Activation Energy Mental Model

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Executive Summary Of Activation Energy

If you only have three minutes, this introductory section will get you up to speed on the activation energy mental model.

In “The Happiness Advantage” (THA review + notes), psychologist Shawn Achor recommends making undesirable behaviors harder and desirable behaviors easier. If you want to watch less TV, remove the batteries from your remote and store them tediously far away – maybe at the back of a cabinet that you have to get a step-stool to reach. Similarly, if you want to encourage healthy eating, many nutritionists recommend placing healthy snacks at eye-level and hiding (or not buying) junk food… thereby reducing the “activation energy” for eating healthy, and increasing it for eating junk. This works for kids and adults alike!

The concept in one sentence: “activation energy” in chemistry (also known as “static friction” in physics) refers to the fact that it takes a lot more energy to start something than to keep it going. Human behavior works much the same way.

Key takeaways/applications: small changes in the ease or difficulty of starting something can yield dramatic payoffs.

Two brief examples of the activation energy mental model:

Passivity is one of humanity’s greatest skills. Many business models take advantage of activation energy combined with status quo bias. Here’s a story that may be familiar to you: I once had an unused credit card with a modest annual fee; I intended to cancel it for two years but was always too lazy to do so because there was no online cancellation option. Every time I thought about canceling, I imagined the horrors of staying on hold for hours, or dealing with a pushy customer service rep.

Similarly, businesses that offer their customers easier payment options often see boosted sales: home-security giant ADT saw a meaningful decrease in subscriber attrition as it enrolled more of its customers in auto-pay options (vs. having to manually cut a check), while vending machine operators that install credit-card readers tend to see meaningful increases in customer transaction count and amount. (Note that this is also a function of salience / vividness bias.)

This explains why I can never find my socks. Shawn Achor’s approach to behavioral modification (mentioned in the caption) is mirrored by Japanese organizer Marie Kondo.

She posits on pages 141 – 142 of her interesting / occasionally kooky bestseller “The Life-Changing Magic of Tidying Up” that clutter is a result of focusing on the wrong activation energy:

“A common mistake many people make is to decide where to store things on the basis of where it’s easiest to take them out […] clutter is caused by a failure to return things to where they belong. Therefore, storage should reduce the effort needed to put things away, not the effort needed to get them out.”

If this sounds interesting/applicable in your life, keep reading for unexpected applications and a deeper understanding of how this interacts with other mental models in the latticework.

However, if this doesn’t sound like something you need to learn right now, no worries! There’s plenty of other content on Poor Ash’s Almanack that might suit your needs. Instead, consider checking out our learning journeys, our discussion of the names vs. meaning, association bias, or power laws mental model, or our reviews of great books like “The Making of the Atomic Bomb” , “The Vaccine Race” , or “The Frackers” .

A Deeper Look At The Activation Energy Mental Model:

“One study tested […] a simplified enrollment form. New employees were handed enrollment cards […] with a ‘yes’ box for joining the plan […] employees did not have to spend time choosing a savings rate and asset allocation; they could just check the “yes” box for participation.

As a result, participation rates during the first four months of employment jumped from 9 percent to 34 percent […] if you dig a channel for [people] to sliden>

That quote, from Cass Sunstein + Richard Thaler on page 112 of “Nudge” helps explain why I adore Nudge (and also Richard Thaler). Sunstein/Thaler, of course, didn’t invent the theory of activation energy, but they sure put it to good use.

They cite decades-old research by psychologist Kurt Lewin on “channel factors” (a version of the same thing.) A follow-up study on Lewin’s ideas by some other researchers yielded fascinating results: some Yale seniors were educated as to the benefits of a tetanus vaccination, and most were convinced enough to agree to go get it. However, only 3% of the control group actually ended up doing so.

Conversely, among students who were asked (at the end of the presentation) to decide on a specific time, and provided with a map with the health building circled (even though they already knew where it was), the vaccination rate jumped to 28% – nine times as high. On the same topic, Dr. Paul Offit’s “Deadly Choices” , wherein Offit notes the following interesting stat:

“Children in states with easy-to-obtain [vaccine] exemptions (granted by the simple signing of a form) were almost twice as likely to suffer outbreaks of whooping cough.”

The story of Nvidia’s development of CUDA software, as discussed in Tae Kim’s “The Nvidia Way,” is a perfect illustration of activation energy – Nvidia’s world dominance today is, in fact, at least partially due to activation energy.

In the early 2000s, GPUs had immense latent power, but accessing it required esoteric expertise. As Tae Kim explains, researchers had to “translate their real-world problems into functions” using obscure graphics languages: essentially, an enormous activation barrier that limited participation to a few highly technical specialists. Even Mark Harris, who coined the term “GPGPU” (general purpose use of GPUs), had to hack an Nvidia GeForce 3 just to simulate clouds. The technology existed, but the usability threshold kept it niche.

Nvidia recognized that the constraint wasn’t hardware; it was activation energy. The GPUs could already outperform CPUs, but the bottleneck was the ease of adoption. So they lowered that barrier by developing and releasing CUDA, a programming interface that extended the familiar C language. Suddenly, scientists and engineers didn’t have to learn an alien dialect of graphics code to harness the massive power of machine learning and parallel computing. They could just… start.

As Kim explains, researchers tried AMD’s competing cards but found them “immature and were not easily programmable” – in contrast, CUDA was a “much easier environment to work with.” This enabled scientists to run simulation software “fifty times faster” on hardware that was far more affordable than racks of servers or supercomputers:

“you could buy a $100 CPU and four $500 GeForce cards and have a workstation that was as powerful as a full rack of servers. It was a game changer.”

The broader lesson: most technologies don’t fail because they lack capability; they fail because they demand too much activation energy to use. Nvidia’s success with CUDA shows how making something easier converted a fringe tool into what is, today, a .

