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AI is changing how companies actually operate. not in theory, but in practice.
In this conversation with the team behind AnchorWatch, we break down how AI is already being used inside a Bitcoin Insurance company.
⚠️ AI isn’t just a better Google, it’s becoming a system that make working more productive.
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Rob Hamilton X: https://x.com/Rob1Ham
Becca Rubenfeld X: https://x.com/BeccaAmilee
Website: https://www.anchorwatch.com
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The reason why ChatGPT, 2022, was a really good, better Google, was because it wasn't
just taking your words.
It was trying to discern the context of why you were asking a question.
You now have this context that lives on your computer.
It's being empowered to basically use the computer as its own personal resource.
And the major improvement for these models across time is they're getting larger context
windows.
Two, three years ago, that wouldn't be possible.
I want somebody now who will say, this tool will allow me to get so much more done.
I am going to create these automations.
I'm going to take advantage of this.
If you don't want to get laid off in the next five years, you must tinker because if
you as a leader are not thinking about the second order effects of these things are starting
to, you are not doing yourself a favor.
Becca and Rob from Anchor Watch, welcome, guys.
Thanks for having us.
Thanks for having us, yeah.
You've both been here for cheat code.
What was your experience like at cheat code?
It's great.
Rob's, this is Rob's third.
Third time in Bedford.
Yeah.
First, cheat code in Bedford.
I went to the cheat code in Australia last year, but for cheat code, it was my first
cheat code Bedford, but anytime we are in London for work like visiting the Lloyd's insurance
markets, I always try to come a day early or leave a day later to be able to see the
games.
I love that town.
I'm Becca.
This is your first time.
First time.
First time.
So, yeah, a cute town.
Really enjoyed being up there, getting to meet the local Bitcoin folks that had come
out to learn and listen and have conversation.
That was really good.
And then, yeah, seeing the soccer was really great.
Football.
Football.
That's right.
That's right.
I think we kind of make fun about the appeal of Bedford, but it really is nice.
It's a nice, quite typical British town in English town, shall we say.
But for the Bitcoin conference, we really take it over and make it out on, but specifically
because it's such a small and intimate affair, you get to know a lot of people, especially
if you're not really, you know, networked or connected, which is most people on to, isn't
it?
So, you can get to meet all sorts of people.
Yeah.
The people, since everyone's coming around mostly VUK, maybe Europe, everyone's so polite
and just very excited to be able to talk to you.
And this conference is unique where there's not like six stages and a bunch of things
you have to run around and do, you get a lot of time just to have long, thoughtful conversations
with the locals.
And they're always very appreciative of the time.
You know, people are welcome to me asking me like technical Bitcoin custody questions or
just talking about anything in life in general and they're all very nice and approachable.
It's a great group of people.
I actually like single stage events.
Same.
So, I mean, I think I can understand the opposite.
I can understand companies and authors of books.
You know, everybody's trying to get on stage and have their opportunity to talk about
what they're doing.
But in terms of attending, you know, a single stage is great.
You sit down and you tend to listen as a speaker when we speak at those events.
We experience that on the other side.
People are very engaged and in their seats listening.
I think when there's a million stages and people getting pulled in different directions
and maybe trying to run between stages to catch something, they end up just giving
up on the content and focusing on the network and networking.
The best part of any conference always are the side conversations you have and not
it, not the what's on stage.
And unless it's a small conference that has like a no recording policy where you can't
watch it later, I very seldomly make an effort to like watch every conversation because
I want to be there in person, engage with people.
And then on the flight home, I'll just watch the stream and I'll watch the talks that
I missed, right, to be able to see the content I want.
That's usually my bias out of conference.
And I don't at all.
That's why I like the single stage event is because I find myself actually sitting down.
If I don't have a meeting going on, if I'm not actively in a conversation, I'll sit down
and listen to the content and listen to the speakers.
Because once I'm on the flight home, there's no way I'm watching.
I'm watching recordings.
I'm either getting back to work work or I'm trying to catch up on sleep.
So yeah, I really, I enjoyed the event.
I thought it was very, very well put on, you know, good production value and...
Thank you, Jess.
Well done, yes.
I thought it was excellent.
Yeah, it was my first time on stage and I have to echo your sentiments, actually, because
if there were multiple stages, I probably wouldn't have met as many other speakers as well,
because that's a key point for me.
I know a lot of people think, oh, it's a big account, say, probably knows everybody
already.
I don't know anybody.
I've been operating this account and I'm sleep for five years.
You will.
We talked previously about how several years into doing Anchor Watch now and traveling
around on behalf of Anchor Watch, you know, the speakers have become our friends, you
know, our personal friends and so going to events is actually amazing from that standpoint
as well as that's our opportunity to catch up with people and appreciate each other
on a personal level.
So today, specifically, we brought you in to talk about AI.
Everyone's talking about AI, hardly anyone's actually implementing it using it in their
businesses day to day.
And so we want to talk about that, but before we do, we need some context about your business.
So tell us about what Anchor Watch is, give us the spiel, but also, you know, it's the
operating context as well, how old it is, what you're looking to do.
And then when we start talking about AI, people will have a better understanding of the
context as you are operating.
Yeah.
So Anchor Watch is an old nautical term referring to the crew of sailors who watch the ship
when it's at anchor at port or at anchor, whatever, right?
And that kind of communicates the big theme here, but what that is is, you know, keeping
Bitcoin safe.
And we have built, there's two main pieces of our business.
We have built our own Bitcoin native custody infrastructure.
I feel pretty good in confidence in saying it's the most secure, large, like enterprise,
like custody systems out there today, and we can get into the details as to why.
And we offer insurance to that.
We are Lloyds of London cover holders and we're able to uniquely offer one-to-one insurance
in your name with the policy directly from Lloyds of London to protect your Bitcoin.
And when we launched to the public last year, I'd say that that combined product is what
people know us for.
So if you think of all the capabilities that a super high-tech, super-secure enterprise
grade custody system can do, I mean, obviously, it can custody.
It can keep Bitcoin very safe and cold storage, but there's all sorts of different capabilities
that need a secure custody layer at the base of it.
So Bitcoin back loan, where's the collateral being held?
And so there's all sorts of capabilities that we have because of the tech solution.
We can ensure any Bitcoin that's being held in our tech solution on the custody platform,
but we're Lloyds of London cover holders, Rob mentioned, and we can do custom insurance
policies.
We can craft things to meet businesses where they are.
So think of it as a Venn diagram.
We've got our insurance capabilities.
We've got our custody capabilities.
What people know us for is pretty much the little overlap of those two things.
They know us for insured custody.
But really, we can fill out that entire Venn diagram, if you will, and continue expanding
our business into different capabilities that take advantage of both.
Take advantage of our insurance capabilities and custody.
The one just short thing I would add to that is that for any financial service or any
use of Bitcoin, custody really sits at the foundation of anything you're trying to do,
whether it's alone, escrow, any sort of futures contract, any sort of financial product
really on Bitcoin, custody is the foundation.
If you don't have good custody, then the rest of it doesn't really matter.
And so we've been really laying a foundation, pouring that concrete, letting it set and
be stable for us to be able to look forward into the future for on that custody side
and also bundling with insurance, or maybe not even using our custody platform, but also
just getting insurance.
Rebecca, you're the Chief Operating Officer of Bank of Watch, and you've got a very strong
background in operations, special projects and retail.
Just give us a quick overview so we get some context of your experience.
Sure.
So my career has definitely had some phases.
So the first 10 years of my career, I was an apparel merchant, so doing both the financial
planning forecasting of apparel at kind of the corporate level, and then also actually
choosing the style.
So planning the fashion aspect of it.
When I went over to Starbucks, we've chatted about that, spent 10 years at Starbucks, all
at corporate, doing a variety of different tasks.
But one of those roles that I had at Starbucks for three years was implementing 26 new,
well, 26 systems, different pieces of software, hooking those up together that orchestrated
all of the company's outbound spend with the exception of mainland China.
There was a very operational aspect to the systems themselves, so I think as we get into
the conversation, we'll definitely go back into that and to kind of how a COO or a really
kind of back office team should be or starting to think about these tools.
Rob, you're in effect the technical partner, but also to CEO, so how did you get to that
position?
I mean, a brief background in my career, my first job, besides busing tables, that was
my first job at 14.
I was actually a video game referee.
I got flown around the states working at Major League Gaming.
It was like the dream job for a high schooler.
We're going to pay a little per diem and fly you around the country to watch people play
video games, and that was at Major League Gaming, back when esports was starting to become
really big things, like 2005 to 2015, but even I went to college, got a double major
math and econ, and then I went and started working for them, managing their ad server.
So it was just a job I could do to help them out when you would sell sponsorship contracts
who'd run a live stream.
We were actually kind of the first to pilot video ads in live streams.
We were like piloting that with Google, because we would get 100,000 people back in 2009
watching these streams, and we would want to serve ads.
I did that for a couple of years.
I did another, went to bounce around ad tech as it's called for a while.
And then I went to go work at IBM as a consultant and turned that into being a data scientist
and consultant.
And my Bitcoin journey just briefly started around 2014.
I had a, but as a big Ron Paul and the Fed, Gold Bug guy, and a bunch of my buddies, a couple
of my buddies would tell me to check out Bitcoin, and I was like, too complicated, you know,
I'm not going to get into that.
And then Dogecoin came out.
And all of our friends had gaming computers.
And so we would all mind Dogecoin.
And I realized that like with the Comic Sans font in the rainbow, like, like, like, this
was like, I knew internet culture that this was satire.
So I was like, okay, this is satirizing something.
Let me go learn the real thing.
And I started going to the New York City Bitcoin developer meetup in, in, in, end of 2013,
start 2014.
And that was kind of my opening into Bitcoin and what, going into those bit devs meetings,
it would be maybe 10 to 20 people in a room, and just going very passionately talking
about all of these concepts, like decentralization and security and cryptography, things that maybe
I was a little bit aware of, but this was such a serious application of all these principles
and designing the software that it kind of fell in love immediately.
So I started going, you know, to all of those meetings, starting to learn about Bitcoin.
And that was kind of, I never really intersected my professional career with Bitcoin until starting
the anchor watch at the start of 2022.
So during COVID and all of the lockdowns in the States, there was a big social audio
app called Clubhouse.
And that's where I met Becca, I met, like, all of our initial angel investors.
And the whole story for anchor watch came out of that, but I was always, uh, in those
clubhouse terms, like, the someone who understood the technical nuances of how Bitcoin works.
And through that, um, realized that there was a really good opportunity to apply that
in the domain of not just maybe working at a Bitcoin company, but starting one.
So all these more than two years now, since Chatchee, VT launch, and it was, yeah, really
quite an explosion, um, a lot of attention, massive adoption, it's almost a vertical line
of adoption.
But, you know, it quickly just settled into being an advanced form of search for most people
right?
Better Google.
Yeah.
I'm much better Google.
And you're like, okay, that's fine.
But now, I guess it's just, it's almost like the second stage with the, um, the advent
of, is it open core, with open clause, the, yeah, open cloud, no, open cloud, the agent
model.
The self-hosted agent.
Yeah.
And so we're, I don't think we saw as much.
I think when, when, when Chatchee, VT came out, there was a huge, uh, level of interest.
And then with the development of the visual model, say, like mid journey, there's a lot
of hype around that.
And video, but nothing really quite matched that initial launch.
Mm-hmm.
I think, as, from my point of view, as much as the core, um, launch did, what, what are
the implications of, first of all, just Chatchee, VT style models, but then the agentic models
and what, what can we do with those things right now?
If we talk about how people kind of went through that transformation.
So originally, you're right.
I think people were using us a better Google.
And I think, uh, I would say, maybe throughout this conversation, it's almost important
to have two answers because I feel like there's a lot of things that Rob, as a developer,
as an engineer, is going to have very different answers than I, which, you know, let's just call
me a normie.
Uh, I'm a technically savvy normie.
It, like, I think that's there.
Yeah.
Yeah, I'm not technically ideas and words.
I don't know any code.
Yeah.
All right.
So if that's how we're defining technical, as I don't know code, whatsoever.
Uh, and so yes, initially, I would say, normies were using Chatch as a better Google.
And it took some months to figure out how to use the better Google and really how to, to
appreciate how much better it was.
And, and so I think there was a little bit of learning there.
And then there was a lot of flurry of activity that I'm not even sure I would have been
aware of if I didn't work in a tech company, where, you know, Rob was starting to go into,
you know, various aspects, which he can talk about himself.
And, and you could tell he was getting very excited, right?
Like, so he was going down rabbit holes and coming up and being like, this is amazing
more of this, just like so excited, um, and telling us what the future was going to
look like.
This is what's going to happen.
I was just like, slow down there, slow down there.
Uh, and then I think, uh, actually, I got a little bit of a, I would say a preview when
Rob pulled our team together and introduced the non-technical team to cursor, which was
some tooling, uh, develop, yeah, it was developer focused tooling, but it was a, enough of,
uh, we'll say like a chat style wrapper on top of it that for the first time, Rob could
see how non-technical people would, would start entering.
And I was very curious about it, uh, and I started playing on it and then I got very
sick.
And so I kind of stopped just because, uh, of medical issues pulling me away.
When I recovered, uh, from that, I kind of recovered and, and woke back up from a couple
of months, uh, of, of just being unwell, right as co-work, right as clog, uh, the new model
in desktop came out and people will talk about this, this big change over the last couple
of months is being clawed co-work.
I, I actually think that is a misnomer.
