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When Incentives Go Wrong: The Hidden Costs of Motivating Creative People

In the early 1900s, commercial fishing interests along the New England coast had a problem: the abundant seal population was reducing the fish supply. Convinced that fewer seals would be the solution, Maine and neighboring states began paying bounties for every seal killed. As proof, one simply needed to bring a seal’s nose to a town clerk and collect the reward. 

But reality turned out to be far more intricate than what the policy’s creators envisioned.

For generations, the Passamaquoddy people of eastern Maine had hunted seals for food and clothing. By 1900, logging, fencing, dammed rivers, and mounting regulations had restricted the Passamaquoddy people’s access to their ancestral lands. Ocean hunting remained one of the few avenues left to maintain traditional subsistence practices, providing both food and clothing. As a result, when commercial interests pressured the state to eliminate local seal populations, they directly endangered an essential Indigenous livelihood.

In response, several Passamaquoddy hunters devised a clever workaround. Guided by traditional values that discouraged killing seals en masse, skilled artisans crafted convincing fake noses using small fragments of seal hide. A single pelt could thus produce dozens of bounty payouts. Suspicions rose in January 1904 after two Passamaquoddy men attempted to redeem 86 noses at once in Portland. The sheer scale of the operation was striking: claims spiked from 208 bounties in 1903 to 2,632 in 1904, prompting authorities to quickly end the program.

The scheme was illegal but reducing it to fraud over-simplifies the story. Commercial fisheries wanted to eliminate competition for fish, leading officials to establish a quantifiable metric (seal noses). Faced with a policy that threatened both their environment and their culture, the Passamaquoddy capitalized on the fact that the state was not truly paying for population reduction. It was paying for physical tokens. 

It is a wonderfully absurd example of a serious management problem. Incentives require us to translate what we really want into something observable enough to reward. But the moment we define that, the measure itself begins to compete with the mission.

This problem is particularly acute in creative work. Leaders want innovation, so they reward patents, product launches, ideas submitted, revenue generated, individual performance, or promotion-worthy accomplishments. 

But creativity works differently. So the right question is not, “Do incentives motivate?” because, of course, they do. The more useful question is: “What exactly do they motivate people to do?”

Research suggests that incentives shape creative work through at least three broad mechanisms: the effort type,  the orientation of thought, and the quality of social interaction.

1. Type of Effort 

In the 1990s, Safelite Glass Corporation changed how its windshield installers were paid. Instead of relying primarily on hourly wages, it introduced piece-rate compensation tied to output. Economist Edward Lazear studied what happened next.

Productivity per worker increased by roughly 44%. Part of the effect came from attracting and retaining more productive employees, but existing workers also increased their output. 

This is an important place to begin because incentives clearly work.

But notice the character of the work. Both Safelite and the workers know what a successful windshield installation looks like. You could consider this kind of work a “routine” or a “predictable” task, where both outcome and the process to achieve that outcome are very well defined. Employees, therefore, have considerable control over accomplishing the goal and incentives encourage people to achieve the goal faster.

Creative work is different because the process (and sometimes even the goal) often do not yet exist.

Imagine asking one engineer to install ten more windshields and another to invent a radically better way of replacing automotive glass. Greater intensity is likely to help the first. The second may need to slow down, explore a strange possibility, discard several promising ideas, or spend a week understanding whether the problem has been framed correctly.

A meta-study analyzed 183 studies involving more than 200,000 participants. They found that both intrinsic motivation and extrinsic incentives predicted performance, but their relationships differed according to the type of performance. Intrinsic motivation was particularly important for performance quality, while incentives were comparatively more important for performance quantity. 

Organizations often use the effort in the sense of intensity like working faster or longer hours. But creative work requires a different kind of effort that’s not as easily measured:  searching broadly for alternative solutions, experimenting, tolerating ambiguity, or persisting through roadblocks. 

A researcher under pressure to produce publications can work extremely hard while avoiding risky research questions. A product team can sprint heroically toward a launch date while failing to ask whether customers really need the product. An executive can relentlessly optimize quarterly results while starving experiments whose payoff lies years away.

In these cases, the incentives simply push effort in the wrong direction. 

If the path is well-known, extrinsic incentives can accelerate progress. However, if discovering the path is the work, pushing harder may simply get people to the wrong destination faster.

2. Cognitive orientation

In an elegant series of experiments, Teresa Amabile and her colleagues, had children and adults perform creative activities under different reward conditions. The key manipulation was whether participants understood the activity itself as something they were doing in order to obtain a reward. Across the studies, explicitly contracting to perform the creative activity for a reward reduced the creativity of the resulting work relative to relevant comparison conditions. 

