All right. Welcome to unsupervised learning. This is Daniel Miessler. And today I'm super excited to announce a project I've been wanting to talk about for a very long time called substrate. Okay, let's get into the project itself. So what is it exactly? That is really the question. What substrate is is an open source framework for human understanding, meaning and progress. And you might be inclined to say, what the hell does that mean? And it's a great question. Right? So the purpose of the project is to make things that matter to humans more transparent. Discussable. And ultimately, because they're transparent and discussable, they'll be more fixable. So what kind of things are we talking about? So we're calling these substrate components. And these are the components of human meaning. Right. When we talk about understanding, meaning and progress, these are the pieces that we're actually talking about. So collections of things okay. So the first thing is an idea collections of ideas a list of human novel ideas, problems a list of our most important human problems, our beliefs, our models, which are our ways of conceptualizing reality, frames a list of narratives or lenses for perceiving reality. A list of solutions that correspond to problems. Information sources. So you'll have, like New York Times, The Hill, Breitbart, uh, lots of different sources of information, government sources, individual sources, different media organizations. It's got to be a comprehensive list, and it's got to span different political ideologies. That's really important here as we'll see later people. So this is just individuals, organizations of different types laws. We're going to collect all different legislation, starting with the US government, but expanding out to pretty much the world at some point claims. So this is a factual claim, a truth claim about the world votes, a list of votes and results from laws that were submitted and voted on to basically say, here's what the votes were from different people. So this is talking about representatives voting on things, right? Arguments a list of arguments that have been made in favor or against a particular thing. Funding sources, lobbyists. So a list of lobbyists in their agendas, missions, donations, goals and facts. So these are actually claims from up here, but these are just verified ones. And important to note that it could become true and then untrue again. So you have to kind of keep this updated. Now each of these in this list here, each of these will be an actual list maintained in a repository within GitHub. In fact, we already have one of these okay. So I'm going to click in here. And if we go into here and look at problems we already have a list of problems here okay. It's already starting. So we've got we've got this going and I'm just going to zoom in a whole bunch for this one. So we've got like ransomware attacks on US health care systems. Uh, teen depression in the UK, nuclear weapons development in North Korea. Each one of them has this PR code so you can associate directly to it. And that's basically the structure. And you can see the structure here as well. And again we have the link here to the GitHub repo that we just looked at. And they're all part of this substrate organization which is here. So if you go into the substrate organization you could see the the main flow here. And if you go into repositories you see that we've populated a whole bunch of these, and we've already got some pretty good activity on some of these especially problems. And keep in mind, this just came out like two days ago. So this is brand new okay. So let's keep going on with the explanation here. So the structure will allow the entire open source community which is basically the world to contribute their own problems, claims, sources, frames, goals, etc. for all those different repositories which are all the different substrate components. Okay, I think I'm starting to get it, but I need more. Fair enough. So one way to think about this is as a way to put handles onto things that are hard to discuss, right? The whole reason that I created this project is so that we can articulate things better to each other, but also to ourselves. Okay, so let's get into some actual examples here so you can see what I'm talking about. Let's start with an argument component okay. This is what this is going to look like. Think of a common argument we might hear on any given day about whatever topic. And in this case it's going to be recycling. So this is just somebody that you hear say something right. I don't know why you recycle, man. It's a total waste. It costs so much to recycle right now. And the programs are poorly run. So it's not actually benefiting the environment like I do it if it worked, but it doesn't. And this is some person watching you put a can in a recycling bin. So we're confronted by this type of argument constantly. We're being pitched these different things like oh I, I would definitely do this because of this boom, that's an argument. Or I would definitely stop doing that because of this. That's also an argument. So that's things like recycling, but it's also things that matter much more about politics or whatever. So what substrate does this is the most important thing, right. It takes an argument like this.

All right. Welcome to unsupervised learning. This is Daniel Miessler. And today I'm super excited to announce a project I've been wanting to talk about for a very long time called substrate. Okay, let's get into the project itself. So what is it exactly? That is really the question. What substrate is is an open source framework for human understanding, meaning and progress. And you might be inclined to say, what the hell does that mean? And it's a great question. Right? So the purpose of the project is to make things that matter to humans more transparent. Discussable. And ultimately, because they're transparent and discussable, they'll be more fixable. So what kind of things are we talking about? So we're calling these substrate components. And these are the components of human meaning. Right. When we talk about understanding, meaning and progress, these are the pieces that we're actually talking about. So collections of things okay. So the first thing is an idea collections of ideas a list of human novel ideas, problems a list of our most important human problems, our beliefs, our models, which are our ways of conceptualizing reality, frames a list of narratives or lenses for perceiving reality. A list of solutions that correspond to problems. Information sources. So you'll have, like New York Times, The Hill, Breitbart, uh, lots of different sources of information, government sources, individual sources, different media organizations. It's got to be a comprehensive list, and it's got to span different political ideologies. That's really important here as we'll see later people. So this is just individuals, organizations of different types laws. We're going to collect all different legislation, starting with the US government, but expanding out to pretty much the world at some point claims. So this is a factual claim, a truth claim about the world votes, a list of votes and results from laws that were submitted and voted on to basically say, here's what the votes were from different people. So this is talking about representatives voting on things, right? Arguments a list of arguments that have been made in favor or against a particular thing. Funding sources, lobbyists. So a list of lobbyists in their agendas, missions, donations, goals and facts. So these are actually claims from up here, but these are just verified ones. And important to note that it could become true and then untrue again. So you have to kind of keep this updated. Now each of these in this list here, each of these will be an actual list maintained in a repository within GitHub. In fact, we already have one of these okay. So I'm going to click in here. And if we go into here and look at problems we already have a list of problems here okay. It's already starting. So we've got we've got this going and I'm just going to zoom in a whole bunch for this one. So we've got like ransomware attacks on US health care systems. Uh, teen depression in the UK, nuclear weapons development in North Korea. Each one of them has this PR code so you can associate directly to it. And that's basically the structure. And you can see the structure here as well. And again we have the link here to the GitHub repo that we just looked at. And they're all part of this substrate organization which is here. So if you go into the substrate organization you could see the the main flow here. And if you go into repositories you see that we've populated a whole bunch of these, and we've already got some pretty good activity on some of these especially problems. And keep in mind, this just came out like two days ago. So this is brand new okay. So let's keep going on with the explanation here. So the structure will allow the entire open source community which is basically the world to contribute their own problems, claims, sources, frames, goals, etc. for all those different repositories which are all the different substrate components. Okay, I think I'm starting to get it, but I need more. Fair enough. So one way to think about this is as a way to put handles onto things that are hard to discuss, right? The whole reason that I created this project is so that we can articulate things better to each other, but also to ourselves. Okay, so let's get into some actual examples here so you can see what I'm talking about. Let's start with an argument component okay. This is what this is going to look like. Think of a common argument we might hear on any given day about whatever topic. And in this case it's going to be recycling. So this is just somebody that you hear say something right. I don't know why you recycle, man. It's a total waste. It costs so much to recycle right now. And the programs are poorly run. So it's not actually benefiting the environment like I do it if it worked, but it doesn't. And this is some person watching you put a can in a recycling bin. So we're confronted by this type of argument constantly. We're being pitched these different things like oh I, I would definitely do this because of this boom, that's an argument. Or I would definitely stop doing that because of this. That's also an argument. So that's things like recycling, but it's also things that matter much more about politics or whatever. So what substrate does this is the most important thing, right. It takes an argument like this.

