S1: All right. Welcome to unsupervised learning. This is Daniel Miessler.
S1: And today I'm super excited to announce a project I've
S1: been wanting to talk about for a very long time
S1: called substrate. Okay, let's get into the project itself. So
S1: what is it exactly? That is really the question. What
S1: substrate is is an open source framework for human understanding,
S1: meaning and progress. And you might be inclined to say,
S1: what the hell does that mean? And it's a great question. Right?
S1: So the purpose of the project is to make things
S1: that matter to humans more transparent. Discussable. And ultimately, because
S1: they're transparent and discussable, they'll be more fixable. So what
S1: kind of things are we talking about? So we're calling
S1: these substrate components. And these are the components of human meaning. Right.
S1: When we talk about understanding, meaning and progress, these are
S1: the pieces that we're actually talking about. So collections of
S1: things okay. So the first thing is an idea collections
S1: of ideas a list of human novel ideas, problems a
S1: list of our most important human problems, our beliefs, our models,
S1: which are our ways of conceptualizing reality, frames a list
S1: of narratives or lenses for perceiving reality. A list of
S1: solutions that correspond to problems. Information sources. So you'll have,
S1: like New York Times, The Hill, Breitbart, uh, lots of
S1: different sources of information, government sources, individual sources, different media organizations.
S1: It's got to be a comprehensive list, and it's got
S1: to span different political ideologies. That's really important here as
S1: we'll see later people. So this is just individuals, organizations
S1: of different types laws. We're going to collect all different legislation,
S1: starting with the US government, but expanding out to pretty
S1: much the world at some point claims. So this is
S1: a factual claim, a truth claim about the world votes,
S1: a list of votes and results from laws that were
S1: submitted and voted on to basically say, here's what the
S1: votes were from different people. So this is talking about
S1: representatives voting on things, right? Arguments a list of arguments
S1: that have been made in favor or against a particular thing.
S1: Funding sources, lobbyists. So a list of lobbyists in their agendas, missions, donations,
S1: goals and facts. So these are actually claims from up here,
S1: but these are just verified ones. And important to note
S1: that it could become true and then untrue again. So
S1: you have to kind of keep this updated. Now each
S1: of these in this list here, each of these will
S1: be an actual list maintained in a repository within GitHub.
S1: In fact, we already have one of these okay. So
S1: I'm going to click in here. And if we go
S1: into here and look at problems we already have a
S1: list of problems here okay. It's already starting. So we've
S1: got we've got this going and I'm just going to
S1: zoom in a whole bunch for this one. So we've
S1: got like ransomware attacks on US health care systems. Uh,
S1: teen depression in the UK, nuclear weapons development in North Korea.
S1: Each one of them has this PR code so you
S1: can associate directly to it. And that's basically the structure.
S1: And you can see the structure here as well. And
S1: again we have the link here to the GitHub repo
S1: that we just looked at. And they're all part of
S1: this substrate organization which is here. So if you go
S1: into the substrate organization you could see the the main
S1: flow here. And if you go into repositories you see
S1: that we've populated a whole bunch of these, and we've
S1: already got some pretty good activity on some of these
S1: especially problems. And keep in mind, this just came out
S1: like two days ago. So this is brand new okay.
S1: So let's keep going on with the explanation here. So
S1: the structure will allow the entire open source community which
S1: is basically the world to contribute their own problems, claims, sources, frames, goals, etc.
S1: for all those different repositories which are all the different
S1: substrate components. Okay, I think I'm starting to get it,
S1: but I need more. Fair enough. So one way to
S1: think about this is as a way to put handles
S1: onto things that are hard to discuss, right? The whole
S1: reason that I created this project is so that we
S1: can articulate things better to each other, but also to ourselves. Okay,
S1: so let's get into some actual examples here so you
S1: can see what I'm talking about. Let's start with an
S1: argument component okay. This is what this is going to
S1: look like. Think of a common argument we might hear
S1: on any given day about whatever topic. And in this
S1: case it's going to be recycling. So this is just
S1: somebody that you hear say something right. I don't know
S1: why you recycle, man. It's a total waste. It costs
S1: so much to recycle right now. And the programs are
S1: poorly run. So it's not actually benefiting the environment like
S1: I do it if it worked, but it doesn't. And
S1: this is some person watching you put a can in
S1: a recycling bin. So we're confronted by this type of
S1: argument constantly. We're being pitched these different things like oh I,
S1: I would definitely do this because of this boom, that's
S1: an argument. Or I would definitely stop doing that because
S1: of this. That's also an argument. So that's things like recycling,
S1: but it's also things that matter much more about politics
S1: or whatever. So what substrate does this is the most
S1: important thing, right. It takes an argument like this.
