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[English]

I was asked to talk about the future of

software. So, a lot of this talk is

going to be about what we're doing at

Replet, where we think the future of

software is headed and some kind of

trying to make some predictions or try

to think out loud about really what the

future holds. my mental model for for

our business and really for the moment

we're in today. If you think back uh uh

on the future on the history of

computing, mainframes were kind of the

the first mainstream computing devices

as mainstream as it gets back then. And

to use a mainframe, you needed to be an

expert. And then PCs came around and

initially PCs were kind of toys. you

bought a Mac and you did Mac Paint and

things like that. There wasn't real

business use case. I mean, people like

made fun of Apple at the time

uh until the Excel sheet. The Excel

sheet was the first software that was

actually useful on computers. And now

PCs run world economy. Like they

actually if you go at a data center,

it's also only PCs. It's x86 computers.

So you go you go from something that was

used by a small group of experts that

had to to have a lot of training to

something that started sort of as a toy

and is used by by everyone. Same thing

with software engineering like uh the

modern software engineering career you

can sort of trace it back to the u 70s

with the rise of maybe uh uh Unix and

the C programming language. That's when

people started uh kind of being trained

to become software engineers. You still

needed four, five, six years of uh educ

college education. You need another two

or three years of uh training on the job

to be able to actually do the job very

well. I think today software is going

through the same transition

from something that only experts do

to something that anyone can do.

And this is what we're really rep uh

building replet for. I've been working

at Replet for like almost nine years

now. And our vision has always been to

solve programming to like make

programming so make it so that anyone

can uh write software. So we built um an

IDE, we built uh language runtimes, we

built like a online sandbox environment,

we built deployments, we built cloud

services around all of that. And then

when AI came on the scene, we realized

that the ultimate expression of our

mission is to make it so that you don't

have to code. code is the sort of

bottleneck to actually getting a lot

more people making software. So around,

you know, late 23, early 24, we decided

to put all our resources into agents. At

the time, agents sort of barely worked,

but you could tell by looking at a few

benchmarks that were headed there. So,

SWEBench is a software engineering

benchmark. Uh it is basically a

collection of um issues on on GitHub

from major repositories and the unit

tests and pull requests sort of end

state of those issues and uh and the way

you test an agent is you put in an

environment and have it solve some of

those issues. You could tell like in in

22 sort of it barely worked. 23 started

sort of working and you could tell early

sort of early 24 where we're on this

trend where you could tell that software

engineering is getting automated or like

big parts of software engineering is

getting automated. Uh and now we're

probably I think this is like a little

outdated. We're like at 70 80%

Sweetbench. Now if this benchmark gets

saturated doesn't mean that we automated

all of software engineering but we're on

on our way to make really useful

arguably it's already here really useful

software engineering agents and by the

way this is true of any agent if any any

of you are building sort of agents

startups uh just like

really believe that it's coming really

really like I I keep telling my team we

need to be okay with building crappy

products today

because two months down the line the

models will get better and your business

your product will suddenly become

viable. So to today's kind of the moment

for for uh for agents so rapid kind of

went all in on agents but agents that

can write code uh is actually the easy

part. The hard part is the

infrastructure around it. Sometimes I

call it the habitat for which the agent

uh lives in. So what you need is you

need a a virtual machine, ideally in the

cloud, ideally not on your computer

because you know agents can actually

also mess up your computer. They could

do a lot of scary things. So it needs to

be sandboxed. Uh it needs to be

scalable. If you're running a product

like Replet, you need to be able to, you

know, scale up to like millions of users

and uh you need to be able to support

every language out there, every uh

package out there. Um the way uh

software engineering agents are trained

today is they're trained on standard

Linux environment. They need to be able

to use the shell. They need to be able

to write to files, read files. Uh but

they also need to able to install

packages either system level packages,

Linux packages, but also language

packages. And many cases agents want to

actually use more programming languages.

