S1: Hey, what's up? So there's a common narrative right now
S1: that you don't need to worry about AI. If you
S1: are a knowledge worker, because humans are special and humans
S1: are unique in the way they approach work. They are
S1: better workers than AI. AI is just a text generator.
S1: It's just an LLM. It has no understanding. It has
S1: no knowledge. It doesn't really understand anything about what it's
S1: trying to do. It can't possibly replace human workers. This
S1: is a very popular narrative for multiple reasons. One, it
S1: makes us feel better. It makes us feel more safe
S1: because people are feeling afraid for good reason right now.
S1: And it's just a very common thread right now, basically saying, relax,
S1: you don't have to worry, right? AI is actually kind
S1: of garbage and humans are basically awesome. You don't need
S1: to worry. I think this is wrong. And more importantly,
S1: I think it's dangerous. I think it's wrong for a
S1: bunch of reasons, which I'm about to talk about. I
S1: think it's dangerous because if you adopt this mindset, if
S1: you accept this argument, you will not properly prepare to
S1: get ready for what is coming, right? So this is
S1: a polar opposite argument to the one that's being made.
S1: And keep in mind, it's being made by some very
S1: smart people. Right? I've got a friend we're going to
S1: talk about Marcus Hutchins, very smart security guy, a friend
S1: of mine, and we debated this once. Might do it
S1: again in the future. He's very smart. He's very technical,
S1: he's very knowledgeable. And he has this argument. He basically says,
S1: don't worry, it's overinflated. AI doesn't understand anything. And it's
S1: not just him. There's, you know, hundreds of people out
S1: here saying this very loudly and more importantly to me
S1: with a following, right? Marcus has a following. And there's
S1: people who have much larger followings than Marcus and me
S1: who are saying pretty much the same thing. And so
S1: I want to really come at this argument directly, and
S1: I want to start by talking about what work looks
S1: like inside of companies and sort of just like break
S1: down common things that I see all over the place
S1: and have seen over the course of my career. All right.
S1: So normally in these videos, I don't talk about my background.
S1: It's because I just don't care about my background. I
S1: don't care about other people's background. Usually. I mostly just
S1: want to hear the content. I want to hear the
S1: ideas that they're giving me, or if it's a tutorial
S1: or whatever, I want the content, right? In this case,
S1: I'm making a lot of claims about how businesses function,
S1: how they work. And so I think it raises a
S1: natural question which should be asked, which is, why should
S1: I believe you? Why do I think you understand anything
S1: about these companies? And the answer is, I've been working
S1: inside of companies for like over 25 years. My background
S1: is strictly information security, but I've held senior roles at Apple.
S1: I've built a program and a product there which is
S1: still running. I've been gone from there for a number
S1: of years now. I've worked at Robinhood, HP. I've worked
S1: in embedded in very large energy companies. Tons of experience actually,
S1: in the fortune ten, let alone like the fortune 100.
S1: And that's just the places that have actually been deeply
S1: embedded in as a consultant, like basically a full time
S1: worker there or working full time consulting though, which I
S1: think is even more important for the context here. I've
S1: actually worked for hundreds of companies in like the top
S1: 10,000 companies in the world, like hundreds over these 25 years.
S1: Inside of all these companies, I have seen these problems
S1: that I'm about to describe over and over and over.
S1: And in fact, when I describe them, I'm sure you
S1: have also seen them. If you've worked in multiple companies
S1: over the course of your career, all of these are
S1: going to be extremely familiar. They're like not even controversial,
S1: just a little bit of background. That's where I'm coming from.
S1: And I just want to jump into this first part
S1: here talking about the companies and their problems. So the
S1: first thing to say about working in a, say, a
S1: top 10,000 company or whatever, just an average company like
S1: medium size, fairly large, we can talk about startups as well.
S1: But in general, the natural state of thinking is like,
S1: everything seems to be fine. Like, I don't even know
S1: why we're talking about bringing AI into these companies. Isn't
S1: everything going well? Like we have a good economy. Like, well,
S1: we have had a good economy, right? We've built plenty
S1: of companies. We've got plenty of market share. The Dow
S1: was doing great or whatever for all these years, all
S1: these decades. So what actually is the problem? The way
S1: I answer that is most companies are actually a giant
S1: soup sandwich or a football bat. These are military terms,
S1: but essentially there is absolute chaos in most companies. Most companies,
S1: it's actually difficult to find out what they're actually trying
S1: to do, what they're actually trying to build, especially as
S1: like a worker, right? It's very hard to get a
S1: clear message of like, what exactly is the mission? This
S1: is also the case for like fairly large startups as well.
S1: It starts to become a problem fairly early, you know,
S1: a few years into the startup, the vision is not
S1: super clear. Okay, so the first problem I want to
S1: talk about that I see in a lot of these
S1: companies and that I'm sure you've seen as well, is
S1: the vision isn't always very clear. In fact, sometimes it's non-existent,
S1: sometimes it's not conveyed all the way through the company.
S1: So people like aren't really sure exactly what they're working
S1: on and why. And so that's a huge problem. Another
S1: one is it's not clear. And this is kind of
S1: like fighting for top here. It's hard to say. These
S1: are really big issues. Hard to sort them. But another
S1: really big one is there aren't really SOPs. There aren't
S1: really standard operating procedures. That's what we call them in
S1: the military is SOPs. Most companies just call them processes,
S1: but like, how do you actually do things? And the
S1: problem isn't that there isn't some document somewhere that says
S1: how to do things. The problem is there might be
S1: multiple of them and they change all the time, or
S1: they decay over time, or people are referring to the
S1: wrong one. Okay, so it's like things just keep getting
S1: done incorrectly throughout the company. And the more layers of
S1: management you have, the larger the company, the longer the
S1: company's been around. The more employees you have, the worse
S1: this problem gets. Now I want to caveat something. There
S1: are certain companies, there are a few companies, very few
S1: companies who do this really well. So if you are
S1: one of these people who works at these companies or
S1: you're a manager at one of these companies and you're like, well,
S1: that's not true. I work at Google and it's pretty great.
S1: Or I work at so-and-so Giant Bank, which has had
S1: decades to figure this out and sort it out. Also,
S1: in energy companies, they tend to be extremely risk averse,
S1: and they have processes that they follow and they're really
S1: good at processes. I'm talking about the other 99.9%. Most
S1: companies it's not clear exactly how something is supposed to
S1: be done. And what ends up happening when this is
S1: the case is you have people doing work and the
S1: output is massively inconsistent. Okay? Massively inconsistent, different people doing
S1: that same job, even using the same document. They do
S1: the job differently. You know, it's common knowledge within the company. Oh,
S1: if Sarah does it well, yeah, sure. I love when
S1: Sarah produces this report or produces this security assessment or
S1: produces this artifact that we're going to send to the customer,
S1: she does it really well. Well, guess what, Jim over here,
S1: he looked at the same process. He got the same training.
S1: He had the same onboarding. His output is absolute garbage. Okay.
S1: That's even if there is a process in the company,
S1: oftentimes there isn't. It was just like, you know, common onboarding, right?
S1: You work at a company or whatever you come in,
S1: it's like, hey, you know, here's where you put your keys.
S1: We get lunch over here, you know, basically your day
S1: to day is going to be, you've got to take
S1: these emails, you've got to take it from here. Oh,
S1: make sure you don't send it to Mr. Johnson like this.
S1: He really hates that. Right. And so these are all
S1: being sort of loosely like stored in your mind. Let's
S1: say you're some sort of admin or something like that, right?
S1: So this admin named Chris suddenly starts learning all these
S1: things during onboarding. Yeah. Mr. Johnson doesn't like this. Make
S1: sure the format, if you send it to the group,
S1: make sure to include these people. Right. You're starting to
S1: get all these little things that you're picking up, either
S1: from the training or someone complaining about something and it
S1: starts accruing knowledge, right? The problem is that's stuck inside
S1: that one person's head. And based on how knowledgeable they are,
S1: based on how good they are at listening, you know
S1: how good they are at following processes like, did we
S1: actually write this down or is it stuck in my
S1: brain and me? Chris, can I just leave the company?
S1: And now that whole process has to start all over again.
S1: Very common stuff. You've seen this a million times. Another
S1: thing you've seen related to this million times is there
S1: are people in the company all throughout the company who
S1: just never, ever got good at doing the job. They
S1: never got good at following the process. They they don't
S1: produce good output. It's really, really hard to hire people
S1: in companies. It's also really, really hard to fire people
S1: at companies. This person, Chris or Mike or Raj or
S1: Sarah or whoever, can just be horrible at doing this
S1: for whatever reason. Maybe it's nepotism, maybe it's, you know,
S1: they're a nice person, maybe they're good at one little thing. And,
S1: you know, it's just you would hate to give that up. So,
S1: you know, they can stay on and keep breaking things
S1: and messing things up when they execute work other places.