The problem is not necessarily the reward itself. It is what the reward can do to attention. Without a salient external incentive, someone absorbed in a creative problem might ask: “What would happen if we tried the opposite?” or “What assumptions are we making?”

But when you make the reward salient, a different question enters the mental workspace:”What do I have to do to earn it?” That question can be highly productive when the task is unambiguous and predictable. But innovative work is often the opposite.

A meta-analysis of 60 studies found that rewards explicitly contingent on creative performance tended to improve creativity, particularly when accompanied by constructive, task-focused feedback and meaningful choice. Ordinary performance- or completion-contingent rewards, however, showed a small negative relationship with creative performance. 

In other words, incentives become attention-directing devices. Tell a team you need twenty ideas and twenty becomes important. Tell engineers that they will be evaluated on lines of code and code volume becomes important. 

Again, the employees aren’t behaving irrationally. They are learning what the organization has made salient and they are simply altering their effort accordingly.

3. Social interaction

The most consequential effects of incentives may not happen inside an individual’s head at all. They happen between people.

In one study, researchers looked at forced-distribution performance systems i.e. the practice of evaluating employees relative to one another. When participants performed a task individually, forced ranking increased their speed. But when work became collaborative, the results reversed. Not only did forced ranking slow down task completion, it also significantly reduced knowledge sharing. 

Consider a scenario where two people are collaborating on a new product. If one person discovers an insight that could significantly enhance the other’s part of the  project, passing that information along makes sense when the mutual outcomes are aligned.

However, if both people are competing for a single promotion, things get tricky. One study investigated this dynamic and found that strong promotion incentives were associated with greater individual effort but lower helping effort toward coworkers. 

A conventional performance-management system may record the first effect but completely miss the second one. For creative work, that blind spot is more dangerous. Innovation is rarely the product of isolated brilliance. Someone supplies an analogy, challenges an assumption, shares a contact, offers technical knowledge, or spends an hour helping a colleague escape a dead end.

Competitive incentives can put a price on that generosity. And under stronger competitive conditions, the consequences can move beyond withholding help.

In one study researchers created experimental tournaments in which participants could improve their chances of winning through productive effort or through actions that reduced a competitor’s performance. As the difference between the winner’s and loser’s rewards increased, participants exerted more productive effort and more sabotage. 

Most workplace sabotage isn’t very dramatic. It often looks more like a delayed reply,  a useful insight kept private, or an idea presented as “mine” rather than “ours.” Individually, these decisions can be rational but collectively, they can destroy the culture needed to support creative work.

That is why incentive design is ultimately also relationship design. It tells employees whether a talented colleague is primarily a source of knowledge, a partner in discovery or a threat to their own reward.

The real question behind every incentive

The Maine officials who paid for seal noses made an understandable mistake. They could not directly purchase their real objective, so they created a measurable proxy. 

More than a century later, organizations do the same thing with sales targets, KPIs, patent counts, performance ratings, promotion tournaments, publication metrics, and innovation bonuses.

The lesson is not that incentives are inherently corrosive. Incentives can increase effort, but they can also redirect effort, narrow attention and change colleagues into competitors. 

So before attaching a reward to creative work, leaders should think carefully about these questions: What behavior will this incentive intensify? What will it cause people to pay attention to? And how will it affect collaboration? These questions shift incentive design from a compensation problem to a problem of organizational architecture.

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Creativity Needs a Map, Not Just a Bigger Imagination

In 1941, Swiss engineer George de Mestral returned from a walk with burrs clinging to his dog’s fur that were surprisingly sticky. Most of us would have brushed them off but Mestral looked closer. Under a microscope, he saw tiny hooks gripping the loops in fabric and fur. That observation led to an idea that eventually became Velcro. 

The story captures something essential about creativity. New ideas rarely appear from nowhere. More often, they emerge when the mind travels from something familiar to something related, but not obvious. The breakthrough is not simply “thinking harder” but finding a different path.

Our latest paper, Graph Enhanced Creative Cognition for Alternate Uses Task, explores whether large language models can be helped to make those associative journeys. The headline finding is: when AI models were guided toward moderately distant concepts before generating an idea, they produced a much broader range of responses. The number of distinct idea categories increased by 147% for Gemma-4B, 70% for Mistral-7B and 33% for OLMo-7B. When a learning system identified the most promising conceptual paths, those paths generated 16–20% more idea categories than randomly selected ones. 

The study suggests that creativity may depend less on explicitly asking for originality and more on designing better routes to discover it.

Why “be more creative” usually fails

Anyone who has led a brainstorming session knows the pattern. Ask a group for unusual uses for a paperclip and the first answers arrive quickly: a hook, a lock pick, a cable holder. Then the room slows down and people begin repeating variations of the same themes.