And.

And.

Turns it into something like this. And I'm going to zoom in. It's probably going to be a little bit blurry, but watch this. This argument consists of these claims, okay, effort required cost of progress. So it breaks down the argument into these different components. Limited results okay. Then it breaks those into further things. Recycling programs require significant effort. Recycling programs are expensive. Most rejected materials end up in landfills. Okay. And then it goes and adds research to all of those. In this case, it's just pulled it directly out of the model. But you can actually add AI on top of this, which we're going to be doing with an agent framework, either that we build ourselves or that we use one of the third parties. And these will actually be tools doing this research down here. Okay. Going and doing separate lookups to find research. Like you could do this at really in-depth level for each stage here, especially this one where you're doing the research and then it comes back and says supports or supports or weakens. Look at this. Supports supports weakens weakens partially supports. Then we have a conclusion ends up being a weak argument. Interesting. So you could see the reasons this is the most important thing. You could see how this argument was formed, how it was researched using what sources. Okay, look at this. Uh, recycling statistics 2021 National Waste and Recycling Association 2022. So we actually have source names here, and we can go into even more depth if we do additional AI work on this. And that's kind of a foreshadowing for future section here. So let's just keep going. Again, what this does is it takes an argument like this recycling example, and it turns it into a graph like we just saw. Okay, the most important thing about this graph is that we could throw it up on a board. We could throw it on the side of a wall. We could each view it inside of our AR glasses or whatever in the in the near future. And now both of us who are having this discussion can be looking at the same thing and saying, uh, yeah, I agree with these two, but I don't agree with this one. And the reason I don't agree is because I don't trust the source that it came from. And then you could do things like, okay, let's grab some content from other sources. Boom. You start adding other sources to it and maybe it changes based on those sources. It changes the conclusion of that subpart, of that argument. And now the whole thing updates. And the conclusion is maybe, oh, we're not sure. Or maybe it switches it from yes to no or up to down or whatever. The point is transparency. Okay. Too many of our conversations about any topic, it becomes emotional because it's too difficult to keep all these things in our minds at once. Okay, a basic argument like it was just made here about recycling has multiple sub claims in it, and those individual sub claims need to be backed by data. And it's very hard to do that just in our brains when we're in the middle of a conversation, especially if you're trying to like blast this out. You want to have this conversation, you want to say, hey, recycling is good or recycling is bad, or whatever the topic is, but you try to put that on social media or somewhere. You're basically writing a text thing, right? Which is trying to convince someone, which is fine. We've been doing that forever. But imagine where we could do this graphically, where we could do this visually and show the connections of how strong an individual claim is based on how much you trust an individual source. Right. So that's the power of this thing. And this is why I'm so excited about it. And there's many, many more examples we're about to get into. So each of those objects in that diagram is another substrate component, right. The claims, the sources, they're all in there and they're all in there inside of another repository inside of the substrate project. So here's an example of a source. New York Times, Associated Press, Breitbart. When people make truth claims, it's important to be able to fact check or research those claims to see their support and substrate. Does this by maintaining a list of those sources that we may or may not trust. That's the point. Some people will trust different sources. Some people will not trust different sources. Right. And that's up to them. Then it just becomes a question of, okay, well, you should trust this source and that becomes an argument by itself. Right Now you can make an argument. Well, you're putting too much weight onto a particular source or set of sources, which let's look at a bunch of claims that they've made recently and let's separately research those. And maybe that argument is strong enough to make them discount that particular source, whether that's The New York Times or Breitbart or whatever it is. Right. So when somebody makes an argument or a claim within an argument, it can be linked directly to those sources that you do or do not trust. And you can see the full argument and all of its support. In one visual look at this claim. Inflation fell by 2% under this particular person's term, right? This argument includes this claim. This claim has this source, the AP, this claim has this source. New York Times. So this argument has a particular strength as a result of that. So argument to claims to sources. And this is why we're so excited about substrate. It's going to make things that used to be almost impossible to discuss actually approachable actually discussable, because we could break it down into its individual pieces. So before you'd be like, you're just not able to counter all my arguments and evidence because I'm too smart. And now you could say, look, here's my argument. Throw it up on the board. Maybe it's like a little bit in the future or, you know, Apple Vision Pro or whatever. And now it's actually a 3D thing. You could turn it around like a piece of DNA or something, right? Show me which claim you disagree with or which source you disagree with that backs up those claims. Right. So now we can just take this thing apart using different sources or whatever and change the output. This will enable far more logical and precise discussions that that is what we're going for. This is just one example. This is just the arguments example. So now I want to take and like back up a little bit and think about real world use cases for substrate. Overall okay. Sounds really cool. What do you actually do with it. Exactly. So this is my favorite. Keep in mind, this is very early. It literally came out like two days ago. But we've already got multiple use cases for this thing okay. So let's get into these okay. The first one here is describing yourself. This one is massive. Look at this. This is pretty cool art. This uh, from Midjourney. It's, uh, part of a design that I did for a previous piece. But anyway. Yeah. So you can see these are meant to represent the different connections of like, uh, goals to mission to problems that she thinks are most important in the world. Solutions. She thinks, uh, are the answers to those problems. Different strategies, different projects she's working on. That's what this, uh, AI art is meant to represent with this graph. Little function here. But then you can have labels assigned to that. And keep in mind this is me looking at her in this coffee shop through my AR glasses and seeing, okay, first of all, purple is associated with engineering. Boom. She's an engineer. She's known for being a friend. She's known for being a writer. So I'm now seeing this person as she is, which she has previously defined. So let's get into that. Many people have trouble describing who they are and what they are about. This is so critical with substrate, you can basically describe yourself in any way you want to write text, audio, video, whatever, even have a conversation with AI, which is not part of substrate yet. But uh, there's lots of different ways to do that, and it will be able to both articulate and visualize you as a person. This is absolutely insane. People have trouble describing who they are and what they're about. This is going to help you do that. And if you share your context or your substrate representations with others, they'll be able to see what you're about. They'll be able to see this. Okay. Now, of course, the visuals here, the AR stuff, I mean that's going to take some time that that's a separate project, right? That's just visualization of data that's already there. Substrate is about creating that data for you for organizations for whatever in these individual components. So learning a person's values substrate will be a wonderful way to start learning about someone or something, what they care about, how they see the world, whatever. So imagine having something like this available when you're looking at someone or you're researching them. Okay, check this out. We got a person here. Okay. Got this guy. It's like the same coffee shop programmer, gamer, organizer. And you've got this grid here, right? Or this graph here. So check this out. Mission merged gaming and programming, continuous learning, community building innovation and game dev values. Goals, projects, annual Pixel Jam AI, MPC framework. These are things that he's working on to further these particular projects. And these are the goals that he's shooting for, for the mission of merging gaming and programming. That's insane. So now I know about this person, and if there's matches, then I could walk up to him and be like, hey, I hear you're into this, or I hear here you're into that. And now I have a conversation to start. Or maybe my eye is having that conversation with his eye. Whatever. It depends how far in the future it is. But this will be a wonderful way to learn about what somebody really cares about and how they see the world. So check this