When governments ask people what truly matters to them, they cite things like being healthy, having a secure and affordable home, enough income to live with dignity, and time for the people they love. As the former leaders of Iceland and Scotland, we have heard concerns first-hand. 

But these people-first priorities are not abstract ambitions. They are the conditions for a decent life, and ensuring they are met should be the first priority of any government.

And yet none of this is captured in the number that most economists, the media, and indeed, governments use to measure a country’s success: Gross Domestic Product (GDP), commonly referred to as economic “growth.”

To be sure, GDP has its uses as a measure of many of the goods and services a country exchanges and produces. But today, it is typically used in a way its architects never intended. For instance, a country's GDP rises when a forest is felled and when an oil spill is cleaned up. It rises with financial speculation. But it does not rise when a parent cares for a child or a child for an elderly parent, when the forest is left standing, when a woman can walk home at night without fear, when voters turn out because they trust their institutions, or when a patient sees a doctor in days rather than months.

A new UN Framework, Beyond GDP, promises to change this by offering a dashboard of more meaningful indicators to complement GDP. Governments must now put this framework into action. Not simply by publishing new statistics, but by using them to set priorities, shape budgets, assess policies and be held to account publicly on whether people’s lives are genuinely improving.

Eight years ago, we set out to put the wellbeing of our people ahead of the narrow pursuit of economic growth. Together with New Zealand's Jacinda Ardern, we founded the Wellbeing Economy Governments partnership, later joined by Wales and Finland, with Canada actively participating. We shared practical lessons about developing national wellbeing indicators, embedding them in government decision-making and ensuring that economic policy considers social and environmental outcomes alongside financial ones.

In Iceland, we introduced 39 wellbeing indicators, built on what people themselves told us they valued, and used them to inform policy. GDP became one measure among many, alongside life expectancy, unmet healthcare needs, material deprivation and work-life balance. Parental leave shows what this means in practice. We extended leave to 12 months, with six months reserved for each parent and six weeks transferable. In doing so, we recognized that care, family life, gender equality, and the economy are inseparable, and that fathers' time with their children is worth protecting. The reform strengthened families and shifted expectations about work and who does the caring.

In Scotland, a Wellbeing Economy Monitor showed us how the economy was really performing for people, alongside a National Performance Framework of outcomes written into law and aligned with the Sustainable Development Goals. Policies followed: the doubling of early years education, a Baby Box to equalize children’s starts in life, the Scottish Child Payment to reduce child poverty directly, and Community Wealth Building to ensure public spending, land and assets create lasting value locally—an approach Scotland has since made the subject of the first national legislation of its kind anywhere in the world. 

While these efforts are still evolving and not perfect, they represent an important shift in how governments understand prosperity. Changes in economic systems take time, but progress begins by changing—and being more explicit about—what we value. Our experience taught us that measuring what matters is only the beginning: wellbeing indicators must also shape budgets, policy decisions and how governments are held accountable. Legal systems need to support this too.

We were, and we still are, far from alone in this endeavor. Bhutan, for example, has been making the argument for more than 50 years. Its Gross National Happiness framework focuses on nine different dimensions of well-being such as living standards, health, education, and ecological diversity. Last year, Malaysia introduced a bold new roadmap that embeds public health, environmental sustainability and economic resilience. The National Planetary Health Action Plan moves away from a narrow focus on economic growth to prioritize the health of people and planet, replacing the short-term idea of return on investment with a “return on values.”

It is worth noting that wellbeing economy approaches often mirror the ways many Indigenous Communities have been providing for collective needs for generations. For example, Buen Vivir—living well together—comes from the Quechua peoples of the Andes, and has been written into the constitutions of Ecuador and Bolivia since 2008. And Aotearoa New Zealand's wellbeing budget draws on Māori understandings of intergenerational wellbeing.

These ideas are finally moving from the margins into mainstream economic policy. A landmark UN report, written by a high-level expert group appointed by the UN Secretary-General after the Pact for the Future, proposes a dashboard of 31 indicators to sit alongside GDP, with recommendations for governments, business, academia, and civil society on how to bring this agenda to life.

Earlier this year, a roadmap for eradicating poverty without relying on endless growth was launched in Geneva, developed with more than four hundred contributors from governments, trade unions, social movements, UN agencies and universities. It sets out measures that already work in different places, from social protection and care; to tax, universal basic services, and the rights of nature; to the governance of trade, debt, and finance. That work and the work on measurement are currently proceeding separately. Bringing them together would be a valuable contribution of the intergovernmental process now underway. 

The greatest risk now is a process in which governments agree in principle and move slowly in practice. New indicators alone will not be enough. The pursuit of growth at any cost is embedded far beyond the statistics: in how credit agencies rate a country's debt, how finance ministries deem a budget responsible, and in the rarely-questioned assumption that more is always better.

What is needed is not further consensus but more early adopters: countries willing to pilot implementation and share honestly what they learn, supported by civil society, business and the UN itself.

The 2015 Sustainable Development Goals ask every country to sustain economic growth per person. As governments begin to design what will follow them, we have a chance to change the instruction and create a different approach.

