The benefits of steering people toward making better decisions has become conventional wisdom. But the evidence suggests it doesn’t work quite as well as we hoped.
5 min
The concept of nudging has become popular in the past few years—using psychological tactics to subtly steer people toward making better decisions that are aligned with their own interests or societal goals.
Companies and governments are using nudges, for instance, by automatically enrolling people in retirement savings plans instead of having them opt in, or by placing healthier snacks at eye level in a cafeteria or by comparing people’s electricity consumption with their neighbours’.
But as nudges became increasingly popular, we wondered: Can they go the distance? Would they keep people on track beyond the initial push, like actually eating healthier foods or saving more money or reducing their energy use over the long term?
We found that, in many settings, they don’t. Lots of people simply don’t follow through on options they have been nudged to choose—making those nudges less effective than many people believe. As the old saying goes, “You can lead a horse to water, but you can’t make him drink.”
Other research has shown this effect. In 2012, a team from Cornell University published research showing that more people grabbed healthy snacks—like apples and carrots—when they were placed in contexts that made them more convenient, such as being put at eye level, among other things. The finding got wide attention and helped spread the idea of nudging.
But another aspect of the experiment didn’t get much attention at all. Those Cornell researchers didn’t just measure what went on at the cash register. They also stuck around to see what people did with the food. The nudged people ended up eating the same amount of healthy food as the ones who weren’t nudged—and the extra that was taken because of the nudge was thrown in the garbage. In the end, the effect on consumption of healthy foods was nil.
“For a long time we had always included language in these published studies lamenting the lack of long-term studies to see exactly how long the effects would last,” says one of the researchers, David R. Just, a professor of applied economics at Cornell.
Just adds: “It makes some sense that nudges would be much more effective in the short term than in the long term. Choices like food that are repeated often over time lead to learning, and eventually people are likely to recognise how the environment is interfering with their choices. This may say that nudges are most important in one-time or rare decisions like organ-donor status.”
To be sure, sometimes a nudge is better than nothing. Let’s say somebody who wouldn’t otherwise join a gym is nudged into becoming a member. In the end, that person probably won’t use the membership regularly, but might use it occasionally—which is better than not exercising at all. And nudges may be beneficial when people don’t have to follow up on their initial choice, such as a plan that automatically puts a part of each paycheck into a 401(k).
That is only some cases, though. In others, no nudging might actually be better than a nudge. For instance, somebody might want to choose to join a gym, and plans to attend three days a week. But if nudged into the choice, this person might go there much less.
But even when nudges are better than no nudges, we have found that nudges don’t provide nearly as much benefit as initial results indicate—or as much as many nudge proponents are counting on.
We conducted studies on three of the most popular nudge strategies. In one, we gave the participants a chance to sign up with a website to get daily trivia. We described one as a way to have fun, the other as a way to get smarter every day. In reality, everybody was directed to the same site, no matter which option they picked.
When we gave participants one website as a default—in other words, we nudged them to choose it—70% opted for it, compared with 48% who chose the same one when it wasn’t preselected. That’s typically how default nudges work: People are much more inclined to pick the default, which presumably will be the one that is best for them or society.
Next came the important part. We waited. We tracked how often the study participants visited their website membership over eight months. Those who were nudged to choose the default plan visited the site 42% less often than people who chose an identical plan without nudging.
This was true for people nudged with a default option, as well as people nudged with what’s known as a decoy: a deliberate dud that makes another option really shine. In this case, the dud was an offering designed for children. So, in effect, the default and decoy strategies had a positive impact on choice, but not on long-term actions. When we nudged participants into the program, they used it less than they would have at all if they hadn’t been nudged.
Another study that we conducted threw cold water on a nudge known as the compromise effect. Think of Goldilocks choosing a bed: Nudgers know that people make choices in the same way, preferring to avoid extremes. Let’s say a store is trying to boost sales of a product that gets high ratings but is considered too expensive. The store might try to nudge customers by offering another version of the product at an even higher price—so the original looks like a better deal.
In this study, we gave people the option of choosing a plant, and steered some of them toward a compromise option (a plant that wasn’t too flashy or high maintenance). As with the trivia website, everyone ended up getting the same plant, no matter which option they chose. But people who ended up with the plant by way of the compromise effect let theirs die 16% sooner than those who chose without a compromise option. In other words, the people who were nudged into the “Goldilocks” choice weren’t as committed to caring for the plant over the long term.
Why don’t people follow through on nudged choices? When people are subtly steered toward options, it can feel as if a decision happens on autopilot. This lack of conscious effort might lead people to feel disconnected from their choices, potentially reducing their engagement with them.
This raises all sorts of questions about social programs designed to help people make better choices. Although nudges can be a powerful lever to increase sign-ups, program organisers shouldn’t conflate the popularity of a plan with the amount of people who actually use it. As our studies show, nudges can increase the latter, but decrease the former.
