The origin story of Kinnu is a bit… unconventional. It was inspired from a scene in the Matrix where Neo has his brain plugged into a computer to ‘learn kung fu’ (one of the coolest scenes in any movie, ever).
While this is currently impossible (and maybe not even desirable for many kinds of learning!) it got us thinking: what would be the closest thing to the Matrix, given everything we know today about the science of learning? Surely the way we learn today can’t be the best possible way… it just can’t be.
After rabbitholing the space and auditing the current state of the art, we can resolutely say that it most definitely is not.
To understand the thinking better, we’ve developed a simple framework: to get information into your brain, you have to address content, access, and learning product, though most businesses typically concentrate on one. In a nutshell, at Kinnu we’re building the ultimate learning product, rethinking everything ground up from first principles.
Content (the what) is obvious – it’s the stuff you learn. In the early days of internet-enabled learning offerings, this was the asset. In today’s world, we think content is largely ‘solved’ – there’s an infinite amount of information on any topic out there, largely for free. AI will further solve this through intelligent summarisation, customisation, and will be able to turn any content into a learning-optimised format, and seamlessly render it across video, audio, or text, essentially commoditizing the content.
Access (the who) is how to get content to learners across various media and geographies. Today this is still very much a core asset and where the bulk of market cap in learning products is represented. Many of the largest edtech companies in the world today became big through increasing access. For instance, Coursera with access to university courses, or Byju’s through access to tutors.
Learning Product (the how), is the last big piece of the puzzle: we are using Learning Product as an umbrella term encompassing all technical assets to encourage learning and actually wire stuff into your brain. Once you’ve solved for content and access, this is the last remaining frontier. There will be no complete solution for intrinsic human motivation, but the upper bounds for what is learnable and in what timeframe, is a combination of intrinsic motivation and the extrinsic limitations imposed by product.
This is what we’re really interested in at Kinnu and where we think the most upside is in the future of learning technology. We believe that to truly elevate the intellectual capital of the world, more accessible information alone is not enough; you need to increase the rate at which it can be learned.
In brief: a gamified learning engine.
Unbundling this, ‘gamified’ here means tweaking the intrinsic motivation bit we referred to earlier. It’s product design that factors in the quirks, biases, and oddities of human behaviour to shift the intrinsic motivation curve towards increased learning:
We’ll never be able to get someone who doesn’t care about physics to learn it anymore than we’d be able to get Planck to not care, but we believe we can shift the curve and get some people over the fence. Even a small shift in this curve will have a profound impact on the number of people who will learn new things. Much as the focus of many tight political campaigns is on swing voters, tipping the scale with swing learners will make the world a smarter, better place.
Unlike previous attempts that have merely paid lip service to ‘gamification’ and thoughtlessly dipped the broccoli in chocolate, this is fundamental to how we think.
At Kinnu we will shamelessly and unreservedly exploit everything we know about behavioural design, to make you learn more instead of deathscrolling social media or clicking on ads. And unlike other companies, we pledge to make that explicit in our terms and conditions of usage: we will give you the power to outsmart yourself.
The goal of this piece is not to describe details and implementation, but share how we think conceptually as that shapes what we will build.
The ‘learning engine,’ which we’ve lovingly dubbed ‘Maia’ after the shared name of two of our cofounders’ daughters, is the brain of Kinnu. Maia has one objective: reduce your TTM or ‘Time To Mastery’ – a self-explanatory figure of merit we’ve developed at Kinnu to gauge engine performance, like how a speedometer gauges the speed of a car. Maia removes the excess cognitive overheard (that we’ve sadly just come to accept as a necessary cost of learning), by predicting when you’ll forget things, adapting content by attention level, and a host of other, largely proven effects, that have shockingly been neglected in learning product design to date.
We’re at that exciting, youthful stage in the life of a company where everything is possible. We strongly believe, to the quark level of every neuron in our brains, that thinking about learning from a product-first view will change the way billions of people learn. That said, we’re experienced founders and know that the journey will be anything but easy, and we’ll be wrong about many things. If we are right about the future, and execute well, here are some of the ways we think our product-first approach at Kinnu can go:
Atomic credentialing without examination
An exciting benefit of brain-optimised learning is that it provides discrete data points of mastery, where the act of learning bakes in proof of understanding. We think that in the future, this could replace traditional, one-size-fits-all learning credentials with more granular atomic credentials, which would allow more flexible and adaptive skills development; a boon for employers and employees.
A virtual learning micro-economy
Our choice to depict knowledge geospatially on a map will also allow learners to meet and interact with other people in a micro-economy. The social serendipity this would create would be an online analog of what happens offline in traditional learning environments, only at a global scale, and create the possibility for new kinds of value exchange, like real-time ‘micro-tutoring’.
AI content and interaction
Even before it can create content de novo, we think AI will have some interesting applications, such as ‘translating’ content into the most learnable format, using, for instance, the atomic credentials above as an input. We’re also interested in new dynamic interaction types – maybe in the future, you could ask Morpheus to explain what NFTs are or converse with Einstein about general relativity, and they’d explain it in a way that was optimised for learning and maximum salience for each individual learner.
Suffice to say, rethinking learning as a product-first problem changes the landscape of the possible. It really feels like we’re embarking on a journey to a fantastic unknown. As a lover of learning, a curious reader, an investor, or a team member – thank you for going on that journey with us.
– The Founders of Kinnu: Chris, Abraham, and Hanna