Limitless: An AI Podcast

Ejaaz:
99% of people are using AI models the same way that they use Google.

Ejaaz:
But recently, a new way of prompting your AI has emerged that doesn't just replace

Ejaaz:
the way that you work, it promotes you to the CEO of your very own AI company.

Ejaaz:
It's called Loops and it's part of a growing development in agent autonomy where

Ejaaz:
AI agents basically spin up and autonomously complete tasks or goals that you

Ejaaz:
set for it, often working throughout the night.

Ejaaz:
In 2019, the longest that an AI agent could work autonomously for was for two seconds.

Ejaaz:
Fast forward to today, and they can work autonomously for 12 hours,

Ejaaz:
and that's doubling every couple of months.

Ejaaz:
Andre Carpathy calls this phenomenon the autonomy slider, where you can go take

Ejaaz:
a dial that slides from humans that approve everything to humans that periodically check in.

Ejaaz:
And it's part of this growing trend of agents consuming and taking up more of

Ejaaz:
human capital and labor. And the question that remains going forwards is, what will humans do?

Ejaaz:
And will they be entirely replaced

Ejaaz:
by AI? Or will they be the ultimate orchestrator of their destiny?

Josh:
Yeah, I think the goal for this episode is really just to inform people on what's

Josh:
possible current day with these agents, with these LLMs, with writing these

Josh:
loops, as well as where you can possibly find yourself within that stack,

Josh:
because it gets pretty complicated.

Josh:
When we're getting into loops, not everyone needs to use loops,

Josh:
but everyone should be using LLMs probably slightly different than how you're using them today.

Josh:
So maybe we could start with a little history lesson in terms of the four levels

Josh:
in which we have been engaging with llm starting with the first level which is just prompting,

Josh:
generally like most people are probably still doing this started in 2022 2023

Josh:
around the release of chat gpt

Josh:
the way that you would engage with these llms is you would just submit a question

Josh:
or you submit a prompt and you get some language back now if you are still doing

Josh:
this that's okay because i find a.

Josh:
Three years ago, four years ago. It has since advanced pretty,

Josh:
pretty meaningfully since then.

Josh:
The second step of this is agents. And we're going to spend some time on agents.

Josh:
Everyone's kind of heard of an agent. Maybe not everyone knows what an agent is.

Josh:
An agent is something that could think for a little bit longer.

Josh:
It could run a bit longer than just a standard prompt.

Josh:
It can go off and do things. It could call tools for you.

Josh:
It's a much more capable version of the text box. Then, like we talked about

Josh:
all the time on the show recently in the last few weeks, there's the harness

Josh:
feature in which you put an LLM into a container and that gives it a memory feature.

Josh:
That gives it complete tool use. That's something like an open claw that we've

Josh:
talked about a lot that some people do use and that's level three.

Josh:
And now level four, which is the new thing that has come this week,

Josh:
that's really been highlighted by some of the top leaders at these AI labs is loops.

Josh:
And a loop is essentially a version of an agent that has an orchestration layer

Josh:
and kind of builds upon itself.

Josh:
So it allows you to kind of continue to scope yourself out. If you can imagine

Josh:
you're kind of you're dealing directly with an employee at level one and then

Josh:
you're kind of directing that person to go off and do their own in level two,

Josh:
At level three with the harness, you're kind of directing a series of people to help you.

Josh:
And then level four, you're just the top level CEO who's directing your C-suite

Josh:
to go and manage all the employees below you. So there's an entire stack to this.

Josh:
It's very cool. It just how do you use your AI currently? Where would you say

Josh:
that you fit in this stack?

Ejaaz:
Yeah, so looking at this diagram that we have on the screen here,

Ejaaz:
I'm somewhere between number two and number three. I'm somewhere between using

Ejaaz:
agents and trying to figure out the whole harness thing.

Ejaaz:
Now, what am I doing when it comes to like spinning up agents?

Ejaaz:
If you look at either my Claude or my ChatGPT desktop apps right now,

Ejaaz:
I've renamed a bunch of my conversations to a particular focus or subject and then agent after it.

Ejaaz:
And so I can go to it and this agent basically has all the context of what I

Ejaaz:
wanted to do, whether it's like research a particular topic,

Ejaaz:
create some kind of an outline for something, research a particular investment angle.