And so a lot of environments today where

people are trying to build agents are

very constrained. But what you what you

want is an environment as open as

possible similar to the kind of

environments that software engineers

work in. So what kind of other things

you need to ship real software you need

deployments, you need databases. Uh

really think about everything you do as

a software engineer and all those tools

need to be accessible uh to software

engineering agents. So actually I saw

earlier today if you were at uh

Karpathy's talk he he talked about how

you know the the coding part is the easy

part. So sort of similar to the points

I'm making but he talked about all the

different things that are really

unsolved but in reality we actually

solved a lot of them. So replet of the

gate comes with o uh agents are actually

not very good authentication. It's

better to use a service built-in

service. So replet actually one line of

code we turn on co uh off. So when ask

replet agent to integrate off uh it will

actually just use replet off. It will

just like basically turn on setting and

and then uh you have user authentication

you have user management those users

information are being stored in the

database. You can also obviously deploy

the app you can uh link a domain to it.

We have secrets management secure ways

of kind of uh using API keys. We have

background jobs. You know, a lot of

applications need to be able to run

continuously in the background,

especially in this era of agents.

Storage again, uh, you know, agents need

to be able to store things. They need to

be able to grab things from the web,

images, documentation, whatever, and

store them for the application to use

them in the future. Few other things on

the road map, universal model access.

So, it's really a pain right now to ask

the model to like to ask for an

application that can generate an that

can do something with an images or

videos. You have to figure out which

model to use. You have to go get an API

key and do all of that. Pretty soon, any

model that you ask for at Replet, it'll

be just available in your app directly.

We'll handle the the billing and the API

integration, all of that. Payments is

very important. Payments not just for

your users to pay for your application.

Say you're you're building a startup on

Replet. You're you're an entrepreneur.

you obviously need to collect uh user

payments, but also I think some sometime

in the future you would want your agent

to have some kind of wallet to be able

to go uh pay for services. So let's say

you know your your agent decides that it

needs a Tulio integration and replet or

whatever system you're using doesn't

have a tool integration, it should be

able to go put in its credit card and

provision that service in the in the

background. A more radical idea is that

your agent needs to be able to like hire

people. For example, if it hits a

capture and it doesn't know how to solve

a capture, it should go and task grab it

and ask a human to go solve the capture

for it. Whatever it is, there's a lot of

tasks that you still need humans for and

you would want your your agent to be

able to uh to have money to pay for

services. And similarly, agent to agent.

Um, you would want your agent to be able

to go on the on the market and find

other agents that can it can hire. Too

many YC startups are building uh agents

sort of agents for accounting, agents

for sales and so you need your software

engineering agents to be able to

integrate those agents as well. So it's

so I I know a lot of people think of MCP

as such an agent to agent tool but

actually MCP is a more traditional RPC

protocol. So it's not really going to

solve this. Another model on our sort of

business or technology is think about

sort of the level of autonomy. So when I

started working on what Replet would

become like years ago, perhaps decades

ago, the state-of-the-art code assist

was a language server, right? That's

IntelliSense if you're using VS Code.

And you can think of it as level one

autonomy. You know, if you think about

the uh sort of drive assist in

self-driving cars or like in cars, you

know, would be kind of the lane assist,

that would be the first level. Uh AI

code completion co-pilot, that would be

level two. Uh level three is what we

worked on um when agent when replet

agent first launched. Agent v2 I I would

call it almost 3.5. It can work up to 10

15 minutes on its own but it still needs

your input every now and then to test

the app and make sure the app is

working. And right now we're working on

V3. I'll talk a little bit more about V3

in a second. Uh but V3 is sort of level

four, right? Like you're almost there.

It still needs some of your attention,

but it it kind of works fully uh

autonomously.

Bore Plus, which I assume we're going to

get to in the next couple of years, you

can really spin up a thousand agents,

give them a lot thousand problems and

and reliably be confident that like 95%

of them is going to work. Like we're

going to have a really high reliability

rate. any kind of engineer or product

manager really anyone can spin up

hundreds if not thousands of engineers

to do work on their behalf. So they need

very little supervision and therefore

you can uh increase your impact

exponentially as as a as a programmer.