Language models face a similar problem. They generate likely continuations from patterns in their training data. This makes them fluent, but it also creates a pull toward familiar answers. Asking a model to “be highly creative” does not necessarily change the territory it explores. Turning up randomness may produce stranger wording, but strangeness is not the same as a genuinely different idea. 

Creativity researchers have long described creative thought as an associative process. Sarnoff Mednick’s classic theory proposed that original ideas arise when people connect elements that are relatively remote from one another. The challenge is to travel far enough from the obvious to find novelty, but not so far that the result becomes meaningless. 

That is the “Goldilocks zone” of creativity: not too close, not too distant.

Giving AI a conceptual stepping-stone

Our study tested this idea using the Alternate Uses Task, a widely used divergent-thinking exercise in which participants propose unconventional uses for ordinary objects. We used six objects (book, table, fork, pants, bottle and coin) and tested three small, open-weight language models, collecting 3,552 valid generations. 

In the baseline condition, the model received a straightforward request: suggest an unusual use for the object.

The experimental condition received one extra instruction to incorporate a concept reached by taking two steps through ConceptNet, a large commonsense knowledge graph. ConceptNet represents everyday concepts as a network of labeled relationships. A “book,” for example, might connect to a “bed” through location, and “bed” might connect to a “plant” through another relationship. The result might be a vertical planter made from stacked books, an idea that is not an obvious association with “book,” yet remains understandable.

The system provides a conceptual stepping-stone: something sufficiently removed to disrupt the default answer, but still connected through a traceable path.

Diversity is not the same as originality

In the paper, we also make a distinction between an individually creative answer and a genuinely diverse body of ideas.

Suppose an AI proposes using a book as a shield. Judged in isolation, the answer might seem original. But if the model returns to “shield” repeatedly, or other models also propose the same, its creative range is narrower than the individual score suggests. We found that standard originality scoring sometimes gave different ratings to semantically similar answers and compressed many responses toward the high end. To address this, we grouped similar responses into semantic clusters. Ideas involving protection might form one cluster; furniture another; gardening a third. The more clusters a model reached, the more widely it had explored the idea space.

Using this approach we saw that graph-guided prompting increased the number of unique clusters by 147% for Gemma-4B, 70% for Mistral-7B and 33% for OLMo-7B. The prompts helped the models explore new categories of ideas. 

For Gemma and Mistral, graph guidance also produced more responses in the rarest (and therefore most original) clusters. OLMo became more diverse overall but showed a small decline in the highest-originality group showing that diversity and originality often reinforce each other, but they are not identical. 

Not every detour leads somewhere useful

Simply wandering through a network does not guarantee inspiration. Some paths loop back to where they started. “Book → cover → book” creates no real distance. Other paths end at concepts already closely associated with the starting point.

We therefore asked whether a system could learn which associative paths were more creatively productive.

We trained a graph neural network to rank pathways through the “book” portion of ConceptNet. Its technical performance was modest, but it learned enough to identify better routes. Compared with 100 randomly chosen paths, its top-ranked 100 paths produced 16–20% more unique idea clusters across the three models and more ideas in the highest-originality category. 

The finding shows that creative support systems may eventually do more than provide information or generate answers. They could help people navigate conceptual landscapes by suggesting which analogy, adjacent field or surprising connection is most likely to open productive territory.

Creativity as guided exploration

While the study is an early proof of concept, it provides some useful insights. One, it is computationally easier to measure novelty and diversity but usefulness requires human judgement. Generating truly creative solutions requires (at least as of now) a healthy collaboration between humans and AI. Two, designing the right process, where AI can suggest different directions to think about, can yield higher levels of creativity. Completely open prompts often leave people and machines circling the most accessible ideas in practice. AI can be a useful partner by suggesting new associations, analogies or metaphors as stimuli. Finally, traceability is a promising theme. A graph-guided idea comes with a kind of “cognitive lineage” that allows us to inspect the route that helped produce it. In scientific discovery, strategy and learning, the path may be almost as valuable as the answer because others can evaluate, adapt and extend the thinking. 

George de Mestral did not invent a fastener by staring harder at fabric. He followed a path from burrs, to hooks, to loops, to textiles that led to his world changing idea. Perhaps the future of creative AI, and of innovative organizations, will not come from demanding more creativity but by being more deliberate about the conceptual journeys that make better answers possible.

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AI, Layoffs and the Innovation Tax

The promise of AI is rapidly becoming a workforce question.

In a memo last year, Amazon CEO Andy Jassy told employees that as generative AI usage spreads through the company, “we will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs.” He went further: “in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.” 

