Introducing Substrate — An Open-source Framework for Human Understanding, Meaning, and Progress

.bh__table, .bh__table_header, .bh__table_cell { border: 1px solid #C0C0C0; } .bh__table_cell { padding: 5px; background-color: #FFFFFF; } .bh__table_cell p { color: #2D2D2D; font-family: 'Helvetica',Arial,sans-serif !important; overflow-wrap: break-word; } .bh__table_header { padding: 5px; background-color:#F1F1F1; } .bh__table_header p { color: #2A2A2A; font-family:'Trebuchet MS','Lucida Grande',Tahoma,sans-serif !important; overflow-wrap: break-word; }Table of Contents

Introduction

Substrate Components

Component Examples

An Argument

A Source

Real-world Use Cases

Describing Yourself

Learning a Person’s Values

Visualize Any Argument

Substrate + AI = Action

SCIENCE: Automated Hypothesis to Results Workflows

LEADERSHIP: Describing A Comprehensive Plan

PRODUCTIVITY: Optimizing Any Process

Summary

How to Get Involved

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.

Ok, what is it exactly?

What the hell does that mean?

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

Interesting. What kinds of things?

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:

Ideas — A list of novel human ideas

Problems — A list of our most important human problems

Beliefs — A list of beliefs about the world

Models — A list of models for conceptualizing reality

Frames — A list of narratives/lenses for perceiving reality

Solutions — A list of potential solutions to our problems

Information Sources — A list of sources of data and information

People — A list of humans

Organizations — A list of organizations

Laws — A list of laws that were proposed and/or passed

Claims — A list of truth claims

Votes — A list of votes and results from legislation/elections

Arguments — A list of arguments that have been made

Funding Sources — A list of groups that fund various projects

Lobbyists — A list of lobbyists and their agendas

Missions — A list of human ideas

Donations — A list of donations made from X to Y

Goals — A list of potential human goals

Facts — A list of verified truth claims

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:

Problem Name

Problem ID

Problem Description

Toxic Drinking Water in Poor US Towns

PR-1097

Many towns with populations with low socioeconomic status have water that’s not safe to drink.

Deforestation of Our Rain Tropical Rain Forests

PR-33082

Our rainforests are being destroyed, which will negatively affect humans on Earth.

GitHub - Substrate/Problems: The Problems people consider worth working on.

A collection of the problems people feel need to be tackled.

github.com/human-substrate/Problems

And all of these live within an over-arching Substrate Organization within Github.

Substrate

An Open-source Framework for Human Understanding, Meaning, and Progress

github.com/human-substrate

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.

Ok, I think I’m starting to get it, but I need more.

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:

A MermaidJS Visualization of this claim (Using Sonnet 3.5) Click for full size.

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:

Argument Name

Argument ID

Argument Description

Recycling Plastic Isn’t Worth The Effort in the US

AR-28445

It’d be good to recycle plastic if it were actually worth the effort, but current systems are so inefficient that they cost more energy than they save.

A Source

Examples of Organizational Sources

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!

An example of Argument → Claims → Sources

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.

OLD: “You’re just not able to counter all my arguments and evidence.”

NEW: “Here’s my argument (throws it up on a shared viewscreen). Show me which claim you disagree with, or which source you disagree with that backs up those claims.”

This will enable far more logical and precise discussions!

Real-world Use Cases

Ok, sounds really cool. But what do you actually do with it?

Intrigued but practical

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

Visualizing Your Being Using Substrate

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.

And if you share your context or Substrate representation with others, they’ll be able to see what you’re about as well.

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.

A Visual Conversation Starter

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

They believe the most important Problems are PR-1097, PR-2210, and PR-2231

They believe the best Solutions are SL-1128, SL-3110, and SL-1012 to those Problems.

They intend to track progress using the following KPIs.

Imagine matching up with someone like that across multiple axes:

Values

Goals

Beliefs

Preferences

Etc.

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.

Click for Full Size

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

They’re making the following Arguments that SL-19992 and SL-44091 are the best Solutions: AR-7781, AR-9812, and AR-9992.

Which include the following Claims: CL-1111, CL-2309, and CL-0002.