Encouraging individuals to save for retirement through nudges, for instance, may boost initial participation rates but may not translate into sustained engagement or prudent financial habits over time. A nudge might get people to enroll, but it doesn’t make them feel ownership, like the choice was really theirs, so they don’t follow through as much.
In designing nudges, the focus should shift toward helping individuals follow through with their decisions, complementing nudges with strategies that promote sustained engagement and behaviour change. For instance, people get more motivated for tasks when you turn the jobs into games and let them share their achievements on leaderboards. (Think of the popularity of Wordle.) It feels good to have a streak and see how you stack up to others. We might be able to transfer those competitive elements to nudged choices: If you nudge people into saving for retirement, for instance, you could show them how their savings stack up against other people’s each week.
In the end, though, the main takeaway from our research is that nudges may be a great first step. But that’s all they are: a first step. Much of the hard work is what comes next.
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AI doesn’t rebel—people design, deploy and profit from it. The real danger lies in allowing tech companies to escape accountability while shaping regulations that protect their dominance.
4 min
A wave of corporate warnings and technical disclosures has flooded the media, with headlines worrying over “swarms” of rogue artificial-intelligence agents launching “unprecedented” cyberattacks, outsmarting their makers, and inching toward a terrifying autonomy. The most revealing part of this narrative isn’t what the software did. It’s who is telling the story—and why. When corporate leaders publicly insist that the systems they financed, engineered and deployed are suddenly beyond their power to contain, skepticism isn’t only healthy; it is essential.
For years, Silicon Valley has drawn scrutiny from civil society and global regulators over tangible harms such as youth mental health deterioration and systematic privacy violations. Today, industry figures seem to be trying to change that public image. Loudly blowing the whistle on their own systems—just as two of the leading companies were preparing for massive initial public offerings—lets AI executives position themselves as a new generation of leaders who have come to terms with their societal responsibilities. They seem to want us to believe that they no longer want to “move fast and break things” but will instead stand as vigilant guardians between humanity and a technological apocalypse.
There is one glaring problem: Software doesn’t rebel. A mathematical model possesses neither intent, malice nor the will to defy its creators, let alone extinguish our species. AI is a human artifact, engineered for profit.
When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.
That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.
Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created.
By leaning on an anthropomorphic narrative, Silicon Valley attempts to repackage its specific human choices that led to experimental, powerful models behaving unexpectedly during tests as an existential peril. Elevating the issue to a cosmic scale leaves the public paralyzed and takes ordinary product accountability off the table.
In the cutthroat race for venture capital and market dominance, building guardrails slows down deployment. Grandstanding about uncontrollable power costs nothing and generates billions of dollars in free publicity, justifying stock prices, all while cultivating an aura of technological capability not only to build the frontier but also ultimately to rein it in.
Governments need to recognize regulatory capture when it stares them in the face. Tech leaders’ strategy looks transparent: Alarm Washington and Brussels into creating a regime in which only trillion-dollar incumbents with fully staffed compliance and safety departments can legally operate. By sitting at the policymakers’ tables before anyone else, these companies can help draft rules digging an impassable moat protecting them from open-source developers and upstart competitors, domestic or international. The real danger is in further concentrating the tech industry into the hands of only a few companies with deep pockets.
Beijing and Washington have brushed off those tech leaders’ calls, albeit for very different reasons. Chinese state media dismissed them as part of the “Cold War playbook” and intended to preserve U.S. dominance. Xi Jinping argued for exactly the opposite at the Brics Summit on Sept. 12, calling on Brics countries to “strengthen cooperation in the field of AI, encourage open source, openness, collaboration and sharing, and break new grounds and scale new heights.” President Trump, steeped in a doctrine of unfettered capitalism and technological supremacy, called fears that AI could destroy humanity a “hoax.” Vice President JD Vance warned that AI companies “begging the government to regulate them” looked like a “Trojan Horse.”
Striving to pursue its “European way” on AI and assert regulatory leadership, Europe, by contrast, welcomed the call. European Union President Ursula von der Leyen made this clear at the State of the EU speech last Wednesday and announced that the EU will invite “the main frontier labs for a discussion on how we can support ongoing industry efforts to pace the frontier.”
Europe has been here before. In an effort to lead global regulation and react to fears borne from ChatGPT, Europe rushed its landmark AI Act into law in 2024. Already the world’s most restrictive rulebook, the framework quickly proved too broad and complex to enforce. Stalled by implementation delays and concerns about European competitiveness, the EU postponed the law’s full rollout, leaving regulations uncertain.
AI should be regulated—risks exist and should be taken seriously. But governments need to act based on available evidence and verified facts, not corporate PR panic, the views of industry insiders, or the desire for quick political wins. The greatest danger facing society isn’t that software will awaken and overthrow its human masters. It is that we will allow the creators of the software to abdicate human responsibility for the systems they choose to build and help them pull up the ladder to market access behind them.