I think co-work was released at the same time as, as other things, but I think the real
change was actually just clawed desktop.
And so you download a desktop app suddenly it was again, just a very basic tooling on top
of these capabilities that made it norm, norm, norm me friendly, specifically you put
a desktop app on your computer, but the files that you create, uh, that you upload to train
things live in the cloud.
What that means is I can now use it when I travel on my laptop, I can use it on my desktop
at home.
I can invite my team, uh, to join a project and to contribute to a project just like
you would share a Google drive, uh, document.
And so something as simple as that, just organization and, and a wrapper, like nothing
really advanced from my point of view, uh, just that was enough that suddenly you could
use it as a non-technical worker.
I could start to organize things, I could work with my team and delegate things, uh, and
it really kind of changed from being a novelty or a personal tool to suddenly a collaborative
tool, which really does change everything.
Uh, so that was a, a big aha.
And then I think as we go on, we can talk about how operators, how, how my team is starting
to get more towards the code side.
But like, let me pause and talk about kind of Rob's entry into it, maybe?
Yeah, I would even just take a, like, one big step back and to talk about your question
about chat GPT versus open claw, why even GPT is a better Google and the initial, like,
in 2022, this came out.
The reason why the, in many of these things of the step functions of improvement beyond
just like the quality of the models, but the user experience and kind of the distribution
of these tools that are becoming more commonplace is the word context.
The reason why chat GPT in 2022 was a really good, better Google was because it wasn't
just taking your words.
It was trying to discern the context of why you were asking a question and use that for
how the answers get returned to you at a very, very high level.
These models are what's called stateless, which means it doesn't have any active memory.
You have this large neural network model that you put something in, you get something
out.
And I'm not sure if most people realize when you talk to one of these, if you talk to
it in response to you and you say something again, what you're doing is you're taking the
initial question, the response that it gave you and your follow-up question are all being
put into the model at once.
And that's more context.
If you're having a dialogue.
Repeditively.
So it's building on itself.
It's constantly reprocessing the entire conversation at every new request or engagement.
Exactly.
And why that's important is because that's more context of understanding what the conversation
is about.
And in 2022, it became a better search very quickly because of that.
Because you were building on a conversation.
Then I would say the next big step forward were these tools where you were pulling something
onto your computer.
And Tobacco's point about why Claude Desktop in particular is good when you, the early
phases of these when you would interact with them, maybe you could drag and drop a file.
And it could have that as context.
What ultimately, though, happened is when it lives on your desktop, rather than having
to go to a chat history and find the conversation and pull it in, you're able to retain context
across conversations as well as the friction of adding context to the model went down a lot.
Not just I'm uploading this document or this conversation.
Also, when I get an output and I curate a really good response, that doesn't just live
on a server somewhere now.
I now have a what's called a markdown file, which is a formatted text file, which lives
on my computer.
And this is where you start seeing the recursive loops of abilities like skills and everything
else like it starts building on each other.
On the developer side in 2022, I started toying with these things.
The issue, though, is the domain of language for conversation versus code, whereas code
is very strict and it has to work or not.
And so throughout 22 and through 23, really, it would be able to build up maybe a basic
prototype, but there would be many, many headaches and friction points.
Like this is, and this is where the common thing of like, it's hallucinating.
It's like became very part of conversations.
The thing to pay attention to here, though, is not just this, this is now existing, but
the rate of acceleration of improvement.
It's funny, like you're saying that you were sick and then you came back.
That's when we had another major step function up, like in that time.
So I had a similar dynamic with my wife, where she went on maternity leave, came back,
and she was gone for three months.
I was like, you need to do this.
Like you need to check this out.
And specifically for open claw, that just took it to the next level.
So rather than me having a conversation, whatever, you now have this context that lives
on your computer.
And it's being empowered to basically use the computer as its own personal resource to
go out and do other things.
Whether it's searching web, whether it's writing code, or maybe there's an ask you make
of it, and it says, okay, I will every day at 7 a.m. send you a text about a briefing
of what happened overnight.
To do that, though, it's writing a program to do that, storing that program on the computer,
and then calling out to that 7 a.m. every morning.
And the further autonomy you give these machines to have access to a computer to be able
to run things, the better able it's to manage context and build things and figure out the
most optimal way of executing things.
And that ultimately powers you.
And that's kind of like these continual stuff, the functions.
Let alone just when a new model comes out, the major jump you get in improvement.
And the major one lesson, the major improvement for these models across time, is they're getting
larger context windows.
You're now in a position where you would quickly overwhelm it, and you wouldn't be able
to add more information.
You'd have to drop information.
They're getting more and more context, which is allowing it more memory, and these are
the things where now we're at a point where even large complicated code bases are able
to be in one session, all be persisted at the same time, whereas two, three years ago,
that wouldn't be possible.
It gets, then I would say, further complicated because of the rate of change, because nothing
is final yet, right?
Everything is very fluid, that even as a non-technical person, there's model development.
And so how well the context and the recursive nature of the models is improving, so you
can have decisions of what model is best or what model do I want to use, what model is
smartest.
But then there's tooling and tooling developments and different companies have developed
different tooling.
And so, for example, right now, actively kind of this week, I've been largely using
cloud desktop and the chat and co-work functions of it, so I don't yet use code, though I think
I will.
But perplexity came out with computer, and they have different pros and cons or different
capabilities.
But I'm confident that's a temporary thing, right?
So it's how much do you dive in and learn and optimize and build to learn versus build
to implement when you know there might be something very better, very much better on
the near horizon.
So for example, cloud desktop is a desktop app, but the files live in the cloud.
But when it executes tasks, it creates a virtual machine on your computer, on your physical
computer, and it runs on your computer, which means your computer needs to be awake and
active doing that work.
You have to take that into account.
Perplexity computer, the virtual machine is in their cloud.
And so if you are creating, say, repetitive tasks that you want happening overnight and
you don't have your own server rack, you know, maybe if the capabilities of the model are
similar and perplexity, by the way, computer lets you use other models.
So you're not constrained by that.
If you really need that, that ability that the virtual machine, the cloud-based virtual machine
provides, which again, for normies is more about when it runs, not on your local machine,
then maybe you need to use, use that.
And the other thing is one of them, one of the two models has the ability to actually make
changes on your personal computer, which has pros and cons and security things to consider,
but can also help you create things, right, because I don't code.
So it can just do things to my computer to implement what I'm wanting.
The other one can browse the internet and control the internet.
So then there's plugins, you know, there's a cloud plugin for Chrome or perplexity has
Comet browser, which has those abilities mixed in.
So just right now, it's very, very agile.
So I think as leaders, we're both assessing the new tools.
Our team is starting to use new tools, which is exciting and seeing what they're creating
and, but watching how both of our teams are using these tools is giving us both in
our respective roles, you know, big eyes, right, because there's a lot of implications
for all these things.
And maybe just the very last thing I would go back to your original question, because
I think a lot of people just don't understand yet this term, and what we're even talking
about.
So when people are using chat, they're just having a conversation, right, and if you're
using cloud chat, you're still just having a conversation.
When you think about agents or sub agents or skills, these are all ways to indicate
that you are training a very limited task.
And you are trying to narrow the context window so that it can't mess up.
So it can't hallucinate and make mistakes.
And so OpenClaw, I've never used, so Rob can speak to OpenClaw in particular, but that
was, I would say, the big technical agent builder infrastructure, whereas now I will be
starting to use either cloud or computer to do it in the normal way.
Yeah, I guess what I briefly add to that is when you're having a conversation with either
chat GPT or a cloud AI, you're just having a chat dialogue back and forth conversation,
it just sits there and it doesn't leave that, like it just leaves, it's that conversation
that's it.
An agent is taking the capability of these chat models and saying, I want you to take
the steering wheel and go build and do things, right?
That is the next level of kind of breaking things out.
So maybe in 2022, if you were writing code, you would, what you first had to do initially
was like you had to take your block of code, copy it and ask a question, take the answer,
copy it out, put it back and check and see if it works and kind of like do that as your
loop.
The agent model, like in these, maybe what's also called a harness, if you heard of
cloud code or open code, code access, opening eyes, is that we're saying, okay, we want
you to run these large language models, but here is the code base and maybe even like
you're talking like an open clause saying, here is your computer.
Now go do a bunch of things.
The one thing because we're just started talking about skills, the way I view skills and
what these, like you're training on a specific sub-domainer task, you give these ultimately
all distilled down to what's called a markdown file, which is a fancy formatted text file.
And these markdown files, what you're doing is you're adding context.
So you have this stateless model, it has no memory or things, but you're saying, hey,
you are a leading expert in insurance or you are an expert in Bitcoin custody, right?
You're giving it guardrails to go through the infinite universe of what this model's potential
is and you're immediately gating it in and saying, this is your focus.
You are a QA, like quality analyst for code.
You're just a tester, right?
And what you can do is you can take the same model and give it different skills and it
can then narrow the scope of what it actually does.
And the way I view skills is if you think about like a craftsman has a big tool belt,
maybe a hammer and a wrench and a screwdriver and they're all kind of circling around his
belt, what it is is you go to the model and say, hey, use this tool or you can have a tool
that isn't just like an agent personality can be, I want you to run like some early examples
of what I've done is like you, I want you to use these tools to go through and verify
a Bitcoin dress.
And this is exactly what you do and make sure it follows X, Y and Z spec.
And I give it context around the business.
And then when these kind of questions come up, rather than from first principles thinking,
hmm, how do I solve this problem?
What it does is it'll look through its skills list and see what skills do I have and that's
like a craftsman looking at the tool belt wrapped around his waist and saying, ah, here's
my tool that does this thing, right?
And this is, and because it's language domain, like the instructions of the, these skill
files are not code in these skill files, you can have sections and say run code like
this or run code like this.
But you don't have to give it specific code instructions if you're not using it for specific
code things.
But what you're able to do though is kind of encode these different personalities to
start thinking about things.
And over time, realistically, as you're improving on these things, you are going to inevitably
have pieces of code that get peppered into them saying, this is my, I want you to make
this call specifically to look at my calendar, or I want you to specifically look at this
formatting code for how I write things in general, right, for like code format.
So that's like the general mental model I like to give people is that these skill files
are like tools on a tool belt and the agent as it walks through problems will be able
to understand, like, ah, this is what I need to go look at and do.
And maybe a real life example.
That's what I want to think about.
Yeah.
So here's, let's say I want to use AI to help me develop new policy, insurance policy.
And you know, at the, first of all, AI, there's a whole new thing to think about of, you
know, at what point you do legal reviews, you know, there's all sorts of change, right?
But let's say I'm going to use AI, that's my goal.
I want to use AI to help me write this new insurance policy.
I already know the policy I want to write.
I know what I want to cover.
And so I might have a normal chat that you're very familiar with in to set the context
for that.
But in terms of agents or sub agents or skills, let's say that, and the way I think of
it in my head is the more generalist an agent is, the more tasks, I think of it more human
and probably over time, I'll name them to have a, have a name, right?
I was watching a video by a guy called Alex Finn, he's really prominent online, doing
tutorials and stuff.
And he literally names every single agent.
He has a chief of staff with the name, the chief of, he only speaks to the chief of staff
who then command is all the other agents kind of kind of, I mean, that's one way to do
it for sure.
But yeah, the more, the more of a generalist it is, I think of it more as a human.
And then if I'm getting into sub agents or tasks, I really don't.
I think of it as like a thing.
So in terms of the policy example, maybe I would set up a project in, in cloud desktop.
And that's where I'm uploading my, the previous policies that we've written, maybe some context
from the internet on research that I did are prior conversations as I was planning for
it.
And overall, I have an agent who takes those things and will spit out a insurance policy
for me.
But I start looking at it and, man, it's hallucinated some crazy stuff and it's, oh, man, this means
so much review.
It's a legal document, so it's, you know, and so what you can do, for example, is in insurance
policy, there's a definition list.
There's a prescribed format for these documents, it's regulated.
And so it must have definitions and so we put a lot of attention into those definitions.
They have to be extremely accurate because they drive really the rest of the document.
So what you can do then is, for example, develop a dictionary sub agent.
So you could name the dictionary a name if you want to or you can just think of the sub
agent is the dictionary.
And so then I would train a sub agent to say, you are the dictionary.
Our job is when I create policy, you are to take our approved list of definitions that
I've written before.
And maybe to train this dictionary skill, all upload load, all the current definitions
and then based on my aspirations for what I want to do, it will help me create, identify
new words that I need to define and I'll work on those.
So you'll use AI to make the dictionary better, the actual list of definitions better.
And then once you have it, you say, that's a job, is you are the keeper of the definitions.
And I can either manually, when I get to the point that I think I have a good policy,
I could say, hey, run the definition or run the dictionary skill.
And it would go through my policy and it would tell me anywhere anything in the policy
might contradict our definitions, where I might need to look or it might tell me, you
know, it will go through and it will not allow a word to be misdefined, because it only
had one job.
It's only job was to make sure the definitions and the policy match the approved list.
And then maybe I'll have a second agent whose job is to check the documents for that.
Are you creating, do you think of it as kind of like an agentic or structure and line
of command?
I don't yet.
I don't yet.
Right now I view it as tasks.
I all report to you sort of, all these agents feed back to you.
I'm not there in my head yet.
I know many people do that kind of see themselves as CEO.
The first time I drew, drew it out, it was both work, but my personal life too.
And so yeah, like I drew myself in the middle of CEO and I just moved away from it.
It didn't, that mental model didn't help me.