S2: And.
S1: Turns it into something like this. And I'm going to
S1: zoom in. It's probably going to be a little bit blurry,
S1: but watch this. This argument consists of these claims, okay,
S1: effort required cost of progress. So it breaks down the
S1: argument into these different components. Limited results okay. Then it
S1: breaks those into further things. Recycling programs require significant effort.
S1: Recycling programs are expensive. Most rejected materials end up in landfills. Okay.
S1: And then it goes and adds research to all of those.
S1: In this case, it's just pulled it directly out of
S1: the model. But you can actually add AI on top
S1: of this, which we're going to be doing with an
S1: agent framework, either that we build ourselves or that we
S1: use one of the third parties. And these will actually
S1: be tools doing this research down here. Okay. Going and
S1: doing separate lookups to find research. Like you could do
S1: this at really in-depth level for each stage here, especially
S1: this one where you're doing the research and then it
S1: comes back and says supports or supports or weakens. Look
S1: at this. Supports supports weakens weakens partially supports. Then we
S1: have a conclusion ends up being a weak argument. Interesting.
S1: So you could see the reasons this is the most
S1: important thing. You could see how this argument was formed,
S1: how it was researched using what sources. Okay, look at this. Uh,
S1: recycling statistics 2021 National Waste and Recycling Association 2022. So
S1: we actually have source names here, and we can go
S1: into even more depth if we do additional AI work
S1: on this. And that's kind of a foreshadowing for future
S1: section here. So let's just keep going. Again, what this
S1: does is it takes an argument like this recycling example,
S1: and it turns it into a graph like we just saw. Okay,
S1: the most important thing about this graph is that we
S1: could throw it up on a board. We could throw
S1: it on the side of a wall. We could each
S1: view it inside of our AR glasses or whatever in
S1: the in the near future. And now both of us
S1: who are having this discussion can be looking at the
S1: same thing and saying, uh, yeah, I agree with these two,
S1: but I don't agree with this one. And the reason
S1: I don't agree is because I don't trust the source
S1: that it came from. And then you could do things like, okay,
S1: let's grab some content from other sources. Boom. You start
S1: adding other sources to it and maybe it changes based
S1: on those sources. It changes the conclusion of that subpart,
S1: of that argument. And now the whole thing updates. And
S1: the conclusion is maybe, oh, we're not sure. Or maybe
S1: it switches it from yes to no or up to
S1: down or whatever. The point is transparency. Okay. Too many
S1: of our conversations about any topic, it becomes emotional because
S1: it's too difficult to keep all these things in our
S1: minds at once. Okay, a basic argument like it was
S1: just made here about recycling has multiple sub claims in it,
S1: and those individual sub claims need to be backed by data.
S1: And it's very hard to do that just in our
S1: brains when we're in the middle of a conversation, especially
S1: if you're trying to like blast this out. You want
S1: to have this conversation, you want to say, hey, recycling
S1: is good or recycling is bad, or whatever the topic is,
S1: but you try to put that on social media or somewhere.
S1: You're basically writing a text thing, right? Which is trying
S1: to convince someone, which is fine. We've been doing that forever.
S1: But imagine where we could do this graphically, where we
S1: could do this visually and show the connections of how
S1: strong an individual claim is based on how much you
S1: trust an individual source. Right. So that's the power of
S1: this thing. And this is why I'm so excited about it.
S1: And there's many, many more examples we're about to get into.
S1: So each of those objects in that diagram is another
S1: substrate component, right. The claims, the sources, they're all in
S1: there and they're all in there inside of another repository
S1: inside of the substrate project. So here's an example of
S1: a source. New York Times, Associated Press, Breitbart. When people
S1: make truth claims, it's important to be able to fact
S1: check or research those claims to see their support and substrate.
S1: Does this by maintaining a list of those sources that
S1: we may or may not trust. That's the point. Some
S1: people will trust different sources. Some people will not trust
S1: different sources. Right. And that's up to them. Then it
S1: just becomes a question of, okay, well, you should trust
S1: this source and that becomes an argument by itself. Right
S1: Now you can make an argument. Well, you're putting too
S1: much weight onto a particular source or set of sources,
S1: which let's look at a bunch of claims that they've
S1: made recently and let's separately research those. And maybe that
S1: argument is strong enough to make them discount that particular source,
S1: whether that's The New York Times or Breitbart or whatever
S1: it is. Right. So when somebody makes an argument or
S1: a claim within an argument, it can be linked directly
S1: to those sources that you do or do not trust.