As with many mental models in the latticework, I don’t think the point is terribly difficult to grasp, so let’s move right along to some of the interactions, each of which are pretty short.

Activation Energy x Inversion x Memory = Structural Problem Solving + Margin of Safety

As discussed in the memory mental model, our memories are notoriously limited, leading us to make plenty of unintended mistakes. Most of the time these mistakes aren’t critical, but in some situations, such as a nuclear power plant, the cockpit of an airplane – they can be.

I never get tired of citing Don Norman’s “The Design of Everyday Things” – a masterful book in which Norman elucidates the topics of memory and “human-centered design” – or, what I call structural problem solving. Activation energy is one of the problem-solving techniques Norman invokes via inversion: by making it harder for us to forget things, “forcing functions” are an example of good design that transforms activation energy into a margin of safety, reducing the likelihood that we engage in an undesired behavior.

What is a forcing function? It’s an annoyance of sorts, an extra step or a constraint that forces you to do things in a certain way. Norman cites the ubiquitous “don’t you want to save your changes?” screen in Microsoft Office as an example. We’ve all seen this:

Spreadsheet copyrighted by Initech… or Initrode… oh, and: HipChat wins all the ad awards. All of them, I tell you.

Examples abound: putting a book in front of the door (with your car key underneath it) so you don’t forget it on your way out; putting important appointments into your calendar. All of these actions are “forcing functions” that meaningfully raise the “activation energy” of forgetting – you can still forget to save your work, but now you have to try!

In higher-stakes situations, the stress of an equipment failure on a plane or a high-stakes surgery can cause even the most talented, highly trained operators to forget things – Laurence Gonzales provides an excellent overview of the underlying neuroscience in “Deep Survival.”

Application/impact: tactically increasing the activation energy of forgetting, and decreasing the activation energy of remembering, can mitigate biological, structurally persistent shortages of working memory.

Activation Energy x Selective Perception

Gawande’s “The Checklist Manifesto” is a thoughtful and nuanced book that is too-often oversimplified and overinterpreted (I was guilty of this the first go around, frankly.) Checklists are a specific solution to a specific problem, and the book also touches on issues of “How Doctors Think” .

Many PAA readers may be familiar with the concept of schema, or selective perception: the idea that our brains are physically incapable of processing all the stimuli that the world throws at as, resulting in our utilization of pattern recognizing heuristics and selective focus to filter out irrelevant information.

The net result of this is some cognitive quirks like contrast bias (our tendency to focus on changes), but like any adaptive trait, these are neither inherently “good” or “bad” – they’re circumstance-dependent.

When it comes to activation energy, the upside of our non-omniscience is that we don’t even have to make things harder or easier to increase or reduce activation energy: we just have to make them look harder or easier. Sometimes it might be inconvenient or costly to make a “real” change, and much easier to make a “seeming” change (that doesn’t actually exist).

Don Norman provides an example of this on page 19 of The Design of Everyday Things, discussing some rubber poles that look like they’re metal, discouraging visitors from driving down a certain path but still easily allowing service/maintenance vehicles through.

Similarly, Sunstein/Thaler provide an example on pages 37 – 39 of Nudge of “fake” speedbumps on a dangerous curve of Lake Shore Drive in Chicago. Achor cites some similar research on loyalty cards on pages 117 – 120 of “Before Happiness.” If you give shoppers a “head start” on achieving loyalty rewards without any actual free points given away – for example, if you theoretically gave them an 8-stamp card with two free stars, rather than a 6-stamp card with no free stars – they’re happier.

We respond to the perceived difficulty of tasks, not the actual difficulty, so the activation energy required to sequentially tackle a set of small chunk is far less than the activation energy required to tackle a huge undertaking – even if the total effort is actually the same!

Application/Impact: Even if it’s impractical to actually change the activation energy, simply making it look like there’s a higher or lower effort required to achieve something can have big impacts.

Activation Energy x Habit

Finally, it bears noting that the ultimate way to reduce activation energy for any desired behavior is to make it a habit.

While my metaphor is not neuroscientifically accurate, habits can be visualized as literal paths. Recall that Sunstein/Thaler used the metaphor of a channel you slide down; I prefer the visualization of blazing a trail through a forest: it can be difficult at first, but once the trail’s there, you can’t help but follow it because it’s easiest to go down the beaten path rather than rin quite some detail, ranging from classics like Stephen Covey’s “The 7 Habits of Highly Effective People” to Duhigg’s “The Power of Habit” , but the idea goes all the way back to “The Autobiography of Benjamin Franklin” , in which Franklin describes his process of creating virtuous habits.

Application/impact: repetition reduces the activation energy of behaviors, so think carefully.

AI Angle: Activation Energy

AI is the ultimate remover of activation energy. Indeed, a big part of why this site was down for so long was the difficulty of getting started on getting it back up. You’re reading this thanks to AI solving that activation energy barrier. AI can turn “I should…” into “Done.”

If you have pesky, partially-used ingredients hanging around your the back of your fridge – or need to clear space in your pantry – just ask AI for a recipe incorporating those. At work, reusable prompts like Gemini Gems or ChatGPT Projects remove the friction of context-setting and copy-pasting. When you move beyond single prompts to AI workflow automation, activation energy drops to zero. Instead of you having to search for information and remember to do stuff, the information arrives automatically and much of the stuff is already “done” – reports delivered on schedule, leads enriched, summaries written, and social posts drafted the moment a new blog goes live. In fact, that last use case is one my own wife used so she wouldn’t forget to post on social media for her business – check out the demo video below.

In short, AI helps you spend less energy starting and more energy refining. Helpful guides and walkthroughs for setting up your own automation or reusable AI helpers are available at our sister site, Ravensight AI.