What got in the way?
I just think it's unnecessary.
It's premature.
We talk a lot about different neuro abnormality, atypical thinking, I'm super ADD, right?
So I've already always described my brain as being a cloud.
And so when I learned something new, I just throw it up there and I just let it flow.
And then my brain takes care of connecting the synapses on itself.
And so that is how my brain works.
And when I got into, yeah, prematurely identifying agents and roles, it just wasn't helpful
because my brain is still in accumulation mode on this topic right now.
I'm still learning.
I'm still like drinking from the fire hose and throwing things up there.
Now maybe later, maybe later when we figured out exactly what we're going to do in a professional
sense, maybe I will get to the point that I've got these X number of human people reporting
to me and then these Y number of AI agents reporting to me, but I'm not there.
I would say in general, like the comment about it being premature is not that you can't
get productive output today.
What I would encourage for people in general is to start iteratively tinkering because this
is an entire new domain and how to get work done in the skills that are reflected in them.
And then we talk about this where non-engineers are inherently going to become more engineering
minded, even if they're not literally writing code.
But since these computers are very literal in understanding walking through these conversations,
you need to be almost like an architect, right?
Or a way maybe from a non-engineering perspective would be like you're like a product manager.
And because you're taking this higher level of abstraction, when thinking about these things,
the underlying tools are changing, not just because the acceleration of this stuff is happening.
What's interesting is not just it's getting better, the rate at which it's accelerating
is increasing, that you don't want to get too invested into this is my exact architecture
because very likely in a couple of months, it's going to be entirely transformed by a new thing,
right?
But the encouragement to tinker comes from a place of this is a whole new domain space of being
able to have a conversation or being able to curate lines of thought, basically what you're
doing, you're creating a skill fire in an agent, you're like this is the world in which you live in.
That is a skill in of itself, and as a general tip for people at home, the most important
skill, Claude, open sources, because they're just markdown files, right?
This isn't code, they have a list of all of these different skills, Claude, Anthropics
specifically has the skill building skill.
So meta skill, this is how you actually build skills, and that's the one I would start encouraging
people to and you can start having convert, you can load up that name again, Anthropics
Claude, which version?
Yeah, so any version, right?
So these markdown files are just blocks of text, so it's not like opus versus sonnet 4.5
or 4.6, just the skill file, and this is like the idea of is it more important to have
an ability or to learn to make a new abilities?
And this is in the Anthropic GitHub page, you can call it like it, so Anthropic is the
company that runs the Claude models.
The skill building skill is what it's called, and you can find it pretty quickly online.
It's on GitHub, but it's just a quick text file, you could just click, pull down, load
it into a skill on your computer, and that's a great place to start because you're going
to have decent guard wells on what it takes to build a skill.
And then the reason why you want to take her to is you want to understand, I was way
too specific here, I was way too general there.
I should have added A, B, and C pieces to it, and that's the part of tinkering I think
should be encouraged, understanding that we're moving, we're changing the domain rather
than having different people, you have different text files that are able to start, and this
all goes back to context.
You're just learning how to manage context, and this is an important thing for you get
to a point in these conversations where there's something called compacting.
So let's say there's a one, so you've may have heard the term token, my two sentence summary
of that is that what you do with words for these models is you actually transcribe them
into numbers, which is called vector, you take a word and you make a vector out of it.
And what you do over sentences, all of these words turn into numbers, they get put into
the model, because it's a mathematical transformation, and on the other side you get numbers, and
they convert those numbers back into words, right?
So that's kind of like the magical sleight of hand here, why are words doing all of these
complicated things?
It's because it's all math under the hood.
And where this goes to for the tinkering of the skills is what you're doing, and when
you get to a compaction point, a million tokens, which is more than a million words, but
let's just for the sake of conversation say it's that the model says, I can't retain,
I can't do more.
You've hit my limit.
Let's make this smaller and it'll go back and make a summary, right?
So this turns into memory management, and it'll make an attempt to summarize, but maybe
you have strong opinions on what should be maintained, right?
So this is like the things you start iterating and thinking about over time, and creating
tasks into small bite size tasks is a great way of managing context.
And so loading your entire code base or every single document you've ever written in your
company into one thing is not going to meaningfully actually do that because you actually, if
you get too much, this is one last thing of it's called like a vector database or retrieval
augmented generation where I have my folder of all of my documents, and I ask a question
and they'll go look at the folder and be like, oh, this one, they'll look at the title
and be like, this, this may be similar and pull the whole thing and go back and forth.
It gets to a point where everything kind of clusters together and it's all noise.
So your own curation of how you think about organizing data, which is funny, because
it's an engineering principle of like how you organize and kind of break things off, that
becomes the important skill.
Your data curation, your organization of how you group data, and then also learning how
to build these skills.
That's those are skills that are going to be across time still important because these
models have fundamental mathematical constraints.
By planning those, it made me really start to understand a new area of management requirements,
I guess, in the future.
But it'd be fair to say, and I know it's, you just said what a token is, right?
So I know it's not literal, but is it fair to say that tokens are more or less energy
or money?
I mean, the more tokens your employee uses, the more money.
Oh, that's true.
Yeah, so just at that level, you say gas pump when you go to top off that you're paying
by the token.
Right.
Currently, let's say a non-technical employee of a company will have very little spending,
right?
They might have a corporate card where they can cover certain expenses, they might travel,
they might pay some vendors for some things.
But they themselves, if you're talking a corporate worker, office worker, is not generating
a bunch of spend.
Suddenly, your team could be generating huge amounts of spend, doing work, but how does
a new manager start to manage their budget?
So I think what's going to happen this year and next year is across the world.
Suddenly, the companies who allow their employees to actually use their computers to do these
things.
Suddenly, companies everywhere, managers everywhere are going to find that they blew their budget,
like blew their budget, right?
You could have a team of 10 people that suddenly has generated a million dollars of spend
over the course of a year.
That's an actual possibility.
Businesses can't sustain an unexpected million dollars of spend.
And then it might not have achieved anything, especially in the next two years, where people
are iteratively learning and trying to build things and it goes nowhere, it doesn't end
up being used and all that spend did not accomplish the business purposes of growth or advancement.
There's just two brief points.
One, at the moment, most people are using either a subscription plan, either it's Clawed
Code, they pay anywhere between 20 and 200 bucks a month, or they're using a version of
that for opening eye and chat GPT.
Those are being subsidized at the moment, 95 cents on the dollar.
So people are already complaining about the cost and or if they reduce the limits, well,
you're only going to give me $4,000 of compute for my $200 buy and people are like, I never
use an anthropic again.
I'm going to go to chat GPT because they let me take my $200 and spend $5,000 of compute
and you guys are only going to spend $4,000 of compute, right?
Someday, the subsidies will go away and then what I'm describing will be even more.
Even in the subsidized world, companies are going to get surprise bills or unforecasted
bills.
When the subsidies reduce and eventually go away and people are actually paying their
way.
Well, in the video I watched last night, it was a peter-domineless, is that his name?
No.
Yeah.
So he had a sort of bit of a panel discussion on his podcast and one of the guys said, yeah,
I could let one of these agents go overnight and you come back in the morning, you don't
know how much it's going to spend unless you put hard limits on it.
And so he could go off on all sorts of sort of wild use chase and you don't know what
it's going to come back.
Well, this is the other thing, too, to back us point about like, there's a, at the moment,
there's, these tools are incredibly productive.
It's so easy to be seduced by the illusion of productivity.
The idea that you can have a cup, you can wake up, answer a couple of emails, have an idea
and then by dinner time, something's built, right?
And you're like, wow, this would have taken weeks to get to this point.
And then I think it's really important to have that kind of freedom to iterate prototype,
think about things.
On the engineering side, for a pure proof of concept, I'll code something up in an afternoon
and then I'll realize, oh, I actually missed like five different things in this product
spec.
And that gives me more information for when it's time to actually build things.
But it's so easy to be seduced and be like, look at all of the work I'm doing.
Look at how many documents I've made, right?
And somewhat like a general principle in data science is like garbage and garbage out.
So like if you're just casually like doing a proof, quick proof of concept, it can be productive
and interesting, but it may not be the actual end piece.
And to your point, like, unless you set hard limits on these things, and that's like,
when you get to this this agent model in general, you're very quickly going to hit your
usage limits.
If you're doing generalized, like go explore overnight and do things, tasks, which means
you have to go to what's per token rather than having this like subscription based thing
where you get a certain amount of usage and whatever, you need to actually use the API.
If you're using the API, that's truly like a gas pump.
And you're paying twice.
You're paying for when I send you data and then when the model responds, those each are
input and output and those each have their own cost to them.
And that's just its own reasonable thing to manage.
And then that turns into a skill of maybe you start discerning these when people talk
about chat GPT or Claude, there's for Claude, there's Sonnet, Opus, and Haiku.
Opus is like the best model.
It's more expensive.
And then Haiku is the cheapest model and it runs faster, right?
And then you start realizing like, do I really need to use Claude Opus to ask about the
best coffee shop in my neighborhood?
Is that really requiring the most use, right?
And then there's also is a way you can sub break out agents as you can actually say that
this is a-
Tell us the selector model?
Well, you can just code saying that whenever I talk to you, I want you specifically
to use the lowest quality mode.
And then on the GPT 5.4 side, like the latest model there, there's different thinking
levels.
And this is interesting, the way the models are increasing their thinking and this was
a really big thing that around the time you were getting back that was really like stabilizing
is thinking mode is not like the model is just a mathematical formula.
If you want to call up that, it's not like it turns on an extra brain.
What it does is if you ask a question, it will have a side dialogue with itself and
recursively think on it.
And they're charging you for that usage, like that's all usage.
But the iterative process, it's actually modeling like an inner dialogue of asking your
question, thinking about it before you go.
And the way that gets emulated in this code is it'll talk to itself for a while to think
about what the best answer is and you can set different levels of thinking.
Because inherently don't cost more or less, but if you're taking the higher think mode
or the extra high think mode, that will just inherently be more costful because it's
taking more time.
And a very simple, just very quick simple example of how I do this is when I'm architecting
a project in an idea, I use the ultra high think mode because that's the time to be really
expansive and think about the opportunities of things.
And then it'll come out with a playbook.
And then I can pull down the, since that high think mode, the output is this very structured
product document.
I can then tone down the level of thinking to then just go execute the specific detail
tasks that are at hand.
I think in the future then, I don't mean in the next two years, I mean the next five
and more years.
I think part of being a good worker is that you will inherently learn how these models work
and you will start to design your own work to be efficient.
That will be partly forced upon you by your employer to manage costs and your own time,
not waste time or waste server time if it's happening locally.
But as a user, this one will not be that hard because users will want to do it because
think of it like, I think of it like using a slow phone or a slow computer that needs
an upgrade is you can't, you can't change the phone, right?
But you will, you will change your own usage of it to meet the internet speed that's
provided to you.
So you will or you won't do certain things based on the amount of time it takes you.
So if you build, let's say, if you don't use an agent model and so every single time
it's doing anything, it's searching this giant context window and taking a long time,
it's going to be so annoying.
We're going to be wanting instantaneous reaction just like we demand from a browser window.
And so I think, I think over time, people will learn to use agents and sub agents and skills.
So it's super efficient.
So that way when they're working or doing their personal life, it's closer to instantaneous.
So I think that kind of stuff, some stuff will happen naturally and some stuff will be
forced upon you by your employer as a cost or efficiency or other HR control related
things.
I want to explore some, I guess, tangible ways that people can use AI now.
And I want to break this down between simplified tasks and perhaps some more complex tasks,
both technical and non-technical because a lot of the discussions I had for the sake of
understanding kind of like a generalized conceptual level.
People watching this at home, I think, and yeah, but how can I actually use this when
I'm seated at my cubicle?
What work can it actually do?
I think that's genuinely an exercise in changing your perspective on how to evaluate problems.
And the reason why I say that, it's entirely, it's entirely, here's a very just, like as
a basic example, I was talking to someone who was asking me how to use these tools and
they're like, well, I know what to do.
I'm like, well, what is it that you work on?
That's a repetitive task, and they said it and I was like, okay, then go do it.
And they're like, what do you mean?
I was like, explain your repetitive task, have a conversation with it, and ask it, how
can you help me automate this?
That's like the ultimate, just like directly, and this is where people are used to with
code and software, like you have to go to school and you have to read a bunch of texts
books, and then you have to like get a code editor out, and then you have to think about
it, you have to design it.
All of that is gone in general principle, especially for someone who's not technical and
is looking to just go from zero to one and start using this stuff, having the conversation
and just doing your own inventory audit and think about where you're spending the hours
of your day, and just start having conversations and it's an iterative process.
And this is why I make the point about tinkering is important.
You want to start flexing that muscle and thinking about how to interface with this new
technology, and that's where you can go in general.
I can tell you where I started.
My first one, my first one is, I hate email, I'm terrible at email.
I had a friend and an exec at Starbucks who said when he came back from vacation, he took
all like 470 emails and he hit delete, and he's like, just because somebody sends me an
email doesn't mean they deserve or like they automatically get my time.
I was gone.
If it's important, don't email me again.
I was like, my mind's blown, right?
I wish I could take that out of it towards email, but that's how much I'm bad at it and
I don't like it.
And so I was just like, I want this problem solved.
And one, some of my problems are there's a certain type of spam that I'm certain you're
familiar with that makes it through the spam filter.
And they do it obviously in certain ways.
It's instantaneously recognizable where they don't have any links.