out. They believe the most important problems are these three problems. This is really cool, and they believe the best solutions are these solutions to those problems. And they intend to track progress using the following KPIs. Boom, boom boom. Here are the KPIs. So imagine if you match up with somebody across all these different axes right? You match with them on values, goals, beliefs, preferences. You could find friends, you can find people to have conversations with. You can find a mate, you could find a partner. This way you could find business partners this way. So we're very excited about the potential to spawn more human connection in this way. Okay, so the next one here is visualizing arguments. So when you have a given narrative or rumor or conspiracy theory going viral, you'll be able to use substrate to analyze the argument or claim and publish the results. We already talked about this one. We already showed it. So I'm just going to show another argument here and zoom in. Pretty heavy. Look at this. We never went to the moon. Look at this. Look at this. We never went to the moon. Contradicted by contradicted by leads to claim moon landings were faked. Look at this claim. We never went to the moon. It leads to moon. Landings were faked by NASA. Okay. And we have actions which is contradicted by leads to. And then we have another claim which is the red one. And then look at this provides attempts to explain demonstrate. So again the actions or the verbs. And then we have results. So reflectors on the moon consistently reflect lasers back from the Earth. I didn't think of this one. I actually dynamically created this one just just for the launch of the project. And that was a great one. Okay, what is bouncing lasers off the moon if there aren't reflectors on the moon, which means somebody put them there. So I guess maybe that would take you into aliens, but that's a separate topic. Okay. Multiple countries have independently verified moon landings. Over £800 of moon rocks have been studied worldwide, so you've got a collection of evidence that starts to add up. Conclusion. False. Overwhelming evidence supports moon landings, and this is just one level deep. You can go even deeper with sub claims and sub claims and pointing to the various sources with actual citations, like you can go as far as you want with this and have it all stored right there inside of substrate. So you'll be able to see, for example, that they're making the following arguments, which include the following claims, which we fact checked using the following sources which resulted in the following results which, using the following methodology, leads us to this conclusion. So I don't know if you remember Snopes, but Snopes was a basically a rumor denier or approver. It was like, yes, this sounds like it's not true, but it's actually true. Or it would be like, that sounds like it's true, but it's actually not true. But this is like Snopes, except for in a way that you can visually explore and you can individually validate each particular component. So any particular background can now evaluate this with more transparency than it's ever been possible. And they could see the pieces. And of course people will be able to add all their favorite sources. Right. They could build arguments, they could evaluate the same argument using a different set of sauces and see if the conclusion comes up different. So this is why we're excited about this particular argument piece, which we've spent a lot of time on, because it's really important. 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. Okay, so there's a tiny teapot orbiting the sun investigated by space mission data claimed the teapot is too small to be detected by our current instruments. Result no unusual objects detected. No evidence of teapot found. Support. Support. Conclusion. False. Insufficient evidence to support the claim, which is exactly what it should say, right? You can't prove that it's not there, but not being able to prove that it's not there is not evidence that it is there. Right. So and that's what we end up getting to okay. So this is all additive. It all starts compounding and adding in with itself okay. So now we're talking about substrate plus I leading to actual action. Yeah yeah yeah I this I that uh, totally get it. But this is different. This is not about I okay. The substrate is not about I. It's about human meaning and progress. I is just a tool for helping that along. So think about this with everything you've heard so far about substrate and what's simultaneously happening inside the world of I. So context sizes, which is basically the size of prompts that you can use, are increasing massively. And inference costs, which is basically a fancy way of saying the cost to run individual AI pulls or questions are massively falling. Okay, what this means is we can basically like chocolate and peanut butter this thing together with Substrate's ability to have all of these things stored in very neat, structured ways and being seen together in graphs combined with AI's ability to hold multiple things in its mind at once, and then perform really cool actions and answer questions and make recommendations. So this combination is absolutely insane. So we could feed AI with our goals. Okay, us as an individual, us as a county, us as a city, us as a company. Us as a country. Okay, goals, KPIs, risks and have it help us untangle these, discuss them, debate about them, vote on them, whatever, and then take action. Okay, so here's some of the examples that we're most excited about for for this combination of substrate plus I. So first one science automated hypothesis to results workflows. So one big problem with science is that it takes so long to do science. And this is why it takes so long. Because look at look at all the different things you have to go through. It's hard to come up with ideas. It's hard to design experiments. It's hard to find funding to do the experiments. It's hard to interpret the results. It's hard to publish the results, and it's hard to get those results in front of the right people who actually have influence and might want to do something. So now imagine that we have our list of problems, our list of proposed experiments, all within substrate, a list of funding sources, also within substrate. They're all there. Now I can help us to do almost every step in that difficulty chain we just talked about above. Okay. So I can help us come up with or collect ideas and hypotheses. It can help us design experiments. It can help us collect and evaluate the best funding sources, because it's got the full list right there. So it could be like, okay, based on this, based on what your goals and your mission and what you're trying to do here, these are the types of groups that are most likely to want to fund you. Okay. Requesting funding now that it knows those groups, maybe it knows how to contact them because that information is inside a substrate. Okay. So we can write the perfect pitch for you to get funding, help. You set up the experiments, and we're going to need some humans and or robots to help with that part. But whatever. Running and monitoring the experiments, interpreting the results, all of this really, really right in the wheelhouse of AI, writing the paper and sharing the paper also possible with AI and getting better all the time. So in other words, we're talking about hypothesis to propose experiment to looking at funding sources, to acquiring funding, to running experiments, to publishing the results, to actually making progress. So in the beginning, this is still going to require a lot of human help, right? Especially at the idea and the running of the experiment phases. But over time, I will get better at that as well. But what we're talking about ultimately with this right here is the acceleration of science. Right. Because this cycle right here is leading to a whole bunch of failures and making us move on to do something else instead. But this whole pipeline, and I want to give credit to Joseph Thacker for thinking about a very similar thing at a similar time, like a year ago, talking about experiments and hypotheses and testing and stuff like that. So a lot of people probably thinking very similar things along these lines. But we are so excited about this. The idea of just being able to experiment and advance our knowledge forward through the most powerful mechanism that we're aware of, which is science accountability. Okay, this one's insane. Monitoring crime and corruption okay. So the reason it's so easy to get away with corruption and crime right now is because there aren't enough people watching gangs, cartels, embezzlers, dirty politicians. They're actually dropping evidence all the time. There's receipts, there's like travel, there's tickets, there's cameras. There's lots of different ways to know that a particular person was in a particular place, and they're not even being that careful, because it's actually so difficult to go and collect that stuff and bring it together into a narrative. So it usually takes a major journalist team or a massive law enforcement operation to dump thousands of hours of highly skilled work to collect all this different evidence. Okay, then you have to do the analysis. Then you have to formulate the conclusions. Then you have to document all of this. Then you have to like take it to the media. You have to get in front of people. And most crime and corruption slips by because nobody is simply watching. There aren't enough journalists. There aren't enough law enforcement teams who have the skills to do this stuff. And even if they had the skills, they don't have the resources and the time. So substrate plus AI versus dirty politician, this is a use case. So let's take substrate with some AI added on. And let's