Duolingo CEO Luis von Ahn’s “AI-first” memo said that they would “gradually stop using contractors to do work that AI can handle,” and that “headcount will only be given if a team cannot automate more of their work.” 

IBM CEO Arvind Krishna offered perhaps the clearest version of the attrition model. Speaking about back-office functions that could be automated, he said, “I could easily see 30% of that getting replaced by AI and automation over a five-year period.” 

Taken together, these statements reveal a new leadership strategy: automate and reduce headcount as AI expands, or in some cases, in anticipation of higher productivity. It is an understandable response to a technology that can write code, resolve customer tickets, draft marketing copy, summarize meetings, and generate analysis at extraordinary speed. 

But there is a danger in this story. It assumes that innovation is produced by tasks. In reality, innovation is produced by people working together in messier ways that defy easy automation – experimenting, discussing, dissenting, and learning through customer interactions. When companies cut too broadly, they may inadvertently remove the conditions that will allow AI-era innovation to compound.

The tricky question for leaders is whether they can distinguish between work that is truly automatable and work that looks inefficient only because its value is hard to capture in a balance sheet.

Impact of Downsizing on Innovation

The danger is not that every layoff is bad. It is that broad layoffs, especially when framed as an AI-enabled operating upgrade, can confuse labor reduction with organizational learning. A company can become leaner and less capable at the same time.

One of the earliest systematic warnings came from Teresa Amabile and Regina Conti’s 1999 study. They examined a large high-technology firm before, during, and after a major downsizing. Their central finding, apart from a worse morale, was that the conditions that support creativity deteriorated. Creativity, productivity, and perceived work-environment support declined during downsizing, while obstacles to creative work increased. 

That finding should land heavily in today’s technology sector. Innovation is rarely born from an isolated genius typing faster with better tools. It is more often a social process: someone notices an anomaly, someone else connects it to an unmet customer need, a third person remembers a failed experiment from two years earlier, and a fourth turns the conversation into a prototype. Remove enough nodes from that system and the org chart may still look coherent, but the creative network underneath it becomes brittle.

Newer research adds an important nuance. Ramdani and colleagues studied 122 UK firms over 22 years, using downsizing and patent data to examine how workforce reduction affects innovation outputs. Their conclusion is not “layoffs always reduce innovation.” It is more precise: downsizing has a dual effect depending on the firm’s resource position. In firms with resource slack, downsizing can have a positive effect on innovation. In resource-constrained firms, it has a negative effect, and the damage appears more quickly. 

This matters because many tech companies do not experience resource slack uniformly. One division may have layers of coordination, duplicated tooling, and unclear ownership. Another may have exhausted engineers, customer-support escalation queues, and neglected technical debt. From 30,000 feet, both may look like “headcount.” But only one will lead to higher innovation on downsizing.

The key question for leaders is: are you cutting fat, or are you cutting connective tissue? Because downsizing in a resource-constrained organization can significantly hurt innovation down the road. 

Psychological Effects of Layoffs

Why do layoffs damage innovation when there is little slack? The answer lies in psychology as much as economics.

The first mechanism is psychological safety collapse. Innovation requires people to take interpersonal risks: challenge assumptions, admit uncertainty, surface bad news, and suggest ideas that may initially sound naïve. Amy Edmondson and Derrick Bransby’s 2023 review of psychological safety research describes its importance for learning behavior, performance, and work under uncertainty. A meta-analysis of 94 studies also found psychological safety positively associated with both employee innovation behavior and team innovation behavior. After broad layoffs, however, the unwritten rule often becomes: do not look expendable. In that climate, people do not stop having ideas; they stop volunteering them.

The second mechanism is survivor syndrome and identity rupture. Research on downsizing survivors shows that employees who remain often experience reduced commitment and performance, a pattern commonly described as survivor syndrome. Van Dick and colleagues found that downsizing can reduce employees’ identification with the organization, which then harms survivor performance. The innovation consequence is direct. The person who no longer identifies with the company may still complete assigned tasks. But will they fight for an unproven customer insight? Will they spend political capital defending a long-term bet? Will they mentor the junior colleague who may one day become a breakthrough inventor? Often, the answer is no.

The third mechanism is job insecurity narrowing attention. When employees fear future cuts, their time horizon contracts. They focus on visible output, defensible metrics, and work that protects their standing. Niesen and colleagues’ research on job insecurity and innovative work behavior notes the paradox that organizations often expect restructuring to enhance innovation, even as insecurity can undermine the behaviors innovation requires. This is the problem with fear-based efficiency: it may increase activity while decreasing imagination. 

There is also a network mechanism. Corporate knowledge is not stored only in documents or AI retrieval systems. It lives in relationships: who knows which customer exception matters, why a particular architecture decision was made, or which workaround keeps a product alive. Broad layoffs sever these ties indiscriminately. AI cannot easily reconstruct what the organization failed to write down.

