Why Mental Models Matter

As they leave school and embark on professional adventures, naive engineers believe their purpose is more or less summed up by this equation:

product = software = code

As they get deeper into their careers, good engineers gradually realize that the raw code baked into a product is not everything. They come to appreciate the role that support folks and tech writers, marketers and professional services play in delivering value to the customer. Eventually many arrive at :

product = (software = code) + augment

I’d put this equation into words as follows: the purpose of dev teams is to create products, which consist of software (a synonym for code) plus auxiliary offerings like support, documentation, and services.

Equations capture mental models… Image credit: xkcd

This is the level of sophistication at which much of the software industry operates. It is taught by academia (at least, if you listen to business professors), and it’s the philosophy that underpins lots of outsourcing decisions, as well as strategic mergers and acquisitions.

I think the second equation is better than the first, but it’s still woefully inadequate.

Easy Critiques

For one thing, it ignores the interrelationships among software, hardware, enabling ecosystems, and customer communities. Products don’t exist in isolation; they are part of an embedded system made possible (and relevant) by societal conventions and other technologies. “Microsoft Word” and “Adobe Photoshop” are not “products” for Kalahari bushmen.

For another, software is more than code. Notice the subtitle of my blog… Software includes people as a fundamental ingredient. In the shadows of every architecture diagram is an assumed human being (or an army of them), providing input or accepting output. How else do we think our systems will be installed, configured, optimized? How will our databases get populated, our backups get mounted, our e-books get typeset, or our web searches get chosen? (See my posts about people in architecture and role-playing in design.)

Both of those critiques are important, I think. But today I have a different bone to pick.

The Deeper Issue

Whenever we put “product” at the front of equations that describe our industry’s output, we make the implicit assumption that product is the major–or even the entire–output of tech companies. This assumption is ubiquitous and almost never articulated, let alone challenged. Ask a tech buddy about what his company does; he’ll say something like “We build products that ___.”

Of course, tech companies do build products–or solve customer problems by delivering products and services, if you want to make economists happy. But they also create another output, and I think this neglected stepchild deserves far more attention.

Besides products, tech companies produce and propagate mental models. Or in other words, they enable and shape our view of the world.

Photo credit: daveelf (Flickr)

These mental models of the world matter. They–not products–are the nuggets of gold for which we prospect. Ask Galileo.

How much of popular culture is built on scaffolding provided by an idea that used to exist only in the mind of an engineer? Engineers didn’t just dream up plasma TVs or radios; they enabled the very idea of broadcasting. They didn’t just figure out how to download files from the internet; they convinced us to think of data blobs in terms of files and folders in the first place. They didn’t just populate the App Store; they thought the concept of “app” into existence. I could go on and on with examples, but I’ll leave that as an exercise for the reader.

As I said in my post the other day about comments, the mental models created by engineers are the most valuable output of the tech industry.


Products are directly sellable, and we have to have them. But products without mental models are pretty darn useless. If you doubt me, try using a sophisticated piece of software without any idea how to think about its problem domain. If you know nothing about accounting, try to use Great Plains to be a bookkeeper. If you know nothing about graphics, try airbrushing an image in Photoshop. If you know nothing about HPC, try keeping Cray’s latest supercomputer busy doing protein folding.

Code is important, but without a mental model of how that code works, it’s not much of a foundation for a product. (This is why outsourcing that doesn’t involve bi-directional knowledge transfer is usually foolish, and why acquiring a company and RIFing all its employees nets the acquirer a lot less than they bargained for.)

Patents look nice in a war chest, but it’s sophisticated mental models, not patents, that are the prerequisite of innovation.


If you understand that tech companies produce mental models, then certain issues take on new significance.

Tech debt isn’t just insidious because it makes code ugly. A kludge lets us get by with a flawed, ill-developed mental model of a problem domain–and if we build on that model, eventually we create a house of cards. Bad mental models bite us, sooner or later.

Competition in a turbulent market is often decided by who has the better mental model. “Better” might mean the one closer to the predilections of the customer, or the one that has better long-term applicability.

Usability is all about conveying a mental model with minimum effort on the part of the receiver–and then using that model consistently and easily.

A product that doesn’t improve the mental model of the customer (e.g., by pruning unnecessary clutter, by visualizing connections that were previously impossible to see, by accounting for a neglected issue that’s been a thorn in the side) is not innovative, no matter which features it touts. It is providing little of value, and will end up on the dust heap of history.

Action Item

Take a minute to ponder how much of your passion and talent is actually centered on the “other” output from product development. What contribution have you made to a helpful mental model for a customer? Where have you invented a term that resonated, or formalized a process that used to be chaos? 

Why I don’t blog about “great code”

Last week I heard a Stanford researcher describe how failure can be a good thing, if we are prepared to learn from it.

I agree, although this mindset is easier to describe than to achieve. So here I’m kicking off a new series of posts about mistakes I’ve made over the years, and what I’ve learned from them. Look in the “Mistakes” category for more like this.

If you’ve followed my blog at all, you’ll know that I regularly return to the theme of what constitutes good code. Ever wonder why I don’t get more ambitious and talk about “great code” instead?

A big reason is that in software, great can be the enemy of good.

If you’re a fan of aphorisms, you’ve probably heard the opposite statement a few times: “good is the enemy of great.” People who say this are emphasizing the value of setting lofty goals, and then aligning our day-to-day lives to deeply held priorities. They remind us that settling for mediocrity is almost guaranteed not to create deep meaning or purpose. And they are quite right.

However, I submit that the greatness you should be pursuing in software is less about producing great code, and more about becoming a great producer of code. And great producers of code know that most of their creations will not optimize business value if they aim for a magnum opus. Not every commission can be the Sistine Chapel.

Detail from the Sistine Chapel, by Michaelangelo. Image credit: Wikimedia Commons.

Don’t get me wrong. I care about the quality and artistry of code, and there is definitely great code out there. It’s just that I’ve got these battle scars…

Daniel builds a tower

In 2001, I helped design and code Continue reading