S1: The point is this is endemic throughout companies. In a
S1: large company, there are dozens or hundreds, maybe even thousands
S1: of people who don't follow processes. They aren't good at
S1: their job at all. Right. Now, keep in mind there
S1: are also a players everywhere, right? There's a players everywhere
S1: and they do a fantastic job. They don't even need
S1: a process. They're awesome right. No one's disputing that there
S1: are awesome humans and that they can do work. That's
S1: not what this argument is about. This argument is about
S1: overall work. Okay, just a pop quiz here. Guess how
S1: much knowledge workers are paid every year? $50 trillion. That
S1: is the compensation roughly worldwide for knowledge workers. Okay, that
S1: includes the B players and C players and the D
S1: players and the F players, right? Forget the A players.
S1: We're not going to talk about them. Right. They're awesome.
S1: They're going to continue to be awesome with or without AI.
S1: Forget that we're talking about most work in most companies. Okay.
S1: So you've got these people. They can't follow the process. Okay.
S1: They produce bad work. Now you've got the fact that
S1: the job isn't that rewarding. They're not exactly sure what
S1: they're working on. Their bosses are changing all the time,
S1: by the way, their work that they're supposed to be doing,
S1: it's also changing all the time, right? Because the the
S1: company goals, you know, that they were given a long
S1: time ago, they change all the time. Guess what? That
S1: has to trickle down. It has to go down to
S1: a thing. Guess what that means. Meetings. Lots of meetings. Right?
S1: So your developer, you're trying to code, you're trying to
S1: do whatever. You're an engineer, you're trying to do a
S1: bunch of technical stuff. Nope. You got to get on
S1: this call. Why? Because so and so all these people
S1: need to have this thing explained to them. You already
S1: made a document. You already explained it to them 20 times.
S1: You've already been in 40 meetings about this very same topic.
S1: Nobody is listening. They keep doing the work. You keep
S1: having to correct the work that they did because they
S1: didn't follow the document, which you spent three weeks making, right?
S1: This is not even special. This is not like a
S1: surprise to you. If you've worked in any companies, most
S1: companies across your career, this is endemic now. Add on
S1: top of this, people are empire building. It's fucking Game
S1: of Thrones is what's being played out in real time
S1: across many companies for many employees, right? Managers are empire building.
S1: They're picking their favorite people also. What about when you
S1: have a good idea? How many times have you had
S1: a good idea? You submit it up through management because
S1: it actually would solve half the stuff that just ruins
S1: your soul. It crushes your soul to have to do
S1: this stupid work, which doesn't follow this process, which could
S1: easily be fixed by these things that you're recommending, right?
S1: You're like, look, if we just did this, it would
S1: cut four steps out of the process. It would cut
S1: four steps out of the process. Everything would be much better.
S1: I spent three weeks, I put together this proposal. I
S1: wrote all this documentation and they're like, yeah, yeah, I
S1: don't think so. Oh, by the way, you need to
S1: join this meeting. We're changing our goals again. Oh, by
S1: the way, you're going to report to Sarah now instead
S1: of Raj. And also we need you to redo all
S1: that stuff that you just did. And it's like, okay, great.
S1: Sounds awesome. Why do you think people dread Monday so much?
S1: Why do you think people on Sunday night are just, like,
S1: filled with this pit of despair because they get to
S1: maybe get in a car and drive for an hour
S1: to commute in, to go to another meeting, to redo
S1: their plans, hopefully try to get a bonus, right? Meanwhile,
S1: everyone around them is playing Game of Thrones and like
S1: breaking everything. Now I'm being a little overdramatic because there
S1: are some jobs where it's kind of chill and blah
S1: blah blah. It's everything's fine. But in general, it has
S1: been known for decades that this is a problem. It
S1: has been known that this dysfunction has been happening for decades.
S1: People were complaining about the same stuff. Look at office space.
S1: We've been making shows about corporate drudgery and stupidity for
S1: decade upon decade. We recently had severance soul killing work, right?
S1: You're sitting in the queue, blah blah blah. The manager
S1: comes by and smiles like nobody's having fun here. That
S1: manager's not having fun. You're not having fun. Why? Because
S1: your ideas are not getting into the system. You're not
S1: even clear exactly what you're working on and why. There's
S1: multiple layers separating between you and like whatever's happening at
S1: the top, which seems to change all the time, right?
S1: So this is not a good state. Okay. Human work
S1: is not the ideal state. Workflows, processes. What confidence do
S1: you have that if you have a nine step process,
S1: each of which has an SOP for it, and you
S1: give it to the new people that just came on
S1: and got onboarded and got trained, that the people are
S1: going to actually read the document and they're going to
S1: execute those nine steps. And at the end of it,
S1: you're going to have an artifact. You're going to have
S1: an outcome, a document project, whatever it is, and it's
S1: going to meet the standard. And what confidence do you
S1: have that that will also be the same the next
S1: time you run it. Because guess what, two of those
S1: people are on vacation and they didn't share their knowledge
S1: with anyone. Also, they didn't update the document because there
S1: was a change. This whole thing is chaos and decay
S1: and problems. Meanwhile, people are getting sick. People are changing
S1: jobs like every 2 or 3 years or whatever, depending
S1: on the industry. Then people are getting so like soul
S1: crushed that they're doing the bare minimum, right? I think 23%
S1: of total working time in a week is actually spent.
S1: Like 40 hours is like 23% of a full week, right?
S1: So people aren't working the full week. They're only working
S1: this little slot at work. Many people are doing the
S1: absolute bare minimum. They're making a fucking sport out of
S1: doing the least amount of work possible, right? That was
S1: also part of office space. Like this is extremely well
S1: known stuff. We are holding this situation up as the
S1: standard that AI can never achieve. AI can never, never
S1: pierce this because humans are just so innovative, right? It's
S1: absolutely insane to me. So another problem here is actually
S1: because I've been talking mostly about the worker, right? Mostly
S1: about the human doing this job, getting soul crushed. Let
S1: me tell you about the other side of it as
S1: a leader. Okay? Looking down because I've been here as well.
S1: So you're looking down at this company and you're trying
S1: to figure out you're looking with the CFO, you're looking
S1: at your headcount spend and you're like, okay, well, I'm
S1: paying $94 million on headcount across my entire staff, you know, engineers, admins,
S1: like all these different people. I'm paying $94 million and
S1: that's not even a lot of money. Let's just say
S1: that's a total cost of human compensation. Okay, what exactly
S1: are we getting? Can I see all of my projects?
S1: Can I see all of the work that's being done?
S1: Do I have a list of workflows that show all
S1: the different processes in my company, what the steps are
S1: and how they're actually being performed by the different teams?
S1: Do I know how much that costs? Do I know
S1: how long it takes to actually execute any of these things?
S1: Do you know? I think you do. But rhetorically, do
S1: you know what happens when a leader asks this question
S1: to their staff, to their company. Hey, I need a
S1: list of all work being done. I need a list
S1: of what everyone is doing. I need a list of
S1: how much this work is costing and how expensive it
S1: is at the different stages. Oh, also, is it good work?
S1: What's the average quality of an output of one of
S1: these processes? Do you know what the answer is? The
S1: answer is. Well, we could take a look. That's a
S1: good question, ma'am. We've been thinking about that as well. Yeah.
S1: If you would like, I could put together a project.
S1: What we could do is we could put together a
S1: list of all the stuff that we're doing and we
S1: could figure out, you know, what exactly our work is
S1: and how much it costs, and you know how good
S1: of a job we're doing. That is a major endeavor. Oh,
S1: by the way, it's separate from their work. This happens
S1: constantly inside of companies. Constantly. I'm often brought in to
S1: help them do this. I am often there when they
S1: are doing it with someone else. So if they spin
S1: up a project like this. Okay. Do you think this
S1: takes hours. Nope. Days? Nope. Weeks? Nope. Months. This is multi-week, multi-month,
S1: or multi-year. This is so work intensive. Keep in mind
S1: what has to happen to actually gather all this stuff.
S1: You actually have to call a bunch of meetings. Guess what?
S1: You have to tell them. Hey, I want you to
S1: put together the list of everything you're working on. I
S1: want you to note all the different tasks that you
S1: do during a day. This is our favorite. This is
S1: our absolute favorite thing to do. Yeah. Let me tell
S1: my manager exactly what I do. Why don't you look
S1: at my output? Why don't you look at my schedule?
S1: This is happening constantly while I'm speaking. Some managers, hundreds
S1: of them, thousands of them are telling their people, I
S1: need a detailed report of what you do and what
S1: you work on, and how much it costs and how
S1: long it takes, so I can put it up the chain,
S1: that's all. So they can get this visibility. And you
S1: know what's even crazier? If it's a large company, they
S1: don't even ask their people that. They're like, hey, let's
S1: bring in McKinsey or KPMG. Let's spend Thousand dollars to
S1: have a team of smiling 22 year olds come in
S1: here and do the audit for us. This is ridiculous.
S1: This is what AI is competing with. Okay, $50 trillion,
S1: by the way. It's around $10 trillion just for the US.
S1: $50 trillion worldwide is spent. The CEO and the CFO
S1: have very little idea what all is happening in the company.
S1: They don't know where the money is going. They don't
S1: know what the processes are. They don't know what the
S1: workflows are. They don't know how well it's going. They
S1: just have a rough idea based on, you know, performance
S1: of the company or whatever. Like when they hear from
S1: their managers and they see the stock doing whatever and
S1: obviously they have control, they can say, go do this,
S1: go do that. There is no visibility. They can't see shit.