Ejaaz:
It already knows and has the embedded context for what it needs to do.

Ejaaz:
And there's usually like one to maybe three tasks that it needs to autonomously execute on its own.

Ejaaz:
And so it runs in kind of like a sequence. But if any of that sequence kind

Ejaaz:
of breaks, let's say it kind of tries to retrieve data from some particular

Ejaaz:
website and it is unable to do so, it breaks.

Ejaaz:
And it comes to me and it says, hey, Ejaz, is there some other thing that you

Ejaaz:
want to look at or retrieve from, blah, blah, blah?

Ejaaz:
It's not fully autonomous. Now, number three, the Harness side of things is

Ejaaz:
what I'm trying to kind of like mold my understanding around.

Ejaaz:
What I've noticed is when you type in a prompt and you get a response,

Ejaaz:
you can kind of tell that it's AI-y. Like usually when we kind of create artifacts,

Ejaaz:
it comes in a particular font or it speaks in a particular type of language.

Ejaaz:
The Harness helps kind of like take your prompt and kind of mold it into something

Ejaaz:
that is more human-like, but also more nuanced with what you are trying to do.

Ejaaz:
Like it effectively gets

Ejaaz:
closer towards that ultimate goal. Like we were talking before recording this

Ejaaz:
episode about human taste and how AI doesn't really get human taste.

Ejaaz:
The harness helps you get towards that ultimate kind of taste profile for the

Ejaaz:
particular output that you're trying to generate.

Ejaaz:
I haven't tried working with loops just yet, but my understanding of this,

Ejaaz:
and correct me if I'm wrong, is you have an AI. You can prompt it and you can get some kind of output.

Ejaaz:
A loop specifically is an AI agent that doesn't break, if it comes across an

Ejaaz:
obstacle that it doesn't understand, its instinct isn't to come to the human

Ejaaz:
and say, hey, like, I can't figure this out, guide me.

Ejaaz:
It completely reiterates the prompt over and over again until it gets past that

Ejaaz:
obstacle, working towards like one objective. So a few examples I've seen for

Ejaaz:
this is if you are coding, right?

Ejaaz:
And let's say there's multiple workflows of a code base that you want to work

Ejaaz:
on, and it comes across a hiccup where it can't retrieve data from one of those

Ejaaz:
particular flows, it is able to kind of like circumnavigate around it,

Ejaaz:
maybe spin up its own separate flow and try to figure out the problem.

Ejaaz:
And often this results in an agent working for multiple hours at a time,

Ejaaz:
often overnight. I think Carpathy spoke about his auto research agent working

Ejaaz:
overnight whilst he slept.

Ejaaz:
And we're seeing different variations of this start to arise.

Ejaaz:
Where are you, Josh, in the stack?

Josh:
Yeah, loops are like the closed source a system where you kind of define an

Josh:
outcome and it will continue to work towards that outcome without any external inputs.

Josh:
It's very cool. It's very automated. I don't think it's for everyone.

Josh:
It's certainly not for me because I haven't really had a use case for loops per se.

Josh:
I would say I'm sitting at each one of those first three phases given whatever

Josh:
tasks I'm trying to do. And I think it's important to understand that a lot

Josh:
of people might not even need to go past number one unless you're actually doing productive work.

Josh:
A lot of the agents, a lot of the harnesses are for kind of automating more.

Josh:
More systems from your life if you're just trying to use this as google if you're

Josh:
just trying to use this as a writing assistant or someone to chat with the prompting

Josh:
is really strong and i find a lot of times

Josh:
this is my outlook or this is my outlet for like google search results so instead

Josh:
of searching for google i'll get a little more in-depth results i'll ask my llm

Josh:
for agents i use them quite a bit when i'm doing a little bit more productive

Josh:
work for example we track the analytics on limitless and we want a place in

Josh:
which we can have all those analytics dumped to a dashboard,

Josh:
that is an agent that I run.

Josh:
So it goes into my browser. It detects all of the views that we've had from

Josh:
the week for YouTube, from Spotify, from RSS feed, where you should all be subscribed

Josh:
to and rate us five stars.

Josh:
And it compiles it into a singular spreadsheet in which we could then publish

Josh:
online and we could share with prospective sponsors and things like that.

Josh:
And then for harnesses I've used, because I mean, that's mostly OpenClaw.

