in the company’s latest Environmental Sustainability Report, published in June 2025, it announced emissions had risen by 23.4% since 2020.
On April 10, Heatmap News reported that Microsoft staff had told suppliers and partners that it was pausing future purchases of carbon removal, though it wasn’t clear whether the company would increase support for existing projects, or when purchases might resume. Bloomberg reported a similar story the next day. In one instance, Microsoft employees said that the decision was related to financial considerations, one source told Bloomberg.
In a statement in response to written questions, Microsoft said that it was not permanently closing its carbon removal program. “At times we may adjust the pace or volume of our carbon removal procurement as we continue to refine our approach toward sustainability goals. Any adjustments we make are part of our disciplined approach—not a change in ambition,” Microsoft Chief Sustainability Officer Melanie Nakagawa said in the statement.
Whatever, exactly, is happening behind the scenes, many in the industry are nervous, says Wil Burns, Co-Director of the Institute for Responsible Carbon Removal at American University. People viewed the company as the foundational supporter of carbon removal, he adds.
“This pause—whether it’s short term or whatever it is—the way it’s been rolled out is extremely irresponsible,” Burns says. The vast majority of firms looking to get carbon removal contracts are probably seeking Microsoft deals. So, while Microsoft has every right to change its plans, the company needs to be open with the industry now, he adds.
“I don’t think you can hold yourself out as the paragon of fostering carbon removal and then treat a nascent industry that disrespectfully,” Burns says.
Carbon removal companies were already in turmoil in the US, particularly because of recent policy shifts: Funding has been cut back, and recent changes at the Environmental Protection Agency were aimed at the government’s ability to target carbon pollution.
Now, if the largest corporate backer is shifting plans or taking a significant pause, things could get rocky.
Depending on the extent of this pause, the industry may need to survive on smaller purchases and hope for support from governments and philanthropy, Höglund says. But for carbon removal to truly scale, we need policymakers to create mandates so that emitters are responsible for either storing the carbon dioxide they produce or paying for it, Burns says.
“Maybe the upside of this is Microsoft has sent a wake-up call, that you just can’t rely on the kindness of strangers to make carbon removal scale.”
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
2026-04-16 18:00:00
As a movement, environmentalism has been pretty misanthropic. Understandably so—we humans have done some destructive things to the ecosystems around us. In the 21st century, though, mainstream conservation is learning that humans can be a force for good. Foresters are turning to Indigenous burning practices to prevent wildfires. Biologists are realizing that flower-dotted meadows were ancient food-production landscapes that need harvesting or they’ll disappear. And the once endangered peregrine falcon now thrives in part thanks to nesting sites on skyscrapers and abundant urban prey: rats.
For decades (two, but that counts), I’ve been writing about how humans aren’t metaphysically different from any other species on Earth. Conservation can’t only be about fencing people out of protected areas. A lot of the time the real trick is not to withdraw from “nature” but to get better at being part of it.
Still, I recognize that living in harmony with nature sounds like a mushy idea. I was therefore stoked to participate in a meeting in Oxford, UK, that sought to build more precise tools to assess human-nonhuman relationships. Scientists have invented lots of measurements of environmental destruction, from parts per million of carbon dioxide to extinction rates to “planetary boundaries.” These have their uses, but they engage people mostly through dread. Why not invent metrics, we thought, that would engage people’s hopes and dreams?
It was harder than I expected. How do you quantify how good people in any given nation are at living with other Earthlings? Some of the metrics the group proposed seemed to me to be too similar to the older, more adversarial approach. Why tally the agricultural land use per person, for example? Environmentalists have typically seen farms as the opposite of nature, but they’re also potential sites for both edible and inedible biodiversity. Some of us were keen on satellite imagery to calculate things like how close people live to green space. But without local information, you can’t prove that people can actually access that space.
Eventually the 20 or so scientists, authors, and philosophers who met in Oxford settled on three basic questions. First, is nature thriving and accessible to people? We wanted to know if humans could engage with the world around them. Second, is nature being used with care? (Of course, “care” could mean lots of things. Is it just keeping harvests under maximum sustainable yield? Or does it require a completely circular economy?) And third, is nature safeguarded? Again, not easy to assess. But if we could roughly measure each of these three things, the numbers could combine into an overall score for the quality of a human-nature relationship.
We published our ideas in Nature last year. Though they weren’t perfect, green-space remote sensing and agricultural footprint calculations made the cut. Since then, a team in the United Nations Human Development Office has continued that work, planning to debut a Nature Relationship Index (NRI) later this year alongside the 2026 Human Development Report. Everyone loves a ranked list; we hope countries will want to score well and will compete to rise to the top.
Pedro Conceição, lead author of the Human Development Report, tells me that he wants the new index to shift how countries see their environmental programs. (He wouldn’t give me spoilers as to the final metrics, but he did tell me that nothing from our Nature paper made it in.) The NRI, Conceição says, will be critical for “challenging this idea that humans are inherent destroyers of nature and that nature is pristine.” Narratives around constraints, limits, and boundaries are polarizing instead of energizing, he says. So the NRI isn’t about how badly we are failing. It speaks to aspirations for a green, abundant world. As we do better, the number goes up—and there is no limit.
Emma Marris is the author of Wild Souls: Freedom and Flourishing in the Non-Human World.