S1: And you can see the full argument and all of
S1: its support. In one visual look at this claim. Inflation
S1: fell by 2% under this particular person's term, right? This
S1: argument includes this claim. This claim has this source, the AP,
S1: this claim has this source. New York Times. So this
S1: argument has a particular strength as a result of that.
S1: So argument to claims to sources. And this is why
S1: we're so excited about substrate. It's going to make things
S1: that used to be almost impossible to discuss actually approachable
S1: actually discussable, because we could break it down into its
S1: individual pieces. So before you'd be like, you're just not
S1: able to counter all my arguments and evidence because I'm
S1: too smart. And now you could say, look, here's my argument.
S1: Throw it up on the board. Maybe it's like a
S1: little bit in the future or, you know, Apple Vision
S1: Pro or whatever. And now it's actually a 3D thing.
S1: You could turn it around like a piece of DNA
S1: or something, right? Show me which claim you disagree with
S1: or which source you disagree with that backs up those claims. Right.
S1: So now we can just take this thing apart using
S1: different sources or whatever and change the output. This will
S1: enable far more logical and precise discussions that that is
S1: what we're going for. This is just one example. This
S1: is just the arguments example. So now I want to
S1: take and like back up a little bit and think
S1: about real world use cases for substrate. Overall okay. Sounds
S1: really cool. What do you actually do with it. Exactly.
S1: So this is my favorite. Keep in mind, this is
S1: very early. It literally came out like two days ago.
S1: But we've already got multiple use cases for this thing okay.
S1: So let's get into these okay. The first one here
S1: is describing yourself. This one is massive. Look at this.
S1: This is pretty cool art. This uh, from Midjourney. It's, uh,
S1: part of a design that I did for a previous piece.
S1: But anyway. Yeah. So you can see these are meant
S1: to represent the different connections of like, uh, goals to
S1: mission to problems that she thinks are most important in
S1: the world. Solutions. She thinks, uh, are the answers to
S1: those problems. Different strategies, different projects she's working on. That's
S1: what this, uh, AI art is meant to represent with
S1: this graph. Little function here. But then you can have
S1: labels assigned to that. And keep in mind this is
S1: me looking at her in this coffee shop through my
S1: AR glasses and seeing, okay, first of all, purple is
S1: associated with engineering. Boom. She's an engineer. She's known for
S1: being a friend. She's known for being a writer. So
S1: I'm now seeing this person as she is, which she
S1: has previously defined. So let's get into that. Many people
S1: have trouble describing who they are and what they are about.
S1: This is so critical with substrate, you can basically describe
S1: yourself in any way you want to write text, audio, video, whatever,
S1: even have a conversation with AI, which is not part
S1: of substrate yet. But uh, there's lots of different ways
S1: to do that, and it will be able to both
S1: articulate and visualize you as a person. This is absolutely insane.
S1: People have trouble describing who they are and what they're about.
S1: This is going to help you do that. And if
S1: you share your context or your substrate representations with others,
S1: they'll be able to see what you're about. They'll be
S1: able to see this. Okay. Now, of course, the visuals here,
S1: the AR stuff, I mean that's going to take some
S1: time that that's a separate project, right? That's just visualization
S1: of data that's already there. Substrate is about creating that
S1: data for you for organizations for whatever in these individual components.
S1: So learning a person's values substrate will be a wonderful
S1: way to start learning about someone or something, what they
S1: care about, how they see the world, whatever. So imagine
S1: having something like this available when you're looking at someone
S1: or you're researching them. Okay, check this out. We got
S1: a person here. Okay. Got this guy. It's like the
S1: same coffee shop programmer, gamer, organizer. And you've got this
S1: grid here, right? Or this graph here. So check this out.
S1: Mission merged gaming and programming, continuous learning, community building innovation
S1: and game dev values. Goals, projects, annual Pixel Jam AI,
S1: MPC framework. These are things that he's working on to
S1: further these particular projects. And these are the goals that
S1: he's shooting for, for the mission of merging gaming and programming.
S1: That's insane. So now I know about this person, and
S1: if there's matches, then I could walk up to him
S1: and be like, hey, I hear you're into this, or
S1: I hear here you're into that. And now I have
S1: a conversation to start. Or maybe my eye is having
S1: that conversation with his eye. Whatever. It depends how far
S1: in the future it is. But this will be a
S1: wonderful way to learn about what somebody really cares about
S1: and how they see the world. So check this out.
S1: They believe the most important problems are these three problems.