They spin up a new domain so it's not a crazy domain like old spam used to be.
I wanted those out.
I wanted things that I have to delete manually like a calendar invite that I've already accepted.
I want those out because with my ADD brain, those things they suck my time away.
I open up email to do legitimate work, to look for that email from somebody important
and respond to it.
But right before I get to that, there's these 17 emails that irritate me and so I end up
doing that.
I get distracted.
Maybe I run out of time and I have to go to a meeting I never did the email.
So I described that I wanted to create an email agent that could help me solve those problems.
So I talked about how to do that.
And so earlier, remember I said certain models can do things to your machine.
Certain things, certain models or extensions can do things to your browser.
And so I started experimenting with those.
I ended up using both Claude and perplexity comment browser specifically.
I've used them both and they are going through Google web apps, script app, web scripts.
And so it is code, but it's doing it into Google's kind of platform that they already had
in place.
It's important for compliance that you're running this logic.
You talk to an AI agent and it gave you an output that you're now just running within
our enterprise account.
It's not like anthropics reading every email that's coming in.
Right.
Right.
And so it was just to help me create this email filter.
So I talked to it for a bit and I said, okay, run it.
Let's see how it does.
And it said, okay, I ran it.
I said, well, I need to know how you did.
So okay, now when you run the email, send me a daily email, recapping what folders you
cleaned out.
I had eight or ten different things I wanted it to do in terms of filtering and cleaning
things out that were a little more advanced than the native filtering skills.
And so now then the next thing was I got a daily email and it listed everything that it
had deleted or moved to spam.
And if I see something that did poorly, I could go retrieve it out of the spam folder
and save it because it was maybe a bill or something like that.
And then every, let's say four or five days, my email would irritate me again.
And I could tell that my email assistance was not doing well.
And so then when I found time, I would open up that kind of conversation.
I'd be like, the email filter is still sucks.
It's still not doing the spam.
It is terrible that there's a reason that stuff makes it through the spam filter.
Even though we can perceive it, apparently it's very, very hard.
So every maybe five days, I would put in a few more requests or I would show it a screenshot
of something and be like, you know, this is happening.
Why is this happening?
And it would improve it to the point that I have like a moderately useful email assistant.
And then I would say the most advanced one I've done is really was done for plain, at
least most advanced in terms of like my thought process and getting it going is actually just
one that is a get me out of the door when I'm traveling, packing to go to the airport,
tool, skill, I guess, where I tell it what I need to do before I leave.
And now it knows, now it's learned how long each task type takes and it will more or
less estimate my amount of time based on what I have going on and it will remind me currently
by sending me a text message, but where I what I want to do when I get home is a final
step where I want to actually connect it to the Alexa, the Amazon Alexa verbal thing,
which is the world's stupidest model, by the way.
It is an LLM now, so you can have conversations with it, but it's really bad one.
But I want to next take my get out the door tool, hook it up to the Alexa, so then I have
audible call outs.
So that's it.
Those are the types of things I did just to play to learn just a very concise one for
most people who use like many people of their desk jobs use Excel.
If there's regular Excel tasks that you're doing all the time, have a conversation, drop
it in there, it could work well with chat, GPT, if you want to make us, if there's something
you're always doing to a spreadsheet, you can use cloud code or co-work and it will
make a script that'll automate that or to summarize, right?
If you have very large complicated documents, give me a bit of a quick summary calls, like
that's where it excels really well to just a or if you work with a lot of large documents
being able to summarize a large.
Do you use it to learn much?
Learn with what?
Well, an example was I needed to write an email back to Lloyds of London, our contact
at Lloyds of London, and I needed to make an arguments based on insurance math and
actuarial data that I didn't natively understand, and so the goal is just to help me craft
an email response, but I didn't want to just send an AI response.
I wanted to be able to make this argument, and so I actually took four hours in all with
the ultimate goal of just an email response, and I had it more or less teach me actuary
math.
Yes.
Which was amazing.
I could have never written that email.
I could have never written that.
I had a similar experience.
There was something technical, I wonder if it was math or chemistry or something that in
high school, I had, oh well, I did a lot of things wrong in high school, but I just had
a question, and I asked it, and I'd explained it in a way that I've never heard anyone explain
it to me because I'm like, oh, yeah, I wonder if I can go back and learn co-learn with
one of these models, all the topics that I struggled with in class, because I mean, as
much as I was in a conventional sort of learner, I'd always get these weird questions popping
my mind, and I'd treat them as roadblocks, because unless it was answered, I couldn't just
focus on the rest of the class, because I'd be so fixated on, but what about that?
Right.
And of course, you can't interrupt the class every 15 minutes.
So it's one, absolutely.
That's a perfect example.
Tangential example is, homeschool my kids, and my wife makes homeschool worksheets, whatever
they look on, and whatever they're interested in that day, as a quick conversation prints
out a worksheet.
Boom.
Now, we have custom tailored learning, and for general learning, I use that all the time,
and kind of checking my assumptions, digging in deeper, trying to read up on things, for
sure, and this is where people have different relationships with AI and how they use it.
I'm very productivity and work focused, or knowledge, or just general, like information
gathering, but some people use it for a therapist, right?
Having a conversation, right?
And that's, that's never been something I've personally used it for.
Careful.
Yeah.
Never saying, I've never personally, I've never personally used it as that.
But that's where I think learning and exploration in general, and I think it's also interesting
too, is like even on personal side, having, like if you were to use that, you could have
it look at patterns over time, devils advocate your own positions, being able to value other
people's perspective, and thinking about things, and how to communicate, whether it's
a work email, or someone that's in your life, like these are all things that you can use
as, I use it as like a recursive, just like, I have a thesis, challenge it, like modify
it, iterate, move forward.
If people want to start to understand the concept of cloud code, I really recommend they
start to, well, not just specifically cloud, by the way, like coding and the future.
Rob one said to me back when he was getting really excited, but before I, the tooling had
developed, and I, I saw, he's like, no, everybody is going to code, and I said at the time,
my response is, no, they're not, that's not how, like I've managed too many people and
just to know, no, they're not, no, they're not.
And he was like, yes, they are.
And the thing is, is we're both entirely correct, which is pleasant for a co-founder relationship.
Well, well, yeah, I mean, Chris's thing, yeah, yeah, that's where I'm headed, but yeah,
we're both absolutely correct, because when I talk about my email, that is building,
that's coding.
And just talk to it and have a conversation, I talk to it and it built something for me.
Not, you know, right?
It actually built something for me.
And so in that way, Rob is entirely correct, everybody is going to code, everybody is going
to build, I would say.
And when I'm saying, no, they won't, what I mean is, no, they won't, they will use a
wrapper and they will interact with it in a normy way and it will do the coding for you.
Now a fun anecdote that like we're excited to talk about is actually kind of a, an in-between
that I think is an opportunity for people who have the interest in, in going this way.
And it is this in-between state, where you're not say to an engineer of today, you're not,
you're not a coder of today, but you're much more advanced than say your average office
worker.
And so Chris on our team is our head of ops, he is very technically oriented and interested
and he, the things I was using Google script for using AI.
So AI was using Google script on my behalf.
He was already the type that was experimenting with out on himself and he was, it was just
very laborious, right, because he was kind of figuring it out as he went.
But somebody with that inclination saw a problem, which is we have one of our backend systems
is, you know, it's a small piece of software, meaning a small company.
And you know, like any kind of back office software, there's parts of it.
Maybe there's a bunch of different modules to serve different clients or different businesses
that license the software.
You use parts of it, you don't use parts of it.
And so what he decided is like this part of my job is very frictiony.
And I know that we pay a lot of money for the software.
So I am going to rebuild it.
And so he and I had had a conversation and I was talking about kind of my ah-haws about
how people are going to use these tools.
And he took that as a kind of go ahead to start experimenting.
And I talked to him on a Thursday, apparently on a Friday, he started.
And by Monday, he showed me that he had rebuilt and we'll tell you what we meant by that.
But rebuilt this piece of software in entirety.
And in terms of looking at it on the screen and how it appeared, it worked.
It pretty much worked.
So if customer info went in here, all the proper things would happen.
It would spit out our desired information.
And so he had created this on the surface and think of it as a prototype effectively.
And I gave him some feedback on it.
I said, well, let's say he's the user himself.
So if you're inputting stuff that's currently on these three pages, wouldn't it be
nicer if they're all on one page?
It would be faster, fewer clicks, right?
He's like, oh, yeah.
So then he just by taking screenshots, right?
So he started with screenshots of existing software that he uses.
And then he told it what he likes about it and doesn't like about it.
He explained how he wants to use it.
He spent a lot of effort into context.
But in terms of cloud desktop, I said, it's chat, co-work, and code.
Chat is chat.
Think of it just like GPT.
Co-work, when I said at the beginning of our conversation, was a bit of a misnomer
that everybody seemed to think it was co-work, was the big unlock.
Co-work is, think of it for really building deep analysis or specific tasks.
I almost never use co-work.
Clawed, or I'm sorry, code is this big step farther where it really will write the code
and can go so much farther.
And he showed it to Rob, and I'll hand this over to Rob in just a moment.
When he showed it to Rob, I think Rob, he can do his own reaction.
But I think he was pretty impressed when we talked about it as executives or managers
over our team.
I was very curious, his point of view.
And he said, look, I mean, on the front end, very impressive, on the back end, very
unsecure, not the right languages.
Everything's Jerry rigged.
And so then I'll pause, but that's ultimately then what starts a conversation here about
how do we want to take advantage of these crazy abilities, this insane ability that this
guy who's not just rebuilt a piece of software that we pay money for, right?
And what could it look like in the future when we can overcome the security and the
jankiness?
So before you learn, so I want to set some context, my personal interest in this question
is immense because there is a challenge at the moment that's in my mind is that there's
never been a solo founder entrepreneur who's built a billion dollar company on his own.
I think the possibility with the tools that we've got emerging, I don't think we're
there yet.
Sounds of it.
But I want to hear your opinion.
No, we're, I think it's possible, like, and this is, I'm not the first to say that's
it's possible that that person's building that company right now right now.
It's very possible.
So for this anecdote, like, of building things, it was funny because I got pulled in when
basically like, because like, oh, I just talked to Chris, he's rebuilding stuff.
And I was like, oh, he's, he's building software, like, you know, like in the company,
I like, I managed the engineers and I'm like, okay, let's see how this goes.
And it was impressive in the sense that tell us what you were impressed by first.
Well, that's what I was getting.
Yeah.
So like, from a strictly, just the front end and looking at it and just building something
you can touch for a prototype.
And I made this comment earlier that I really enjoy using these coding tools and actually
it's funny for your distribution.
I almost entirely only use cloud code, even for analysis.
And only if it's like very superficial, quick question, well, I use chat.
And that's basically, but 95% of my work is with cloud code or codex or like these,
like the coding level tools.
I'll get to that back in a second.
But for what Chris did was so impressive was, this is something we would have hired a
junior engineer a year ago to work on for a week or two and just to get to the prototype.
Just to get to the prototype of what like what Chris had done.
And it looked nice.
It was functional code like the front end was like, it was real framework like there was
a library there.
There are certain things that I'll get to in a second that are easily addressable.
There wasn't a database.
There wasn't any security validation of when you talk to a front end website, when you
put data in somewhere, you don't want to let any arbitrary blob through.
It used an insecure version of TypeScript, like which is just a coding language, right?
And in the post mortem of talking about this was one, like we were very happy, like for
Chris to go off and run and do this, even though it's not really like his day job.
But like this was a good exercise to start figuring out what the future of work looks
like for people that are highly motivated office workers.
So as managers, us kind of looking at this as like a post mortem experience and understanding,
okay, this was a fun little experiment.
Took a couple days.
Chris wasn't burning API token credits, right?
So this was all within his subscription limit.
So as a quick iterative loop of understanding, I want to build something.
I'm a motivated office worker.
How do I kind of go to that next step?
What we did was talking about it, I realized that there were several things we could do
across teams on the first side.
When looking at, you know, building this out, I may have mentioned this earlier thinking
like a product manager.
You need to be very disciplined and specific up front because it's way easier as these
machines are building things to have the good context and the good patterns up front.
Then to say, oh, I made a misdissumption or I forgot something, go bolt this back in.
So what does that mean?
Does that mean work on a full product requirement description or product requirement document
first?
Yeah.
Absolutely.
And say, build me, like if it's a photo editing app, build me a photo editing app.
Exactly.
Exactly.
Exactly.
I think it is important to know going into a test, I guess, or a project knowing how it
is intended to be used.
I think doing that in general is not generally necessary if you're building something for
yourself.
If you're dog fooding something for your own tool, it's not going to go elsewhere.
Then as long as you're using your time and resources wisely, you know, it really doesn't
need to have that level of review.
The question is, if we want to use something as a company, if we want to start building
the tools that we need to be successful and do them in-house, what does that look like
if it's involving non-technical workers?
And that's where the PDD, I think, becomes very important and that's what Rob shared
is.
That's where we need to begin now.
That's what we learned here.
Yeah.
Before anything's written, you're going to get way further better returns.
I made this comment earlier about how maybe this is where you'd use your higher quality
models, extra thinking time, being very slow and methodical, and just very detailed building
out specs.
I think what's important for this is having the engineers aware of it because if Chris
were to build something, he's going to build it and then someone else doesn't have to
maintain it.
Software devs in general, if at all possible, don't maintain other people's code because
if there's issues or problems, they are issues and problems that were created by someone
else.
Now it's their job to be janitor.