think about a dirty politician who is taking massive gifts from a particular lobbyist. Let's say this is some dirty Democrat, some dirty Republican. Independent doesn't matter. This has nothing to do with that. Problem is, there are so many donations. There are so many lobbyists, so many representatives, so many actual laws and bills and so many votes. But guess what? It's all public. We're talking about the US here. This is all public information. It is required by law that all the stuff is posted. The lobbyist groups must actually register themselves. The donations that they make to any particular representative, they have to be public records of meetings. I believe those are also public, and so are all the votes that representatives make on bills where lobbyists have been donating and trying to influence. So a nonprofit or even just a project, a small project that comes out of substrate or whatever, just a bunch of open source people could use AI to collect all of these different things, continuously put them in substrate, and they're already going in substrate because we're about to start dumping all the laws, all the different voting records, all the different histories for each lobbyist and also for each representative, all that goes right into substrate to inspect. And then I can ingest all of that at any given moment and basically tell us this. Here are all the bills written by that person. Here are all the summaries of those bills. Basically, what are they trying to do? Here's who those bills helped and who they hurt. Right? Here are all the lobbyists that care about those particular issues. Here are all the donations that those groups made to those representatives or that particular representative. And here's how the representative voted on every single bill. Then guess what? The AI is really good at, which we already know. Okay. Perform a comprehensive analysis of all legislation created and voted on from Bill Myers, Senator from Arkansas. I made that up. Hopefully there is no one like that cross-referenced with every single donation ever made to him, every dinner he's ever attended with them, every gift he's ever received, etc. finally, give me your assessment of whether or not he is being unduly influenced by this lobbyist and give me your reasons for this conclusion. Then it comes back with something like this assessment. This is a compromised politician reasoning. Osint reveals you can have Osint going and researching various things that this person is doing. Again, all public, all legal. Right? We're not talking about nasty stuff here. We're talking about legal. Public documents reveals that he was illegally gifted a small yacht last year, which he tweeted about and later deleted. He's had 31 dinners in the last 18 months with them totaling over like almost $15,000. Osint reveals that the Lobbyist's president used considerable influence to get Bill Meyer's daughter admitted to an exclusive private school that she wasn't actually qualified for. Every vote he's made about this particular issue has been in the direction that the lobbyist actually wants to happen, and previous votes before they started the relationship, the politician used to vote in the opposite direction. Therefore, the conclusion from this I. And by the way, you published the algorithm for the AI as well. We've got another project called fabric, where you publish the actual thoughts and directions given to the AIS so that they're inspectable. Right? You you don't want people to think, oh, they came to that conclusion because it's biased. No. You can look and see the actual instructions given to the AI to evaluate this thing impartially. So now you could trust this thing with a very high rate of confidence, because you could see how it's thinking and you could see all the data sources, just like before with arguments. So basically, the incredibly important objects of legislation, votes, etc. are all things that can be monitored and collected and stored within substrate. So that's that one next one here. Leadership. This is absolutely, absolutely powerful okay. I've been a consultant for a couple of decades now and I've worked with hundreds of startups, so many large corporations, and a big, big problem for most organizations. And that includes governments, families, individuals, startups, corporations. Everything is that they don't have clarity. Just like a person. It's hard to know what they think the issues are, what they specifically plan on doing, and how they plan to measure progress. So we see this with business leaders and we see this with politicians. So with substrate, we intend to make it so that check this out. Every leader will need to have a full detailed plan that has the following components. Imagine if everyone had this when they were pitching some plan for the high school, or some plan to be a principal, or some plan to be a community leader, or some plan to be a politician or whatever it is. Here's what I think the problems are. Remember, this is a problem object inside of substrate so you can actually look at them. Here's what I think the problems are. And coming off of the problem here's what I think the solutions are. Here are my proposed strategies for accomplishing that. Here are the KPIs. These are the metrics. Here's how actually going to measure myself. And how about this fire me if I don't get the KPIs to this number by this date. So at the end of the three and a half years when I'm going back up for reelection, I expect whatever the thing is, uh, the number of kids who don't go hungry in this high school, the literacy level in this high school, the unemployment rate, whatever it is, doesn't matter if I don't improve that number by X amount. By this date, you have my permission to vote me out. Imagine having that level of clarity and accountability for any leader trying to get any job doing anything. So really excited about that one. Okay, next one here. This is the best one okay. I saved the best for last. These are all adding on each other. Watch this. Look at this. I did a post a while back about how companies are essentially graphs of algorithms, and I encourage you to go check that out. I've got the link down here below, but it's like, okay, you have a company that processes bad images come in, they send them to you on the website, and you then do a number of things to that photo, right? You do a high quality scan, you repair it, you stylize it, you caption it, you send it to the user. Then there's a marketing group. A marketing group has an idea. It shares it with a team. They decide on the best version. They do this, they do a final vote. They do a launch campaign. Okay, or the uploading process. Visit the website create account, click the upload link. These are all individual steps okay, these are just algorithms. And this is a graph of algorithms where they're all connected. And you can break these into smaller and smaller pieces until you eventually see the world in this way. Okay. I'm just going to click into this thing just to highlight this a little bit. This is what it ends up looking like. Okay. It looks like this is what your companies are going to look like before too long okay. Not your companies. Everyone's company okay. AI consultancies are going to come in. They're going to see the world in this way. Everything is a process. Everything is a set of components. Okay? You got human decisions here. You got human teams here. Maybe the red are all the places that it's human and AI wants to replace that. Maybe the red is it's a little bit too manual. Maybe the red is the efficiency numbers are too low or it takes too long. Doesn't matter. You could drill in. You could see this is actually a human team. They actually need more people. We need to hire more people, etc. the point is, you can look at any company or any process as a series of these components, which is part of a larger process which has a flow. Okay. That's the important part here. Okay. Yeah. This is the piece here. Companies are just graphs of algorithms. But think of it this way I don't think I went far enough with that. Everything can be conceptualized in this way. Everything can be conceptualized in this way as a process. So essentially what you have is you have a current state of things, right? State of the universe, but smaller down to a scale that we're dealing with, like a company or a process or a team or a department. Okay. Then we have an action or event, like a decision is made. Um, you know, customer does a particular thing. They buy a thing. We need to pay out an insurance policy, whatever the thing is. Right? And then that results in a new state of things. So previous state action thing happens a cause. And then you have like an effect which is the outcome, which is the result. And if we add human components into this like people's jobs or making decisions, and we do this for like running a business or a country or family, we have additional pieces, right? We have people, we have decisions, we have strategies. We have lessons learned. We have conclusions. We have reasons. All of these are substrate components. There aren't infinite numbers of these. These are all look, what was the decision made? There aren't that many decisions okay. Buy more stock. Um, hire more people. This is the type of decision in there, right? Again, which we can connect and see visually. And what that results in is a way to tie together all of this into much larger graphs, graphs that we could use to describe the operations of anything a family, company, or even a country. So here's one for a small company. And I made this live. This is actual like I put in some fake company stuff. Look at this. No customers website visitor quality lead. Yes. No. Okay. Goes down. Look at this. Breaks down the thing. This is how we're about to see our companies. We're about to see all of anything that humans do as a set of processes like this. And it's fine if you want to resist this, but trust me, this is coming because the consultancies are coming. I got to stop for you. 40% of McKinsey's business right now is already in AI consulting. 