S1: This is really cool, and they believe the best solutions
S1: are these solutions to those problems. And they intend to
S1: track progress using the following KPIs. Boom, boom boom. Here
S1: are the KPIs. So imagine if you match up with
S1: somebody across all these different axes right? You match with
S1: them on values, goals, beliefs, preferences. You could find friends,
S1: you can find people to have conversations with. You can
S1: find a mate, you could find a partner. This way
S1: you could find business partners this way. So we're very
S1: excited about the potential to spawn more human connection in
S1: this way. Okay, so the next one here is visualizing arguments.
S1: So when you have a given narrative or rumor or
S1: conspiracy theory going viral, you'll be able to use substrate
S1: to analyze the argument or claim and publish the results.
S1: We already talked about this one. We already showed it.
S1: So I'm just going to show another argument here and
S1: zoom in. Pretty heavy. Look at this. We never went
S1: to the moon. Look at this. Look at this. We
S1: never went to the moon. Contradicted by contradicted by leads
S1: to claim moon landings were faked. Look at this claim.
S1: We never went to the moon. It leads to moon.
S1: Landings were faked by NASA. Okay. And we have actions
S1: which is contradicted by leads to. And then we have
S1: another claim which is the red one. And then look
S1: at this provides attempts to explain demonstrate. So again the
S1: actions or the verbs. And then we have results. So
S1: reflectors on the moon consistently reflect lasers back from the Earth.
S1: I didn't think of this one. I actually dynamically created
S1: this one just just for the launch of the project.
S1: And that was a great one. Okay, what is bouncing
S1: lasers off the moon if there aren't reflectors on the moon,
S1: which means somebody put them there. So I guess maybe
S1: that would take you into aliens, but that's a separate topic. Okay.
S1: Multiple countries have independently verified moon landings. Over £800 of
S1: moon rocks have been studied worldwide, so you've got a
S1: collection of evidence that starts to add up. Conclusion. False.
S1: Overwhelming evidence supports moon landings, and this is just one
S1: level deep. You can go even deeper with sub claims
S1: and sub claims and pointing to the various sources with
S1: actual citations, like you can go as far as you
S1: want with this and have it all stored right there
S1: inside of substrate. So you'll be able to see, for example,
S1: that they're making the following arguments, which include the following claims,
S1: which we fact checked using the following sources which resulted
S1: in the following results which, using the following methodology, leads
S1: us to this conclusion. So I don't know if you
S1: remember Snopes, but Snopes was a basically a rumor denier
S1: or approver. It was like, yes, this sounds like it's
S1: not true, but it's actually true. Or it would be like,
S1: that sounds like it's true, but it's actually not true.
S1: But this is like Snopes, except for in a way
S1: that you can visually explore and you can individually validate
S1: each particular component. So any particular background can now evaluate
S1: this with more transparency than it's ever been possible. And
S1: they could see the pieces. And of course people will
S1: be able to add all their favorite sources. Right. They
S1: could build arguments, they could evaluate the same argument using
S1: a different set of sauces and see if the conclusion
S1: comes up different. So this is why we're excited about
S1: this particular argument piece, which we've spent a lot of
S1: time on, because it's really important. This has the potential
S1: to significantly strengthen our shared understanding of reality, and will
S1: allow us to disagree with each other in a far
S1: healthier way. Here's one for the claim that there's a
S1: tiny teapot orbiting the sun. Okay, so there's a tiny
S1: teapot orbiting the sun investigated by space mission data claimed
S1: the teapot is too small to be detected by our
S1: current instruments. Result no unusual objects detected. No evidence of
S1: teapot found. Support. Support. Conclusion. False. Insufficient evidence to support
S1: the claim, which is exactly what it should say, right?
S1: You can't prove that it's not there, but not being
S1: able to prove that it's not there is not evidence
S1: that it is there. Right. So and that's what we
S1: end up getting to okay. So this is all additive.
S1: It all starts compounding and adding in with itself okay.
S1: So now we're talking about substrate plus I leading to
S1: actual action. Yeah yeah yeah I this I that uh,
S1: totally get it. But this is different. This is not
S1: about I okay. The substrate is not about I. It's
S1: about human meaning and progress. I is just a tool
S1: for helping that along. So think about this with everything
S1: you've heard so far about substrate and what's simultaneously happening
S1: inside the world of I. So context sizes, which is
S1: basically the size of prompts that you can use, are
S1: increasing massively. And inference costs, which is basically a fancy
S1: way of saying the cost to run individual AI pulls
S1: or questions are massively falling. Okay, what this means is
S1: we can basically like chocolate and peanut butter this thing
S1: together with Substrate's ability to have all of these things
S1: stored in very neat, structured ways and being seen together
S1: in graphs combined with AI's ability to hold multiple things
S1: in its mind at once, and then perform really cool
S1: actions and answer questions and make recommendations. So this combination
S1: is absolutely insane. So we could feed AI with our goals. Okay,
S1: us as an individual, us as a county, us as
S1: a city, us as a company. Us as a country. Okay, goals, KPIs,
S1: risks and have it help us untangle these, discuss them,
S1: debate about them, vote on them, whatever, and then take action. Okay,
S1: so here's some of the examples that we're most excited
S1: about for for this combination of substrate plus I. So
S1: first one science automated hypothesis to results workflows. So one
S1: big problem with science is that it takes so long
S1: to do science. And this is why it takes so long.