Chris gets to have a fun four or five days sabbatical building something and then throws
it over the fence.
Now my team's like, well, this sucks.
Chris is great to work with.
This is now my problem.
Now I have to own all of the technical data associated with this.
I have to own all of the complexities or the misassumptions and I have to kind of
bolt and do these things on later or maybe even possibly rewrite things, which then just
becomes tedious because it's way easier for a developer going from the start, actually
building this in methodically building out in a way that makes sense to them that may
not be exactly the way a model has generated something.
I was going to say, yeah, so this is a very real-world non-AI problem, but it's exacerbated
because you don't have anyone you can go to and say, hey, why did you build them like
this?
I'm talking about this up front with the software developers.
I think it's very like why I say this is a cross-team functional thing is that it would
also be really helpful for our developers to say, these are the languages that we use.
This is how our general system is architected, so it's not like, I'm going to do a database,
so I'm going to build it for no sequel, which is a database type where we use Postgres,
right?
It's like one of these things where you want to immediately up front kind of call these
things out.
It's like as in going through our code and thinking about, okay, whenever we build a front
and website, we do things like form validation.
Let's talk about how we do form validation up front and maybe even show some patterns
of how we use that and give that to someone like Chris, so then when there is an intersection
of work streams, it's something that's already following the basic code patterns of things
that we are exhibited to.
No, that's not the only way a company can or should allow their teams.
That was our learning and our decision for how we're going to use this if it's going
to be deployed.
Think about what else we accomplish there and how else you could choose to use it.
At the beginning of talking about this anecdote, Rob said that he was impressed on the front
end because it would have taken a front end dev weeks to mock up this, whereas it took
a non-technical person a few days.
He didn't mention is actually the time to hand over the request to that engineer to start
building.
The way we used to, like if it's a back office tool or whatever, if I wanted a certain
functionality, generally what I would do is I've got a big digital whiteboard or an iPad
Pro with a pencil, something like that.
Usually I would start by drawing my own wireframes, so I'd have here and maybe a rough button
here and then arrows and then goes here.
I would be using words with the devs to be like, and here's what I want this to do and
here's what I want that to do, and they're already maybe starting to get frustrated because
I'm making requests that they know how difficult or challenging something might be.
I don't know.
I just want the button here.
I just want the button here.
You might get to a point where you have drawn out approximately what you want.
Then you hand it over to that front end dev and then you get back something from them,
which may or may not match what I envisioned in my head when I made the request of the tech
team to start with.
Even if you don't want to have your team go as far as deploying software and you need
to have that security and infrastructure done properly, what if it's just a very fast
way to communicate now because he's now built a working prototype that even if they can't
use a speck of code from it, he has now communicated more completely than any hand drawn conversation
could have ever communicated.
He has built exactly what he wants and so a company could choose to be like, look, this
is already a better PDD than I could have ever received.
The product type is when you've got teams, cross functional teams working with one another.
You have the end user, so I want something like this here and then the product manager
or the engineering team would be like, what do you mean?
It doesn't make sense to us.
If you can actually show them what you're doing, this is a really important point.
This was the strength of any person within an organization as people have cross-disciplined
understanding.
To be the bridge, whether it's information or understanding, whatever it is, to be the
bridge is a really good place to be in an organization.
One of the best strengths I had when I was on a consultant at IBM working with people
and they'd be like, oh, that would take two weeks and I'm like, can you explain how that
takes two weeks?
And they'd be like, oh, because X, Y, Z, and I'm like, well, A, B, and C, right?
Whatever the specific mechanics are, the ability to materially collapse inward the knowledge
gaps and make the bridge shorter to move faster.
But then to also, in running an organization, being able to quickly discern who's working
hard and who's being productive, is immensely valuable now.
That is something that is just very inherently where we have a very small engineering team.
We don't have this problem, but at large organizations, if we're working at Starbucks, I want
to update SAP or whatever it was to do X, Y, and Z, and you have to go, you have to file
a help test ticket.
You have to go do a meeting, right?
Not only would you run into things when you're using external software, I did work on
SAP.
So let's use it SAP Ariba.
Some people out there are like, oh, I know you're paying.
It might just be a time thing.
So I might come in and say, hey, the users of the software, the 10,000 users of the software.
People paying for it.
Yes.
They all want to button here because this task takes, I counted, 17 clicks.
But if you put a button here, it would solve that problem.
Sometimes when you're using external software, like SAP or an Oracle piece of software, sometimes
the engineers can do that for you.
You have to wait for the Devcycle and wait your turn, but sometimes they can put that
button where you request for your company.
But many, many times they would say, no, we can't because other clients use the SENDAT
is at that level.
And so we can't customize it for you.
And so it was just like, OK, so these 10,000 people are all spending nine minutes six times
a week that they shouldn't need to.
And then you do the math and across an organization like the lack of a button cost, you know, X,
X many minutes and therefore dollars of productivity.
And that will be a thing of a pass.
And then Rob also mentioned kind of workers and knowing if people are working.
That was actually my first AHA when I got into using these tools for work versus just
playing and using chat and learning.
When I actually started using them to make myself more productive and move faster, I immediately
saw the change in the workforce, immediate.
People say like entry level is done, certain thing.
I don't actually agree with that.
I say middle of the bell curve is done.
That could be middle of the bell curve intelligence because they're just not smart enough to use
these tools to their maximal ability.
And even the first to go are going to be mid curve effort.
Because mid curve, mid curve effort has a large element of BS built into it.
Mid curve effort is somebody intelligent, ish, or maybe fully intelligent, who uses
that intelligence to make themselves very efficient.
They do their job very quickly and then they steal the rest of their time.
And then they BS about how long tasks take.
So I want somebody now who will say, wow, this tool will allow me to get so much more
done.
I am going to create these automations.
So I'm going to take advantage of this.
And then when they've collapsed, they're 40 hours of work into eight hours of work.
They're going to look up and look for more opportunities to contribute.
And they're going to continue producing deliverables.
When I said the BS was over, what I mean is in the past when somebody said they're working
on it, I can simply ask to see the iteration.
And with my team, for example, we've kind of changed that daily iterations are new
norm because there's nothing that takes more than a day to at least get it to the next
level.
Right.
So there's nothing of like, well, do you want me to do this or this?
I want you to do both of those.
We were having this chat the other day, but just to give people an understanding of how
much this compresses time, give us an example of something that may have taken one or two
weeks, that suddenly it is very reasonable for you to ask for by this afternoon or tomorrow
morning.
At least in iteration.
That intervention.
Not for perfection.
Just to drive the conversation further.
Yeah.
So maybe in the past, I might have asked somebody on my team to investigate or start doing
research on a particular customer vertical because I sense that there's an opportunity
there.
I don't know much about it.
I want to know more about it.
But then also, I want to figure out where is there like a distro list of this profession.
So let's say a state attorneys and trust attorneys.
So I want them to go deep on kind of this as a profession, who they serve.
And based on that, I want a marketing proposal on how we're going to find and locate these
people and go all the way to some sort of deliverable at the end of it.
And if it's starting from something that they know nothing about, I would say, I would
say in the old world, I would have expected it to be about a 10 hour task.
And I understand with meetings and emails on other things to ask a 10 hour task of somebody,
I'm going to generally ask, give them about a week to fit that 10 hours into their other
work.
Now I expect a version tomorrow.
Just an iteration.
And if somebody does BS you and I'll give it to you then, if somebody does BS you, not
that I've had to do this, but I now know what I would do, especially if I was in a corporate
environment where I couldn't just, you know, I literally could be like, if somebody
kind of explains why something's taking a long time and I don't believe them, literally
I could be like, you know, just so I understand your thought process and how you approach
the problem.
So just send me a mark down file of your actual conversation history with the model, just
so I can see how, if they didn't do it, it's not there to show.
I don't know how you think the model's output is.
Well, yeah, that and the way I would even think about this further is that whenever you're
doing work in general, there is the, the moment of I need to do X, Y and Z, doing those
things, taking a half step back, reflecting on it and just as an iterative like with any,
any task that you're doing, whether it's a spreadsheet, a document, a PowerPoint, getting
ready for a pitch, whatever it is, research, organizing things, all of these things like,
just have this little loop.
And what this does is this instantly collapses the loop and what's so exciting about highly
motivated workers is we're collapsing all of the work of the tedious part of getting the
things on page and going back to using your brain.
It's like, all right, I'm going to use my brain, I'm going to use my brain, I'm going
to use my brain, right?
And there's certain people who are just highly motivated.
And this is not in like, in the belt curve of like intelligence, I think it's just strictly
motivation.
I think I would take a one, I would take a 105 IQ person who was highly motivated over
a 140 IQ lazy person any day of the week when it comes to using these tools.
And there's a, the, the fundamental model just in life in general, so you can view words
as spells.
You're having a conversation and you're either trying to persuade or you're trying to
convey or you're trying to do something, right?
And maybe an old work culture, people who would be able to ascend corporate hierarchy structures
because they were really good spellcasters.
They knew how to posture things and they knew how to, you know, phrase things.
And then when it came to work, you had all of these meticulous details of why your work
was so involved and important.
And that's all just gone.
Like it's just completely obliterated.
Like this is going from a swords to muskets kind of just like jump, right?
And the people that are highly motivated are able to execute better.
And additionally, when it comes to like, I'm like, expecting iterations, it's not just
like, this is something that's been very apparent for code for a long time.
Where if someone's like, oh, I worked on it.
Here's my update and you look at it and you're like, okay, you literally just asked a coded
question.
I'm looking at the code changes.
You did not look at this.
You just said, you know, make an update looking at x, y and z.
And what you're actually looking for in these iterations is not just, I had a conversation
and the computer did my work for me and here's what the computer did for me.
It is the additional loop of, okay, I'm going to use my judgment now.
I have an output.
And now I want to think very critically about what this output is doing and maybe do that
two or three times.
Look at it one more time and then hand it over, right?
And this is where I think most people in the workforce, we're such a small company.
It's not relevant to us because it's very tight knit and we're all kind of like sprinting
along.
But in the larger workforce, there are a lot of people are like, someone wants me to make
a report and I make the report, okay, and here's what, here's my report and they are totally
surrendering their autonomy, their own intellectual brain, like they are going into autopilot.
And those are the people that are very quickly, with the compounding ability of these tools
that they're, it's going to further differentiate.
Everyone gets more productivity, but the people that are going to leverage this on top of
it and compound on top of it, we're going to get way further ahead than someone who's
not.
Well, the ones who don't will get fired, I mean, I'm transparent, right?
They really will because we, we will go ahead, very quickly.
This is a funny thing because like you saw maybe with cash at block, like they'd laid
off a bunch of people.
So 40% in the stock rows, and one, I think in general, there was a massive overhiring during
COVID and salary inflation.
This was a great way to clean it up.
You see these massive layoffs and it just very quickly became like, oh, these are the
people who are just not even trying, right?
Like it very quickly, you can discern out and understand.
And that's a general statement, like I'm not saying that everything on the individual person
and there's always office politics and mechanics and details of things.
And luck.
Yeah.
Like especially at a large work like that, like there are some arbitrary things, like it's
not a conclusive thing, the people are highly motivated are going to be okay.
But when you're thinking about this and these like large layoffs, I think it's just very
quickly going to compound, I think people at the moment are using, because AI is here,
we had to do layoffs because we have an increase in productivity and I just think that it's
less of that.
And more of AI very clearly is starting to show who your highest performers are.
And it's not that, it's another way of saying it, like AI, like things, it's not just
like, because the computer is augmenting anything, they're quickly seeing these are our
high performers.
These are the ones that are really leveraging these tools and that we're able to get a large
amount of output with less.
And this is a general principle like Mark and Jason or someone else with any organization,
the square root of the number of employees is half of the work.
So if you have 100 employees, 10 do half the work.
And if you have 150, 12 are doing half the work.
And I think just AI is very quickly showing that like, it accelerates that principle in
general because they're the ones that are be highly motivated.
I did the math on that last night actually with Starbucks and because I'd always set
os parato, 20% of people do 80% of the work, there will be little bumps along the way
of learning.
I'd like to think I'm a top performer.
I had more or less two embarrassing things like three days apart where I accidentally shipped
out slop externally and had to, you know, in neither case did it create any negative
repercussions, but it was embarrassing because usually I wouldn't typically send out mistakes,
but what I quickly learned and learned with my team as well because in some, in one of
those two cases, also like my team had also checked it.
And I believe they did.
I believe they put in the time in their old way of checking things.
So you think of it like in Excel form if you built it.
So let's say you built a little calculator that does whatever you were trying to do.
If I built that, I'm the one that typed in the formulas when I make a change, or if
I type in a number and I see everything else change, I know what's happening because
I'm the one that built it and I actually understand.
And so when I'm checking my work, if I built it, I can spot check.
So I can see like I'll check cell C11 and F22.
And if those are correct, I'm reasonably sure the document is correct and I can send it
out.
Same thing in words.
Like if I'm the one that wrote something and I'm just kind of spell checking and do grammar
stuff and I do very little time editing and most of the time in creation.
I was taking that way of working into this new world and I did some spot checking on
an Excel and the cells were correct and I sent it out only to figure out after the fact
that like there were totally made up numbers on it.
Like I'd sent out absolutely just incorrect information.
And then I did a similar thing on a written document where I just, it had hallucinated
a different custody model than ours.
And because I didn't go back and read every page, reread every page, you know, it sent
out this garbage.
So now I know that creation is not the time consuming thing, editing and judgment like
Rob mentioned is the time consuming thing.