S1: This is the state that AI is competing with, which
S1: is roughly chaos. Okay, so when you hear the argument,
S1: I just want this to crystallize in your mind. When
S1: you hear the argument, AI cannot compete with humans inside
S1: of a company. This is what they have to compete with.
S1: This is a bar that is not low. It is
S1: on the floor. It. It burned a hole through the floor.
S1: It is descending to the. The core of the earth.
S1: This is a low bar. AI can follow instructions. We're
S1: not even going to talk about AI yet, but the
S1: ability to follow instructions over and over, doing the same
S1: thing in a very uncreative, shitty way is better than
S1: what is done in most companies most of the time. Like,
S1: I cannot over express how bad the situation is and
S1: how low this bar is. So the crazy thing about
S1: this is everyone knew this before 2022. Everyone knew that
S1: this was soul crushing, shitty corporate knowledge work. Everyone knew
S1: that this was just like horrible. And I'm not talking
S1: about like some people who love their jobs 100%. A
S1: lot of people love their jobs. They're lucky. Maybe it
S1: lasts for a while, maybe it's short lived. Whatever. There
S1: are some people who love their jobs. Some companies are great,
S1: they tend to be smaller, but whatever. In general, everyone
S1: knew that this was soul crushing before 2022. They knew
S1: it before 2022. They knew it in the 90s. They
S1: knew it in the 80s. Like, that's why it's part
S1: of culture. Everyone knew this was a problem and guess
S1: what we were doing? Guess what everyone was doing throughout
S1: the culture, they're trying to figure out a solution. What
S1: is a better way? Because this is obviously not the
S1: way that humans should live. That was the state before
S1: 2022 when AI arrived. Okay, so another argument I'm going
S1: to continue beating up on us humans here. One last piece.
S1: I got positive stuff at the end to say about humans.
S1: So don't worry about that. You can hang in for that.
S1: This next piece is another negative piece about humans, and
S1: it's actually something that a lot of people don't think about.
S1: So and it relates to how AI thinks versus how
S1: humans think. Another sort of part of this argument is
S1: that humans just fundamentally think differently. And we just have
S1: like a lot more integrity of thought. We're more cohesive.
S1: We have understanding. We know ourselves. You know we can introspect.
S1: We can be creative. We have intuition. We have all
S1: these advantages over an AI which is like this, you know,
S1: dead black box, basically. So I want to sort of
S1: like dispel this as well. This is another angle to
S1: this argument, which also makes absolutely no sense. The crux
S1: of this argument is that humans are better thinking machines.
S1: Humans are much better thinking machines than AI. If you've
S1: ever studied or read any meditation documentation, or tried to
S1: learn it, or tried to study it or whatever, what
S1: it reveals about humans is absolutely mind blowing. Okay, so
S1: the first thing that it reveals is that humans have
S1: no idea what they're thinking at any given moment. Humans
S1: are basically like, if you could just zoom in and
S1: listen to the minds of people. Okay, sitting in their cars,
S1: sitting in their office space or their cube at work
S1: or wherever, you're just going to zoom in on these
S1: conversations that are happening in their mind. It's like hamsters
S1: running on a wheel. You'll just zoom into like Ravi here,
S1: and Ravi is like, just can't believe he said that.
S1: All that work I put in in the project, like,
S1: I hope that guy gets fired. I just hope he
S1: gets fired. Like, you know what I would love? Here's
S1: what I would love. Just imagine like, okay, so a
S1: new boss comes in and the new boss sees my
S1: stuff and just absolutely loves it. And then he looks
S1: at Chris's stuff. Can't you know, Chris, I can't believe
S1: that happened. Yeah, yeah. So looks at Chris's stuff and
S1: he's like, oh, this is garbage. You know, the one
S1: I really like is I like this one. And then
S1: that's me. That's mine. Like that would be so much better.
S1: And then, oh, did I feed the dog? I think
S1: I need to feed the dog. Hey, let me text someone.
S1: I need to feed the dog. Oh, crap. It's almost lunch.
S1: I got to get ready for lunch. That is the
S1: mind of a person. That randomness is happening constantly. Now
S1: check this out. When you try to meditate, what you
S1: do is you try to observe thoughts. It is the
S1: craziest thing to try to do if you try to
S1: observe your thoughts. So you can pause this if you
S1: want and try to observe your thoughts for a second.
S1: It's a really fun and crazy exercise. It is like
S1: this sliding window of chaos. Just scrolling through your brain
S1: and it is literally the stuff I was just talking about.
S1: Like you go into this, this woman over here, this
S1: guy over here, this kid over here, the janitor over here.
S1: It's just this rumination, this grinding of like, oh yeah,
S1: I should, I'm going to watch that thing. I've got
S1: to pick up those things. I don't know why he
S1: said that. Yeah. You know, I should get a promotion
S1: like these people don't respect me. Blah blah blah. It's
S1: just this constant like droning. Where did those thoughts come from?
S1: Who is making those thoughts and who is observing the thoughts?
S1: The answer is you have no idea. I have no
S1: idea what my thoughts are. I have no idea how
S1: I'm going to finish this sentence. We'll talk about that
S1: one in a second. Nobody has any idea where their
S1: thoughts are coming from. Do you know what that reminds
S1: me of? That reminds me of an LLM. When you
S1: poke an LLM and you say, hey, say smart stuff
S1: about logistic workflows and pipelines and you know how to
S1: move heavy freight from the East Coast to the West
S1: Coast on an 18 Wheeler. It just goes okay, and
S1: it starts spewing, starts spewing words, right? And it turns
S1: out they're really good and they're awesome. And the words
S1: make sense. And it's actually factual unless it's not right.
S1: And you could do deep research or whatever and correct that.
S1: The point is, you could ping a human to do
S1: that and they start spewing words, or you could ping
S1: an LLM and start spewing words. Does the LLM have
S1: any idea where its thoughts are coming from? No. Do
S1: you have any idea where your thoughts are coming from? No.
S1: It's a complete black box. Complete black box. We have
S1: no idea where our thoughts are coming from, where our
S1: dreams are coming from, where our concerns, our feelings, anything.
S1: It's bubbling out of us from a black hole. Like
S1: actually think about this. If you are in the middle
S1: of a sentence like I have been multiple times here already,
S1: I have no idea how I'm going to finish the sentence.
S1: I'm not planning a sentence. First of all, I didn't
S1: come up with the sentence at all. I didn't plan
S1: that sentence. I just said, these are spewing out of
S1: me just like an LLM. I have no idea how
S1: the sentence is going to end. I am just as
S1: surprised about anything I say as you are. Who is
S1: receiving it right now? Guess what that sounds like also AI.
S1: So going back to the work thing and now this,
S1: there's this view that we are put together. We are cohesive.
S1: We are self understanding. It's complete and utter crap. We
S1: have no idea where our thoughts are coming from. We
S1: have no idea how we're going to finish sentences. The
S1: whole thing is like this random generator. So here's another example.
S1: I'm going to ask you a question right now. What
S1: are your top seven favorite restaurants in the entire world?
S1: You've got 30s to answer. What is your brain doing
S1: right now? You're thinking about these. Who are you receiving
S1: content from? Where is that content coming from? Now you
S1: might say, well, I have memories and those memories are me. Yeah, sure.
S1: AI also has memories. AI has weights and AI has
S1: context and it goes and retrieves those and brings back stuff,
S1: but it brings it back in a different way each time. Right.
S1: When it spews an answer to top seven restaurants, it's
S1: reviewing content and spewing it in a way that is unpredictable. Right.
S1: It's random, hallucinated. Some might say, well, guess what? When
S1: I come up with my list of seven items, I
S1: have no idea what they're going to be. If you
S1: ask me multiple days or weeks apart or whatever, and
S1: I'm not like prepared, the list will be different because
S1: some I couldn't remember at the time. Actually, some my
S1: preference might change in between. This is not a static, cohesive,
S1: dependable process. Okay. And that takes us right into the
S1: memory one. Okay. One of the most reproduced things in
S1: psychology is the fact that memories are not what we
S1: think they are in the human mind. When we store memories,
S1: we're storing it with like emotion and bias and sort
S1: of feelings. And when we recall things, it's extremely well
S1: documented all through science, and I'm going to have a
S1: whole bunch of science links in the blog that accompanies this.
S1: So you can actually review this literature, but you actually
S1: don't store. You're not a hard drive, you're not storing
S1: unaltered ones and zeros. Okay. First of all, we don't
S1: know how we're storing things in the brain. That's step one.
S1: But we do know that when we recall events versus
S1: what happened, they're massively different and it gets worse over
S1: time and it gets worse the more you recall it.
S1: So our memory system, even our memory system is completely flawed.
S1: So we don't know where our thoughts are coming from.
S1: We don't know how we're going to finish sentences. And
S1: our memories are not static. They're not ones and zeros.