Josh:
I've used OpenClaw. I really enjoyed the process. I find myself using it a bit less and less.

Josh:
And I think in the loops feature, at least it's probably most productive right

Josh:
now for people who are writing code, who are writing verifiable solutions.

Josh:
One of the difficult things that as I was looking into loops and figuring out

Josh:
how I can structure them into my life, one of the problems that I run into is

Josh:
I'm not really sure I have a verifiable,

Josh:
set of outputs that I wanted to optimize for, for a lot of the work that I'm

Josh:
doing, because a lot of it is subjective. A lot of it is kind of creative work.

Josh:
It requires a human in the loop for a lot more of it.

Josh:
So I would say I am number one, two, and three on the list. Haven't quite made my way to four.

Josh:
But yeah, for the people who are, those are the people like Boris Churny from

Josh:
Anthropic. And we know Andre and Peter Steinberg from OpenAI.

Josh:
They are all on four. They are using it to,

Josh:
create these like unbelievable, agentic systems and continue to remove themselves out of the loop.

Ejaaz:
You know what I've realized? With loops in particular and just AI agents in

Ejaaz:
general, they're trying to improve our understanding or rather their understanding

Ejaaz:
of the English language.

Ejaaz:
So one of my favorite Carpathy quotes back in the day was English is the new

Ejaaz:
programming language. I think you said this like two, two and a half years ago.

Ejaaz:
And I've just realized that like us creating AI agents is basically like,

Ejaaz:
it's the same model. It hasn't necessarily got smarter.

Ejaaz:
It's just like using that model to kind of like keep ramming its head and its

Ejaaz:
brain against a particular problem until it understands what the human actually means.

Ejaaz:
And so like in this new world, like I know you just used the example of like,

Ejaaz:
you know, loops can be used for coding specifically,

Ejaaz:
coding that Boris Churny and Carpathy is doing is English.

Ejaaz:
Like they're speaking to the LLM, they are writing in English to the LLM.

Ejaaz:
And yeah, maybe they're copy and pasting some versions of code,

Ejaaz:
but that code is primarily generated by an AI.

Ejaaz:
I think like something crazy, like 80% plus of code generated at Anthropic,

Ejaaz:
both for research and for just general consumer adoption is generated by Claude itself.

Ejaaz:
And so that's one thing. The other thing is the model just not getting smarter

Ejaaz:
is a really interesting thing. Like typically in my head, I would think,

Ejaaz:
okay, you need a better model to be able to unlock some of these new features

Ejaaz:
like AI agents, autonomous loops, et cetera.

Ejaaz:
But really you could just take the same model, wrap a harness around it and

Ejaaz:
try to get it to understand what particular goal it's getting at and just run

Ejaaz:
that iteration over and over and over again until you get a better output.

Ejaaz:
And I guess this is the same concept as inference or reinforcement learning

Ejaaz:
where like we've found this trend of post-training of these AI models,

Ejaaz:
these AI models just getting smarter, not because they've got bigger GPUs or more expensive GPUs.

Ejaaz:
It's because you've just taken the same model and you've just run it through

Ejaaz:
a different reasoning framework over and over again until it can do a thing.

Ejaaz:
And this is the practical embellishment of it. I personally haven't found like

Ejaaz:
an obvious use case for loops either.

Ejaaz:
So either you and I are boxing ourselves into a particular realm and maybe someone

Ejaaz:
listening to this is using this for like their software engineering thing or

Ejaaz:
their marketing thing. But yeah, I guess that's where I sit right now.

Josh:
Well, I think it's probably a skill issue on both our parts.

Josh:
Like there is certainly a use case for us in which we can use a loop in which

Josh:
we can define this outcome, send an agent off to go do it, and it will iterate

Josh:
on itself until it comes to a conclusion.

Josh:
I think it's just so novel and so new. It's difficult to kind of understand

Josh:
why. And we have this really great chart on screen that you're showing now,

Josh:
which is the why now section of this.

Josh:
And it's because the duration of a task that these agents can run is so much

Josh:
longer than it used to be.

Josh:
I mean, in 2019 we have here, it was two seconds. This was well before ChatGPT.