And that's what I'm paying people for.
If I'm going to pay anybody six figures ever again, it's for the use of their judgments
and their minds to interact with these things.
It's not for tasks ever again.
That's the big evolution change.
And that's bottom of the bell curve.
I might still employ people if we have tasks that somehow are not automatable.
But those mid-effort people are gone gone forever.
And that's just on the coding side, it's turning more to code review.
Not actually writing the code.
It's okay.
You've proposed a change.
Let me now look at a poll request, which is like the changing of the code and let me
look through that and understand it, ask it questions, and try to avoid the orboros
of the snake in its own tail of, well, I have an AI agent that reviews the code before
I deploy.
I mean, for the work we do specifically with Bitcoin custody, I'm one, I'm very happy
that we've built all of this before the big AI boom happened.
I say that strictly because seeing people like vibe code a quick wallet for a fun project
is fine.
But with Bitcoin money, the code is money, like in the money is the code.
So like there really isn't a margin of error for any of that, and I feel very good about
that.
And when these tools started coming out, as I started red teaming, like to red team
is like to try and attack your own infrastructure.
And felt very good getting to them.
I was like, oh, wait, this is great.
I was like, oh, let me check out like the ledger hardware.
Let me check out the code card.
Like I started like looking at things and poking around and felt pretty good from like
this initial assessment of things.
That's some, not sure how much of this is hype and marketing.
The latest models that are becoming out in middle end of April are so good at the moment
actually at cybersecurity where they're finding bugs in the Linux kernel, they're finding
bugs in the Chrome browser and Firefox like very serious bugs.
And they're trying to really like distribute to cybersecurity teams before like it gets
to them.
Do you think, do you think in terms of, so I would say cybersecurity has always been an
arms race, right?
You know, so hackers, you know, coming in and hackers are increasing their abilities and
defenders are increasing their, so this goes up and up, right?
I have to imagine that these tools simply exacerbate that or, you know, does this ever
get so resilient that somehow like hacking is a thing of the past or the opposite?
And it's just like indefensible or is it just the same that we're in today?
It's mutually, everyone just has the same quality of arms now.
So the complexity of the domain in general, just like going from swords to guns to going
to missiles and nukes, just so now you're doing it, you're walking away 10 paces turning
around and then shooting ballistic missile at each other instead of, right?
So it's just constantly going back and forth like I don't think it goes away fundamentally.
Have you tried the Replet platform briefly a little bit?
Give me your thoughts on that because I've used that.
I think it has a really good non-engineering experience for being able to do that.
I think it's a great for prototyping, it'll also help you build out like your architecture
for your database and kind of like go through these things and having conversations.
I saw the latest Replet announcement, which is really cool where they basically have your
code base and what you're building also live with like your marketing documentation and
having them kind of recursively like it kind of memory management and context is really
well handled and it just very cleanly for someone to be able to just click a button
and deploy code to so all of the infrastructure complexities are just totally abstracted
away.
So I think it's a very powerful tool.
That's like cool, like sorry to read with, we don't build on it, like we have, we own
our own infrastructure, we go through all these different pieces like engineering team.
I have an engineering team.
Yeah, that's right.
Yeah, an engineering team and also have crossed the threshold of skill domain where even
if I wanted to have agents write the code and do that, I would have them help me build
my own infrastructure rather than using something that's entirely self-hosted.
It is something that's within my knowledge and context that I personally have that I
could use AI tools.
And that's the funny thing, right?
You and I, this is actually just a comment, a beautiful thing about these AI models is
they're massively democratized.
There isn't like a secret cabal sort of the people who work at these companies.
It's not like the, it's not like, let's pick it like Amazon has access to like the super
duper secret model and you and I have like a pleb model, like everyone has access to
the things.
Everyone has access to the best, right?
That's just an interesting democratizing thing, but I can use that to build cloud infrastructure,
but since that's not your area of domain, I mean, we do that, but there are probably
many things that you have unique domain expertise on that I do not that you'd be able to
way further accelerate and use it for production of a podcast for like a thousand other things,
right?
And that's just like the differentiation.
Everyone's going to be even more specialized.
Rob's expertise is really important.
I think earlier I said I will use code, cloud code.
And the reason for that or the reason is that I can, I have already run into limitations
of letting it think for me that because I don't have the domain expertise, I cannot yet
or at least really quickly anyway, really quickly redirect it to do things better more
efficiently.
For example, I wanted, I had a series of Google Drive folders that had like 50 PDFs on them,
and I wanted to have it go into these folders.
I had a list of documents.
I wanted to go into these like six folders and download each of them onto my desktop into
its own folder.
That was, that was the task.
And it spent like all day doing this and what it was doing was taking like a screenshot
of my list.
And then like based on that screenshot, it was doing this and there was opening each
folder and comparing things to things.
And you know, I would leave my computer and come back in like four hours later, I see
what it's been doing.
And I'm like, no, you idiot, like why would you do it like that?
That's so dumb.
And right now all I kind of know is that Rob, if he was the one doing this, he would have
said something along the lines of, well, I'm just going to write a script.
That's kind of like, and he would write it and because he knows the best way to instruct
a computer to do that, I don't know the best way.
And so all I can do is gradually learn over the next six to 18 months by watching it do
stupid things.
That's why it's important to tinker.
This is exactly it.
It's you must tinker.
I tell my friends, look at how I like it.
This is exactly how I like it.
It's amazing.
That's not efficient.
And this is why I have spent the last six weeks having coffee and lunch with all my
friends and my family and my sister who are all still in corporate America where I am
urging them to tinker.
You must do this.
All these things we've been talking about today, if you don't want to get laid off in
the next five years, you must, I care about you so much, I care about you, you must tinker
because if you haven't, and all my friends at this point in my career are senior managers
through executives, right?
If you as a leader are not thinking about the second-order effects of these things are
starting to or you can't even picture because you've never had these thoughts in your head
yet because you haven't tinkered, you are not doing yourself a favor for sure.
Really funny, just like near image of that exact anecdote is, so my wife works in biochemistry
at a publicly traded company.
I went to their quarterly earnings and I started just poking around and I looked through
it all in there and I was trying to start how important this is as she's getting back
to work.
I just started asking questions about the research and have you thought about X-Wines
and she's looked at me and she's like, okay, I like to do this podcast and I was like,
no, I just did it and she's like, wait, you, and it was just one of these funny inflexions
which was like, wait a second, this general, this democratizing factor of knowledge in general
that's happening right now is something that many people aren't appreciating, unless
they're tinkering.
That's what you want to tinker.
You don't need to be able to tinker and understand, because the way you describe your
problem, I would have used the Google Drive API and I would have made a very simple thing
and just talk about it like a computer system would and that's why everyone's turning
more into writing code, it's that you're a product manager and the product managers understand,
maybe not the lines of code, but they understand the connectors and the tools, just speaking
the language that can go to the engineers to do that and now your engineer is a chat
box, right?
So you and I could do a race of like, how do you want to do this?
And I, since I understand the, you know, when architecture, but like the closing, the
kind of understanding general architecture, like, you easily could have asked me like, how
would you think about this problem?
I would have been like, boom, boom, boom, and then you would have been done.
Totally.
A 90, a 30 to 90 second conversation would have transferred sufficient knowledge for her
for in perpetuity whenever interacting with Google Drive, you would have known how
exactly.
Now and then I'll ask him, but sometimes I'm actually purposely not because it's the
same thing I actually pushed my team to do, which is they're like, well, I don't, how
to do this, or I don't know how to do this, or I don't want it to like, that's intimidating
to me.
And, and we keep being like, ask it, ask it how to do it, let it tell you how to do it.
And so I'm trying to kind of live that to a certain extent, even when I can see that
this is happening in a stupid way and I'm not figuring it out yet.
I am trying to learn how to communicate with it to wrestle from it what I need and do
some of that learning.
I wouldn't, I would recommend actually getting the tips and tricks for most people because
why waste a bunch of effort and time.
I think it's important to learn those skills.
I mean, I've had funny things where I'll have a bug in software and my camera can help
me fix it.
And what it'll do is it'll just remove the code.
Yeah.
Bugs fixed.
Like, I just, I just remove the code, isn't that better?
Like, I think no more, no more, our test suite has no more errors because you just remove
the code or it'll remove the test, you know, like, you can't fail a test, you know, right?
Right.
So, like, you have to go like, yeah, that's the old Soviet saying you have a man, you have
a problem, you have no man, no problem.
That's right.
That's right.
Now, we've spoken a lot about how people can, uh, position themselves as employees, so
that they can get more out of their work and make themselves more valuable in environment
where, you know, a lot of people are going to come under pressure.
Yes.
As an entrepreneur, how do I qualify someone as with, uh, having potential to work well
with AI or not?
I'm so far for me.
It's not very complicated.
It's looking for top performers and attitude and intelligence.
I don't quite, I don't agree as much as Rob's kind of extreme statement, which may have
been for effect that he would take a 105 IQ with good effort.
105 is pretty, pretty dull, um, but I would take one, yeah, definitionally, one, 105 is
pretty dull, but I would take one 20 and good effort over 140 and lazy.
Um, so honestly, I'm just looking for people.
If I were hiring two day, I'm fighting for my life here.
If I were hiring literally today, I would certainly get into this conversation about
using the tools.
How are you using it today?
How do you think about it?
Give me some examples of things that you've done.
Yeah.
And if they're like, if their answer is something along the lines of, well, you know, I've,
um, I've been using chat and I've been using it more and more and I did some, uh, really
good research or, uh, spreadsheet, uh, one, once, uh, I did that.
That was really useful, but it becomes evident that they have not used it beyond a better
Google because it's so, but it's so new, right?
So it's so new that maybe I, it's fine.
That is a non technical person.
Maybe you haven't, but when I press into that and I start talking about this, if they're
getting excited, uh, and you see their eyes opening up and like, oh, wow, like that is
true.
And like you see these things, um, that means a lot versus a lot of people have this initial
reaction that is like, especially non technical people is I don't want to learn to code.
I don't want to gain that knowledge because it's boring to me.
If I had wanted to do that, I would have become an engineer to start with.
And if, if their initial reaction is to say no, they're, they're probably going to have
a very rough five years because all we're going to be is change, change, change, more
and more efficient.
And the only people who are successful who see that and are like, oh, cool, oh my god,
I want to try that.
Like, oh, cool.
Oh my god.
Oh, so interesting.
Like, and those are the people who are going to use their intellectual curiosity, uh, even
if they have not had a chance to yet, that I, that's what I'm looking for is the spark,
I think.
Uh, and, and not much more than that because the tools are going to enable, uh, a, say
an entry level person with intelligence and a great attitude, they're not going to have
the same judgment in that using the tools as a experienced career professional who has
lots of examples and personal anecdotes to, to bring in.
But if they're smart and have a, you know, good judgment with the tools, suddenly they
can be on the same level.
Let's say I'm talking about a 22 year old university hire.
I think a, a good 22 year old university hire will be performing at the same as somebody
with 10 years experience because of the tools.
Hmm.
I think we, we can't finish this podcast without talking about Bitcoin and AI.
I was about to wonder if we're going to the data center conversation.
So, um, where do you, where do you want to pick this up?
Because the obvious one is transactions, right?
And so the, the assumption has been, we think, well, for me, perhaps you have a different
, um, place to start.
But for me, would be the obvious place between the combination of AI and Bitcoin would
be a transaction.
Oh, okay.
Okay.
So like agents being empowered to go off and do got it a couple of random thoughts, just
as a little bit of, a little bit of, like a little bit of background, um, these data
center.
I thought, well, we'll talk about Bitcoin as money first because I, I, my, my immediately
to data centers, um, I think fundamentally, I'm not a big fan of these, like, mall,
like, like, I know, like, I love BPI, but they did this study that like, we surveyed
a bunch of models and they looked using Bitcoin as money and ultimately these things are,
are tools that are pointed in a direction and they are going to use whatever their system
directs them to do.
I do think a natively digital money makes the most sense for these things to be able
to do instantaneous settlement when you use the token and it's gone and the work has
gone, like, the money has to live somewhere, like these things are critically important
and for transaction stuff.
I think the, it removes much of the friction around Bitcoin because you no longer
have to be a Bitcoin developer and deploy a server that running the lightning node and
like all of these things, like, all of those like intermediary complexities start collapsing
inward.
Um, Bitcoin for transactions and like a machine machine context, I think makes a lot of
sense.
I think also most people haven't put thought into what good security looks like for managing
that and, um, you don't want to be in a position where you have some random code that
spins up that's starting running on a server and you're not software to have all of a sudden
is managing large flow, right?
So I think custody comes back to being important, whether it's within a lightning channel
or you're using R or state chain, stable coins for dollars.
I think all of this stuff, just Bitcoin's a medium exchange can very easily start collapsing
because a lot of the technical barrier starts dissolving.
You no longer have to be a dedicated lightning engineer to be able to figure out how
am I going to send and receive payments.
Well, won't we immediately start hearing stories of, for example, agents that more or less
drain another agent's Bitcoin wallet and things like that that you've given it kind of
permission to charge and more or less create an invoice.
Let's say that's happening behind the scenes.
So you've given permission to agents on both sides to both create an invoice and then
another agent effectively pay and a lightning invoice say, uh, but there will, that will
go badly for some people like there will be accidents where, where suddenly, you know,
you're creating too many invoices and the other agents is just paying them no problem.
Yeah.