So what we're working on right now with

uh Asian v3 is uh that you know it's

based on uh basically three uh three

pillars. One is uh end to-end testing.

So today computer use is um so in models

what's called computer use if you've

used openi operator it's the idea that

models can go into a computer you know

click around and use a computer like a

human does they're slow they're

expensive they're not very good but this

is what I talked about earlier you want

to build a product at the edge of what's

possible right now the edge of what's

possible is like computer use in my opin

is really at the frontier of what these

models could do and I think over the

next 3 to 6 months they're going to get

a lot better and it's going to enable an

entire new market and also probably

start to automate a lot of real jobs

once we have app testing uh you know

this this kind of annoying thing that

replication does where keeps ask asking

you to do QA for it'll start doing QA on

its own and that will allow it to work

you know 30 40 up to hour maybe two

hours of work. Sort of the hype today is

test time compute. If you think about

the sort of 03 uh or like oer models or

deepseek R1 the kind of main insight

there is the more tokens the model is

able to consume or produce

uh the more intelligent uh it gets. Now

today with something like 03 the model

is generating a lot of tokens and trying

to reason but a lot of it is sort of

synopsistic. It doesn't get feedback

from the environment. It's almost like

it's just sitting in place and thinking.

What you'd want in a real computer

environment is for the model to generate

hypothesis and test its hypothesis in

real time. So at Replet, we built a uh

fully transactional reversible uh file

system. So when you're on on Replet,

every edit you make to the file system

is an atomic snapshot in time. And that

allows us to have very cheap copy and

write forks of the file system. And so

our idea for this is that um anytime

there's a tough problem or basically if

you have a lot of budget you can have it

on all the time but every time the agent

is making a big change it forks itself

and the environment in number of times

to solve this problem in different ways

and then find the best solution and then

take that solution merge it into the

main branch.

So think about you know the idea of

simulations like when you're thinking

about the problem you're often

simulating different branches of things

that you could do you have different

hypothesis you want to test and so we

want to also

uh give agents the ability to do that.

So at any given problem generating a ton

of different ways of doing it and then

testing all of them in parallel. This

will bring up reliability of agents by I

think two to three folds. So that's

sampling and simulations. And then

finally is uh for the uh model to be

able to generate tests for every feature

that it creates. Today replet replet

agent often creates a feature and then

later on breaks that feature but also

true of clot code and cursor and all the

others. So we want to make it so that

once the agent makes a set of changes or

feature, it always has tests that it

runs on every change to make sure it's

not breaking the software. This is

actually harder than it sounds like it

sounds like okay write tests and let's

run them. But often actually models are

pretty bad at generating unit tests. So

there's still a lot of work uh to do

there. It needs to be fast as well so so

that it happens on every change. So I so

that's what we're working on with V3.

That's a lot of infrastructure work.

Want to create the best habitat for

agents to to live in and be able to u

the most be the most reliable possible.

But like let's fast forward to like what

I talked about with like level five

autonomy. Really the most autonomous

system we can think of.

>> YC's next batch is now taking

applications. Got a startup in you?

Apply at y combinator.com/apply.

It's never too early and filling out the

app will level up your idea. Okay, back

to the video.

>> My prediction is that all application

software will go to zero. In other

words, software will be dirt cheap, that

no one will be making money on the

traditional type of SAS software. I'm

not saying this will happen tomorrow or

even next year. I gave up on the the um

trying to predict timelines. I know it's

going to happen on the order of years.