S1: Because look at look at all the different things you
S1: have to go through. It's hard to come up with ideas.
S1: It's hard to design experiments. It's hard to find funding
S1: to do the experiments. It's hard to interpret the results.
S1: It's hard to publish the results, and it's hard to
S1: get those results in front of the right people who
S1: actually have influence and might want to do something. So
S1: now imagine that we have our list of problems, our
S1: list of proposed experiments, all within substrate, a list of
S1: funding sources, also within substrate. They're all there. Now I
S1: can help us to do almost every step in that
S1: difficulty chain we just talked about above. Okay. So I
S1: can help us come up with or collect ideas and hypotheses.
S1: It can help us design experiments. It can help us
S1: collect and evaluate the best funding sources, because it's got
S1: the full list right there. So it could be like, okay,
S1: based on this, based on what your goals and your
S1: mission and what you're trying to do here, these are
S1: the types of groups that are most likely to want
S1: to fund you. Okay. Requesting funding now that it knows
S1: those groups, maybe it knows how to contact them because
S1: that information is inside a substrate. Okay. So we can
S1: write the perfect pitch for you to get funding, help.
S1: You set up the experiments, and we're going to need
S1: some humans and or robots to help with that part.
S1: But whatever. Running and monitoring the experiments, interpreting the results,
S1: all of this really, really right in the wheelhouse of AI,
S1: writing the paper and sharing the paper also possible with
S1: AI and getting better all the time. So in other words,
S1: we're talking about hypothesis to propose experiment to looking at
S1: funding sources, to acquiring funding, to running experiments, to publishing
S1: the results, to actually making progress. So in the beginning,
S1: this is still going to require a lot of human help, right?
S1: Especially at the idea and the running of the experiment phases.
S1: But over time, I will get better at that as well.
S1: But what we're talking about ultimately with this right here
S1: is the acceleration of science. Right. Because this cycle right
S1: here is leading to a whole bunch of failures and
S1: making us move on to do something else instead. But
S1: this whole pipeline, and I want to give credit to
S1: Joseph Thacker for thinking about a very similar thing at
S1: a similar time, like a year ago, talking about experiments
S1: and hypotheses and testing and stuff like that. So a
S1: lot of people probably thinking very similar things along these lines.
S1: But we are so excited about this. The idea of
S1: just being able to experiment and advance our knowledge forward
S1: through the most powerful mechanism that we're aware of, which
S1: is science accountability. Okay, this one's insane. Monitoring crime and
S1: corruption okay. So the reason it's so easy to get
S1: away with corruption and crime right now is because there
S1: aren't enough people watching gangs, cartels, embezzlers, dirty politicians. They're
S1: actually dropping evidence all the time. There's receipts, there's like travel,
S1: there's tickets, there's cameras. There's lots of different ways to
S1: know that a particular person was in a particular place,
S1: and they're not even being that careful, because it's actually
S1: so difficult to go and collect that stuff and bring
S1: it together into a narrative. So it usually takes a
S1: major journalist team or a massive law enforcement operation to
S1: dump thousands of hours of highly skilled work to collect
S1: all this different evidence. Okay, then you have to do
S1: the analysis. Then you have to formulate the conclusions. Then
S1: you have to document all of this. Then you have
S1: to like take it to the media. You have to
S1: get in front of people. And most crime and corruption
S1: slips by because nobody is simply watching. There aren't enough journalists.
S1: There aren't enough law enforcement teams who have the skills
S1: to do this stuff. And even if they had the skills,
S1: they don't have the resources and the time. So substrate
S1: plus AI versus dirty politician, this is a use case.
S1: So let's take substrate with some AI added on. And
S1: let's think about a dirty politician who is taking massive
S1: gifts from a particular lobbyist. Let's say this is some
S1: dirty Democrat, some dirty Republican. Independent doesn't matter. This has
S1: nothing to do with that. Problem is, there are so
S1: many donations. There are so many lobbyists, so many representatives,
S1: so many actual laws and bills and so many votes.