After all, the responsibility of every leader is to ensure the wellbeing and safety of his or her people, and to do that, we need to go beyond GDP and think about what truly matters

Claude now watermarks AI-generated text to comply with European Union transparency rules. OpenAI and Google add invisible fingerprints to AI-generated images. And Substack is touting a feature that scans pieces for signs of AI. Will we finally be able to tell what’s real on the Internet? My take: not even close. 

In fact, AI watermarks and detectors may leave us worse off by creating a false sense of confidence in content marked as genuine.

Watermarks and detectors are gaining traction as we lose our ability to trust our senses online. Look up the Will Smith eating spaghetti test, and you’ll see just how far AI has come. A 2023 AI-generated video shows the actor slurping spaghetti, face distorted, in a way that breaks physics. By 2025, AI was producing lifelike renditions. Deepfakes are so good that experts recommend families develop secret codewords to identify one another. 

“But I know a fake when I see it,” someone might say. 

Unfortunately, research consistently shows that you do not. This can feel especially hard to accept given the abundance of AI slop rocketing around the Internet. You may even start to think you can sniff out offending content. It might work, for a little bit. It almost never lasts. Any signal that becomes discernible is one a sophisticated actor will find ways to avoid. 

We’ve seen this story before. During the earliest days of the Internet, visual polish at least told you something. Major institutions had the resources needed to produce well-designed websites. Janky-looking sites, on the other hand, screamed “scam!” Information experts directed Internet users to dwell on features such as design, broken links, and typos. But when the Internet changed, the advice didn’t. 

A study I led, published in 2022, found that 96% of America’s leading colleges and universities offered outdated advice on how to evaluate online information—long after platforms like Wix, Squarespace, and Photoshop made it easier for bad actors to create fake but convincing-looking websites. Inexpensive software made slick graphics ubiquitous. Educators, however, continued to instruct Internet users to search for visual clues like a game of Where’s Waldo?

The most dangerous legacy of this aesthetic fixation is the inverse illusion: the cognitive tendency to believe that if the presence of a signal proves one thing, its absence proves the opposite. Yes, a site with misspellings that claims to show aliens still isn’t legit. But a beautiful site with a dot-org domain can also be harmful. In 2019, our research group found that nearly half of hate groups had dot-org domains. Bad actors know how to adopt the trappings of credibility. 

The same is true with AI. Even if visible flaws sometimes linger, their absence doesn’t mean content is genuine. Yet, too often, experts offer surface-level clues to identifying AI-generated content. This is why in the lead-up to the 2024 elections, Stanford Professor Sam Wineburg and I warned about public officials who advised citizens to pay attention to lighting, strange shadows, or other visual cues to identify deepfakes, even after AI content stopped making these errors. Many 2026 guides to spotting AI content mislead readers with the same poor advice. 

Which brings us to AI watermarks and detectors. These approaches, based on hidden signals in content, promise that while we can’t always spot the signs, their algorithms can. 

I’m not a software engineer. Yet I was able to easily strip metadata from some AI-generated images just by screenshotting them. Anthropic confirms that file metadata can be “stripped through format conversion, re-saving, screenshots, or other means.” Watermarks like SynthID are stronger and can persist after screenshots. But I was able to use a free online tool to remove a SynthID watermark. 

Google admits that the accuracy of detecting watermarked AI text is “greatly reduced” when users thoroughly rewrite what they generate, and that it “is not designed to directly stop motivated adversaries from causing harm.” More broadly, open-weight AI models that can run locally, outside platform terms and conditions, guarantee the spread of unmarked content.

Third-party detectors, too, have a spotty track record. I’ve regularly run AI-generated text through detectors that said it was human and vice versa. Many studies of text, image, and audio detectors find that they don’t work very consistently, and yet, their findings are used as the basis for public accusations. Every detector must confront an arms race with humanizer tools and other workarounds motivated actors find. 

I would argue that the biggest problem for detectors and watermarks remains the inverse illusion. Just because content lacks a watermark doesn’t mean it wasn’t produced or edited with AI. As Anthropic notes: “lack of a detected mark doesn’t mean the content wasn’t AI-generated or processed.” Deferring judgment to AI detectors leaves us vulnerable to bad actors who know how to launder content and make it pass muster.

This is a confusing time. Many of us are, understandably, uncertain. In one recent pilot, our research group showed 117 students a confident chatbot answer about local history with hallucinated facts. Half said they weren’t sure if it was true. One student said AI is sometimes right and sometimes wrong and “you never know which is which.” 

But just because we can’t trust our eyes or place full faith in detectors doesn’t mean we can’t trust anything. Rather than hunt for visual clues or outsource judgment to detectors and watermarks, we can turn to reputation and context. It’s easy to fake content. It’s much harder to fake a good reputation that’s validated by credible sources. 

The next time you see unfamiliar content online, resist the urge to ask, “Does this look like AI?” or run the content through a detector. Instead, ask yourself, “Do I trust where this information is coming from?” Open a new tab and check if reputable people and organizations confirm what you’re seeing. 

In an era of dwindling trust, we should not fork over ours to cheap signals or cheap software.