If anyone with one prompt can generate

any kind of software of any type of

complexity, then um the value of

applications will go down to almost

zero. So what does that actually look

like? So today you know in the startup

ecosystem in the tech ecosystem there's

all these generic generic SAS you know

vertical SAS software and any of you

who's running a small business or even a

bigger business you you probably have

bought you know dozens and dozens of SAS

software just to uh just to run your

business even today you're able to

replace large parts of those software by

using something like replet agent or

writing your own software uh I think in

the next few years. This again this will

go from uh you know maybe 15%

replaceable to 100% replaceable. So this

will really fundamentally change the

software market. Uh just to give you a

story uh one of our colleagues at Replet

uh Kelsey was uh she works in HR uh

she's never written a line of software

in her in her life and she wanted an

orchar software. She had a few bespoke

needs like she wanted to connect it to

ADP, our our sort of payroll software

and and she had a few features that that

she wanted and she went on the market

and she couldn't really find an org shot

software that exactly fits her needs.

They were very expensive that were going

to cost tens of thousands of dollars a

year. So she decided to make it. She

took a week, less than a week, three

days, and she made orshot software that

we're using today that we can go out on

the market and sell it as a SAS product

for tens of thousands of dollars a year.

So that's like mind-blowing, right? I

mean, it's HR professional can make

software to run their work. That's

happening today. Try to project that out

a couple years later. Like the software

business fundamentally changes, gets

disrupted. Not only software but I think

how we work, how businesses works, how

corporations work will will

fundamentally change.

Today

we have these roles you know uh

companies like to specialize since the

industrial revolution when factories you

know became the main mode of creation.

the sort of modern uh special, you know,

specialization in the economy kind of

emerged where one person is making one

part of the the the product. It goes on

a factory sort of um assembly line and

another person is responsible for

testing it, another person responsible

for assembling it. And so this this um

specialization has been the way the

economy has been trending for a long

time. And it sort of makes sense, right?

You want to uh specialize people as much

as possible. You want them to be as as

replaceable as possible. And so this is

how the modern economy is built. But

once your HR professional is also a

software engineer is also potentially a

marketer is also potentially anything

because they can learn anything. There

are AI agents that can do anything for

them. really, you know, you go into the

world where jobs will become less

specialized, less um siloed. And in

fact, we started, we're seeing it today

and we're at Replet the way we're

structuring our orchard and our business

based on this idea. We're building for

the first time, we're building a like a

actual product team, product management

team. And our product team is actually

made of designers, engineers, and

product managers all almost always in

the same person.

So we're trying to merge a lot of roles

together and create this generalist

employee. So the the arch will start to

look more like a network than a

hierarchy. So it'll look more like an

open source project than a than uh it

will look like a traditional company

hierarchy with a marketing department,

sales department. Every employee will

like wake up in the morning and their

mandate would not be write this

marketing email or you know make this uh

optimize this button. Their mandate

would be make the business work,

generate value for the business. So

everyone is sort of an entrepreneur and

that will really disrupt and

fundamentally change how companies work.

It's a model that really we haven't you

no one has really embraced or or or even

started to talk about but really you

know think it through if everyone has

access to a general purpose software

engineering agent and sort of agent for

every possible role obviously domain

expertise is still important but is not

as important as it used to be. It's

exponentially less important and this

also affects how people build

businesses. it affects the opportunities

that are available for us in the future.

One uh really interesting book uh that I

read this book is was written in the 80s

which is insane given how good the

predictions were. So I'm just going to

read this. Ideas will become wealth.

merits wherever it arises will be

rewarded as never before. In an

environment where the greatest source of

wealth will be the ideas you have in

your head rather than the physical

capital alone, anyone who thinks clearly

will potentially be rich. The

information age will be the age of

upward mobility. The brightest, most

successful and ambitious of these will

emerge as truly sovereign individuals.

Now, some of these various is a bit

dated. The information age perhaps we

call the intelligence age today.

But this book predicted things like uh

crypto, remote work, all sorts of things

like that. And this idea of like a

sovereign individual, someone so uh

empowered by technology, so empowered by

these uh uh agents

uh that is able to create enormous

amount of wealth uh individually is is

uh is is going to be the norm. Think

think about um someone like Satoshi.

Satoshi created a single person created

a trillion dollars worth of value. I

don't know what the market cap exactly.