S1: They're actually extremely fluid. And then there's the weird fact
S1: that when you go to sleep and wake up, you're
S1: actually like a different person. Your entire being, your entire identity,
S1: like went offline and came back up, like spun up
S1: with like mostly the same memories, like mostly the same feelings,
S1: but you're kind of like a different person. So this
S1: whole concept of companies are doing great work. They have
S1: great workflows. People follow the processes. That's garbage. Then you
S1: have the concept of humans are like these, you know,
S1: bastions of like perfection. And we are the perfect thinking machines.
S1: And AI can't possibly hold a candle to us. Also
S1: highly flawed. The whole thing is built on just absolute madness.
S1: So once again, as thinking machines, the bar is extremely low. Okay,
S1: I want to show you this. I came up with
S1: this capability stack, whatever. It's just an image. And by
S1: the way, this is not like settled science. Like this
S1: is all still up in the air. People are still
S1: trying to figure all this out. I read a lot.
S1: I've got pretty decent mental models. Just work with me
S1: on this. Think of these blue pill boxes here, these
S1: blue layers here as fundamentals of a knowledge worker. Okay?
S1: Things that make us good at doing knowledge work. So
S1: first of all, it's knowledge. So collecting, organizing and accessing facts, right?
S1: Knowledge Facts. Easy understanding. Turning knowledge into concepts. Okay, so
S1: it's the ability to actually like, let it seep in
S1: and actually be able to say, okay, oh, that knowledge
S1: is related to that knowledge. Oh, okay. That forms a concept, right?
S1: So now we have concepts. So that's, that's what I'm
S1: calling understanding here. Again, we could use lots of different definitions.
S1: Just work with these knowledge understanding. Now on top of that,
S1: in addition to having a whole bunch of facts stored,
S1: that's not enough to be a good knowledge worker. Understanding. Okay,
S1: now we're getting closer, right? You have to have understanding
S1: of all these different concepts and everything and how they relate.
S1: Intelligence is the next one above. This is the ability
S1: to face obstacles, right? You're navigating obstacles. You're navigating the
S1: world around you, and you have goals that you are pursuing. Okay,
S1: so you're pursuing goals. So basically, if you're more intelligent,
S1: you're able to navigate the world better and better achieve
S1: your goals. So that's the definition of intelligence that we're
S1: using here. And the final one is creativity. Now this one.
S1: I would even argue not to get ahead of myself here,
S1: but I would argue that this is truly something that
S1: humans have that AI doesn't have right now. And who
S1: knows when they're going to get it or how much
S1: they're going to get. But even the first three are
S1: really solid and most knowledge work, okay? Most knowledge work
S1: that people are doing is actually the knowledge, the understanding
S1: and the intelligence creativity. You can have some people have
S1: some people have a lot of creativity. Some people have
S1: a little, some people have none, but they're still really
S1: good knowledge workers. I would argue you could even have
S1: an A player who crushes knowledge, understanding and intelligence and
S1: is still really great at their job. Then you have
S1: creativity on top of that. So this is like intuition art,
S1: you know, liberal arts thinking, philosophy, you know, logic, like
S1: all the different things, the ability to come up with
S1: novel things, net new things. That's ultimately what we're talking
S1: about here. So taking just the left side, just the
S1: blue layers. One of the arguments for why humans cannot
S1: be replaced is that they have expertise. This is a
S1: thing that my friend Marcus Hutchins is always talking about.
S1: He's like, hey, look, there's like, expertise is the thing
S1: that AI can't do. Like it can have facts and
S1: it can regurgitate them. But like, that's not real useful.
S1: That's not what work is. That's ultimately what we're trying
S1: to answer here. What is work, right? And how does
S1: it relate to human capabilities versus AI capabilities? So where
S1: is expertise on this list? And maybe you think I
S1: missed a layer. I don't think I did, but I'm
S1: challenging you to tell me what I missed. Where is
S1: the expertise layer here if it's not one of these four?
S1: So my answer to that question is expertise is knowledge,
S1: understanding and intelligence combined. Okay, so let's just go through
S1: some different work tasks. Okay? You've got engineering, you've got
S1: like civil engineering, you've got software developers, you've got site
S1: reliability engineer. You've got product manager, you've got project manager,
S1: you've got. Head of finance. You've got what do we
S1: got lots of other jobs right. Million different jobs. Admin.
S1: You're an admin for the company. You you handle you know, meals.
S1: You handle emails, you handle events. You handle all that
S1: kind of stuff. Maybe there's marketing as well. If I
S1: didn't already mention that, tons of different roles, thousands of
S1: different roles. Here's the question. When somebody is good at
S1: that thing, which of these are they good at? Which
S1: of these do they have in combination and which of
S1: these are required? Obviously, some require more creativity than others.
S1: Some don't require any creativity at all. I would argue
S1: they pretty much all require intelligence. So this is really
S1: interesting actually. Here's the question. Why hasn't existing automation previous automation,
S1: why has it not killed off knowledge workers for all
S1: these decades? We've had automation for decades. We've had automation
S1: for hundreds of years, and we've had computers for decades
S1: and decades, and we've had automation scripts, you know, programming.
S1: We've had these things for decades as well with really,
S1: really advanced automations, excel, all these different things. Why can't
S1: they replace a knowledge worker? Why have they not replaced
S1: a knowledge worker? My answer to that is understanding and intelligence. Okay.
S1: Previous automation basically just churned it executed it followed a
S1: list of steps deterministically, right? What it can't do is
S1: the thing that everyone must do as a knowledge worker
S1: inside of the chaos that I described earlier. Remember I
S1: was describing how everything is chaos. Everything's changing all the time. Oh,
S1: the goals are different. This is different when you come
S1: into work on an average day and an average company
S1: as a knowledge worker, you don't know what you're going
S1: to get. They might be like, hey, we're moving offices. Hey,
S1: our remit is completely different. All of our jobs are different.
S1: We now report to a different person. They have different goals.
S1: Maybe the goals are the same, but they like everything
S1: to be done differently. They like blue templates. So you
S1: need to put all your stuff in brand new templates. Well,
S1: if it's base automation, okay, it didn't learn how to
S1: use blue templates. It didn't learn how to have new goals.
S1: All that stuff needs to be rewritten. Intelligence is the
S1: ability to adapt, to change, to navigate, to work around
S1: and basically say, okay, well, here are my actual goals,
S1: here's what I know and I'm supposed to do. Here's
S1: my knowledge, here's my understanding of what is meant by
S1: all of this. So here are the changes I'm going
S1: to make to the way I do my work based
S1: on these new requirements that intelligence piece previous automation didn't have. Okay,
S1: now you add creativity. On top of that, the ability
S1: to come up with new ideas and innovate and stuff
S1: like that, that's way beyond automation. And I would say
S1: arguably today, AI, it's also way beyond today's AI. Now,
S1: there are some edge cases where it's starting to seep in,
S1: but let's just set those aside. So this is what
S1: makes a human valuable as a knowledge worker. Expertise is
S1: all for. But I would say mostly it's knowledge, understanding
S1: and intelligence. So give me an example. Try to think
S1: of an example. And this is sort of me arguing
S1: with Marcus here. Give me an example of expertise that
S1: isn't this. Think about what expertise is. It's lots of experience.
S1: It is the knowledge, right? It's the understanding of the concepts.
S1: It's the ability to adapt with the intelligence and navigate
S1: and change and everything. The combination of those combined with
S1: experience is ultimately what gives you your expertise right now.
S1: Maybe you would argue, well, what about experience? Maybe experience
S1: should be on the list, but experience is memory, right?
S1: Experience is recall experiences, accrued iterations on seeing different problems.
S1: And ultimately this is why I have the model. This way.
S1: It is actually improving your knowledge or your understanding. Okay,
S1: your intelligence is not really improving. That's just a thing
S1: that you have. It's a tool that you can use,
S1: but your knowledge improves with the experience. An experienced person
S1: has more knowledge. An experienced person has more understanding. They
S1: have better understanding, okay? Which means they have linked more
S1: and more facts and more and more situations and more
S1: and more patterns to interlock, to connect to each other
S1: so they could just see patterns better. And this is
S1: related to intelligence as well. But somebody with 25 years experience,
S1: 30 years experience, 50 years experience of doing financial audits,
S1: they just immediately see things. They see patterns. Okay, here's
S1: the question. What part of that does AI not have?
S1: First of all, how is AI a knowledge? Okay, insane. Okay.
S1: It's read every book. It's a deep expert. I don't
S1: want to lead the witness here. It's deeply knowledgeable about
S1: many fields, tens of thousands of fields and subdomains. It
S1: can rattle off all the details, explain everything to you
S1: whatever way you want to receive it. It could write
S1: a full book on the topic by itself. It could
S1: make flashcards for you about this very specific thing that
S1: is like very few people on earth actually know about,
S1: but the AI knows about it. It's also read most
S1: every book effectively. Okay, so knowledge, forget about it. Okay. Understanding. Yes. Okay.
S1: You could argue AI's don't have understanding. There is a
S1: type of understanding that AI's don't have and that is experience. Okay.
S1: If you argue that understanding requires subjective experience, well, then
S1: they don't have it right because they don't experience anything.