A 2024 HBR article based on a study of 146 companies found that engagement, morale, and loyalty can take years, not months, to rebound after layoffs. The authors cite Pixar director Brad Bird’s memorable observation: “If you have low morale, for every $1 you spend, you get about 25 cents of value. If you have high morale, for every $1 you spend, you get about $3 of value.” Whether or not one takes the math literally, the leadership lesson is still valid: the same dollar value produces radically different returns depending on the emotional state of the system. In a frightened organization, talent becomes defensive. In a committed organization, talent becomes generative. That difference will determine whether AI becomes merely a substitute for human contribution or a catalyst for the next wave of innovation.

The Way Forward

So, how can leaders figure out if layoffs are the right move? It starts with three key diagnostic questions.

First, where is work waiting? Slack isn’t always visible in just headcount ratios. Look for queues: unresolved customer issues, delayed experiments, or rising technical debt. If critical work is already backing up, you’re resource-constrained and cuts will only make things worse.

Second, where has learning slowed? Beyond simple experimentation metrics, track behavioral indicators. Are people trying fewer new things? Are you seeing fewer dissenting views? A drop in any of these suggests learning is stalling.

Third, where is human judgment still doing hidden work? AI can automate routine tasks, but leaders must map the judgment layer. This critical layer handles edge cases, ethical tradeoffs, and customer reassurance. Removing people who interpret these ambiguous situations could make the process faster, but the organization will get less intelligent.

Klarna learned their lesson the hard way. They cut about 700 employees citing the productivity of their AI assistant, but later had to reverse course when they had to reassign engineers and marketers back to customer-support roles when AI didn’t hit the mark. 

However, the deeper challenge for leadership is a philosophical one. The industrial-age instinct was simple: reduce labor when a machine increased productivity. That made sense when work was predictable. But in the innovation age, the question is entirely different: when a new tool boosts productivity, where do you redeploy the freed human capacity to gain a learning advantage?

That is the fork in the road. Companies using AI mainly for headcount reduction might win a margin story but lose their innovation future. Conversely, companies that reinvest human attention into discovery, customer understanding, and experimentation will compound their advantage.

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Why Education Needs an “AI-in-the-Loop” Model

Twelve year old Leo sat in his room, staring at his history assignment on the Industrial Revolution. Usually such an assignment would take several hours of reading relevant material, picking his thesis, finding supporting evidence to back his claims and then drafting his essay. But today, he simply fed a prompt into ChatGPT, made some quick and simple revisions, and hit submit. 

On paper he looked like a star student. His essay even got him an ‘A’ from his teacher. But did any real learning take place? 

This is the crisis we face in education. We are currently obsessed with the “Human-in-the-Loop” model, where humans oversee AI outputs. But in a classroom, that model is backwards. If we want to raise a generation of innovators rather than mere prompt engineers, we have to flip the script to an “AI-in-the-Loop” approach that prioritizes the student’s cognitive effort before the algorithm ever enters the chat.

Renzulli’s Three Ring Conception of Giftedness

To understand why current AI integration is risky, we have to look at what actually creates high-level human performance. One of the most respected frameworks in educational psychology is Joseph Renzulli’s Three-Ring Conception of Giftedness.

Renzulli argued that “giftedness” (or what we might call high-level creative productivity) lies at the intersection of three distinct clusters of human traits:

  1. Above-Average Ability: The core competency and foundational knowledge in a specific domain.
  2. Creativity: The ability to generate original ideas, see new patterns, and think divergently.
  3. Task Commitment: The grit, perseverance, and “productive struggle” required to see a difficult project through to the end.

When these three rings overlap, giftedness emerges. However, the current “plug-and-play” integration of AI in schools threatens to thin out every one of these rings, potentially preventing students from achieving high accomplishments.

The Risk to Ability

The most immediate danger of AI is cognitive offloading—the tendency to use external tools to reduce the mental effort required for a task. While offloading is great for mundane chores (like GPS for driving), it is quite harmful for learning.

We already know that easy access to external information changes what people remember and how they allocate mental effort. The classic “Google effect” research showed that when people expect information to be accessible later, they are less likely to remember the information itself and more likely to remember where to find it. That isn’t necessarily bad. Humans have always used tools and transactive memory, but in schooling, ability is built through repeated retrieval, reconstruction, and sense-making.

Research has already begun to show the impact of cognitive offloading. In a large field experiment with high school math students, researchers found that a “ChatGPT-like” tutor improved performance during practice, but when the AI support was removed, those students performed worse than students who learned without the AI tool, an effect consistent with dependence and reduced durable learning.