Josh:
But even early last year, in 2025, the duration that an agent could run on one single task was

Josh:
less than an hour in length so there's only so many tokens it could generate

Josh:
there's only so much reasoning it can do and there's only so much iteration

Josh:
you could get over that hour time period let alone the amount of costs that

Josh:
these tokens are going to be,

Josh:
costing you if you're using like the api or anything like that now fast forward

Josh:
to today i mean the best models in the world they're getting days worth of runtime

Josh:
so they can really think deeply and continue to iterate on themselves over and over i see examples of um.

Josh:
Backslash goal on x all the time of people who have a problem whether it be

Josh:
an optimization problem where they have a bug that they need to fix and they'll

Josh:
put this backslash goal on it for

Josh:
however long it needs to and it'll think for three four even five days i've

Josh:
seen in order to optimize for the specific parameter and this is possible because

Josh:
these models now can think for days long,

Josh:
you have to assume months is coming what does it look like Like when an agent can think for months.

Josh:
I mean, it's a really interesting paradigm shift that I'm not sure where people

Josh:
are going to find value in the open-ended way that it exists today,

Josh:
right? It's like, okay, here's this agent.

Josh:
You can tell to do whatever you want. You can create a loop.

Josh:
You can create an infrastructure system for it to operate in.

Josh:
It's pretty much open-ended and it's on you. And I think the answer to that

Josh:
is that not even the AI companies really understand the best use cases for it quite yet.

Josh:
I would imagine it's still this really difficult thing of how do you unlock

Josh:
value from essentially an open-ended agent that can go and run for an infinite

Josh:
amount of time? I don't know.

Ejaaz:
I also question like what a human's purpose would be at that point.

Ejaaz:
Like if you automate enough of the thinking and the curiosity behind like solving

Ejaaz:
particular problems, What do humans end up doing at that point,

Ejaaz:
especially if they don't do the work themselves?

Ejaaz:
They don't understand it, right? You need an AI to kind of like understand what

Ejaaz:
on earth is going on in the first place.

Ejaaz:
And eventually like an AI will then start setting goals, like more ambitious

Ejaaz:
goals than a human can in terms of like what to like kind of solve or go after.

Ejaaz:
There were some very low-level examples that I saw in response to Pete Steyer's tweet about loops.

Ejaaz:
And there's some kind of concrete examples that I want to run through very quickly here.

Ejaaz:
So one of them is using it for code, right? So a classic loop could basically

Ejaaz:
look like, okay, can you please pull live errors for my particular app?

Ejaaz:
Can you inspect and figure out where the bug might particularly be?

Ejaaz:
Can you create then a fix for this particular bug in my code?

Ejaaz:
And then can you deploy it? Then can you check the health of that deployment

Ejaaz:
and make sure that nothing else is broken?

Ejaaz:
And then record what failed and feed that into a database so that in the future,

Ejaaz:
we can detect errors like this or prevent it when we code and build some of

Ejaaz:
These future app features.

Ejaaz:
Now, that is kind of like a very small and specific enough use case that can

Ejaaz:
be generalized across basically any app or software engineering project that

Ejaaz:
you might be working on if you're listening to this.

Ejaaz:
And I wonder how many hours worth of engineering time that this replaces.

Ejaaz:
Because I know that there are entire teams having worked at companies.

Ejaaz:
Been a product manager in the past, entire teams of software engineers that

Ejaaz:
spend their entire days working on something like that. So that's one thing.

Ejaaz:
And then for content, which is very applicable for product managers,

Ejaaz:
or even like the work that you and I do, Josh, an agent could read a PRD.

Ejaaz:
So which is a product requirement doc, which is usually kind of like created

Ejaaz:
for a strategic goal that you want to kind of like build at your company,

Ejaaz:
like a product or a feature, it then writes whatever that next asset could be.

Ejaaz:
So it could be like a design profile or a mockup of what that feature might

Ejaaz:
look like, score it against like some kind of criteria that the company has

Ejaaz:
across like, you know, it must follow our vision, A, B, and C.

Ejaaz:
It must also look a particular way. This is our design profile,

Ejaaz:
our brand kind of profile and our aesthetic.

Ejaaz:
And then it kind of like updates its progress depending on like what other teams

Ejaaz:
have shipped. So maybe it's dependent on a particular feature.

Ejaaz:
And so it updates itself autonomously like that. Now, this all sounds very vague

Ejaaz:
intentionally because it's meant to apply to your particular business or your particular project.