S1: But guess what? It's all public. We're talking about the
S1: US here. This is all public information. It is required
S1: by law that all the stuff is posted. The lobbyist
S1: groups must actually register themselves. The donations that they make
S1: to any particular representative, they have to be public records
S1: of meetings. I believe those are also public, and so
S1: are all the votes that representatives make on bills where
S1: lobbyists have been donating and trying to influence. So a
S1: nonprofit or even just a project, a small project that
S1: comes out of substrate or whatever, just a bunch of
S1: open source people could use AI to collect all of
S1: these different things, continuously put them in substrate, and they're
S1: already going in substrate because we're about to start dumping
S1: all the laws, all the different voting records, all the
S1: different histories for each lobbyist and also for each representative,
S1: all that goes right into substrate to inspect. And then
S1: I can ingest all of that at any given moment
S1: and basically tell us this. Here are all the bills
S1: written by that person. Here are all the summaries of
S1: those bills. Basically, what are they trying to do? Here's
S1: who those bills helped and who they hurt. Right? Here
S1: are all the lobbyists that care about those particular issues.
S1: Here are all the donations that those groups made to
S1: those representatives or that particular representative. And here's how the
S1: representative voted on every single bill. Then guess what? The
S1: AI is really good at, which we already know. Okay.
S1: Perform a comprehensive analysis of all legislation created and voted
S1: on from Bill Myers, Senator from Arkansas. I made that up.
S1: Hopefully there is no one like that cross-referenced with every
S1: single donation ever made to him, every dinner he's ever
S1: attended with them, every gift he's ever received, etc. finally,
S1: give me your assessment of whether or not he is
S1: being unduly influenced by this lobbyist and give me your
S1: reasons for this conclusion. Then it comes back with something
S1: like this assessment. This is a compromised politician reasoning. Osint
S1: reveals you can have Osint going and researching various things
S1: that this person is doing. Again, all public, all legal. Right?
S1: We're not talking about nasty stuff here. We're talking about legal.
S1: Public documents reveals that he was illegally gifted a small
S1: yacht last year, which he tweeted about and later deleted.
S1: He's had 31 dinners in the last 18 months with
S1: them totaling over like almost $15,000. Osint reveals that the
S1: Lobbyist's president used considerable influence to get Bill Meyer's daughter
S1: admitted to an exclusive private school that she wasn't actually
S1: qualified for. Every vote he's made about this particular issue
S1: has been in the direction that the lobbyist actually wants
S1: to happen, and previous votes before they started the relationship,
S1: the politician used to vote in the opposite direction. Therefore,
S1: the conclusion from this I. And by the way, you
S1: published the algorithm for the AI as well. We've got
S1: another project called fabric, where you publish the actual thoughts
S1: and directions given to the AIS so that they're inspectable. Right?
S1: You you don't want people to think, oh, they came
S1: to that conclusion because it's biased. No. You can look
S1: and see the actual instructions given to the AI to
S1: evaluate this thing impartially. So now you could trust this
S1: thing with a very high rate of confidence, because you
S1: could see how it's thinking and you could see all
S1: the data sources, just like before with arguments. So basically,
S1: the incredibly important objects of legislation, votes, etc. are all
S1: things that can be monitored and collected and stored within substrate.
S1: So that's that one next one here. Leadership. This is absolutely,
S1: absolutely powerful okay. I've been a consultant for a couple
S1: of decades now and I've worked with hundreds of startups,
S1: so many large corporations, and a big, big problem for
S1: most organizations. And that includes governments, families, individuals, startups, corporations.
S1: Everything is that they don't have clarity. Just like a person.
S1: It's hard to know what they think the issues are,
S1: what they specifically plan on doing, and how they plan
S1: to measure progress. So we see this with business leaders
S1: and we see this with politicians. So with substrate, we
S1: intend to make it so that check this out. Every
S1: leader will need to have a full detailed plan that
S1: has the following components. Imagine if everyone had this when
S1: they were pitching some plan for the high school, or
S1: some plan to be a principal, or some plan to
S1: be a community leader, or some plan to be a
S1: politician or whatever it is. Here's what I think the
S1: problems are. Remember, this is a problem object inside of
S1: substrate so you can actually look at them. Here's what
S1: I think the problems are. And coming off of the
S1: problem here's what I think the solutions are. Here are
S1: my proposed strategies for accomplishing that. Here are the KPIs.
S1: These are the metrics. Here's how actually going to measure myself.