Without guardrails, AI can act as a “cognitive crutch.” By providing hints and solving intermediate steps, it removes “desirable difficulty” or the kind of struggle that leads to deeper learning and long-term retention. If the AI does the heavy lifting, the students learn less and don’t reach higher levels of competence required for high achievements. 

The Risk To Creativity

Creativity is not just producing something. It’s producing something both novel and appropriate.

AI is remarkably good at the obvious. Large language models generate outputs that reflect patterns in their training data. That makes them useful, fluent, and fast but it also means they can pull learners toward the statistical center of what’s been said before.

A recent experiment on creative writing found that access to AI ideas helped individuals produce stories judged as more creative (especially among less creative writers) but it also made the stories more similar to one another, reducing collective novelty. In other words, AI can raise the floor while lowering the ceiling of diversity.

Separate work has begun to quantify this “echo” effect more directly, showing measurable limits on plot diversity in LLM outputs under the same prompts. And broader research reviewing LLM creativity suggests that while models can appear creative through recombination, there are persistent questions about originality, intent, and the difference between pattern completion and human creative agency. 

When students brainstorm with AI first, they often anchor on the suggestions they see and fall into an “associative rut.” If they took the time to think by themselves before reaching out to AI, they would discover more original and personally meaningful ideas. 

And we’ve seen a similar phenomenon long before AI. Research on brainstorming has shown that nominal brainstorming (individual idea generation before group discussion) produces more ideas and more original ideas than purely interactive brainstorming. In the AI context, the LLM acts like a “dominant personality” in a group meeting. It speaks first, speaks confidently, and sets a baseline. Once a student sees those AI suggestions, their brain finds it incredibly difficult to think outside those parameters.

The Risk to Commitment

Task commitment includes persistence, delayed gratification, self-regulation, and the willingness to stay with ambiguity.

Modern technology has already been impacting this ring. Research shows that higher use of digital devices is tied to concentration difficulties, lower academic performance, and poorer self-regulation. 

Now layer generative AI on top. In a world where a chatbot can produce instant essays and workable code, the emotional “cost” of effort feels high. Why wrestle with the challenge when the answer is right at the fingertips?

Emerging education research is also starting to map how generative AI intersects with self-regulated learning—highlighting both risks (over-reliance, reduced monitoring) and opportunities (scaffolds for planning, reflection, and feedback) depending on design and pedagogy. And survey-based findings have reported associations between ChatGPT use and procrastination or lower performance in some student samples, suggesting that without strong norms and supports, AI can drift from scaffold to shortcut. 

Then there’s an additional twist: changing expectations. Once AI is available, teachers and workplaces may (implicitly or explicitly) expect faster output. But speed is not the same as depth. Many breakthroughs require a long dwell time. If we compress the timeline before students have built the inner muscles of persistence, we don’t get high performers.

The Way Out

In many AI discussions, “human in the loop” sounds reassuring: the human checks the AI’s work. But in education, that framing can be backward. It puts students in the role of evaluator rather than constructor, as if learning were mainly about spotting mistakes in someone else’s thinking.

Decades of learning science tell us that durable learning is constructive and interactive. The ICAP framework, for example, shows that learning activities that are Interactive and Constructive generally outperform merely Active or Passive engagement. Students learn more when they generate, explain, debate, and build meaning, rather than just consume or lightly manipulate information.

In education, we need a “Learning-First” model that prioritizes human cognition before algorithmic assistance. A sample 5-stage framework could look like this:

Phase 1 (Individual): students write an initial thesis, solution path, or set of ideas before using AI. This protects original cognition and forces retrieval, sense-making, and ownership.

Phase 2 (Group): students critique and build together. This is where misconceptions surface and learning becomes social where students learn from each other.

Phase 3 (AI): only then does AI enter as a gap-finder, alternative perspective generator, or a Socratic questioner. It reveals elements that students might have missed and stretches their thinking.

Phase 4 (Group): the group revises their solution based on the feedback from AI, synthesizing aspects that are reasonable and rejecting those that don’t fit well.

Phase 5 (Individual): individuals reconstruct the argument/solution in their own words because self-explanation is a reliable way to accelerate understanding.

This scaffolded approach protects each ring of Renzulli’s model. Ability is built through retrieval and reconstruction. Creativity is protected through first-thought originality and peer divergence. Task commitment is strengthened through social support and reflection.

Conclusion

Education has a choice to make. Without creating the right guardrails on how to use AI, we risk teaching the  models instead of students. 

The danger of a default “human-in-the-loop” stance in classrooms is that it casts students as editors of machine work. But learning isn’t editorial. Students must build mental models, connect ideas, and develop the internal fluency that only comes from doing the cognitive work themselves.