And Bitcoin is like some people will probably get rich and some people will probably get
poor accidentally because of this dynamic.
So I think we'll hear things about that.
For sure.
And I think it's interesting too because Bitcoin as money is an abstract concept, right?
Like when people start getting to Bitcoin, like, wait, but it's not physical, like, where
does it live?
How do I look at my Bitcoin?
Like, how do I touch my Bitcoin?
And a lot of the interactions we do day to day, if like you make a, and like if you're
using these tools and you're like doing some research, right, go do some research, you
get an answer, keep on going through your day.
There's a whole chain of abstract things happening in the background of the agent going
out and searching and looking for information and organizing things and we're cursively
thinking to itself where like, that's actually a great way where you can use Bitcoin as this
abstracted money and being able to understand, it's actually probably really good for the payment
of these things that are happening all around on the background and you don't have to
understand the exact value proposition of Bitcoin.
It's just from the properties of being natively digital money, the best option possible for
all of these things.
Stablecoins can be seized, they can be reversed.
You know, they have their own kind of baggage and problems and the reason why stablecoins
have just been so massively successful in general is that everyone just doesn't have to
do worry about 4x risk.
But people are probably willing to take some 4x risk and have sovereignty and the ability
to know that I have my Bitcoin.
I don't have to worry about something going wrong later and I can deploy all of these
machines in architecture and just so natively, as I mentioned earlier, the money is the code
and the code is the money.
It just so nicely plugs into how you manage all of that within these tools.
And I think right now, I'm not sure if you've seen money dev kit, Nick, he used to work
at spiral.
He started money dev kit and he actually has replet plugins that allow you to actually
do lightning interactions back and forth, right?
He bought a couple domains on human coffee and on human domains where you actually can
have your AI agent go and buy some of the bag of coffee and ship it to him.
And it's all, and when you go to the website, it's really cool because there's two ways.
You go to the website and it's like, here, buy some coffee and then there's an agent
tab and it's just a markdown file.
And it explains to an agent who visits to this website, how do you buy coffee using the
tools within it?
And rather than it being like, here's the website and the button that clicks to go to Shopify
and do all the things and you pay in Bitcoin, it just says, okay, here's the API link
to get the invoice and this is the payload and how you send me the address and information
and you just have a quick conversation over telegram or your chatbot, whatever it is,
and it'll just send a bag of coffee to you.
Is that visible?
Like when you visit their website, you can see it.
But would it be correct that in the future or the way this can work?
Is it doesn't need to be visible at all?
You're because the information is there.
I mean, it could be say in like transparent text, it could just be in a JSON file.
Yes.
So that we get to the point that it's not a novelty, right?
Like they're doing as a novelty to like, look what you can do.
Look what you can do.
And in the future, I think it's a novelty today.
Your agent will just go to that website and that will be there and it will read it
and do it if that's what you wanted it to do.
And it'll just be a bunch of data in the back.
They'll basically be probably a parallel web that people don't go to.
It'll be a parallel web.
That's right.
And like in the background, it'll just be JSON, APIs and markdown files.
And to us, it'll be tedious and annoying, but like things that we need to as visual creatures
to look at things they don't actually have sight.
They're just reading the code and the words.
So we'll have a very, so today websites are effectively.
There's a front end on the back end, but like there are two versions of the same.
The front end is going to be a markdown file and the back end is going to be like,
there's servers and here's my API to talk to me.
Well, it's almost like there's a third layer.
There's still the front end, which that's what we see.
That's what we see that.
But the difference is so that in the pre-markdown file,
there was the front end, which is what we see.
And there was the back end that corresponds to it.
And there, I mean, maybe you could have hidden stuff, but like that wasn't the point.
You had a front end and a back end.
Now you have a front end, you have a back end, and then you have a whole parallel back
end that's only speaking to other agents.
And the front end is effectively, go to this domain and I'll give you a markdown file.
Or it might have content that's made for humans.
That has nothing to do.
That's what I'm, so the front end will just be about humans.
Right, yeah, yeah.
And the AI version just is invisible, because it doesn't matter.
And if you want to, you can just go to it and it's just going to give you a bunch of
documents, like text files, and it's not going to really be visually appealing to you, right?
I think that's a big potential in the future.
It will fundamentally change commerce.
You can already start seeing with the ads being added into like GPC that like,
this is becoming the new like Google's massive dominance wasn't, was search that allowed
it to sell ads.
And now we're getting to a point where these things are going to be able to start selling
ads.
And then not only are you going to sell ads to humans, they're going to be selling ads
to agents.
What about product placement where like, let's say we wanted to pay for anchor watch to
be recommended as the best resource for Bitcoin insurance.
And you're willing to pay as a company, you know, where is that going that we're allowed
to influence the models for money?
That's already called like agent optimization.
And the malicious way is called content poisoning where people are able to like actually,
with a couple of pointed articles on the web when these models go do a big training run,
they'll have very, very narrow specifically tailored keywords.
And people are able to like, with very specific combinations, just always be the top
hit because they basically self ref, they created a concentration point while these models
were training.
And the model wasn't aware of it to always, and that's like, you can do as grill marketing
too, right?
So you could pay for advertising or you could just do organically by building like a network
of things to be able to pull into there.
And that just creates a whole new domain of like what it is to advertise now the, what's
really changed is that you're not trying to persuade the human.
You still are in some cases, but you're also trying to get these computers to automatically
engage with you first.
Do we get an entire sort of marketplace like Amazon that's solely for these agents to
go showing?
Yes.
Yeah.
Yeah.
Like the thing with Amazon, it has like this network, Lindy effect of them, the warehouses
the distribution of the machines, very well, Amazon may just be the winner because they
have the physical supply and chain infrastructure that's already in place.
But the marginal cost of friction to go from one to another is going to be way lower, especially
if you're using Bitcoin as money because now you don't know, oh, I pre-save my credit
card information into this website, and it's so convenient.
I hit a button and it arrives.
That also kind of starts collapsing away too, if you just use it or using it.
Have these marketplaces popped up already?
I just know enough about physical supply chain because of my background and apparel and
food that immediately I start thinking about the manufacturing aspect.
So less thinking about kind of consumer products right now, but more thinking about manufacturing
and how, you know, if I order, if I need to make breakfast sandwiches, for example, the
primary supplier provides Starbucks with breakfast sandwiches.
The secondary suppliers provide that supplier with English muffins, with sausage patties,
with pieces of cheese, et cetera, and sometimes their tertiary suppliers as well.
If you then take that and go to a much more, many more parts, so like some sort of manufacturing
thing that when you order a part, it needs to source hundreds of secondary things.
And maybe all of those today come from different suppliers all over the world.
It's very manual.
I'm certain that there will be manufacturing type warehouse kind of secondary.
In the food industry, there's something called broadliners.
And that's more or less the grocery store for the grocery store.
It's giant warehouses that supply grocery store warehouses, which supply stores.
So I think all of that is very, very primed for agentic optimization that that's all
just a recipe.
This part is made from so much aluminum and so much plastic and that's a recipe and anything
you make can be drilled down to a recipe and if you can put into a recipe, it can be
automated.
It will happen.
One question I have for you that is to Bitcoin is you mentioned a few minutes ago that
you could do guerrilla marketing or context poisoning on the other side.
It reminded me of when chat came out a couple of years ago, I was still working for Brad
Mills on the side and we would, you know, he had funded certain projects and other projects
were kind of popping up and several of them, you know, were Bitcoin agents of some variety.
But this is a 18-month ago version of what an agent was.
Really just trying to create Bitcoin expertise.
And there is this momentary big effort by the maxi community to start training and trying
to put info out there to teach it correct information about Bitcoin because answers were
coming back very crypto-oriented.
Did any of that effort by those people go anywhere or was it?
I think it did.
I mean, does that actually do something?
No, it does.
It does.
It's a model answer.
No, you're wrong.
Here's the right answer.
No, very much did.
Where a lot of this data was actually trained on Reddit and that's a funny, just cultural
observation.
Sometimes it's like, this is why I would never use it for relationship advice because it's
using Reddit data.
We both leave.
She's cheating.
Every time we read it, it's like, and it's like, yeah, like, and so it's totally like
slow.
It's terrible.
Yeah, right.
Exactly, right?
Boyfriend?
No.
Not for the straights.
For the straights.
That's right.
That's right.
But so like, this is why I would never talk to these models for anything related to
relationship advice because it trained on Reddit.
And people observe that like, hey, these models have a left-of-center, cute, cute skew.
And like, that's because like, the employees too, and they nudge it in a direction.
It does speak incredibly favorably of Bitcoin compared to everything else.
It does.
And we had our own little, and the thing is like, it's funny, you've talked about us
a quarter.
You talked about us like a coordinated thing, and maybe there was a little bit of that.
One, the lag of these models is like, you know, things are like six, eight months,
and then it starts training.
It doesn't get any new data.
So there's a considerable lag in these.
We just truly believed in Bitcoin and talking about an evangelizing public for 15, 12, 15
years that there was already a strong, corpus base of not only like these are our arguments,
but this is how we respond to said arguments, and this is why we've put so much thought
and thorough into it.
And that got baked in and making Bitcoin as a unique asset, for sure.
Can you directly, let's say, content poisoning, but with good intent.
Can you...
Well, let's say guerrilla crowdsource education, where let's just...
We just like an attack.
Like that's where you...
No, it's sure.
If you wanted to go in, so maybe Bitcoin is already well thought of by AI, but like,
let's say it wasn't, and you had interacted with it a lot, and you got frustrated, and
you started educating the tool and said, you know, I just, I keep getting these answers
from you.
You've clearly trained from somewhere, but here's a bunch of information that I think
is better quality information, and you really should be using this when you talk to other
people.
How directly can you actually...
Well, we can't, because we use the enterprise licenses, and they explicitly do not train
on our data.
Sure.
But I mean models and general models, how does that work?
Putting content out there, putting out a podcast like this.
Public.
Public for come soon.
A lot of the coding examples came from Stack Overflow.
A lot of the code stuff came from open source GitHub repos.
This is also an observation of why sometimes it's not great code, because the average coder
who uploads code to GitHub is not a great developer.
And that's just like...
We were talking earlier about like hiring, like hiring like the proficiency for AI.
My engineers actually, they will use it, but they begrudgingly will use it, and they're
very skeptical, because the things that they're working on with Bitcoin and Custy is so
narrow domain specificity, where it'll ask to solve it, and you're like, here's seven
reasons why that doesn't work.
And I'll have these conversations, right?
So what I like about, from an engineering side, and hiring people, is attention, where
you do not want to be delegating, going full autopilot vibe coding for anything that's
a serious software project.
Hey, if you're going to...
The first software project I coded was I built a bot that scraped my local shelter's
website, because they got a bunch of golden retriever puppies in, and it was first come
first serve.
And that's a perfect thing to vibe code.
Yeah, that is a perfect thing to vibe code.
Find me.
Customer data.
I need a website.
And just send me a text.
Right?
Like the most trivial thing.
That's actually...
If you're looking for kittens, like...
I was like, I think I need to let everybody know I'm looking for two adorable Russian
blue kittens, if they can help me.
Did you want to talk about data senties?
I was just going to just briefly say, as an intersection with Bitcoin, because we're
in town this weekend, London, to talk to insurance, and we got the first conversation we sat
down with.
I was like, well, what happens to the Bitcoin network when all of these minors go over
to AI?
I was like, well, every two weeks, the difficulty adjustment updates.
And I was like, oh, I didn't realize that's exactly how it worked, right?
So, like, it's not an existential threat to Bitcoin.
I think in general to these data centers, the first thing to think about is there's two
types of these data centers.
You have training ones that are like Elon's massive one in a Memphis or in Tennessee.
Things like training the new Brock models.
They're all there and they're thinking and they're building the new model versus inference,
which is, hey, ask a question.
It goes to a server, it does the math, and it gives me an answer.
That's inference.
You're talking to it and you're getting it out put versus the training.
Those are just two fundamental different loads to be separate, yes, because the training
task is like, this is where all of the frontier, like, this is what they spend their time
on, right?
Obviously, that's the products and like little bells and whistles around the end.
But the core thing I'm hearing these step functions is we're plugging more and more
energy into solving these problems and analyzing data, and that's where you're seeing this
marketed, like, compounding, accelerating increase.
Inference though is like, once you have the output, you're like, here's my model.
I'm going to now load it on a server and then words come in, words come out, right?
And they just have fundamental different power needs, whereas like a Bitcoin data center,
you can trivially if the power gets too expensive or there's a natural disaster or something
you can just turn it off instantly.
You can do that with inference.
You may get slower response or may go down.
If you do it on the training side, you may actually meaningfully lose like days or weeks
of progress.
So they're more sensitive and there's way more fiber optic networking, like, there's
a lot more complicated things because it's a true data center as opposed to a bunch of
energy boxes that are sitting there and just humming along.
Funny that no one's talking about data centers, boiling the oceans anymore.
Yeah.
They are actually.
Are they?
Actually, they have seen much of it.
In the States.
No, no, no, no.
It's USA.
Because I've been doing this little friends and family tour, you know.
Yeah.
No, no, they're definitely.
They're definitely still talking about it and it's used as a momentary resistance to
change.
Yeah.
So they're like, yeah, like I hear what you're saying, but like I've never really been
into coding.
So then you have to like talk through that and then you're like, I hear, I mean, I'm interested.
I'm interested.
But like, you know, data privacy and I'm like, come on.
When have you ever taken care of the data privacy?
Give me a break.
You use the same password for 900 different websites.