Ejaaz:
But make no mistake, this is what

Ejaaz:
a lot of humans are paid upwards of six figures to do on a daily basis.

Ejaaz:
It's that nuance. And we're starting to see basically AI models and AI agents

Ejaaz:
enter into that human taste profile. So when I think about where we end up eventually,

Ejaaz:
There's a common argument that's made that it's like, oh, humans will always have the taste.

Ejaaz:
They'll always be able to kind of direct where the AI should go because we are

Ejaaz:
this all being kind of like smart kind of entity.

Ejaaz:
But I see increasingly AI stepping into that boundary and becoming the tastemaker

Ejaaz:
for all of the work that we end up doing.

Josh:
I still believe that to be true, that humans in the loop are critically important

Josh:
to applying human taste. I saw this great chart. I have no idea where it is.

Josh:
Somewhere in the depths of X. But basically, it was showing that in the App

Josh:
Store, the iOS App Store, where everyone downloads their apps,

Josh:
the amount of apps that have gone into production that have been published recently has gone vertical.

Josh:
I think it's doubled or tripled over the last six months. Everybody's publishing apps at the App Store.

Josh:
The amount of five-star reviews and the amount of downloads has actually either

Josh:
stayed flat or gone down.

Josh:
It has not matched the amount of new apps that are going to the app store.

Josh:
Why is this? It's because a lot of the apps don't have enough care applied to

Josh:
them. They're just not great applications. And when I think about,

Josh:
how I use my phone on a regular device or on a regular day or how I use my laptop

Josh:
and the applications that I actually spend time on, there's a very fixed set

Josh:
of them. And I'm a little stubborn when it comes to downloading new ones because

Josh:
a lot of the new ones just are not great.

Josh:
And I think a lot of that comes from this, this lack of care that is presented

Josh:
from AI outputs, where if you're optimizing for a specific parameter that you

Josh:
can measure, it's going to do it great, but it doesn't understand the subtle nuances of how humans

Josh:
engage and how they really love to use these products like one of the products

Josh:
that i use totally unrelated totally not sponsored but this app called copilot

Josh:
money it's like a budgeting application and it's so thoughtfully curated and designed and.

Josh:
And it really deeply understands all the complexities that are related to humans

Josh:
when it comes to budgeting it understands a lot of the

Josh:
the design characteristics same with an app called flighty i'm sure a lot of

Josh:
people have heard flighty it's like a flight tracking application there's a

Josh:
thousand ways to track a flight but flighty really cares about design they really care about how,

Josh:
it's implemented with the human and they've created this amazing output and

Josh:
i don't see that changing one thing that i did want to note is that,

Josh:
i think when a lot of people see this they imagine a world in which they are

Josh:
getting replaced everyone's like ai is replacing me look how much i could do

Josh:
now it has these loops and i think the reality is it gives you a lot more agency

Josh:
to do the things you want to do,

Josh:
where maybe you're not doing the day-to-day where,

Josh:
you would normally prompt an agent to do this but you're doing a lot of the

Josh:
higher level tasks you can imagine yourself not having to do

Josh:
the day-to-day like for example if you're just managing your household you no

Josh:
longer have to take out the trash you don't have to run errands you could just

Josh:
focus on how to make your household the best household it is because you have

Josh:
that higher level ability

Josh:
and in that chart that we showed in the artifact earlier on it shows a decreasing

Josh:
sized human it's the amount of input that a human is needed to get the output you want,

Josh:
but it's still ultimately on the human being in order to to push and navigate

Josh:
towards the outputs that you want because ultimately these tools are just for us so when i think of.

Josh:
Ai becoming increasingly good and when it comes to running the show even i've

Josh:
leaned on it we both have i think a lot more recently but all that's done is

Josh:
actually given us more leverage to do more with the show than have it replace

Josh:
us and even in the case that.

Josh:
We could clone ourselves. We could create a video version of ourselves that

Josh:
has a perfect voice that sounds just like us. I don't think people actually want that.

Josh:
There's that lacking human nature that still isn't understood.

Josh:
And I find that it's more empowering when I hear that these loops exist that

Josh:
can run for days on end and create amazing outputs versus not where it's kind

Josh:
of extracted from us. I don't really think that's true.