S1: And how about this fire me if I don't get
S1: the KPIs to this number by this date. So at
S1: the end of the three and a half years when
S1: I'm going back up for reelection, I expect whatever the
S1: thing is, uh, the number of kids who don't go
S1: hungry in this high school, the literacy level in this
S1: high school, the unemployment rate, whatever it is, doesn't matter
S1: if I don't improve that number by X amount. By
S1: this date, you have my permission to vote me out.
S1: Imagine having that level of clarity and accountability for any
S1: leader trying to get any job doing anything. So really
S1: excited about that one. Okay, next one here. This is
S1: the best one okay. I saved the best for last.
S1: These are all adding on each other. Watch this. Look
S1: at this. I did a post a while back about
S1: how companies are essentially graphs of algorithms, and I encourage
S1: you to go check that out. I've got the link
S1: down here below, but it's like, okay, you have a
S1: company that processes bad images come in, they send them
S1: to you on the website, and you then do a
S1: number of things to that photo, right? You do a
S1: high quality scan, you repair it, you stylize it, you
S1: caption it, you send it to the user. Then there's
S1: a marketing group. A marketing group has an idea. It
S1: shares it with a team. They decide on the best version.
S1: They do this, they do a final vote. They do
S1: a launch campaign. Okay, or the uploading process. Visit the
S1: website create account, click the upload link. These are all
S1: individual steps okay, these are just algorithms. And this is
S1: a graph of algorithms where they're all connected. And you
S1: can break these into smaller and smaller pieces until you
S1: eventually see the world in this way. Okay. I'm just
S1: going to click into this thing just to highlight this
S1: a little bit. This is what it ends up looking like. Okay.
S1: It looks like this is what your companies are going
S1: to look like before too long okay. Not your companies.
S1: Everyone's company okay. AI consultancies are going to come in.
S1: They're going to see the world in this way. Everything
S1: is a process. Everything is a set of components. Okay?
S1: You got human decisions here. You got human teams here.
S1: Maybe the red are all the places that it's human
S1: and AI wants to replace that. Maybe the red is
S1: it's a little bit too manual. Maybe the red is
S1: the efficiency numbers are too low or it takes too long.
S1: Doesn't matter. You could drill in. You could see this
S1: is actually a human team. They actually need more people.
S1: We need to hire more people, etc. the point is,
S1: you can look at any company or any process as
S1: a series of these components, which is part of a
S1: larger process which has a flow. Okay. That's the important
S1: part here. Okay. Yeah. This is the piece here. Companies
S1: are just graphs of algorithms. But think of it this
S1: way I don't think I went far enough with that.
S1: Everything can be conceptualized in this way. Everything can be
S1: conceptualized in this way as a process. So essentially what
S1: you have is you have a current state of things, right?
S1: State of the universe, but smaller down to a scale
S1: that we're dealing with, like a company or a process
S1: or a team or a department. Okay. Then we have
S1: an action or event, like a decision is made. Um,
S1: you know, customer does a particular thing. They buy a thing.
S1: We need to pay out an insurance policy, whatever the
S1: thing is. Right? And then that results in a new
S1: state of things. So previous state action thing happens a cause.
S1: And then you have like an effect which is the outcome,
S1: which is the result. And if we add human components
S1: into this like people's jobs or making decisions, and we
S1: do this for like running a business or a country
S1: or family, we have additional pieces, right? We have people,
S1: we have decisions, we have strategies. We have lessons learned.
S1: We have conclusions. We have reasons. All of these are
S1: substrate components. There aren't infinite numbers of these. These are
S1: all look, what was the decision made? There aren't that
S1: many decisions okay. Buy more stock. Um, hire more people.
S1: This is the type of decision in there, right? Again,
S1: which we can connect and see visually. And what that
S1: results in is a way to tie together all of
S1: this into much larger graphs, graphs that we could use
S1: to describe the operations of anything a family, company, or
S1: even a country. So here's one for a small company.
S1: And I made this live. This is actual like I
S1: put in some fake company stuff. Look at this. No
S1: customers website visitor quality lead. Yes. No. Okay. Goes down.
S1: Look at this. Breaks down the thing. This is how
S1: we're about to see our companies. We're about to see
S1: all of anything that humans do as a set of
S1: processes like this. And it's fine if you want to
S1: resist this, but trust me, this is coming because the
S1: consultancies are coming. I got to stop for you. 40%
S1: of McKinsey's business right now is already in AI consulting. 40%.
S1: This thing happened like a year and a half ago.
S1: They are already doing almost half their business doing this.
S1: They're doing this. They're coming in. They're looking at companies
S1: and saying, what can I optimize? What can I improve?
S1: So that's pretty cool. We can create that. But that's
S1: not the full power of this yet. Check this out.