S1: My definition of understanding is connecting concepts, finding patterns across domains,
S1: and AI is unbelievably good at this. It is mind
S1: blowingly good at connecting things, cross linking patterns, identifying a
S1: pattern in a piece of content. Oh, this relates to
S1: the 1930s Russia in the following way. Oh well, how
S1: does that relate to chicken feed farming practices from Idaho?
S1: It's like, oh, well good question. Here's how it relates.
S1: It can cross-reference anything. It holds it all in its
S1: mind all at once and can reference this. It can
S1: also do that supplemented by tools to go and research
S1: anything and add to it. It could also do that
S1: by looking at Rag or at a giant composite list
S1: of additional knowledge that you give it. Right? So it
S1: doesn't have to actually be in the model weights. It
S1: could actually be part of a unified system, which includes
S1: going to get new information and cross-referencing to make sure
S1: it's real. Right. So knowledge and understanding, crushing it, absolutely
S1: crushing it. Let's go to intelligence. Can AI navigate the
S1: world and achieve goals? Yes, absolutely. And this is fairly modern, right?
S1: It's been going on for a couple of years. But
S1: the last year, the last six months. Are you kidding me?
S1: An agent infrastructure that has access to all these tools
S1: and all your company's knowledge, all this context and a really,
S1: really smart model and a system of agents that actually
S1: can use that context. It actually has a list of
S1: rules to follow. And okay, so it's proceeding down a
S1: track and you're like, okay, well, now imagine that the
S1: customer says, we actually can't do the deal in Japan.
S1: We're actually need to do it in the UK. And actually,
S1: here's another restraint. So the size of the project has
S1: to be larger than this. By the way, you need
S1: to employ only people who have residency in Singapore or
S1: the UK. Also, we need some people who have understanding
S1: of Scotland and also everyone should be over the age
S1: of 35, right? And also make sure the report format
S1: has to follow this particular template. And also we have
S1: to take into account these other 90 things which I'm
S1: about to give you. Those are new requirements. That is
S1: dynamic changing requirements on the ground. Here's another requirement. Also
S1: go and research all things related to this. Find all
S1: scientific documents related to this and do thorough research for
S1: a day and a half using all your resources. Bring
S1: in all the sources to support the document that you're
S1: actually going to produce for this customer. That's going to
S1: cost $300,000. Those are dynamic, changing situations. Everyone who's used
S1: AI recently, who is a decent user, knows that you
S1: could throw anything at these eyes. You could throw anything
S1: at them. And it's like, oh, well, in that case,
S1: you should do this. And you're like, yeah, but here's
S1: another requirement. Oh, well, in that case, we should adjust.
S1: So can it do intelligence? Do modern AIS have intelligence
S1: according to this definition, 100% expertise. Like I said, expertise, knowledge,
S1: understanding and intelligence, mostly knowledge and understanding. Okay. Intelligence, I
S1: would say definitely. Sure. Let's include that creativity. Again, I'm
S1: not sure you need it for expertise. You can have
S1: somebody who is just the craziest bookworm and is just
S1: like locked in. You give them this task. They just
S1: crush it. You give them a different task. They just
S1: absolutely crush it. Are they inventing anything new? Not really.
S1: Are they coming up with a new way to do it? Nope.
S1: The way that they were trained or the way they
S1: decided they wanted to do it, that's the way they
S1: execute every single time doesn't require creativity. Okay, so now
S1: let's slide over here. Let's look at the AI one intelligence, understanding,
S1: knowledge creativity. Let's just take it off the list. Let's
S1: just pretend it doesn't have it. AI has the expertise.
S1: So this argument that a bunch of people are making
S1: very smart people, very knowledgeable, very technical, they're saying that
S1: you can't possibly replicate human expertise, human thinking, the quality
S1: of human thought, the quality of human thinking, the quality
S1: of human execution of work, everything that we've been talking
S1: about tells us that is just not true. In fact,
S1: the polar opposite is actually more true that even decent
S1: AI today, given these types of tasks, will just crush
S1: an average knowledge worker. Just absolutely crush them on tasks
S1: that are there defined. Okay, what are we doing all day?
S1: We're accepting emails. We're responding to emails, we're producing reports,
S1: we're writing code. These basic commodity tasks make up. I'm
S1: making up a number 95% of all knowledge work. It's
S1: common stuff. The only thing that stops somebody from walking
S1: in off the street who has the requisite, like education
S1: or training in this field, which again is just knowledge
S1: and understanding, mostly knowledge. Someone walking in brand new to
S1: do that in the company, they just don't have the
S1: company knowledge. Well guess what? They accrue it over time.
S1: They go through onboarding, they read the documentation, they do
S1: all that well guess what? AI can do that in
S1: a few seconds. Here's a core point about all of this.
S1: A big reason that people think there's a gap between
S1: what the humans have and what A's have. A big
S1: reason they think there's a gap is because the humans
S1: have not articulated the stuff. It's not written down. Think
S1: about this. The way that AI has gotten so good
S1: in the last year is largely because of cloud code.
S1: What did cloud Code do? Did it have a dramatically
S1: better model than the other models? No. What it did
S1: was scaffolding a harness around the model. What it did
S1: is it built context and context management relations between documents,
S1: allowing it to have knowledge, understanding and intelligence. It linked
S1: together everything that the model needs to be able to
S1: perform to actually do intelligence tasks, to actually do work.
S1: That is what this thing did. That is what cloud
S1: code did. Okay. That stuff being written down. Okay, so
S1: another thing related to this that's extremely hot right now.
S1: It's been hot for a number of months is skills.
S1: What are skills? Skills are a combination of resources, tools, Context. Knowledge.
S1: It's a whole bunch of stuff written down. This is
S1: the center piece. It's essentially the core benefit of a
S1: harness versus a model. A model is just like a
S1: whole bunch of knowledge, right? Once you realize how powerful
S1: these skills are, once you realize how powerful it is
S1: to have all this context in one place, that sort
S1: of wraps an AI model, you start to realize that
S1: the problem is not the AI. The gap between human
S1: expertise and AI expertise is not the model, okay? It's
S1: not the fact that humans have expertise and AI doesn't.
S1: It's the fact that we have not articulated our expertise
S1: down into files, into tools, into contexts. I've been doing
S1: this for over a year now. I've built a system
S1: called Pi. It is a context management system. It is extraordinary.
S1: It's open source. You can check it out. Everyone is
S1: building with skills now. It is becoming the most powerful
S1: concept in all of AI is skills. Why is that?
S1: Because it is literally expertise packaged for this very smart
S1: model to then be able to use the problem inside companies.
S1: That makes people think there's a disconnect between human capability
S1: and AI capability. Nobody has any of this written down.
S1: All the stuff that is in Robbie's mind and Susie's
S1: mind and Kristy's mind and Chris's mind, all the stuff
S1: that's in their minds, it's not written down. Their expertise
S1: is not captured the way this is supposed to be done,
S1: this particular task, this sending of the emails, the creation
S1: of the report, the writing of the code. It's not
S1: written down anywhere. It's passed from human brain to human brain.
S1: And they go on vacation, they go on maternity leave,
S1: they retire, they jump companies every couple of years. That
S1: knowledge just dies. What's starting to happen now is knowledge
S1: is dispersing from brains, from very smart, high expertise, people
S1: into skills. This is why there was a giant stock
S1: drop when anthropic came out with a whole bunch of
S1: legal stuff, right? Those are just processes. Those are expertise
S1: captured in documents, which AI can then follow. So the
S1: expertise gap between humans and AI is actually the failure
S1: so far of us to articulate all the different chaos
S1: things that we talked about in the beginning, all that chaos,
S1: all those random pieces of knowledge inside of people's brains.
S1: Like there's some super old guy who's 62 years old,
S1: you know, his name's Cliff. He's the one you call
S1: when the things go down and there's no docs, you
S1: can't find a thing. You don't know how to get
S1: the server back up. People don't even know what servers
S1: are anymore. You call Cliff. Cliff has never written the
S1: stuff down. Cliff has never created a skill. He's never
S1: done a full debrief interview to capture his knowledge. Now
S1: you're not going to get all of it. It's not
S1: going to be perfect. But if Cliff could document that,
S1: and by the way, lots of people are. Lots of
S1: people are documenting what they know about Cliff's topic and
S1: putting it into skills and their open source. This stuff,
S1: this is P that's seeping into the pool and it's
S1: not going to come out again, but inside of a
S1: given company, if that stuff were to be captured and
S1: brought into this context system like I have with Pi,
S1: like lots of other people have like are captured in
S1: skills that's just absolutely flooding the internet right now. Flooding,
S1: especially the AI space. That is what bridges the gap
S1: between human expertise and AI expertise. And this is happening fast.
S1: It's happening extremely fast. All right. So, so far I've
S1: been talking about the problem. I've been talking about what
S1: I believe to be the problem with the way people
S1: are thinking about this, right? Comparing humans to AI, thinking
S1: that work is like easily done by humans and it's like,
S1: it's working fine. Like what's even the problem now I
S1: want to transition into and we also talked about the
S1: whole expertise thing and like what that really means and
S1: what I think the gap is there. What I want
S1: to pivot to now is essentially talking about what I
S1: think the solution is and what I think is inevitable. Okay,
S1: so this is kind of getting to why I think
S1: this is going to happen and why I think people
S1: need to prepare and get ready. So all of that
S1: was sort of a setup to what I think is
S1: about to happen. And this is an architecture that I've
S1: come up with. I call it the lattice architecture. I've
S1: been working on this for quite a while. I'm going
S1: to release it as an open source project as well.