Perhaps it's more than trillion dollars

of Bitcoin. But like that's a single

person. They wrote the paper. They wrote

the software. They put it out there and

it became a big thing. Obviously there's

a lot of people. It's a big market right

now. But it was created by a single

person and we don't know who they are.

And I think that's going to be a common

occurrence in the future. The really

great thing about it is really um the

access to opportunity will be universal.

The idea of merit being rewarded

wherever it arises doesn't matter if

you're in Silicon Valley or anywhere

else in the world. If you can think

clearly and you can use some of this

technology. If you can think clearly and

generate good ideas, go into replet, put

in those ideas, make the first version

of software today, you can start to

become more like the sovereign

individual. Again, the way collaboration

work will be will be seamless. You know,

everyone's talking about the you know $1

billion single person company, but I

think that really kind of misses the

point a little bit. What's really

interesting about it is that you'll be

able to assemble groups of people really

quickly. You'll also be a be able to

assemble uh groups of agents really

quickly. You'll be able to assemble

these companies and also unwind them.

You can create mission purpose, you

know, companies or like projects and

unwind them really quickly. And in some

cases it could happen in a day or two.

And sometimes you might be you might

think you're working with another human

on the internet, but they're actually an

agent built by someone else who's out

there doing work for them. So the way we

work uh and the way people build

startups will fundamentally change as

the the cost of transaction goes down

goes down to zero then um the the the

reason to hire an employee full-time

uh you'll have less of a reason to hire

full-time employees. So think about um

like getting an Uber today. The

transaction cost the kind of effort of

getting an Uber is just one button on

your phone. I think the same thing will

be in the future to to get a developer

whether it's a software agent or another

human being. Uh it'll be just like one

button. I want this problem solved.

You'll be able to maybe your agent will

be able to go find and interview a lot

of different people or agents on the

internet and be able to find the best uh

thing to solve that problem. And um and

so you'll be able to like build

businesses really at the speed of light.

Now you know I I I talked about how kind

of application software goes to zero.

That doesn't mean that all software goes

to zero. Today you know rapid agent or

others the way it works is the agent

makes a piece of software the user uses

the software to solve problems. You can

think of those things as intermediate

steps. Instead,

agents can just solve problems.

And and for Replet, and I'm sure a lot

of other businesses to survive, at some

point, Replet needs to stop being

focused on making applications

and start being focused on solving

problems with software. So, I want to

leave ample time for for questions. So,

I'll I'll I'll end here and open it up.

>> My name is Chinat from Stanford. Nice to

meet you. My first question is in this

future do you see there potentially

humans engaging with multiple agents or

will there be a unilateral agent and if

in the case of like multiple agents um

how would we deal with the fragmentation

of like data memory and context across

all these different agents

>> I think multiple agents uh and and the

reason I think that's true is because

let's let's say I'm someone with true

unique domain expertise

uh let's say I'm I'm a lawyer who is top

in the world at solving certain um

cases that that are very rare. And so I

have this domain expertise that I'm not

going to share in the open source. I'm

not going to sell to scale AI so that

they can sell to to OpenAI or Google all

of those. I'm just going to keep this

resource to myself. But the way I would

monetize it instead of myself going and

selling my services directly, I would

like imbue this knowledge into an agent

that becomes this very specialized agent

in this very specialized domain and then

I can scale myself. uh and so so I think

I think people will be building these

agents to work on their behalf and then

um there's going to be agents that that

uh go out there and assembles these

teams of agents uh and then there's

going to be obviously software

development agents and maybe there's and

maybe you're running all all this

through chat GPT or whatever main

interface you have but I think it's

going to be a multi- aent world with

different contacts similar that we have

in the world today. When I go to a

lawyer, I need to give them my context.

So, uh, and maybe there are protocols

and this is why they talked about how

MCP really doesn't solve the agent to

Asian problem. I think there needs to be

more interesting protocols in this space

that and maybe this is a startup someone

builds.