S1: The smarter the AI gets, it's not only going to
S1: see this and describe it and make some recommendations, but no,
S1: it can optimize the stuff. In other words, this is
S1: just the current state. What about future state? What about
S1: recommended state? Should this company merge departments? Where can we
S1: add more people? What kind of people should we hire?
S1: Which processes here are inefficient which can be replaced by AI?
S1: That's going to be the number one question somebody is asking.
S1: Where can we use more human decision making? If we
S1: wanted to grow, where would that happen? Now imagine this
S1: for a family, a corporation, a church, a city, a county, etc.
S1: and keep in mind, the more data that you have here,
S1: the better, right? You can. You can pull in all
S1: the data about conversion rates, churn rates, like whatever data
S1: you have that comes in. That's part of this whole equation.
S1: So here's an example for a security team because my
S1: background is in security. So it currently takes 3.5 business
S1: days to complete a security assessment. Delays in security assessment
S1: turnaround are the number one complaint in the engineering survey.
S1: So the security group sends a survey to engineering and says, hey,
S1: what do you love and what do you hate about security?
S1: This is their number one complaint. It takes too long
S1: to get stuff back. So you're slowing us down. So
S1: if we switch to the new flex scan model, which
S1: is a new model for doing security assessments in a
S1: more flexible way using fewer generalist security testers were able
S1: to complete type B and type C assessments 94% faster.
S1: This will give our senior testers two extra days to
S1: do a high impact security assessments, and this will also
S1: likely make engineering much happier with security and make them
S1: more likely to cooperate on our goals. So there's multiple
S1: steps the full articulation and breakdown of how a process
S1: is currently running. Visualization of that process to help with
S1: human understanding, and then analysis by AI of how to
S1: optimize the process to optimize the stated goals of the entity.
S1: That's the three pieces. And remember, the AI will also
S1: have access to the mission of the organization as well,
S1: and its goals and its strategies and its team members
S1: and its projects and its budget. Everything. So we'll have
S1: the full context on how resources are being spent relative
S1: to the desired outcomes, and we'll be able to see
S1: how the actual KPIs that we care about, the actual numbers,
S1: the actual metrics, are moving as we adjust these. So
S1: it'll be able to do all sorts of recommendations hiring
S1: new people, hiring people with specific skills, using more AI
S1: and high volume and low creativity areas, adjusting strategies based
S1: on goals and market conditions. Canceling projects like this one
S1: and that one. Like they're not even related to the goals.
S1: They're costing way too much. Way too much of the
S1: team is working on them. Let's get rid of those.
S1: This project over here, this human powered project, it's way better.
S1: It's producing way more revenue. It's way more tied to
S1: our actual goals and KPIs. So we're taking those people
S1: and putting them over here instead. So ultimately we're talking
S1: about the ability to continuously analyze and optimize any system
S1: using full knowledge of its goals and progress. And the
S1: more data about the system it has, the better it
S1: will actually work. And the smarter the AI gets, the
S1: better it actually works. Completely insane. All right, to summarize,
S1: the world is hard to understand, and things that are
S1: hard to understand are hard to discuss and improve. The
S1: goal of Substrate is to address this problem by making
S1: the things humans care about more visible. Discussable and Improvable.
S1: The framework is open source, lives on GitHub. At its core,
S1: it's a collection of crowdsourced lists of the things humans
S1: care about and that make up our discourse and society.
S1: One major problem that people and organizations have is not
S1: knowing and or being able to communicate what they are about.
S1: Using this framework, people and organizations will be able to
S1: articulate their values and purpose more clearly, which will not
S1: only help them, but everyone that they interact with. Substrate
S1: is magnified by I because I can or will soon
S1: be able to hold all of substrate in its mind
S1: at once. And from there, we'll be able to ask
S1: all sorts of meaningful questions such as what does that
S1: person or organization about? Are we pursuing the best path
S1: towards our goals, or what are the most critical mistakes
S1: I'm currently making? Ultimately, this will allow us to take
S1: action on these things. What action should I do right
S1: now to optimize this workflow? What should I do right
S1: now to achieve the best possible outcome that's aligned with
S1: my goals? In short, substrate is a way to better
S1: understand and optimize the things that we care about as humans.
S1: So we would love for you to get involved. I
S1: want to give thanks to people who are already involved
S1: and are like, really, really into this. Along with myself,
S1: Jonathan Dunn, Jason Haddox, Clint Gibler, Joseph Thacker, Joel Parrish
S1: and Robert Hansen, some of the few that are already
S1: involved and excited about this. And if you are interested,
S1: please go to the Substrate Project and, uh, let us
S1: know that you'd like to contribute. Thanks for your time.