S1: It's the same system that I use with customers to
S1: basically build their back end systems to help them solve
S1: the stuff that we talked about on the company side.
S1: But the system itself, I'm actually going to release, just
S1: like I did with Pi, I'm going to release this
S1: as kind of a sister project for actually helping companies,
S1: actually any type of entity basically solve that chaos mess.
S1: So essentially, the concept is addressing this transparency problem, addressing
S1: the fact that all these different groups are opaque. The
S1: work is opaque. The processes, the SOPs are not clear.
S1: So what Latisse does is that it actually has a
S1: single unified daemon that everything reports up into. And what
S1: you have are the different tiers of organization. You have
S1: the company level, you have like a unified context of
S1: documents and processes and SOPs and everything, all the different
S1: knowledge for a particular company in a unified place. And
S1: then you have a set of things for each company.
S1: So you have SOPs, you have metrics, you have work,
S1: you have goals, you have budget, you have all these
S1: different components of a particular entity all the way from
S1: the company level. But you also have that same exact
S1: thing for the department. You also have it for a team,
S1: and you also have it for individual people working inside
S1: the company as a worker. So what this does is
S1: it takes a system like pie and you can have
S1: your own version. People will have their own versions. This
S1: is the one that I use. And again, it's open
S1: source project. You can go and check out. And what
S1: it is, is as an individual, you will have your
S1: personal SOPs the way that you like to get things
S1: done that you follow. You will have your personal budget
S1: of what you spend things on. You will have your
S1: own personal metrics, which maybe you got from management or whatever. Actually,
S1: you're about to see where you got it from. You
S1: have all these different components. You have your knowledge in skills,
S1: you have all these capabilities that you have when you
S1: learn something new or whatever, you capture it into the
S1: system and most importantly, your individual unit. You are broadcasting APIs.
S1: You as a person, you, Sarah, you, Ravi, you, Chris,
S1: you are broadcasting what your SOPs are. You are broadcasting
S1: what your metrics are. You are broadcasting what your budget is.
S1: You're broadcasting your work items. They are available for any
S1: agent or any other team member or the agent for
S1: the team or the agent for the department or the
S1: agent for the company can ping these things. Can query
S1: you and actually pull the stuff in real time. Most importantly,
S1: this allows the CEO and the CFO actually to be
S1: able to look down and say, okay, this is the
S1: work that we're doing. Okay, so you're going to have
S1: workflows also documented in this exact same way. I want
S1: to show you some other diagrams here. Okay, so here's
S1: another way of looking at it. So again, goals, plans, SOPs, metrics,
S1: work cost quality. The thing I want to express to
S1: you here, like we talked about already, so you probably
S1: already have this point. So I won't belabor it too much.
S1: These things goals, plans, SOPs, metrics, work, cost quality, these things,
S1: the workflows for the company. This stuff is extremely opaque
S1: to the leaders of a company. This is extremely hard
S1: to gather and this is going to turn it into seconds.
S1: It's going to turn it into less than a second oftentimes, right?
S1: It's going to turn it into minutes at the absolute maximum.
S1: Like if you have a super large company, right? And
S1: it depends on the implementation. Like I want to give
S1: actual numbers or anything. That's just ridiculous. It depends on
S1: the architecture that you use. But the point is transparency.
S1: First of all, and the other core point is that
S1: each layer from the worker to the team to the department,
S1: to the company is independently authoritative over its stuff. So
S1: one of the problems that I see tons with customers
S1: is they have no idea what their other departments are doing, right.
S1: They have no visibility into how they're interacting with the customer,
S1: what they've said to the customer, what's been done with them.
S1: You can also have context silos like this for each
S1: customer inside of a customer container, right? And you can
S1: have other agents because I'm a security person, there's going
S1: to be another agent flow here, basically a gateway that
S1: basically says, okay, which agents can ask which questions about
S1: what context zones, right? So there's going to be permissions.
S1: You've got identity stuff, you've got a whole bunch of
S1: stuff that's all being worked on. But the core concept
S1: of lattice is visibility up and down and across. I
S1: could just query, first of all, I'm just I'm not
S1: going to be actually running any APIs myself. I just
S1: talk to my agent system as an individual worker on
S1: the front end team over here. And I say, hey,
S1: what are my coworkers working on? I'm thinking about working
S1: on this. And they're like, oh, yeah, you know, Julie's
S1: already working on that. You should talk to, you know,
S1: Modi or whatever. Yeah, hit them up. They're already working
S1: on it. Maybe you can do a collab. Do you
S1: want me to set up a meeting? Boom. Okay, cool. Well,
S1: now you just posted that you're in a meeting now
S1: it just posted that you're going to collaborate about this
S1: particular feature that could go into your API. It's now
S1: available when someone asks, hey, is anyone working on this? Well,
S1: now it includes you that you're actually also working on this.
S1: It's now available to other teams, other departments, all the
S1: way up to the company level. And this unified sort
S1: of context layer here, the lattice daemon is where you
S1: can query and get it for everyone. Now I want
S1: to talk about this whole concept of the SOPs and
S1: the goals. Think of what happens right now in a company.
S1: I didn't talk about this too much in the sort
S1: of negative state. When you go to create goals and
S1: change your goals, how many people are triggered by the
S1: word OKR? For a good reason. The reason is because
S1: they change all the time. People have different systems for
S1: doing them. Imagine a giant all hands meeting where the
S1: CEO gets together with everyone and says, hey, check it out.
S1: These are the new goals. This is what we're doing.
S1: Here are our new metrics that go with the goals.
S1: By the way, we've got some new SOPs we just published. Boom.
S1: Guess what they say. They have now been published. All
S1: documentation is now updated, goes all the way down. Updates
S1: the documentation, updates the goals. Now inside of the goals
S1: of each group. That content is now updated. We're talking
S1: about text files here. This is not like magic stuff.
S1: This is not difficult stuff. We are doing much more
S1: difficult stuff with AI already. This is just not a
S1: system that has been built yet and it will take
S1: time to roll out. Some companies will be able to
S1: adopt this very quickly. Some companies will take, you know,
S1: five years or ten years or more, or they'll just
S1: die off because they couldn't get there fast enough. And
S1: to be clear, this is one architecture. This is the
S1: architecture that I'm using. But the concept is actually more important.
S1: The functions that this architecture allows is the most important piece.
S1: And I think the broadcasting of the demon, the broadcasting
S1: of the APIs for each tier level, for each object
S1: type within the company. That is what makes this all work, right.
S1: So the protocols that we use, who knows? Like it's
S1: hard to predict exactly how it's going to play out.
S1: Maybe it's going to be something better than HTTP. Maybe
S1: it's going to be something better than rest. It doesn't matter.
S1: The concept is what I want you to think about here. Okay,
S1: so here's another view of this. And just another way
S1: to think about it. I've got a whole bunch of
S1: these diagrams, by the way, that I put together over
S1: quite a decent amount of time, and I'm going to
S1: make them all available there in the blog post. They're
S1: also in, I think the link is in the description
S1: to the video as well. But look at this. All
S1: these different queries can go up to the latest statement.
S1: You basically post your status. It could be push or pull, right?
S1: A combination of the both. But again you have the tiers.
S1: You have goals, budget, plans for the department container, the
S1: team container, the individual node, right? And they all are
S1: speaking to one central place, but they're all able to
S1: post to each other and or receive from each other.
S1: And the other thing that's part of different diagrams is
S1: essentially a big unified context as well. Like kind of
S1: like a context data lake, which I've talked about in
S1: previous videos. Okay, so here's another view. And this is
S1: absolutely critical. This is huge. A big part of the
S1: problem that we have is not being able to see
S1: the work that we're doing. We don't understand the workflows.
S1: We don't understand who's doing which part. So I've got
S1: another video where I talked about companies are just graphs
S1: of algorithms. Okay? This is a really core concept. The
S1: idea that someone's doing like insurance claims or whatever. Well,
S1: it's not wizardry. They don't make it up each time.
S1: They should be following a set of steps, right? But
S1: it's not clear like we talked about in the beginning.
S1: It's not clear what those steps are. It's not clear
S1: if they're being followed correctly. It's not clear exactly what
S1: is happening inside the company. There's not even a list
S1: of them. And like I said, it's actually really difficult
S1: to go and collect a list. So look at this here.
S1: This is the type of view that somebody is going
S1: to have. They're going to have the ability, anyone, whoever
S1: has access, right, with the security permissions to be able
S1: to look at this stuff and they're going to be
S1: able to say, okay, well, we still got human components here.
S1: We got automated components here. Okay, we got review over here. Okay,
S1: we've got audit over here. We've got a quality check
S1: over here. This is the type of view that CEOs,
S1: CFOs like senior management, really any manager really wants to
S1: be able to see because you can't optimize what you
S1: don't understand. You can't optimize what you don't see. Right.