You do not care about data privacy.
Yeah.
But what if like, it's a lot of energy.
Don't even get me started on energy and like, and like, you are just making excuses right
now.
Like things cost energy.
Things require energy.
You are making excuse to not start tinkering and protect your entire career.
Stop getting distracted by the anxiety that a lot of people, most people have with respect
to AI.
Yes.
The potential, I guess, political consequences as well.
So we're seeing on the political side, I was going to say to go from the anecdotal to
the political is specifically these local towns that they're trying to build more data
centers and they're they're bullying the town boards to stop the build.
I think there's actually a fair local political question of what's happening, especially in
the United States.
There's so much energy abundance and Bitcoin miners for a long time have played really
in a good synergistic, friendly position with these local communities.
I think people are starting to see that their power costs are starting to increase and that
they're actually sharing the load for this, which is not a, like I listen, I'm a capitalist,
like I think that people should be able to go build and do things.
But if you're going to incur extra load on the grid and make power more expensive, that
is your unique responsibility to pay for because all of a sudden now you just have mom and
pop stores and families that are now subsidizing the cost for these companies that are getting hundreds
of billions of dollars of venture capital money to go make these builds to try and take
everyone's job away is a very perverse.
So there isn't, I think there's a fair tension even to someone who is not a Luddite and
thinks these tools are amazing and incredibly important for the human species to go for.
There needs to actually be a fair conversation here and like why are we subsidizing Sam
Altman's cost to build power?
And that's, it's also a hyper local thing so every single data center has a different
power agreement with the different utility companies.
So like it is not an umbrella characterization of everything.
I think it is something to be conscious of and I think that's actually a fair citizen's
like point to push back on.
Well, that's very interesting because I speak to a lot of people and some of the more
ideological than others, you know, and different ideologies, but I guess it has to be balanced
with we live in a society.
Yes.
There are consequences and you can't just dismiss the repercussions of second, third order
effects.
Yeah.
You can't just dismiss them.
It's like, well, you know, that's, in order to hold my sort of worldview together, I'm
willing to overlook that.
But I think I don't, I don't even know if I agree.
Even though the circumstances certainly there, I just think that ultimately solves itself
and so I don't put a bunch of time on it because right now, you know, like right now
it's moderate enough for modest enough of an impact that most people are barely aware
of it, of cost increases, energy increases.
That will change as the subsidies go away and so people's costs will go up because of
using the tools and then energy will go up even more because this will, this will increase.
And I just think over time that that changes naturally.
So people will start spending, for example, more of their income on energy than they used
to.
And we all just will kind of morph and be a river around this new technology.
But obviously, maybe.
I have much to say, but just one percentage of people, there's a study that actually
used paid AI services, what percent of the population?
No.
Human population or American?
I think this is American.
Well, we'll check.
I'm going to say 50 million customers, right, I mean, I'm saying in the world.
Oh, paid.
I'm going to say 3% of adult Americans are like, oh, it might be too low than that.
I reckon.
I just don't think that Americans just point that percent.
Yeah.
Point five.
Point four, okay.
All right.
I'll also find the exact one.
Oh, but it's lower in the world.
Yeah.
I made way lower in the world.
I bet it's you.
Yeah.
But like, especially for people that are paying for more, like, I know chat has their like
$20 a month thing.
Like, and people are using that like, as Google search, they're not doing 85 to 95% of
the things we've been talking about in this conversation.
You're not doing that on a $20 a month chat GPT, like, you could do a little bit, like,
you could do a little bit.
But you at the $200 a month here is kind of like your, your point is that at point five,
like if we're already starting to feel the stuff at point five, I think it actually proves
both their points because at point five percent, we're already seeing these impacts.
So when it's 20% and then more, we're kind of in a bubble and like, and this is a very
small conversation that most people aren't even aware is going on.
And so the reason why I say I don't worry about isn't because I don't see that there's
a tsunami of, of a, of a situation brewing.
It's because it is a tsunami of a situation brewing.
And I'm like, there's no fighting it when that wave crashes over.
Like you're going to go where the wave takes you.
And so I just, I'm more, like, I can only worry about so many things.
And this is one where I'm just like, well, yeah, that's, there's going to be a lot of
impacts to this, but eventually, like, it'll get worked out because people do both need
to use tools and do need to heat their homes both and also feed themselves.
So it'll, it'll balance.
I was just thinking about the cost, you know, like imposing costs on rest of society,
like extensities.
Yeah.
Yeah, a lot of people with ideologies, they write off the externalities of their ideologies
as well, something that's, you know, the means is the end of business, but that's always
a costing post on other people.
And I had a really good conversation with the other day because, you know, he's quite,
he's libertarian, quite hardcore libertarian.
And, and I did give him the example of, you know, so if someone's got a piece of land
between two valleys and a road's got to go through the national highway and he doesn't
want to sell, you know, shouldn't the government be able to acquire that for a fair value?
Given that, you know, if there's, if there are fair appeals mechanisms and valuation mechanisms
as well, he's like, no, if he doesn't want to sell, he shouldn't sell, go around it.
I'm like, okay, yeah, I don't agree with that, but, but he's quite ideologically, sort of
fixed on that.
That's, he's, he's quite hardcore, he's committed to that.
So I do think, yeah, that we do have an obligation to, um, to make sure society still functions.
And I'm not sure about, I see, in, I mean, two minds and in one respect with this wave
of AI adoption that's going to happen, it's extremely exciting for me as an entrepreneur.
And then I look at all of the advice that we've given today to people who are employees
right, but they're still going to be a huge toll.
I view it the same as Bitcoin, actually, Bitcoin adoption, Bitcoin adoption, which is when
I first came to kind of believe in the benefits of Bitcoin, there was a big need to evangelize
to kind of everyone.
And then maybe it's like you kind of come down and just try to convince your own family
and friends, uh, and you stop worrying about everybody else.
I think with the AI stuff, I'm to the point that like, look, the tsunami is coming for
sure.
Okay.
And the best I can do, honestly, is look out for my family and make sure, and I, and
I don't feel responsibility for other people.
I think everybody can adopt Bitcoin and seek shelter from Bitcoin with Bitcoin from the traditional
financial system.
My point is that the combination of AI and robotics doesn't matter.
How much of a positive attitude you have toward these technologies, and the grand scheme
of things, there's going to be a massive displacement of human labor.
I agree with that.
Permanently.
Potentially.
Yes.
I think it's almost certain, but, but again, that's where I'm like, I, so again, I believe
that with Bitcoin too, I believe the, the wealth gap between the rich and poor is going
to get wider, not narrower.
And so the best I can do at the end of the day, and maybe and others don't take this point
of view, which is fine.
But for me is say, like, look, I'm going to try to raise my children and, and encourage
my family to make decisions to ensure that they're on the top half of that divide.
And I think with AI and these things, it's the same is like, and that's what I stress
to my friends.
Sometimes in very uncomfortable conversations where I really pressured them to, to pay attention
here, because ultimately there is going to be a permanent underclass and I care about
you and I don't want you to be part of it.
Yeah.
So do everything you can in the next five years to set yourself aside or your family.
See why we're still going to have to deal with that situation of a permanent underclass
and I think we have the political terminology, framework, discourse.
People are still talking in terms of left and right, communist, professional.
We don't have people of the proper ethics to handle the situation before we have a giant
dislocation.
True, it worries me.
Sorry.
Don't.
It will be an incredibly balls of time.
I think the permanent underclass meme as it's spread around is incredibly catchy and
I think it actually conveys as was good, all good means compresses very complicated ideas
into a very short form thing.
In the long arc of history, I'm an optimist in the human condition and in mankind and
that we would get to the other side of it.
And so the word permanent is doing a lot of lifting in the phrase.
Everything you guys said that was true.
Like next five years, this is a very serious inflection point.
All of that urgency that's made in that, like all of those things are absolutely
factually true.
I just think, so all of that, agreeing with all those points, I do think that there
is, I'm an optimist in the human condition, all these things, and that there is a better
end to it.
I don't profess to understand exactly how the mechanics about all goes and works.
But I try and ground myself in that because I don't want to be moving in a sense of like
flailing urgency and overextend or do something dumb, right?
So that's just like the bouncing and everything you guys said, I agree with.
I just think, I agree as well.
I think maybe, and I actually have not used the phrase permanent underclassed, but you
mentioned it sounded, sounded nice.
So it's a good meme.
Exactly.
So I agreed.
I think what I see more is instead of the word permanent and what I'm trying to stress
to my friends is I think the, the volatile period will span the rest of our careers at
minimum.
That's why the word permanent, it's, it's permanent enough for us, yeah, it's permanent
enough, right?
I mean, for five years, that's a pretty, yeah, you know, I mean, you might, yeah, you
might come back, but your total earning potential will never recover.
So yeah, that's what I'm trying to encourage my friends and family is like, you're young,
you have many years to protect here.
You can't just count on luck of your company being slower to adopt and slower, you know,
you really, if you want to be safe, you just, you have to always be a top performer in
that today, from our point of view, means potentially using these tools to great effect.
One small piece of just people are retiring, massive income.
I don't think the, I'm, I have enough money to save it, I'm going to retire, it's going
to be strictly okay.
If you have, everything we're talking about individuals, like this proportionate outcomes
of like certain people, like are going to do way more of the work in 80, 20 world whatever,
that's going to extend the companies.
It means if you're just buying the general basket of the S&P 500, you're not going to
get your guaranteed seven to 10% return that you've had for the past 45, 40, 50 years
that Warren Buffett had, like, made so popular in champion.
So like, it, it's not like if you're retired and you're okay, like, like, this is a time
of vigilance and awareness in general, as just a general point in life.
And just because you're going to buy stretch, but optimist, I mean, I'm just, but I'm
optimist, right?
But yeah, I feel like we could talk about this for another three hours to be able to
answer if you had the final final thoughts on, on what we're talking about now, start
encouraging what we're talking.
A lot of this conversation was around context and iterating, starting with that, even in
your own internal process and how to use these tools and just start questioning and thinking
to yourself, get your hands dirty, don't get analysis paralysis about the overwhelming
options.
Just jump in and start doing things and just start tinkering and playing have fun.
That's actually my biggest advice.
You can make your work fun because you're learning a new skill and one, you're just going
to have fun because you're automating your work and all of a sudden what took three hours
only takes 10 minutes.
And like, that's, that's an initial door thing because you're like, wow, I'm going
to have so much more free time.
Don't get complacent.
Keep chasing that high of specifically getting that dopamine hit of learning new things and
empowering yourself and using this technology as a force multiplier and a lever for your
own freedom, your own autonomy to specifically engage with your mind.
That is my general advice and don't, don't get complacent.
That's my general thing, get your hands dirty, just try things, iterate, be comfortable throwing
things away and just, you know, start engaging in metacognition, just start engaging with
that like thinking about how you think and how you use these tools and stay curious and
amuse yourself.
Becca?
I'll try to not repeat all that because I couldn't agree more with all of it.
It reminds me though of a conversation that I had with our friend Zach Shapiro who owns
a law firm and has been very public about his law firm's use of AI tools.
And we were just having a very similar conversation.
We were both excited.
We were talking about kind of our mutual experiences and he said this phrase that was a body of
work that used to be intimidating no longer is.
And I thought about that a lot, a lot because I've managed so many people either directly
manage or indirectly manage that I have become very, very tuned to when somebody procrastinates
because it's very common that procrastination comes from intimidation.
I don't quite know where to start.
I'm not quite comfortable with this and so I'm not, I'm going to do familiar tasks and
I'm going to continue pushing out this and procrastinate more and more.
What Zach said that really got me thinking and excited is suddenly these tasks that used
to be quite intimidating.
You know, like this is outside.
I'm just paying contract review or something like that.
It could be that or trying to send a very detailed email that has real repercussions for
my company based on actual math that I'm not an expert in, right?
Things that would have just been like, I can't do this.
The only way for me to do this would be to hire an expert.
I cannot do this or if I was going to not hire an expert, oh man, like what do I do?
Do I order a book on actuaries, right?
Where do I start on this?
It is no longer intimidating.
Now everything starts with, here's my problem and here's what I wish I had and that's
also just, it's an absolute game changer and if you can open your mind to that just
to be like, if you find yourself intimidated, don't be intimidated.
Just ask.
Just ask it.
Just tell it what you want.
Just use your words if we're talking to a toddler or a priest cooler, right?
Use your words, right?
Just use your words.
Tell it what you want and yeah, then you will have fun.
The fun will, you just got to get in there.
The fun is very, I told a friend during these conversations, I was like, it's crazy
out there because I work in this tech industry, tech forward industry, it's like everybody's
on emphetamines right now, like everybody is big eyes and they're like tense and excited
but they're so happy.
People are having so much fun building so just don't be intimidated, get in there, everything
Rob said.
I totally agree.
Thank you so much for joining us on our website, anchorwatch.com.
Anchorwatch.com.
And where can people find you guys on X?
My Twitter handles at Rob1Ham, ROB, the number one HAM.
And I am Becca Amole, B-E-C-C-A-A-M-I-L-E-E on most of the apps.
Thanks guys.
Thanks for doing this.
Thank you guys.
It's such a pleasure getting to know you guys out of the weekend as well.
Yeah.
Don't wait till we connect again.
Yeah, thank you for having us.
Thanks for having us.
Cool.
Appreciate it.
Thank you.
Bye.

Bitcoin Archive w Archie

Bitcoin Archive w Archie

Bitcoin Archive w Archie