40%. This thing happened like a year and a half ago. They are already doing almost half their business doing this. They're doing this. They're coming in. They're looking at companies and saying, what can I optimize? What can I improve? So that's pretty cool. We can create that. But that's not the full power of this yet. Check this out. The smarter the AI gets, it's not only going to see this and describe it and make some recommendations, but no, it can optimize the stuff. In other words, this is just the current state. What about future state? What about recommended state? Should this company merge departments? Where can we add more people? What kind of people should we hire? Which processes here are inefficient which can be replaced by AI? That's going to be the number one question somebody is asking. Where can we use more human decision making? If we wanted to grow, where would that happen? Now imagine this for a family, a corporation, a church, a city, a county, etc. and keep in mind, the more data that you have here, the better, right? You can. You can pull in all the data about conversion rates, churn rates, like whatever data you have that comes in. That's part of this whole equation. So here's an example for a security team because my background is in security. So it currently takes 3.5 business days to complete a security assessment. Delays in security assessment turnaround are the number one complaint in the engineering survey. So the security group sends a survey to engineering and says, hey, what do you love and what do you hate about security? This is their number one complaint. It takes too long to get stuff back. So you're slowing us down. So if we switch to the new flex scan model, which is a new model for doing security assessments in a more flexible way using fewer generalist security testers were able to complete type B and type C assessments 94% faster. This will give our senior testers two extra days to do a high impact security assessments, and this will also likely make engineering much happier with security and make them more likely to cooperate on our goals. So there's multiple steps the full articulation and breakdown of how a process is currently running. Visualization of that process to help with human understanding, and then analysis by AI of how to optimize the process to optimize the stated goals of the entity. That's the three pieces. 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 its projects and its budget. Everything. So we'll have the full context on how resources are being spent relative to the desired outcomes, and we'll be able to see how the actual KPIs that we care about, the actual numbers, the actual metrics, are moving as we adjust these. So it'll be able to do all sorts of recommendations hiring new people, hiring people with specific skills, using more AI and high volume and low creativity areas, adjusting strategies based on goals and market conditions. Canceling projects like this one and that one. Like they're not even related to the goals. They're costing way too much. Way too much of the team is working on them. Let's get rid of those. This project over here, this human powered project, it's way better. It's producing way more revenue. It's way more tied to our actual goals and KPIs. So we're taking those people and putting them over here instead. So 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 will actually work. And the smarter the AI gets, the better it actually works. Completely insane. All right, to summarize, 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, 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 this framework, people and organizations will be able to articulate their values and purpose more clearly, which will not only help them, but everyone that they interact with. Substrate is magnified by I because I can or will soon be able to hold all of substrate in its mind at once. And from there, we'll be able to ask all sorts of meaningful questions such as what does 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. What action should I do right now to optimize this workflow? 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 that we care about as humans. So we would love for you to get involved. I want to give thanks to people who are already involved and are like, really, really into this. Along with myself, Jonathan Dunn, Jason Haddox, Clint Gibler, Joseph Thacker, Joel Parrish and Robert Hansen, some of the few that are already involved and excited about this. And if you are interested, please go to the Substrate Project and, uh, let us know that you'd like to contribute. Thanks for your time.

Turns it into something like this. And I'm going to zoom in. It's probably going to be a little bit blurry, but watch this. This argument consists of these claims, okay, effort required cost of progress. So it breaks down the argument into these different components. Limited results okay. Then it breaks those into further things. Recycling programs require significant effort. Recycling programs are expensive. Most rejected materials end up in landfills. Okay. And then it goes and adds research to all of those. In this case, it's just pulled it directly out of the model. But you can actually add AI on top of this, which we're going to be doing with an agent framework, either that we build ourselves or that we use one of the third parties. And these will actually be tools doing this research down here. Okay. Going and doing separate lookups to find research. Like you could do this at really in-depth level for each stage here, especially this one where you're doing the research and then it comes back and says supports or supports or weakens. Look at this. Supports supports weakens weakens partially supports. Then we have a conclusion ends up being a weak argument. Interesting. So you could see the reasons this is the most important thing. You could see how this argument was formed, how it was researched using what sources. Okay, look at this. Uh, recycling statistics 2021 National Waste and Recycling Association 2022. So we actually have source names here, and we can go into even more depth if we do additional AI work on this. And that's kind of a foreshadowing for future section here. So let's just keep going. Again, what this does is it takes an argument like this recycling example, and it turns it into a graph like we just saw. Okay, the most important thing about this graph is that we could throw it up on a board. We could throw it on the side of a wall. We could each view it inside of our AR glasses or whatever in the in the near future. And now both of us who are having this discussion can be looking at the same thing and saying, uh, yeah, I agree with these two, but I don't agree with this one. And the reason I don't agree is because I don't trust the source that it came from. And then you could do things like, okay, let's grab some content from other sources. Boom. You start adding other sources to it and maybe it changes based on those sources. It changes the conclusion of that subpart, of that argument. And now the whole thing updates. And the conclusion is maybe, oh, we're not sure. Or maybe it switches it from yes to no or up to down or whatever. The point is transparency. Okay. Too many of our conversations about any topic, it becomes emotional because it's too difficult to keep all these things in our minds at once. Okay, a basic argument like it was just made here about recycling has multiple sub claims in it, and those individual sub claims need to be backed by data. And it's very hard to do that just in our brains when we're in the middle of a conversation, especially if you're trying to like blast this out. You want to have this conversation, you want to say, hey, recycling is good or recycling is bad, or whatever the topic is, but you try to put that on social media or somewhere. You're basically writing a text thing, right? Which is trying to convince someone, which is fine. We've been doing that forever. But imagine where we could do this graphically, where we could do this visually and show the connections of how strong an individual claim is based on how much you trust an individual source. Right. So that's the power of this thing. And this is why I'm so excited about it. And there's many, many more examples we're about to get into. So each of those objects in that diagram is another substrate component, right. The claims, the sources, they're all in there and they're all in there inside of another repository inside of the substrate project. So here's an example of a source. New York Times, Associated Press, Breitbart. When people make truth claims, it's important to be able to fact check or research those claims to see their support and substrate. Does this by maintaining a list of those sources that we may or may not trust. That's the point. Some people will trust different sources. Some people will not trust different sources. Right. And that's up to them. Then it just becomes a question of, okay, well, you should trust this source and that becomes an argument by itself. Right Now you can make an argument. Well, you're putting too much weight onto a particular source or set of sources, which let's look at a bunch of claims that they've made recently and let's separately research those. And maybe that argument is strong enough to make them discount that particular source, whether that's The New York Times or Breitbart or whatever it is. Right. So when somebody makes an argument or a claim within an argument, it can be linked directly