5 Questions is a recurring Science Diplomat series exploring how practitioners, scholars and policymakers view the systems shaping our future.
Laurie Smith spends his working life trying to see around corners. As Head of Mission Discovery at Nesta, the U.K.'s innovation agency, he helps governments and institutions tackle long-term challenges, from mission-driven policy to emerging technologies.
When we spoke, one tension kept resurfacing: institutions say they want to think long-term, but almost everything about how they are organized rewards the opposite.
1. Why do institutions struggle to operationalize long-term strategic thinking, even when they know foresight matters?
For Smith, the problem is less about political will than institutional incentives. “The incentives often point in a different direction,” he said.
“Electoral cycles are quite quick, media cycles are quite quick. You’ve got a politician who may genuinely intend to think long-term, but if the pressure is that you get rewarded for being short-term, not for long-term, people end up behaving accordingly. You’ve got a systemic problem.”
Yet Smith does not think democratic systems are condemned to short-termism. Countries like China may be able to plan further ahead because they face less electoral pressure, he said, but democratic governments have their own tools, from future generations acts to climate legislation and other long-term policy instruments.
Institutional design is only part of the story.
“I’m not a psychologist,” he said, before suggesting that human decision-making may also reinforce short-term thinking. Rationally, he said, gains and losses should carry equal weight, yet people experience them differently.
“If you die, that’s the biggest loss. You cannot do anything else.”
That instinct, combined with institutions that reward immediate results, makes long-term thinking difficult even for leaders who genuinely want to do it.
2. How do you distinguish real transformative signals from hyped-up innovation noise?
Policymakers often want a formula for recognizing the next transformative technology. Smith’s experience has convinced him that one doesn’t exist.
“There isn’t an algorithm,” he said. “A lot of it’s built-up experience. Some of it’s just like muscle memory.”
Instead, he looks for recurring patterns. General-purpose technologies tend to matter more because they reshape multiple sectors rather than solving a single problem.
“If a technology affects lots of things, the more general-purpose it is, I think that’s one thing that makes it more important.”
He also pays attention to technologies that accelerate further discovery.
“AI probably increases your ability to find other technologies, and also improve the subsequent ones recursively.”
Even then, timing can matter as much as the technology itself.
“It’s not that the technology exists,” he said. “It’s: when does it become useful?”
For Smith, foresight is less about following a prescribed method than developing the judgment to recognize when those moments arrive. As he put it, “the least worst way of thinking about the future.”
3. What separates effective mission-driven governance from aspirational branding?
A mission only becomes meaningful when success can be measured, according to Smith.
“Our missions are measurable and specific,” he said, pointing to Nesta’s own approach. A goal such as reducing emissions by fifty percent by 2030 works because “it’s a specific measure, you know you’ve done it.”
But defining the destination does not mean prescribing the route. “Be firm about your goal, but give people freedom in how to achieve it.”
Rather than trying to design every step in advance, Smith favors an iterative approach.
“You start small, do practical experiments, de-risk your assumptions fast, and then get bigger.”
4. Are large institutions actually capable of embracing uncertainty and experimentation, or are they just built for stability?
Large institutions, Smith argued, are fully capable of experimentation.
“It depends on the institution,” he said. “Institutions are, in principle, capable of doing it, because there are institutions that do do it.”
He pointed to technology companies and test-and-learn units within government as evidence that experimentation can become part of institutional culture.
Just as important, he said, is recognizing that different disciplines often use the same language differently. Even a word like “experiment” can mean one thing to a designer and something quite different to a scientist, creating misunderstandings before substantive work even begins.
His solution is simple: bring people together, surface those differences early, and make terminology explicit rather than assuming everyone means the same thing.
5. How has your view of the science-policy-society relationship evolved as tech outpaces government?
“Everything needs to be more agile,” he said. “You can’t have these rigid, fixed systems. You need really flexible systems. It’s imperfect, but it’s the best solution.”
He illustrated the point with regulation of genetically modified organisms, arguing that rules built around specific technological processes can quickly become obsolete as science evolves.
His preference is to regulate outcomes instead, while involving policymakers and the public earlier in the development of emerging technologies.
That flexibility matters because technologies often end up having their greatest impact in places no one anticipated. GLP-1 drugs, he noted, were originally developed for blood pressure or diabetes before becoming significant for entirely different uses.
That unpredictability, Smith said, is why institutions need governance systems that can adapt as technology does.