Which we fact-checked using the following Sources.

Which resulted in the following Results (Claim = False / True).

Which—using the following methodology—leads us to this Conclusion.

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

Yeah, yeah, yeah. AI this—AI that.

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:

Context sizes (prompt sizes) are increasing

Inference costs (the cost to run AI) are plummeting

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:

It’s hard to come up with ideas.

It’s hard to design experiments.

It’s hard to find funding to do experiments.

It’s hard to interpret results.

It’s hard to publish results.

It’s hard to get the results in front of the right people.

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!

Coming up with—or collecting—ideas and hypotheses

Designing experiments

Collecting and evaluating the best funding sources

Requesting funding by writing a perfect pitch

Helping set up the experiments (eventually with robotic help as well)

Running and monitoring the experiments

Interpreting results

Writing the paper

Sharing the paper

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

A visual representation of a political platform (Click for Full Size)

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:

Here’s what I think the Problems are

Here’s what I think the Solutions are

Here are my proposed Strategies for accomplishing that

Here are the KPIs we’ll use to track progress

Fire me if I don’t get the KPIs to _________ by ___________ date.

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.

From Companies Are Just Graphs of Algorithms

In a recent piece, I talked about how Companies Are Just Graphs of Algorithms. True, but I don’t think I went far enough with it.

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

State of things

Action / Event

Result = New State of Things

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:

People

Decisions

Strategies

Lessons-learned

Conclusions

Reasons

Etc.

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:

A Company Process Flow (Click for Full Screen)

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.

Should this company merge departments?

Where can we add more people?

Which processes here are inefficient?

Which can be replaced by AI?

Where could we use more human decision-making?

If we wanted to grow, where should that happen?

Now imagine this for:

A family

A corporation

A church

A city

A county

Etc.

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

It currently takes 3.5 business days to complete a security assessment

“Delays in Security Assessment Turnaround” are the #1 complaint in the Engineering survey

If we switch to the new FlexScan model using fewer generalist security testers, we’ll be able to complete Type B and C assessments 94% faster.

This will give our senior testers 2 extra days to do high-impact assessments

This will also likely make Engineering much happier with Security, and make them more likely to cooperate on our goals.

So this is really multiple steps here:

The full articulation and breakdown of how a process is currently running

Visualization of that process to help with human understanding

AI analysis of how to optimize the process to optimize the stated goals of the entity

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:

Hiring new people

Hiring people with certain skills

Using more AI in high volume and low creativity areas

Adjusting strategies based on goals and market conditions

Cancelling projects X and Y to work on Z instead because it’s more aligned with the goals

Etc.

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.

The world is hard to understand, and things that are hard to understand are hard to discuss and improve.

The goal of Substrate is to address this problem by making the things humans care about more visible, discussable, and improvable.

The framework is open-source and lives on GitHub.

At its core, it’s a collection of crowdsourced lists of the things humans care about, and that make up our discourse and society.

One major problem that people and organizations have is not knowing —and/or being able to communicate—what they are about.

Using the framework, people and organizations will be able to articulate their values and purpose more clearly, which will help not only them but everyone they interact with.

Substrate is magnified by AI because AI can—or will soon be able to—hold all of Substrate in its mind at once.

From there, we’ll be able to ask all sorts of meaningful questions, such as, “What is that person or organization about?”, “Are we pursuing the best path towards our goals?”, or, “What are the most critical mistakes I’m currently making?”

Ultimately this will allow us to take action on these things., e.g., “What actions should I take right now to optimize this workflow?”, or, “What should I do right now to achieve the best possible outcome that’s aligned with my goals?”

In short, Substrate is a way to better understand and optimize the things we care about as humans.

How to Get Involved

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

Jonathan Dunn — MD, Hacker

Jason Haddix — Cybersecurity Expert, Hacker, Trainer

Clint Gibler — Cybersecurity Expert, Hacker

Joseph Thacker — Cybersecurity Expert, Hacker

Joel Parish — Cybersecurity Expert, Hacker

Robert Hansen — Cybersecurity Expert, Hacker

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

Email

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

Powered by beehiiv