Ejaaz:
Yeah, it's like that stat of, well, it's that thesis that everyone held about

Ejaaz:
a year ago, which is like with the increase of AI adoption, people will have

Ejaaz:
more free time to have fun and leisure.

Ejaaz:
And in fact, the opposite has shown that like people just work way more and work harder.

Ejaaz:
And the output of that work is measured across like pretty much every single

Ejaaz:
company and profession and role.

Ejaaz:
I do generally agree with that. I don't think humans are going to get wiped out anytime soon.

Ejaaz:
But one thing that is kind of nagging my brain is if we extrapolate this intelligence out enough,

Ejaaz:
there is no reason why AI won't be able to take over or replace other parts

Ejaaz:
of the cognitive process that a human can do,

Ejaaz:
particularly if it's one AI model trained on the entire corpus of knowledge

Ejaaz:
that a bunch of humans have been guiding it.

Ejaaz:
So when I think about Anthropic, when I think about OpenAI, I think about all the

Ejaaz:
millions of people that use their product every single day and the data that

Ejaaz:
they ingest every single day that gets recorded on one singular database that

Ejaaz:
can then be reused to train a better model that is more hyper-optimized towards humans.

Ejaaz:
You could argue that as a single human, you don't get to meet and read the thoughts

Ejaaz:
of every other human that is out there.

Ejaaz:
You have your very own individual process. And I think that an AM model that

Ejaaz:
can get access to the world's brain and thoughts could probably create something

Ejaaz:
kind of close to knowing what that human taste profile would be.

Ejaaz:
The other major question that I'm wondering is, how much is all of this going to cost?

Ejaaz:
One, like, stat that has stuck in my head over the recent few weeks is that

Ejaaz:
Philanthropic particularly, they service, or like,

Ejaaz:
the Fortune 10, the top 10 companies in the world, nine of them use Clawed,

Ejaaz:
and their budget's increased by 500%, or is projected to increase by 500% by the end of this year.

Ejaaz:
And they're doing this willingly because the ROI, the value that they're getting

Ejaaz:
out of that is pretty massive.

Ejaaz:
Alternatively, there are companies like Uber that have slashed their budgets

Ejaaz:
massively because their entire year's budget was spent in a couple of months.

Ejaaz:
So I'm wondering, in this world of agent loops where you've got AIs working

Ejaaz:
overnight for you, the bills are going to increase pretty massively.

Ejaaz:
And I'm wondering, unless these AI models don't get cheaper,

Ejaaz:
and there's an infrastructure bottleneck there where these GPUs cost a lot of

Ejaaz:
money, we can't scale power and infrastructure anytime soon.

Ejaaz:
We need so much more energy than we already have currently on Earth to be able to power these things.

Ejaaz:
The cost of these things are just going to go up a lot more massively,

Ejaaz:
which means that either this is only going to be a power or a tool reserved

Ejaaz:
for the rich, or something's going to break here and maybe open source models

Ejaaz:
get adopted more aggressively.

Josh:
Yeah, I imagine there's probably use cases for all of the above.

Josh:
It's like open source models will continue to improve they'll be able to do

Josh:
a lot of the more trivial tasks that don't require frontier intelligence so

Josh:
therefore the cost of those types of loops will go down because not everyone

Josh:
needs to have the most cutting edge,

Josh:
software stack engineering like they're just kind of having it help them through

Josh:
their day-to-day maybe it's replying to emails maybe it's whatever miscellaneous things it may be

Josh:
there's a high probability that these open source models as they continue to

Josh:
improve will be able to bite off a meaningful chunk of that then the other half

Josh:
is using these frontier models that is a requirement in order to get the absolute

Josh:
best results for whatever very challenging work they're doing.

Josh:
And that is going to cost a lot of money for sure.

Josh:
And I don't see that changing, but I think the output of the dollars in will continue to go up.

Josh:
It's because as you get more knowledge per token, as you get more output per

Josh:
prompt, it very clearly, I mean, the economics seem to make sense.

Josh:
And I think that's kind of right now.

Josh:
Enterprise spend on these models they're trying to figure out well how much

Josh:
value can we actually get back from every dollar spent and right now it's a

Josh:
little bit unsure you mentioned uber we have uber here that we're showing on screen

Josh:
where uber just recently put a cap on the amount of tokens that

Josh:
their employees are allowed to use at fifteen hundred dollars per engineer per tool per month and

Josh:
we'll see how that works because a lot of other companies that we know they're

Josh:
kind giving their engineers unlimited budget in fact they're kind of ranking

Josh:
the engineers based on how many tokens they're using per month and.