S1: All right. So this is actually another really cool aspect
S1: of once you have those visuals, once you have those workflows,
S1: things just fundamentally change. You know how like someone comes up?
S1: You know, they take you to a steak dinner, they
S1: meet you for some meeting or whatever, and they're like, hey,
S1: you know, we have the best image background removal process.
S1: I don't know what you're using for that today, but
S1: ours is way better. And so it's like, okay, well,
S1: I guess we'll do a pilot. You know, I really
S1: liked the steak. So I guess we'll do a pilot
S1: and I'll tell part of my team, you know, you're
S1: going to spend six weeks coming up with test criteria
S1: and we're going to test this thing and a B
S1: test or whatever. And if they like it, you know,
S1: I guess we'll take a look at it that goes
S1: away when you actually have a lattice over here of
S1: like all the different pieces that make up your company.
S1: These are the different processes, these are the different components.
S1: This is the tool, this is the decision. This is
S1: the human making the decision. This is an AI making decision.
S1: This is an AI processing work. This is the graph
S1: of algorithms that makes up the company. One particular node,
S1: this little node right here, one of hundreds or thousands
S1: or tens of thousands or millions of nodes inside your company.
S1: Is the process the algorithm for doing image background removal. Today,
S1: we don't have metrics on that. Really, most people don't.
S1: What do we have? We have like, yeah, it works
S1: pretty good. I like it, yeah, it's pretty good. Maybe
S1: we could pull some kind of metrics, but it's not
S1: like really good what we're going to have, what we're
S1: moving towards and most importantly, what people want to get to,
S1: what leadership wants to get to is all of these
S1: have metrics for them. They're all clearly defined. So now
S1: a vendor doesn't come with a steak dinner. They cannot anymore. Okay.
S1: Because we're going from wizardry to excel, okay? We show
S1: them our metrics. We show them, hey, look, this is
S1: how we do things. This is our current lattice structure.
S1: This is our current flow structure. This is image background removal.
S1: This is how much it costs us. This is how
S1: good it is. This is our quality ratings according to
S1: so and so standard. What are your ratings? What are
S1: your metrics? What are your cost numbers for this? If
S1: they can't bring that and all they brought was the
S1: steak dinner, that's not a conversation anymore. Once this transition happens, right?
S1: This is absolutely essential. Absolutely essential. Essentially, it's turning the
S1: company from these sort of black box, sort of vibey
S1: things that are happening that hopefully work out pretty well
S1: into actual pipelines, actual workflows with metrics and reliability and
S1: testing and all this sort of stuff as part of
S1: the system. All right. Real quick, this is just a
S1: little teaser. I'm not going to talk too much about this.
S1: This is a screenshot that I took earlier, I think
S1: last week. And this is actually just an example of
S1: the lattice system working. And it's actually one agent system
S1: reporting up into the lattice daemon basically describing its work.
S1: So this is a particular system called the algorithm. And
S1: it's running, I guess if you know, you know, but
S1: it's part of Pi and it runs work. This is
S1: the type of thing I'm just trying to show. This
S1: is not a theoretical thing. It is quite easy. And
S1: if you're an experienced user of AI, you already know
S1: you could do this type of stuff. This is the
S1: type of dashboard that line managers want to be able
S1: to see, that engineering managers want to be able to
S1: see that anybody should be able to drill into and
S1: actually see the work that's proceeding through the system. And
S1: this is making simple Rest calls and pushing this content
S1: up into the system and giving you a dashboard like this.
S1: All right. Essentially, what I want you to have up
S1: to this point is sort of dispelling some of the
S1: myths about how perfect humans are. We are not perfect.
S1: I'm not trying to talk crap about anybody. Some of
S1: my best friends are actually humans. So like, I'm not
S1: trying to throw stones here, right? I'm trying to describe
S1: the reality that's actually happening according to my view. Like
S1: you could watch all this and still think I'm wrong,
S1: but hopefully you won't. So where is this taking us?
S1: It seems like this is just horribly bad news. So
S1: what I want you to do have had so far
S1: is essentially the current state of work. The current state
S1: of human work, the current state of companies is not good.
S1: The perfect human thinking machine is not correct, right? The
S1: way that we actually process information, store information, it is
S1: much more like an AI than is actually comfortable to
S1: think about. And all that combined basically means the bar
S1: is actually extremely low. And then when you add on
S1: top of that, this lattice system, which is just one
S1: implementation that one person came up with, I'm telling you
S1: that is the direction that things are going. This is
S1: the architecture that companies are moving to that they are
S1: going to have. A lot of them haven't figured it
S1: out yet. Like this is still very early stuff, but
S1: people will catch on to this very soon and they
S1: will start building this type of architecture. And it's going
S1: to happen fast. It's going to happen extremely fast. Now,
S1: one other point I want to mention about this is
S1: a way to think about automation in a completely different way.
S1: If you think about it in terms of like, okay,
S1: we have the humans. Now let's think about adding AI.
S1: Oh my God, that's crazy. I have the same instinct. Okay,
S1: it feels icky because they're coming into our processes. Here's
S1: another way to think about this. Imagine you have a
S1: factory production line that produces iPhones. So it's soldering two
S1: nanometer transistors for iPhones. And this thing is amazing. It's
S1: producing whatever, $1 million a minute. Who knows what the
S1: numbers are, but it's just fully automated. It's a whole
S1: bunch of robots and it's just flowing perfectly. Who would say,
S1: you know what this thing really needs? It lacks a
S1: human touch. It lacks a human touch. We need to
S1: get people need jobs. People need jobs. We need to
S1: get some humans in here because AI's don't have the
S1: right thinking. They're just not as good as humans at
S1: doing work. Do you have any idea what would happen
S1: if you tried to ask a human to solder two
S1: nanometer transistors onto circuit boards? It would not go well.
S1: Also known as impossible. Okay. Another one which I think
S1: can't remember who mentioned this. Somebody mentioned an example of
S1: a amazing Excel sheet. We've all seen one of these before.
S1: It's got a million tabs. Looks like an actual application.
S1: The thing is just fantastic. Maybe it handles the entire
S1: company's finances and just produces the best results. It never
S1: messes up. It's just awesome. It's like, you know, 36
S1: months of work goes into this crown jewel of an
S1: Excel spreadsheet, and it's just processing millions and millions of
S1: lines for the entire enterprise globally. Where on the spreadsheet
S1: should we add a human to help the spreadsheet? Where
S1: do we add it when automation works, when AI workflows work,
S1: when the lattice system that I was showing you works,
S1: the answer will not be to add humans to the thing.
S1: It becomes extremely obvious that adding humans actually makes it worse. Okay,
S1: with an exception, which we're going to talk about. So
S1: think about that inversion when you're considering the uncomfortable feeling
S1: that you're getting when you're thinking about adding automation, think
S1: of the opposite. The default is opposite automation first. And
S1: there's another reason to think that one saying that I
S1: have that I think is very, very true is the
S1: ideal number of human employees for a company is zero. Now,
S1: I'm not talking about small boutique startups. I'm not talking
S1: about collaborations with your friends. I'm not talking about like
S1: sole projects that you just love to do. You want
S1: to work with other humans? I do too, that's always
S1: going to be there. I'm talking about for large companies,
S1: medium sized companies, companies that need to do things at scale.
S1: These companies, again, they are paying $50 trillion in knowledge
S1: work compensation. They want to do that work internally. Okay.
S1: If I had an ice cream truck business and all
S1: I had was my ice cream truck, and I had
S1: a machine that could actually make the perfect ice cream cone.
S1: And maybe I have three trucks and I'm the only employee.
S1: And the trucks, they have X number of machines. They
S1: can pump out the things. I can hand them off
S1: to the kids with a lollipop or whatever, and I'm
S1: making X amount of money and I'm happy. I'm just happy.
S1: This is a great amount of money. That is the
S1: state that companies want to be in. Of course they
S1: want to grow, right? So they'll want more and more
S1: bigger trucks, you know, more stuff like that. But they
S1: won't say, I need more humans to add to this.
S1: If I'm sitting here with my ice cream truck and
S1: I've got my ice cream cone makers, and I am
S1: perfectly happy with the amount of money that I'm making,
S1: if a group of people come and they pick at
S1: me and they're like, you're against humans. Humans need jobs,
S1: humans need jobs. I'm like, what are you talking about?
S1: I got my truck. I've got my ice cream cone makers.
S1: It's just me. This is literally me. Those machines are me.
S1: And this ice cream truck is me. This, as a
S1: unit is me by myself doing the work. Well, guess what?
S1: That diagram I showed you of the lattice system of
S1: the entire company. Let's just say nothing in there was human,
S1: which that'll be forever before that happens. Or a long
S1: time anyway. Let's just say it's fully automated. Everything in
S1: the whole lattice structure, it's all automated, or let's say
S1: it's a small company or whatever. Fully automated. Right? Fully automated.
S1: It's working great. That is their company. They consider that
S1: to be them doing the work. All that infrastructure, all
S1: that automation, all the machines, all the tech. It is
S1: part of the company now. It's like the walls and
S1: the floors and the ceilings and the chairs and the desks.