So the guiding question for AI in education should be, “Which phase of learning does this tool strengthen and which phase might it accidentally replace?”

If we stay student-first and learning-first, we’ll use AI the way every great teacher uses support: not to remove the mountain, but to help students become the kind of climbers who can scale it, long after the tool is gone.

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From Kitchen To Code: Lessons in Radical Innovation from El Bulli

When patrons were seated at El Bulli, during its prime, the first thing they would get was an olive. 

Or, at least, what looked like one. They would pick up the “olive” resting in a spoon and bite gently, only for it to collapse into a warm, intensely flavored liquid that instantly flooded the palate and then disappeared.

That small bite of the now legendary spherical olive was much more than a novelty. It was the successful outcome of countless experiments using a technique called spherification, which turns olive juice into delicate spheres using alginate and calcium salts. The El Bulli team had to tackle real-world challenges to create it: How do you make a membrane thin enough to melt in your mouth, but strong enough to survive being plated? How do you make the process reliable so you can repeat it hundreds of times a night? And how do you make sure every guest has the exact same moment of surprise with that very first bite?

It is a case study in radical innovation.

Everything the tech world struggles with—finding the sweet spot between creativity and discipline, quickly moving from idea to experiment, collaborating across different fields, and building teams focused on growth rather than just resumes—was being figured out in this remote kitchen on Spain’s Costa Brava.

So, here are four key lessons from El Bulli’s kitchen that translate well to today’s product and innovation teams.

1. Creativity is the core system, not a side project

El Bulli did something revolutionary: it closed for half the year, from roughly October to March. This allowed Ferran and Albert Adrià and their core team to focus entirely on creativity. They spent those months inventing new techniques, testing fresh ideas, and developing the next season’s tasting menu, which often featured 30 or more incredible courses.

By the time the restaurant closed its doors for good in 2011, the team had created about 1,800 dishes and pioneered game-changing techniques like spherification, foams, and warm gels that reshaped high-end cooking worldwide.

A few things stand out:

  • Dedicated time: Creativity wasn’t an afterthought, squeezed into weekends or the “10% time” left after service. Half the year was deliberately reserved for exploration.
  • Permission to break rules: Inside that creative window, the brief was to question everything. Dishes could be deconstructed, recomposed, or turned inside out. Tradition was a reference point, not a constraint. 
  • Discipline in service of magic: For all the experimentation, the final measure of success was simple: did it create a magical experience for the guest? El Bulli eliminated the à la carte menu so that every guest received a carefully choreographed tasting sequence, built from scratch each season around these new creations. 

This raises some important questions for tech companies: Is creativity truly built into your structure, or is it just something people are expected to do after the “real work” is done? How do you maintain a high standard that encourages teams to experiment but still ensures a compelling user experience at the end of the day?

2. Start from first principles: “What is a tomato?”

Ferran Adrià is famous not only for his bold dishes, but for the questions behind them. Again and again, he and his team would come back to deceptively simple prompts: What is a tomato? What is soup? What is a salad?

In interviews about his work, Adrià often challenges common assumptions. For example, he points out that the original “natural tomato” in the Andes was actually inedible. What we enjoy as a tomato today is the result of human intervention through breeding, selection, cultivation.  In other words, even the most ordinary ingredient is already a designed product. 

This is what first-principles thinking looks like in action. Instead of just the category of “tomato” as fixed, he breaks it down:

  • Where does it come from?
  • What is its essence—its acidity, sweetness, aroma, and texture?
  • What aspects should stay unchanged, and which parts are negotiable?

This intellectual groundwork is what powered the deconstructionist cuisine that made El Bulli famous: taking a familiar dish, radically changing its form, texture, or temperature, but making sure its underlying essence remains intact.

In the tech world, we often talk about first principles, but in practice we work from mental templates: “It’s a CRM, so it needs to look like a sales funnel; it’s a learning platform, so it has to have modules and quizzes.”

The El Bulli approach would sound more like this for a product team:

  • What is a meeting? Is it really just a slot on a calendar, or is it a ritual for making decisions that could take on a completely different shape?
  • What is a classroom? Is it defined by a physical room and a timetable, or is it actually about a set of relationships and feedback loops that could be designed differently?

For Adrià’s team, these questions weren’t theoretical. They were directly linked to real kitchen experiments. When they clarified the true essence of a dish, it gave them permission to change everything else about it.

Product teams can adopt this powerful discipline, too: clearly define the non-negotiable essence of the user problem or the desired outcome. Once you have that clarity, you’re free to completely rethink the structure, the interface, or even the business model.

3. Keep the idea-to-experiment loop radically short

One of the most revealing aspects of El Bulli’s creative culture is not on the plate, but on paper.

