---
title: Thinking Cap RD
description: Day to day, Thinking Cap delivers what our customers expect from an LMS.
created: 2026-09-10
updated: 2026-09-21
authors: ThinkingCap R&D
topics: []
status: published
canonical: https://console.thinkingcap.com/rd/Thinking-Cap-RD
date: 2026-09-10
---

###### We build an LMS. We ponder what comes after it.

Day to day, Thinking Cap delivers what our customers expect from an LMS.

Courses work. Learners learn. Administrators administer. Reports report. We make the platform faster, more reliable and more capable.

But after more than twenty years of building learning technology, we can't help looking a little harder at what we're actually doing.

**At the first scale, the LMS is sharp.**

Courses, learners, assessments, completions, skills. You can see the objects and build software around them.

Then you look harder and everything goes blurry.

*What exactly is a skill?*

*What does “learned” mean?*

*What does an assessment establish?*

*Why did one person learn and another not?*

*What constitutes evidence that someone can actually do something?*

Suddenly the familiar objects stop having crisp edges.

**But if you keep going, a new level begins to snap into focus.**

Now you can see things like capabilities, observable behaviour, uncertainty, interventions, evidence, plasticity, transfer, problem-solving strategies and behavioural change.

Those become the sharp objects.

And when you turn around and look back at the original LMS world from this new resolution, it starts to look blurry.

*“They completed the course and passed the test.”*

Okay…

What does that actually tell us?

Not nearly as much as it used to seem to.

We've come to recognize a recurring pattern:

**sharp model → anomaly → blur → deeper investigation → new primitives → new sharp model**

We don't think the blurry middle is something to be frightened of. It may simply be the sign that we've reached the explanatory limit of the abstraction we were using.

The danger is stopping in the blur and building an enormous pile of complexity around things we don't yet understand.

**So we keep looking.**

We keep delivering the LMS our customers depend on today while asking much harder questions about what learning technology should actually be able to do tomorrow.

###### The model is not the moat.

We don't think we're going to figure all of this out ourselves.

More importantly, we don't want to.

There is no part of what we're building that we would not happily license to someone else in this industry.

And there is no part of what we're trying to build that we wouldn't happily license from someone else if they've already solved it well.

Our list of things to figure out is enormous. We have no particular desire to make it longer by rebuilding something simply because someone else's name is on it.

If we develop a better way to describe skills, we'll tell you.

If we find a better way to determine whether somebody can actually do something, we'll tell you.

If we learn something about how people solve problems, how different interventions change behaviour, how learning transfers into the real world, or how AI can create better learning experiences, we'll tell you.

And we intend to commercialize every useful thing we discover.

We hope you do too.

If we solve a piece first and it can make your LMS, AI, assessment system or learning experience better, we'd like to sell it to you.

If you solve a piece first and it can make ours better, we'd like to buy it from you.

We'll both charge each other.

That's fine.

We're not trying to own every piece. We're trying to get all the pieces solved.

Build products from what we learn. Build services around it. Put it in your LMS. Use it in your AI. Turn it into games. Teach with it. Test it. Challenge it. Improve it.

Find something we've missed and build a company around it.

Your success doesn't require our failure, and ours doesn't require yours.

There is simply too much here for any one company to figure out.

**Help us find out.**

###### We also need data.

Lots of it.

There are questions about learning and problem solving that we don't think can be answered by sitting around a table and thinking harder. At some point, you have to watch what real people actually do.

So we're building small experiments.

Some might take five minutes.

We might give you a problem and watch how you approach it.

*What do you investigate first?*

*What do you ignore?*

*When do you change direction?*

*When do you keep digging?*

*When do you decide you know enough?*

*What happens when the evidence doesn’t support an answer at all?*

And when it's over, we may ask you what we got wrong.

*Was the problem fair?*

*Did something accidentally give you a hint?*

*Was there something you wanted to investigate but couldn’t?*

*Did we misunderstand something you did?*

*What should we have measured that we didn’t?*

You may see ten things we've missed.

**That's the point.**

We're not asking people to participate because we've already figured this out.

We're asking because we haven't.

Our hypotheses aren't secret. Our model isn't a secret. The problems we're trying to solve aren't secret.

We'll tell anyone who will listen what we currently think the problem of learning is, how we've broken it apart, what we're trying in each part, what seems to be working, what has failed, and what remains blurry.

If you think we're wrong, we'd like to know.

If you can help us find out, we'd like the help.

If you've already solved something on our list, we'd really like to hear from you.

###### Keep looking.

Our objective isn't to make the blur more complicated.

**It's to keep looking until things become sharp again.**

Then we'll probably look closer and make them blurry all over again.

That's okay.

**That's the journey.**

If you think it's a journey you'd like to join us on, **we should talk.**