to those sources that you do or do not trust. And you can see the full argument and all of its support. In one visual look at this claim. Inflation fell by 2% under this particular person's term, right? This argument includes this claim. This claim has this source, the AP, this claim has this source. New York Times. So this argument has a particular strength as a result of that. So argument to claims to sources. And this is why we're so excited about substrate. It's going to make things that used to be almost impossible to discuss actually approachable actually discussable, because we could break it down into its individual pieces. So before you'd be like, you're just not able to counter all my arguments and evidence because I'm too smart. And now you could say, look, here's my argument. Throw it up on the board. Maybe it's like a little bit in the future or, you know, Apple Vision Pro or whatever. And now it's actually a 3D thing. You could turn it around like a piece of DNA or something, right? Show me which claim you disagree with or which source you disagree with that backs up those claims. Right. So now we can just take this thing apart using different sources or whatever and change the output. This will enable far more logical and precise discussions that that is what we're going for. This is just one example. This is just the arguments example. So now I want to take and like back up a little bit and think about real world use cases for substrate. Overall okay. Sounds really cool. What do you actually do with it. Exactly. So this is my favorite. Keep in mind, this is very early. It literally came out like two days ago. But we've already got multiple use cases for this thing okay. So let's get into these okay. The first one here is describing yourself. This one is massive. Look at this. This is pretty cool art. This uh, from Midjourney. It's, uh, part of a design that I did for a previous piece. But anyway. Yeah. So you can see these are meant to represent the different connections of like, uh, goals to mission to problems that she thinks are most important in the world. Solutions. She thinks, uh, are the answers to those problems. Different strategies, different projects she's working on. That's what this, uh, AI art is meant to represent with this graph. Little function here. But then you can have labels assigned to that. And keep in mind this is me looking at her in this coffee shop through my AR glasses and seeing, okay, first of all, purple is associated with engineering. Boom. She's an engineer. She's known for being a friend. She's known for being a writer. So I'm now seeing this person as she is, which she has previously defined. So let's get into that. Many people have trouble describing who they are and what they are about. This is so critical with substrate, you can basically describe yourself in any way you want to write text, audio, video, whatever, even have a conversation with AI, which is not part of substrate yet. But uh, there's lots of different ways to do that, and it will be able to both articulate and visualize you as a person. This is absolutely insane. People have trouble describing who they are and what they're about. This is going to help you do that. And if you share your context or your substrate representations with others, they'll be able to see what you're about. They'll be able to see this. Okay. Now, of course, the visuals here, the AR stuff, I mean that's going to take some time that that's a separate project, right? That's just visualization of data that's already there. Substrate is about creating that data for you for organizations for whatever in these individual components. So learning a person's values substrate will be a wonderful way to start learning about someone or something, what they care about, how they see the world, whatever. So imagine having something like this available when you're looking at someone or you're researching them. Okay, check this out. We got a person here. Okay. Got this guy. It's like the same coffee shop programmer, gamer, organizer. And you've got this grid here, right? Or this graph here. So check this out. Mission merged gaming and programming, continuous learning, community building innovation and game dev values. Goals, projects, annual Pixel Jam AI, MPC framework. These are things that he's working on to further these particular projects. And these are the goals that he's shooting for, for the mission of merging gaming and programming. That's insane. So now I know about this person, and if there's matches, then I could walk up to him and be like, hey, I hear you're into this, or I hear here you're into that. And now I have a conversation to start. Or maybe my eye is having that conversation with his eye. Whatever. It depends how far in the future it is. But this will be a wonderful way to learn about what somebody really cares about and how they see the world. So check this out. They believe the most important problems are these three problems. This is really cool, and they believe the best solutions are these solutions to those problems. And they intend to track progress using the following KPIs. Boom, boom boom. Here are the KPIs. So imagine if you match up with somebody across all these different axes right? You match with them on values, goals, beliefs, preferences. You could find friends, you can find people to have conversations with. You can find a mate, you could find a partner. This way you could find business partners this way. So we're very excited about the potential to spawn more human connection in this way. Okay, so the next one here is visualizing arguments. So when you have a given narrative or rumor or conspiracy theory going viral, you'll be able to use substrate to analyze the argument or claim and publish the results. We already talked about this one. We already showed it. So I'm just going to show another argument here and zoom in. Pretty heavy. Look at this. We never went to the moon. Look at this. Look at this. We never went to the moon. Contradicted by contradicted by leads to claim moon landings were faked. Look at this claim. We never went to the moon. It leads to moon. Landings were faked by NASA. Okay. And we have actions which is contradicted by leads to. And then we have another claim which is the red one. And then look at this provides attempts to explain demonstrate. So again the actions or the verbs. And then we have results. So reflectors on the moon consistently reflect lasers back from the Earth. I didn't think of this one. I actually dynamically created this one just just for the launch of the project. And that was a great one. Okay, what is bouncing lasers off the moon if there aren't reflectors on the moon, which means somebody put them there. So I guess maybe that would take you into aliens, but that's a separate topic. Okay. Multiple countries have independently verified moon landings. Over £800 of moon rocks have been studied worldwide, so you've got a collection of evidence that starts to add up. Conclusion. False. Overwhelming evidence supports moon landings, and this is just one level deep. You can go even deeper with sub claims and sub claims and pointing to the various sources with actual citations, like you can go as far as you want with this and have it all stored right there inside of substrate. So you'll be able to see, for example, that they're making the following arguments, which include the following claims, which we fact checked using the following sources which resulted in the following results which, using the following methodology, leads us to this conclusion. So I don't know if you remember Snopes, but Snopes was a basically a rumor denier or approver. It was like, yes, this sounds like it's not true, but it's actually true. Or it would be like, that sounds like it's true, but it's actually not true. But this is like Snopes, except for in a way that you can visually explore and you can individually validate each particular component. So any particular background can now evaluate this with more transparency than it's ever been possible. And they could see the pieces. And of course people will be able to add all their favorite sources. Right. They could build arguments, they could evaluate the same argument using a different set of sauces and see if the conclusion comes up different. So this is why we're excited about this particular argument piece, which we've spent a lot of time on, because it's really important. 