Museums such as The Drawing Center in New York have mounted exhibitions called Ferran Adrià: Notes on Creativity, displaying his sketches, diagrams and visual maps. These sketches offer a glimpse of how he thought and emphasize drawing as a tool for thinking. It helped externalise ideas quickly, organise knowledge, and communicate concepts to the team. 

The creative process started with a rough sketch that captured the initial thought. That sketch immediately leads to a simple kitchen prototype which the team evaluates and iterates till the idea is perfected. 

What you do not see are lengthy slide decks, layers of approval, or months spent debating concepts before anyone picks up a pan. Instead, the kitchen becomes the thinking environment. The sketches move an idea from an internal hunch to a shared experiment quickly, not to impress anyone in a meeting. 

When we’re building products, we often do things backward. We spend weeks making perfect presentations about an idea before a single user ever sees it. As a result, we invest a lot of time justifying something that hasn’t been tested in the real world.

What if we took inspiration from the El Bulli approach to ask:

  • Could you sketch out a major idea in less than five minutes?
  • Could you build the very first version of a new concept in a day and get it in front of a real user within a week?
  • Could we simplify the number of sign-offs needed to run a small experiment?

This isn’t about being reckless. At El Bulli, the final menu was obsessively refined. But the path from idea to first test was deliberately short. And autonomy was real: talented people were trusted to try things without seeking permission for every iteration.

When teams make the idea → experiment → learning loop shorter, they tap into creative energy that can turn a crazy idea about a “spherical olive” into a world-famous dish.

4. Treat innovation as a team sport across disciplines

El Bulli’s breakthroughs were not only the work of a couple of geniuses.  Every dish came to life thanks to a whole team of experts: chefs, pastry specialists, food scientists, industrial designers, and even the folks running the front of the house.

Albert Adrià’s own journey illustrates this. He joined El Bulli as a teenager in 1985, spending his first two years rotating through all the stations in the kitchen before focusing on pastry. Over time he became head pastry chef and then director of elBullitaller, the Barcelona-based creative workshop that served as the restaurant’s R&D lab during the closed season. 

In interviews and profiles, he always emphasised that it was the team, not individual brilliance, that made El Bulli exceptional. The creative work depended on people who were:

  • Deeply curious and willing to learn fast.
  • Comfortable collaborating across roles rather than jealously guarding territory.
  • Motivated by the shared goal of creating something extraordinary for the guest, rather than building personal fame. 

The spherical olive itself was a multidisciplinary artefact. It required understanding the chemistry of alginate and calcium, mastery of textures and temperatures, and careful design of the serving ritual so that each guest ate it in a single bite at the right moment. 

For business leaders, there are two intertwined lessons here.

Multidisciplinary structures

Radical ideas often sit at the intersection of fields yet many organisations still arrange teams in narrow silos.

El Bulli suggests a different model for breakthrough ideas: Instead of keeping teams separated in silos, bring diverse perspectives together. In practice it would mean getting designers, engineers, data analysts, and subject-matter experts collaborating side-by-side in small, cross-functional “innovation pods.” This way, everyone can see and shape the idea from the very start, using shared visual tools like maps and sketches. It’s about co-creating, not just passing a task down a line.

Hiring for growth mindset, not just pedigree

Albert did not arrive at El Bulli with great credentials. He came as a 16-year-old apprentice and grew into one of the most influential creative forces in modern pastry, precisely because he was willing to experiment relentlessly and learn from others. 

Translating that mindset into hiring means asking:

  • Does this person show evidence of rapid learning across domains, or only depth in one?
  • Do they light up when they talk about collaboration, or only when they describe solo achievements?
  • Are they comfortable with ambiguity and experimentation, or do they need everything defined upfront?

In a world where the most interesting problems are inherently multidisciplinary like climate tech, future of learning, human–AI collaboration, it is often more valuable to hire people who can grow into the unknown than those who perfectly match yesterday’s job description.

Bringing El Bulli’s lessons into your organisation

For all its mystique, El Bulli was, at heart, a working laboratory. It dealt with constraints familiar to any leader: limited time, finite resources, high expectations, and the pressure to keep surprising a demanding audience.

Its response was not to work harder in the same way, but to redesign the system around creativity:

  • Carve out time for exploration.
  • Ask first-principles questions again and again.
  • Move ideas quickly from conception to experiment.
  • Allow multi-disciplinary teams to work closely.
  • Hire people for their capacity to learn and collaborate.

These principles apply just as well to innovative companies. When you do so, you begin to treat creativity not as a garnish, but as the main ingredient—tempered by discipline, grounded in first principles, and always aimed at giving the people you serve a truly memorable experience.

Image credit: The Drawing Center