Table of Contents

Introduction

I’m excited to share a project I’ve been working on for a number of months called Substrate. Fair warning: it’s quite ambitious.

Yep, fantastic question. The purpose of the project is to make the things that matter to humans more transparent, discussable, and ultimately—fixable.

Yes, exactly. Here are some of the main ones we’re starting with.

Substrate Components

When we say “human understanding, meaning, and progress” in the description, we’re talking about these types of conceptual objects:

Each of these will be an actual list, maintained as a repository within Github. Each list will have a schema, similar to this one for the Problems repository:

This structure will allow the entire open-source community (i.e., the world) to contribute their own Problems, Claims, Sources, Frames, Goals, etc., that others can use.

Fair enough.

One way to think about this is as a way to put handles on things that are hard to discuss.

Here are a couple of examples.

Component Examples

Here are some more examples of Substrate Components in everyday scenarios. Let’s look at an Argument component.

An Argument

Think of a common argument we might hear on any given day about whatever topic. This one is about recycling.

We’re confronted by this type of thing constantly. About things like recycling, but also about things that matter much more, like politics.

What Substrate will do is take an argument like this recycling example, and turn it into something like this:

Each of those objects in that diagram will be Substrate Components! The Claims, the Sources, etc.

Here’s what the Arguments repository might look like:

A Source

When people make truth claims, it’s important that we be able to fact-check or research those claims to see their support. Substrate does this by maintaining a list of Sources that we may or may not trust for new information, such as an Organization, or a Person (both of which are also Substrate Components).

When someone makes an Argument, or a Claim within an Argument, it can be linked to Sources that people can choose to trust or not trust.

But either way, people can see the full argument and its support in one visual!

This is why we’re so excited about Substrate. It is going to make things that used to be murky and opaque into transparent objects that can be inspected, analyzed, and discussed.

This will enable far more logical and precise discussions!

Real-world Use Cases

Yes, so now we’re getting to the best part—how to actually use this thing!

First, keep in mind that this is very early. We’re just getting started. But we already have many use cases planned that we want to talk about below.

Also, keep in mind that some of these you can do starting immediately, some will take time, and many of them will get magnified significantly by AI.

Let’s take a look.

Describing Yourself

Many people have trouble describing who they are and what they’re about.

With Substrate you’ll soon be able to just describe yourself in text, audio, or video, or even have a conversation with an AI—and it will be able to both articulate and visualize you.

Learning a Person’s Values

Substrate will be a wonderful way to start learning about someone, e.g., what they care about, and how they see the world.

Imagine having something like this available when you look at someone, or research them.

This will be a wonderful way to learn about what someone really cares about, and how they see the world.

Imagine matching up with someone like that across multiple axes:

We’re very excited about the potential to spawn more human connection in this way.

Visualize Any Argument

Another great use will be when a given narrative, or rumor, or conspiracy theory is going viral. We’ll be able to use Substrate to analyze the Argument or Claim and publish the results.

Here’s an argument that we never went to the moon.

Using this kind of visualization, you’ll be able to see (for example) that:

Think Snopes, but as a graph that everyone can visually explore.

What’s amazing about this is that someone from any political background can now evaluate this with more transparency than has ever been possible. They can SEE the Arguments, the Claims, and the Sources that were used to validate them, etc. It’s all right there.

And, of course, people will be able to add all their favorite sources of ground truth, so they can make sure the Substrate visualization is trustworthy to them. At that point, the question just becomes which sources you trust, but you can then see how the logic and sources flow to the conclusion.

I think this has the potential to significantly strengthen our shared understanding of reality, and will allow us to disagree with each other in a far healthier way.

Here’s one for the claim that there’s a tiny teapot orbiting the sun.

These aren’t using Substrate yet, but they will be soon, making each component of the argument community-sourced and transparent.

Substrate + AI = Action

I hear you, but this is different. This isn’t about AI. It’s about human meaning and progress. AI is just a tool for helping that along.

Consider this about what you’ve heard so far about Substrate, and what’s simultaneously happening with AI:

What this means is we can Chocolate-Peanutbutter Substrate with AI’s ability to hold multiple things in its mind at once.

So we can feed AI with our Goals, KPIs, Risks, etc.—and have it help us untangle them and take action.

Here are some examples that we’re most excited about.

SCIENCE: Automated Hypothesis to Results Workflows

One big problem with science is that it takes so long. Look at the set of things that have to happen:

So now imagine we have our list of Problems, a list of Proposed Experiments, a list of Funding Sources, etc. They’re all there.

Now AI can help us do most every step in that chain—completely automated!

So in other words:

Hypothesis ➡️ Proposed Experiment ➡️ Look Up Funding Sources ➡️ Acquire Funding ➡️ Run Experiments ➡️ Publish Results ➡️ Make Progress

In the beginning, this will still require a lot of human help—especially at the idea and the running of the experiments phases. But over time AI will only become more useful in those areas, too.

We’re talking about accelerating science!

LEADERSHIP: Describing A Comprehensive Plan

Many leaders struggle with clarity. It’s hard to know what they think the issues are, what they specifically plan on doing, and how they plan to measure progress.

We see this with both business leaders and politicians.

So with Substrate, we intend to make it so that every leader will need to have a full, detailed plan that has the following components:

Imagine having that level of clarity and accountability for any leader trying to get a job, doing anything.

PRODUCTIVITY: Optimizing Any Process

Ok, I saved the best one for last. This is the one that I’m personally most excited about.

Everything can be conceptualized in this way—as a process.

And if we add human components in there, like peoples’ jobs, or making decisions—like we do for like running a business, or a country, or a family, we have additional pieces:

And what that results in is a way to tie this all together into much larger graphs. Graphs we can use to describe the operations of a Family, or a Company, or even a Country.

Here’s one for a small company:

That’s pretty cool that we can create that, but that’s not the full power of Substrate combined with AI.

The smarter AI gets, the better it will get at optimizing flows of any kind.

In other words, this is just the current state. We can now ask AI what it would do to optimize this.

Now imagine this for:

And keep in mind, the more data you have here the better. You can feed such a system all the various efficiency metrics for the various pieces as well.

A security team analysis example

So this is really multiple steps here:

And remember—the AI will also have access to the mission of the organization as well. And its goals. And its strategies. And its team members. And their projects. Etc.

So it will have the full context on how resources are being spent relative to the desired outcomes, and it will be able to see how the actual KPIs are moving.

From there it will be able to make all sorts of recommendations, such as:

Ultimately we’re talking about the ability to continuously analyze and optimize any system using full knowledge of its goals and progress.

And the more data about the system it has, the better it’ll perform. And the smarter AI gets, the better it’ll perform.

Insane.

Summary

Ok, that was a lot.

Here are the main points.

How to Get Involved

Here are some of my friends and colleagues who have already signed on to help with this project.

If you are interested in contributing, you can do so through the Github projects themselves, or you can connect with us directly.

Thanks for your interest, and please share this page with anyone else you think would like to contribute.