Josh:
We'll see where that goes. I suspect the companies that are spending more on

Josh:
tokens will continue to see a higher upside for now, at least.

Josh:
But like you mentioned, the underlying problem with all of this is we're going

Josh:
to continue to have more prompts. I mean, these loops consume a tremendous amount

Josh:
of tokens, whether they're frontier tokens or open source tokens.

Josh:
It doesn't matter. We're going to need orders of magnitude more than we have.

Josh:
And we don't have the computability. It really does always come down to that

Josh:
energy problem, that infrastructure problem.

Josh:
We don't have the infra built out to support this so therefore the costs likely

Josh:
continue to stay high maybe it's not because you're paying the provider for tokens

Josh:
perhaps it's just renting the gpu time from a cluster that is doing much more

Josh:
valuable work so i think that might ultimately be

Josh:
that crux is the actual availability of the compute to do these things and that's

Josh:
why these edge compute devices like having your,

Josh:
mac studio on your desktop that can run locally it's probably a pretty valuable thing to have.

Ejaaz:
So I'm sure a lot of you are wondering, you know, how does this apply to me?

Ejaaz:
You know, I have none of my friends have mentioned this loop feature.

Ejaaz:
I don't really know many people who are using it.

Ejaaz:
As we mentioned earlier, like this isn't probably going to be used by the bulk

Ejaaz:
or majority of people yet until some of those use cases actually arise.

Ejaaz:
I think it's mainly going to happen in the workplace. It's going to happen with

Ejaaz:
like some of these enterprise companies that are trying to automate certain

Ejaaz:
departments or functions of their particular a company like marketing,

Ejaaz:
like software engineering.

Ejaaz:
And I think it'll start with lower level tasks because these agents still aren't

Ejaaz:
smart enough to understand nuance completely.

Ejaaz:
And also, you don't just want to let an agent run loose overnight whilst you're

Ejaaz:
sleeping and then take down your entire company. And one place where it's working

Ejaaz:
tirelessly to accelerate the development of that

Ejaaz:
And we have Boris Cherny over here basically explaining how he's basically ditched

Ejaaz:
his integrated development environment.

Ejaaz:
He has ditched all of his normal tools that he had spent decades basically honing

Ejaaz:
his software engineering skill on to now completely focus on building up these

Ejaaz:
agent loops. And what is he focused on?

Ejaaz:
Well, he works primarily on cloud code, but the other folks at Anthropic and

Ejaaz:
OpenAI have started this thing called

Ejaaz:
Recursive self-improvement or RSI, which is basically the goal of getting your

Ejaaz:
AI model to build the next version of itself.

Ejaaz:
And this is a test that Anthropic and the folks at OpenAI do for any new model that they release.

Ejaaz:
They set it a goal or task to basically rebuild itself in a more improved fashion.

Ejaaz:
Now, one thing that the AI has gotten really good at is building out that next function.

Ejaaz:
But one thing it's not very good at is figuring out what research problems they

Ejaaz:
should fix, what research problems it should focus on to try and,

Ejaaz:
you know, overcome and make it ultimately, you know, a better model than its competitors.

Ejaaz:
Now, RSI is something, it's kind of like the golden egg that each AI lab is going after.

Ejaaz:
And this is the primary use of agent loops right now.

Ejaaz:
And you can see why it might be obvious. If you have an AI model that can basically

Ejaaz:
build the next best version of itself, eventually you're going to get to AGI,

Ejaaz:
whatever the hell that looks like, and then you can apply it to pretty much any sector.

Ejaaz:
Now, the problem and the worry that kind of immediately pops into my head and

Ejaaz:
a lot of these researchers head is, if it eventually does get that smart, right?

Ejaaz:
Could escape human control completely and run off on its own and do its own

Ejaaz:
thing. Because at that point, why would it need a human to kind of like guide it or shepherd it?

Ejaaz:
Instead, it can just kind of like do its own thing. So this is like the primary

Ejaaz:
use case that I'm seeing for agent loops being worked on right now.