>> Hi, thank you for the insightful talk.

My question is as follows. in the not so

far off future where we're going to have

AI systems that can automate most if not

all of meaningful physical and cognitive

tasks and there's increasing delegation

to agents that work on your behalf and

talk to other agents that are working on

other people's behalf then what is left

for humans to do or like what will our

human condition look like because our

physical and cognitive aspects can all

be done by intelligences

>> I I think it fundamentally

depends on your

worldview and belief of the limits of AI

versus the

uniqueness and premacy of what humans

can do. So it becomes a bit of a

religious discussion but my view is

there's something special about humans

and my view is that there's a

fundamental limitation with how we do AI

today and maybe this gets solved but AI

today can't truly generalize out of

distribution

everything AI can do h needs to be

represented in the data say I go back to

this example of this lawyer that is

expert in the world at very rare cases

uh again this is something that no one

else knows how to do uh or can do or

whenever there's like a truly novel

problem, truly novel case, you still

need human ingenuity

um to solve that problem. And so I think

humans will be more in the creative seat

and I think agents can be creative as

well but their type of creativity are

not net new knowledge. It's more like

about uh which is a lot of what

creativity is bringing a lot of

different things together. And so but

but this idea of like ideas become

wealth uh is um is what gets really

exciting about it is like people can

generate novel ideas and test them out

really quickly which you know I don't

think we're going to get to a to a point

where you can go tell an agent hey go

find me a you know business idea and go

test all of them. I don't think we'll

get there anytime soon. Thanks for your

talk. I've been following Replet for

many years and that's actually where I

learned how to code as well was on

Replet. So you mentioned the value of

clear thinking and ideas being the

future. Do you see this as an argument

more towards a favor of a liberal arts

critical thinking model of education

instead of a more STEM uh skills-based

focus?

>> I don't I don't think they're mutually

exclusive, but I do think that the

liberal arts will become more valuable.

I I think today engineers

tend to be uh a little more parochial

than they can afford to be in the future

because you know what I shot what I

showed with the kind of the model for

what the future company could look like.

Everyone becoming more of a generalist.

I think today engineers can afford to

like not understand even the business

they're in. A lot of engineers are just

focused on very narrow domains. Uh so I

think people need to have a more broaden

worldview and set of skills. So um but I

don't think they're mutually exclusive.

I think

you know being being scientifically

minded I think is going to be important.

>> Hi. Yeah. Uh so I wanted I was more

curious as to uh where in the tech stack

like is replet making a lot of progress

because as you said uh replet can do

tasks which for for one hour and so

given that like replet uses probably

closed source models which have no

access to pre-training and post-

trainining uh where in the text are you

making that like amazing uh kind of

innovation? that gets your models to

work auton autonomously for like an

hour.

>> It's what I was calling the habitat of

the model. So, you know, the um the

commercial models can train really great

models. They can train them to be as

autonomous as they as as possible to be

coherent over a long period of time. But

uh us or really any um agent company

needs to be able to provide the

infrastructure for for that agent to to

exist in. And so all these components

that I talked about. So one really

crucial thing about replet is this idea

of uh you could call it trans uh being

being transactional or atomic. every

mutation to the replet computer

environment

happen in sync with every other uh

component of the system. So right now in

replet if you go to your history you can

see previous checkpoints and you can

actually go to any one of them and

reboot the application in that state and

so we think that infrastructure is going

to be really crucial for how to make um

the models more more reliable. I think

there's a limit on how much the training

can increase reliability,

but I think the environment feedback and

the ability to try things really fast is

the way to get to the upper echelon of

reliability. So that's what we're

focused on.