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. Okay, so there's a tiny teapot orbiting the sun investigated by space mission data claimed the teapot is too small to be detected by our current instruments. Result no unusual objects detected. No evidence of teapot found. Support. Support. Conclusion. False. Insufficient evidence to support the claim, which is exactly what it should say, right? You can't prove that it's not there, but not being able to prove that it's not there is not evidence that it is there. Right. So and that's what we end up getting to okay. So this is all additive. It all starts compounding and adding in with itself okay. So now we're talking about substrate plus I leading to actual action. Yeah yeah yeah I this I that uh, totally get it. But this is different. This is not about I okay. The substrate is not about I. It's about human meaning and progress. I is just a tool for helping that along. So think about this with everything you've heard so far about substrate and what's simultaneously happening inside the world of I. So context sizes, which is basically the size of prompts that you can use, are increasing massively. And inference costs, which is basically a fancy way of saying the cost to run individual AI pulls or questions are massively falling. Okay, what this means is we can basically like chocolate and peanut butter this thing together with Substrate's ability to have all of these things stored in very neat, structured ways and being seen together in graphs combined with AI's ability to hold multiple things in its mind at once, and then perform really cool actions and answer questions and make recommendations. So this combination is absolutely insane. So we could feed AI with our goals. Okay, us as an individual, us as a county, us as a city, us as a company. Us as a country. Okay, goals, KPIs, risks and have it help us untangle these, discuss them, debate about them, vote on them, whatever, and then take action. Okay, so here's some of the examples that we're most excited about for for this combination of substrate plus I. So first one science automated hypothesis to results workflows. So one big problem with science is that it takes so long to do science. And this is why it takes so long. Because look at look at all the different things you have to go through. It's hard to come up with ideas. It's hard to design experiments. It's hard to find funding to do the experiments. It's hard to interpret the results. It's hard to publish the results, and it's hard to get those results in front of the right people who actually have influence and might want to do something. So now imagine that we have our list of problems, our list of proposed experiments, all within substrate, a list of funding sources, also within substrate. They're all there. Now I can help us to do almost every step in that difficulty chain we just talked about above. Okay. So I can help us come up with or collect ideas and hypotheses. It can help us design experiments. It can help us collect and evaluate the best funding sources, because it's got the full list right there. So it could be like, okay, based on this, based on what your goals and your mission and what you're trying to do here, these are the types of groups that are most likely to want to fund you. Okay. Requesting funding now that it knows those groups, maybe it knows how to contact them because that information is inside a substrate. Okay. So we can write the perfect pitch for you to get funding, help. You set up the experiments, and we're going to need some humans and or robots to help with that part. But whatever. Running and monitoring the experiments, interpreting the results, all of this really, really right in the wheelhouse of AI, writing the paper and sharing the paper also possible with AI and getting better all the time. So in other words, we're talking about hypothesis to propose experiment to looking at funding sources, to acquiring funding, to running experiments, to publishing the results, to actually making progress. So in the beginning, this is still going to require a lot of human help, right? Especially at the idea and the running of the experiment phases. But over time, I will get better at that as well. But what we're talking about ultimately with this right here is the acceleration of science. Right. Because this cycle right here is leading to a whole bunch of failures and making us move on to do something else instead. But this whole pipeline, and I want to give credit to Joseph Thacker for thinking about a very similar thing at a similar time, like a year ago, talking about experiments and hypotheses and testing and stuff like that. So a lot of people probably thinking very similar things along these lines. But we are so excited about this. The idea of just being able to experiment and advance our knowledge forward through the most powerful mechanism that we're aware of, which is science accountability. Okay, this one's insane. Monitoring crime and corruption okay. So the reason it's so easy to get away with corruption and crime right now is because there aren't enough people watching gangs, cartels, embezzlers, dirty politicians. They're actually dropping evidence all the time. There's receipts, there's like travel, there's tickets, there's cameras. There's lots of different ways to know that a particular person was in a particular place, and they're not even being that careful, because it's actually so difficult to go and collect that stuff and bring it together into a narrative. So it usually takes a major journalist team or a massive law enforcement operation to dump thousands of hours of highly skilled work to collect all this different evidence. Okay, then you have to do the analysis. Then you have to formulate the conclusions. Then you have to document all of this. Then you have to like take it to the media. You have to get in front of people. And most crime and corruption slips by because nobody is simply watching. There aren't enough journalists. There aren't enough law enforcement teams who have the skills to do this stuff. And even if they had the skills, they don't have the resources and the time. So substrate plus AI versus dirty politician, this is a use case. So let's take substrate with some AI added on. And let's think about a dirty politician who is taking massive gifts from a particular lobbyist. Let's say this is some dirty Democrat, some dirty Republican. Independent doesn't matter. This has nothing to do with that. Problem is, there are so many donations. There are so many lobbyists, so many representatives, so many actual laws and bills and so many votes. But guess what? It's all public. We're talking about the US here. This is all public information. It is required by law that all the stuff is posted. The lobbyist groups must actually register themselves. The donations that they make to any particular representative, they have to be public records of meetings. I believe those are also public, and so are all the votes that representatives make on bills where lobbyists have been donating and trying to influence. So a nonprofit or even just a project, a small project that comes out of substrate or whatever, just a bunch of open source people could use AI to collect all of these different things, continuously put them in substrate, and they're already going in substrate because we're about to start dumping all the laws, all the different voting records, all the different histories for each lobbyist and also for each representative, all that goes right into substrate to inspect. And then I can ingest all of that at any given moment and basically tell us this. Here are all the bills written by that person. Here are all the summaries of those bills. Basically, what are they trying to do? Here's who those bills helped and who they hurt. Right? Here are all the lobbyists that care about those particular issues. Here are all the donations that those groups made to those representatives or that particular representative. And here's how the representative voted on every single bill. Then guess what? The AI is really good at, which we already know. Okay. Perform a comprehensive analysis of all legislation created and voted on from Bill Myers, Senator from Arkansas. I made that up. Hopefully there is no one like that cross-referenced with every single donation ever made to him, every dinner he's ever attended with them, every gift he's ever received, etc. finally, give me your assessment of whether or not he is being unduly influenced by this lobbyist and give me your reasons for this conclusion. Then it comes back with something like this assessment. This is a compromised politician reasoning. Osint reveals you can have Osint going and researching various things that this person is doing. Again, all public, all legal. Right? We're not talking about nasty stuff here. We're talking about legal. Public documents reveals that he was illegally gifted a small yacht last year, which he tweeted about and later deleted. He's had 31 dinners in the last 18 months with them totaling over like almost $15,000. Osint reveals that the Lobbyist's president used considerable influence to get Bill Meyer's daughter admitted to an exclusive private school that she wasn't actually qualified for. Every vote he's made about this particular issue has been in the direction that the lobbyist actually wants to happen, and previous votes before they started the relationship, the politician used to vote in the opposite direction. Therefore, the conclusion from this I. And by the way, you published the algorithm for the AI as well. We've got another project called fabric, where you publish the actual thoughts and directions given to the AIS so that they're inspectable. Right? You you don't want people to think, oh, they came to that conclusion because it's biased. No. You can look and see the actual instructions given to the AI to evaluate this thing impartially. So now you could trust this thing with a very high rate of confidence, because you could see how it's thinking and you could see all the data sources, just like before with arguments. So basically, the incredibly important objects of legislation, votes, etc. are all things that can be monitored and collected and stored within substrate. So that's that one next one here. Leadership. This is absolutely, absolutely powerful okay. I've been a consultant for a couple of decades now and I've worked with hundreds of startups, so many large corporations, and a big, big problem for most organizations. And that includes governments, families, individuals, startups, corporations. Everything is that they don't have clarity. Just like a person. It's hard to know what they think the issues are, what they specifically plan on doing, and how they plan to measure progress. So we see this with business leaders and we see this with politicians. So with substrate, we intend to make it so that check this out. Every leader will need to have a full detailed plan that has the following components. Imagine if everyone had this when they were pitching some plan for the high school, or some plan to be a principal, or some plan to be a community leader, or some plan to be a politician