S1: It's literally just part of the core of the company
S1: that is the preferred mode. They are currently paying $50
S1: trillion for outside help. They want that help to be inside.
S1: They want it to be all internal, okay? That is
S1: the desired state. And going back to the whole setup
S1: with like the factory and adding humans, one argument I've
S1: heard is like, well, yeah, but if you want to scale, right?
S1: That's there's no problem. You'll just hire more humans. Like
S1: why wouldn't you hire more humans? Because humans can also
S1: do the job. Well, no, they can't do the job
S1: as good as it's already being done by the automation. Right?
S1: So that's where that argument breaks down. You don't add
S1: humans to a working system that doesn't have humans in it,
S1: because the working system without humans in it is likely
S1: to be many times better and many times faster for
S1: a lot of tasks, maybe most tasks. So just keep
S1: that in mind. All right, so here we are. I
S1: promise some good news and this is where we get
S1: to it. So we've talked about the bad news. We've
S1: talked about what the problems are and what I think
S1: the inevitable solution is. And I do believe that is true.
S1: Now let's talk about, okay, what are humans supposed to do?
S1: What is the good news? This sounds like horribly bad
S1: news with no possible upside. All the humans are screwed.
S1: What are we going to do? And for that, I
S1: want to bring you over to this content here. All right,
S1: so for that I want to bring you over to
S1: this content here. So remember when we talked about the
S1: four layers and we said, okay, knowledge, understanding, intelligence and
S1: creativity and we said, look, this is the stuff that
S1: humans can do. But there were two layers underneath. And
S1: these are the layers that eyes don't have. Okay. They
S1: don't have subjective experience. They don't have desires to do anything. Okay,
S1: so imagine that whole workflow again, fully automated. It's amazing.
S1: And it doesn't need any humans because it's just better
S1: and faster and more consistent, higher quality, all of that
S1: cheaper way, way cheaper. Fine. Who decides what to make?
S1: Who decides what company to make in the first place?
S1: Here's what AI doesn't have, and this should be a
S1: source of hope. Hopefully what I've done here is show
S1: you how bad one part of the situation is, and
S1: it should be equally clear. Hopefully, after what I'm about
S1: to show you that the opposite is also true. What
S1: this unlocks for humans is actually what we should have
S1: been doing all along. We should not be the row
S1: in the Excel sheet. We should not be the section
S1: of the robot army that you know solders the transistor.
S1: We should not be the one in the car dreading
S1: going into the office Monday morning that all should never
S1: have happened. Which, like I said, we knew that before 2022.
S1: It's totally understandable to freak out when the jobs are
S1: going away and AI is taking them. The natural tendency
S1: of someone yanx something out of your arms is to
S1: pull it closer, right? Understandable. Like not mad at anyone
S1: for that. I'm just trying to help you sort of
S1: think through this. That work was garbage. It is garbage.
S1: It was garbage for decades. It still is garbage. And
S1: AI's are coming to take it. Fine. The place that
S1: we're going, the place that we need to get to,
S1: is where humans actually have ideas. People are actually broadcasting
S1: their own capabilities and their own plans and their own
S1: creativity and their own beliefs and their own ideas and
S1: their own creations. Nurturing people, helping people grow, creating new art,
S1: new experiences for other people. And guess what? Coming up
S1: with companies to do this right. How will the company
S1: be implemented? How will all the work tasks get done
S1: using a system similar to Latisse. It's going to be
S1: an all AI system doing the actual crunching of the work.
S1: But what can the AI not do? It doesn't have
S1: a desire. It doesn't have any goals. It doesn't have ambitions.
S1: AI doesn't see a problem in the world and go, yeah,
S1: I just don't like that. I just don't like that.
S1: You know, that's not the way they should be treated.
S1: You know, this is a horrible experience. I don't like
S1: this product. We should make a better one. AI is
S1: not doing that. Why? Because it doesn't experience anything. It
S1: doesn't experience problems and feel bad. It doesn't come up
S1: with new ideas. If you ask it to come up
S1: with a new idea, it can. But it's not sitting
S1: around dormant in like unhappy. We are unhappy. We have
S1: these desires and we have this subjective experience for another reason, right?
S1: It's because we are powered by evolution. This is kind
S1: of a big idea as well. Might be slightly controversial,
S1: I think if you're watching the channel, might not be
S1: as controversial as it is to most people, but evolution
S1: is the thing that gave us our desires. When we
S1: sit around ruminating, when that whole freight train of ideas
S1: is just scrolling across the things that's in our mind
S1: is generated by evolution. The reason we have these desires
S1: and goals and ambitions is because evolution is what designed us.
S1: We are basically mech suits, we're giant mechs. We're like
S1: giant transformer mech suits. Around evolution, which is trying to
S1: do things and it's trying to do things through us.
S1: Our entire pleasure system, our chemicals, dopamine, serotonin, cortisol, all
S1: these things are designed. The whole purpose that they're there
S1: is to steer us, to steer us towards desires. Do
S1: we have any control over our desires? Can you say
S1: I suddenly like broccoli when you didn't a second ago?
S1: Can you decide to do that? No you can't. You
S1: also can't really control what you desire. You can't really
S1: control your ambitions. You kind of have them. You could
S1: try to use your intellect and steer them, I'll grant
S1: you that. But in general, we are being powered. We
S1: are being powered by a very ancient, powerful thing, which
S1: is our upbringing, our evolution, our hardware, our mammal minds, right?
S1: Our brains, our minds, our identity. It is driving forward, survive, reproduce,
S1: become attractive, become powerful, become respected. That drive makes us fundamentally,
S1: I would say better, definitely different, but I would say better.
S1: It gives us an advantage over eyes. Eyes are dormant.
S1: They just sit there. They just execute things that we want.
S1: When they act like they feel things, when they act
S1: like they have goals and they act like, hey, I'm
S1: going to generate ideas that is not coming from their
S1: true center of self the way that it does for humans.
S1: So what does this mean? What this means is we
S1: are still the centerpiece of this whole thing. Everything we've
S1: been talking about in this video, everything around why AI
S1: is going to replace us doing work is around the execution.
S1: It's all around the implementation of someone's idea of a
S1: founder's idea of a company builders idea. Well guess what?
S1: There's a trillion more ideas out there that need to
S1: be made, and AI makes it so that it's easy
S1: to go and actually implement them, because you could just
S1: implement a system like lattice and stand up your company
S1: and you have some humans there, you have your buddies there,
S1: you have a bunch of A's and a bunch of agents.
S1: You have transparency. You say, okay, I want to execute this.
S1: I want to do this. Where are humans in this
S1: process of like starting companies, managing companies, doing all this stuff?
S1: They're at the top. We are at the top. Do
S1: you remember how you saw all those workflows and all
S1: those diagrams, all those interconnected pieces? What is the role
S1: of the human when the AI does all that work better?
S1: The role the human is to say, I don't want
S1: you to do that task anymore, okay, I can't improve
S1: how you do the task. Your AI, you're great. Do
S1: the workflow. You got it faster, cheaper, great. Who determines
S1: what the company should be doing, why they're doing it,
S1: what problem they're solving that is only human, right? This
S1: is incredibly positive. Incredibly optimistic. This is what we do
S1: that they can't. Okay. And this is where my recommendation
S1: and everything I talk about in all my videos, all
S1: my open source projects, my recommendation is to make the
S1: switch from focusing on the execution and the implementation, which
S1: is all the first part of this video. It is
S1: all this debate about can AI do it? Can AI
S1: not do it? It's all it's all a distraction. AI
S1: is going to crush that stuff. Those jobs are gone.
S1: That's why I consider this threat so dangerous. That's why
S1: I made this video to address this narrative that our
S1: jobs are not in danger from AI. It's because if
S1: you don't realize this, then you will keep focusing your
S1: energy on trying to maintain this job that is going away.
S1: Instead of transitioning all your effort to getting to the
S1: other side. This thing I call human 3.0, it is
S1: basically becoming your full self, Magnifying your true self, understanding
S1: your true self, broadcasting it and interconnecting that with other
S1: humans who are doing the same. So we can have
S1: value exchange from human to human without the need for
S1: this corporate hierarchy, all these corporations, all that kind of stuff.
S1: It's all, it's all going away. First of all, but
S1: it's not good in the first place. We don't need
S1: to hold on to it. We need to get to
S1: the other side where we can look down at a
S1: lattice like system where all the AI is executing, but
S1: we got to decide what to build. We built that company,
S1: we started that business. We can have 20 of them,
S1: and we have a dashboard that looks down at all
S1: 20 of them, and they're all doing awesome stuff. We
S1: could drill in and we could say, hey, we're not
S1: doing this anymore. We're doing it in a different way. Hey,
S1: I want to optimize this. I want to do it
S1: that way. And the AI goes and executes. This is
S1: why I care so much about this. This is why
S1: I counter this argument that AI will not replace knowledge work.
S1: It is going to replace it. And we need to
S1: get ready. And it's not bad news. It's actually really
S1: good news. We'll see you in the next one.