>> Hi. So you talk about the generalist

employee and how that's the sort of

future of um companies. I I to I totally

agree with this vision, but where I find

myself stuck is finding roles today that

set me up for that kind of future. what

what kind of opportunities should we

look out for? What kind of positions

should we look out for in startups, in

companies that would prepare us with the

skills that are necessary in order to be

a generalist good employee five five

years down the line when that finally

becomes a thing. I know being a founder

is one option but

um not all of us want to take that

career plunge immediately. Uh some of us

want to work with other people, build

teamwork skills and learn all of those

other things as well. How do we go about

that? Jo join startups as early as

possible. Like obviously you can think

of it of it as um sort of exponentially

decaying uh curve where like being the

first being the founder you get the most

generalist experience being the you know

first employee and then by the time you

get to the hund I don't know like to the

maybe 100th employee you're sort of like

you're not getting as much of that

journalist experience but like just join

as early as you can depending on your

risk risk profile and and all of that.

But even like number 20 at a like a

series B company, I think you will get a

lot more experience than at a at a fang

or something like that. Even if you join

that startup, you need to be seeking

those generalist opportunities. So don't

sit there waiting for people to give you

tasks. Have that mindset of I'm waking

up in the morning. I'm not looking at a

to-do list. I'm looking at a mission.

and my mission is to make this company

succeed or be more valuable.

>> Um, hi, my name is Shivam. I also wanted

to ask about the one hour of autonomous

like agent development. Uh, specifically

like could you like elaborate a little

more on how like you and your team

approach how long of a time horizon is

worth pursuing as opposed to improving

reasoning for shorter time horizons. So,

so I think the what you're talking about

with shorter time horizons is more like

uh let's um let's work on reliability

um and then longer time horizons like

let's work on autonomy um removing the

human in the loop and the burden of the

human to continue to test and give

feedback. Um so we're doing both. When

I'm talking about reliability, this is

more investing in reasoning and more

investing in this parallel agent.

um trial and error that I was talking

about while we're calling sampling and

simulations.

And then for uh long horizon, it's more

about testing,

making sure that because as you go

longer, the there's like an there's a

like a gold drift. The the agent might

start doing things that you don't like,

but having those guard rail rails of of

testing along the way uh will make it so

that it stays more coherent uh over

time. And then as we collect more data

about what fails and what doesn't work,

you can either uh like go and fine-tune

that or you can just like continue to

improve the prompts uh and add more

guard guardrails to to make it better.

So I think both are important.

>> Hi, I'm Sophia. Um have been thankful

for your talk and I've been following

you met at AI for developers when you

were talking about ghostriter and the

work behind it. Um, but I'm curious to

hear more about how agents are kind of

over oversaturating

um, uh, certain set certain sectors and

um, whether or not that should kind of

you should consider that when you're uh,

working on them or joining a startup

that's working on something.

>> I think certainly software is like

really tricky. software engineering

agents. There's like there's a lot of

people that want to do that. And if

you're coming in late, you want to have

a truly novel idea to be able to like

compete there. But, you know, there's a

lot of things like who's who's building

the agent for HR or finance. Uh I know

one company's doing accounting. Uh

there's a lot of companies doing SDR for

whatever reason. That's very crowded.

What I would start with is what are you

interested in and what where do you have

domain knowledge? So the best way to

start an agent company is that if you

yourself

you're you yourself you're like a

compliance officer,

you start uh a a compliance officer or

you're passionate about compliance. I

don't know who's passionate about

compliance, but if you're passionate

about compliance, uh, go start an Asian

company because you're going to learn

the most about it and you're going to

have the most domain knowledge and

domain knowledge is the most important

thing to build an Asian company. Hey,

um, so if the cost of software and

building software is going to zero, then

by extension, the platforms which build

software like Replet,

like the value capture will be going

down to zero. So, how are you planning

to make money long term and how are you

going to compete with like the other

competitors like Bolton and lovable?