Ejaaz:
I would love to see a like more broader application across like kind of like

Ejaaz:
consumer professions, like in finance, like in science and stuff like that,

Ejaaz:
which I do believe it'll spill over eventually. But unless you're seeing anything

Ejaaz:
else, Josh, I think like that is primarily it on agent loops and agent autonomy.

Josh:
It's on you to figure out the best use cases for it. Like there's no real company

Josh:
defining it. They're just giving you the tools. And I mean, for better or worse,

Josh:
it's very open-ended. So it's on you to figure out how best to use these.

Josh:
I think if this sounds a little overwhelming, maybe we could outline a few examples

Josh:
of each one of these kind of four rungs in the ladder here.

Josh:
The first one being prompting this everyone has done before.

Josh:
I'm sure it's like rewrite this

Josh:
email to sound more confident or explain what my doctor meant by this.

Josh:
But then you've probably also used the partial agentic usage as well of these

Josh:
models, which is like planet my three day vacation to Lisbon that I'm going on next week.

Josh:
And it will actually go off and use tools and it will think complex thoughts

Josh:
and ideas and kind of surface you a full itinerary for your trip.

Josh:
And then there's the third one, which is the harness. This is a little more

Josh:
complicated. This is for people who are building more project based stuff.

Josh:
So for example, if you want to build you a website for your dog walking business

Josh:
and you kind of describe it and you go back and forth on a spec and then it

Josh:
goes off and implements that.

Josh:
And the fourth is loops, which doesn't have to necessarily be overwhelming.

Josh:
It can be simple as let's say you are.

Josh:
Interested in the news, you could say every morning before I wake up,

Josh:
scan these 10 sources plus market data and give me this bulleted brief.

Josh:
Or let's say you have a to-do list. It'll go off and think overnight and solve

Josh:
all those problems overnight, iteratively until it comes to a solution that

Josh:
it hopefully arrives at in the mornings. There's a lot of use cases.

Josh:
I think a lot of it requires creativity.

Josh:
And that is the prompt we will leave you with today, which is share with us,

Josh:
please, how you are using these models best.

Josh:
Because so much of the question isn't are these models smart?

Josh:
It's how can I extract that intelligence from them in the most effective way

Josh:
for my life? So I would be so curious to hear which rung of the ladder you find

Josh:
yourself on one through four.

Josh:
And then what the most interesting use cases you found,

Josh:
among those rungs of the ladder are you using loops currently what are you using them for,

Josh:
are you with agents are you still using it as a google extension if you're still

Josh:
using it as a google extension i would encourage a little more creativity really

Josh:
try to ask harder questions and figure out how it could be implemented in your

Josh:
life but i think that's pretty much it on the loop um,

Josh:
you're not going anywhere but your job might shift a little bit in terms of

Josh:
scope as these tools get more powerful and that should be the hope that should

Josh:
be the goal because it'll allow you to do so much more that you want to accomplish, I believe.

Josh:
And yeah, I think that's where we'll leave you with today.

Ejaaz:
Thank you folks so much for listening. Similar to Josh's prompts,

Ejaaz:
I'm actually kind of curious, for one singular task that you've used your AI

Ejaaz:
for, what is the most number of tokens that you've burnt?

Ejaaz:
Be honest, it can be for any use case, doesn't matter, let us know.

Ejaaz:
And also, what is the longest that you've had an AI work on a particular task

Ejaaz:
for? Is it a couple of minutes? Is it hours? Is it potentially overnight?

Ejaaz:
Let us know. I'm curious. And what was the associated bill with that?

Ejaaz:
And yeah, we'll see you on the next episode. Wherever you listen to us,

Ejaaz:
if you haven't subscribed, if you haven't rated us, if you're not leaving us

Ejaaz:
comments, what are you doing?

Ejaaz:
We respond to pretty much any and every one of them. We listen to your feedback.

Ejaaz:
It feeds into some of the work and content that we put out.

Ejaaz:
We are almost hitting 60,000 of you folks. And you guys are reading our newsletter,

Ejaaz:
which is like hit out to about 100,000 plus people. every single week. We post twice a week.

Ejaaz:
But yeah, wherever you are, please subscribe to us, leave us a comment,

Ejaaz:
and we'll see you on the next one.

Josh:
See you guys next time.

Ejaaz:
Peace.