or whatever it is. Here's what I think the problems are. Remember, this is a problem object inside of substrate so you can actually look at them. Here's what I think the problems are. And coming off of the problem here's what I think the solutions are. Here are my proposed strategies for accomplishing that. Here are the KPIs. These are the metrics. Here's how actually going to measure myself. And how about this fire me if I don't get the KPIs to this number by this date. So at the end of the three and a half years when I'm going back up for reelection, I expect whatever the thing is, uh, the number of kids who don't go hungry in this high school, the literacy level in this high school, the unemployment rate, whatever it is, doesn't matter if I don't improve that number by X amount. By this date, you have my permission to vote me out. Imagine having that level of clarity and accountability for any leader trying to get any job doing anything. So really excited about that one. Okay, next one here. This is the best one okay. I saved the best for last. These are all adding on each other. Watch this. Look at this. I did a post a while back about how companies are essentially graphs of algorithms, and I encourage you to go check that out. I've got the link down here below, but it's like, okay, you have a company that processes bad images come in, they send them to you on the website, and you then do a number of things to that photo, right? You do a high quality scan, you repair it, you stylize it, you caption it, you send it to the user. Then there's a marketing group. A marketing group has an idea. It shares it with a team. They decide on the best version. They do this, they do a final vote. They do a launch campaign. Okay, or the uploading process. Visit the website create account, click the upload link. These are all individual steps okay, these are just algorithms. And this is a graph of algorithms where they're all connected. And you can break these into smaller and smaller pieces until you eventually see the world in this way. Okay. I'm just going to click into this thing just to highlight this a little bit. This is what it ends up looking like. Okay. It looks like this is what your companies are going to look like before too long okay. Not your companies. Everyone's company okay. AI consultancies are going to come in. They're going to see the world in this way. Everything is a process. Everything is a set of components. Okay? You got human decisions here. You got human teams here. Maybe the red are all the places that it's human and AI wants to replace that. Maybe the red is it's a little bit too manual. Maybe the red is the efficiency numbers are too low or it takes too long. Doesn't matter. You could drill in. You could see this is actually a human team. They actually need more people. We need to hire more people, etc. the point is, you can look at any company or any process as a series of these components, which is part of a larger process which has a flow. Okay. That's the important part here. Okay. Yeah. This is the piece here. Companies are just graphs of algorithms. But think of it this way I don't think I went far enough with that. Everything can be conceptualized in this way. Everything can be conceptualized in this way as a process. So essentially what you have is you have a current state of things, right? State of the universe, but smaller down to a scale that we're dealing with, like a company or a process or a team or a department. Okay. Then we have an action or event, like a decision is made. Um, you know, customer does a particular thing. They buy a thing. We need to pay out an insurance policy, whatever the thing is. Right? And then that results in a new state of things. So previous state action thing happens a cause. And then you have like an effect which is the outcome, which is the result. And if we add human components into this like people's jobs or making decisions, and we do this for like running a business or a country or family, we have additional pieces, right? We have people, we have decisions, we have strategies. We have lessons learned. We have conclusions. We have reasons. All of these are substrate components. There aren't infinite numbers of these. These are all look, what was the decision made? There aren't that many decisions okay. Buy more stock. Um, hire more people. This is the type of decision in there, right? Again, which we can connect and see visually. And what that results in is a way to tie together all of this into much larger graphs, graphs that we could use to describe the operations of anything a family, company, or even a country. So here's one for a small company. And I made this live. This is actual like I put in some fake company stuff. Look at this. No customers website visitor quality lead. Yes. No. Okay. Goes down. Look at this. Breaks down the thing. This is how we're about to see our companies. We're about to see all of anything that humans do as a set of processes like this. And it's fine if you want to resist this, but trust me, this is coming because the consultancies are coming. I got to stop for you. 40% of McKinsey's business right now is already in AI consulting. 40%. This thing happened like a year and a half ago. They are already doing almost half their business doing this. They're doing this. They're coming in. They're looking at companies and saying, what can I optimize? What can I improve? So that's pretty cool. We can create that. But that's not the full power of this yet. Check this out. The smarter the AI gets, it's not only going to see this and describe it and make some recommendations, but no, it can optimize the stuff. In other words, this is just the current state. What about future state? What about recommended state? Should this company merge departments? Where can we add more people? What kind of people should we hire? Which processes here are inefficient which can be replaced by AI? That's going to be the number one question somebody is asking. Where can we use more human decision making? If we wanted to grow, where would that happen? Now imagine this for a family, a corporation, a church, a city, a county, etc. and keep in mind, the more data that you have here, the better, right? You can. You can pull in all the data about conversion rates, churn rates, like whatever data you have that comes in. That's part of this whole equation. So here's an example for a security team because my background is in security. So it currently takes 3.5 business days to complete a security assessment. Delays in security assessment turnaround are the number one complaint in the engineering survey. So the security group sends a survey to engineering and says, hey, what do you love and what do you hate about security? This is their number one complaint. It takes too long to get stuff back. So you're slowing us down. So if we switch to the new flex scan model, which is a new model for doing security assessments in a more flexible way using fewer generalist security testers were able to complete type B and type C assessments 94% faster. This will give our senior testers two extra days to do a high impact security assessments, and this will also likely make engineering much happier with security and make them more likely to cooperate on our goals. So there's multiple steps the full articulation and breakdown of how a process is currently running. Visualization of that process to help with human understanding, and then analysis by AI of how to optimize the process to optimize the stated goals of the entity. That's the three pieces. 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 its projects and its budget. Everything. So we'll have the full context on how resources are being spent relative to the desired outcomes, and we'll be able to see how the actual KPIs that we care about, the actual numbers, the actual metrics, are moving as we adjust these. So it'll be able to do all sorts of recommendations hiring new people, hiring people with specific skills, using more AI and high volume and low creativity areas, adjusting strategies based on goals and market conditions. Canceling projects like this one and that one. Like they're not even related to the goals. They're costing way too much. Way too much of the team is working on them. Let's get rid of those. This project over here, this human powered project, it's way better. It's producing way more revenue. It's way more tied to our actual goals and KPIs. So we're taking those people and putting them over here instead. So 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 will actually work. And the smarter the AI gets, the better it actually works. Completely insane. All right, to summarize, 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, 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 this framework, people and organizations will be able to articulate their values and purpose more clearly, which will not only help them, but everyone that they interact with. Substrate is magnified by I because I can or will soon be able to hold all of substrate in its mind at once. And from there, we'll be able to ask all sorts of meaningful questions such as what does 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. What action should I do right now to optimize this workflow? 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 that we care about as humans. So we would love for you to get involved. I want to give thanks to people who are already involved and are like, really, really into this. Along with myself, Jonathan Dunn, Jason Haddox, Clint Gibler, Joseph Thacker, Joel Parrish and Robert Hansen, some of the few that are already involved and excited about this. And if you are interested, please go to the Substrate Project and, uh, let us know that you'd like to contribute. Thanks for your time.