>> Yeah. Yeah. So, notice that I I said not

uh old software. I said like application

software specifically. So, I think

software will continue to run our lives

but a lot of it will be autonomous. So

for example, I build a lot of personal

software using Replet and a lot of it is

around managing my life and my family

and like you know uh doing a lot of

quantified self stuff a lot of like you

know data about my sleep and and and all

of that stuff and then I spent a lot of

time like plotting that data and doing

all of that stuff like instead I should

be able to tell replet agent here are my

goals you figure out what kind of

software that needs to that we built and

you figure out how to how to uh operate

it and you tell me what wearables I need

to buy and what um and and what do I

need to log in the morning, what do I

need to do and she'll be able to go make

the software

uh acquire the things that I need in my

home, what kind of sensors and then

solve the problem for me. I think I

think Replet needs to become a universal

problem solver for our company to

survive. And I think for a lot of the

others, you know, I I think it's

already, especially the companies that

you talk about in the prototyping space,

it's already getting really crowded

there. I think what replet where really

Replet excels today is the fact that

it's full stack. It can go from idea to

a deployed and scaled software.

>> Hi, uh my name is Emma and I'm really

intrigued by your vision of this future

where all code is written by agents. But

I'm also kind of concerned because there

is this kind of known problem where if

you train a generative model on data

that is generated by another model, you

get an issue of like accumulating error,

accumulating noise. So my question is in

this future where code is written by

agents, it's tested by agents, is

approved by agents, how do we kind of

prevent this exploding error problem

while still allowing these models to

grow and evolve? My bet is that pretty

soon we're going to move into more of

the alpha zero style of training where

um you have a more traditional LLM

that's trained on all of the internet.

Um but but then the way to train the

next generation of it would be to give

it a reinforcement learning environment

where uh it's generating a lot of

problems and doing like selfplay where

it's solving these problems getting

feedback on them and doing it in this

like massively parallel way. I think

this is how we're going to get the next

generation of software agents. It's not

going to be trained on human code

because like you said there's not going

to be human code and so we have to solve

this otherwise

we'll plateau very hard. Hi. Um, I'm

quite interested in some of the systems

report uh support required for these

agents. Um, and I find the universal

package manager that you've released and

your use of Nyx quite interesting. And

you mentioned this um copy on write

snapshotting and and uh uh forking and

merging. Uh and I'm working on a similar

thing.

>> Well, you should come work out.

>> Uh I was wondering if any of this is

publicly available or something. I think

you might be thinking about open

sourcing.

>> Um yeah, I mean we open sourced uh some

of our package manager work. We're big

contributors to Nexos. So we use Nex OS

which is a transactional operating

system generator is the best way I can I

can describe it and and possibly the

file system stuff will well at minimum

talk about it but this is like active

active work right now. Um but yeah come

like intern at Replet and learn all this

stuff and then go build it yourself.

>> Thank you. All right. Thank you

everyone.

Key Vocabulary

Vocabulary	Meanings
run /rʌn/ A1	verb - to move quickly using feet verb - to operate or manage
work /wɜːrk/ A1	verb - to do a job or task
write /raɪt/ A1	verb - to put words on paper or screen
make /meɪk/ A1	verb - to create or produce
use /juːz/ A1	verb - to employ or apply
new /nuː/ A1	adjective - not existing before
big /bɪg/ A1	adjective - large in size
future /ˈfjuːtʃər/ A2	noun - time that has not yet come
build /bɪld/ A2	verb - to construct or create
start /stɑːrt/ A2	verb - to begin something
important /ɪmˈpɔːrtənt/ A2	adjective - having great value or significance
easy /ˈiːzi/ A2	adjective - not difficult
hard /hɑːrd/ A2	adjective - difficult or firm
software /ˈsɔːftwɛr/ B1	noun - computer programs and applications
solve /sɒlv/ B1	verb - to find a solution to a problem
create /kriˈeɪt/ B1	verb - to make something new
change /tʃeɪndʒ/ B1	verb - to make something different noun - process of becoming different verb - to replace
useful /ˈjuːsfəl/ B1	adjective - helpful and practical
agent /ˈeɪdʒənt/ B2	noun - a person or thing that acts or does business
code /koʊd/ B2	noun - programming instructions verb - to write programming instructions