Vibe Coding: Everything You Need To Know — With Amjad Masad

Channel: Alex Kantrowitz

Published at: 2025-08-11

YouTube video id: dCZc2kwtUIg

Source: https://www.youtube.com/watch?v=dCZc2kwtUIg

Don't go into this thinking you can just
have prompt and have an application pop
up at the other end. At least set an
afternoon uh to to give it some good
effort and try to get like your first
app in. And once you do that, you just
get addicted. I have the stat here that
Replet uh has multiplied uh by its
revenue by 10x in less than 6 months to
100 million in annual recurring revenue.
Is that growth vibe coding or is that
growth AI coding?
>> Vibe coding. Is AI coding just a hobby
or the beginning of a technological
revolution that empowers everyone to
build? Our guest today, Amjad Msad, the
CEO of Replet, has some answers and
we're here in Replet headquarters in
Foster City to speak with him. Amjad,
great to see you. Welcome to the show.
>> Thank you. I'm excited to be on the
show.
>> So, we're going to talk today about vibe
coding and AI coding, which are two
similar but different things. I first
wanted to speak with you about vibe
coding which is effectively you write a
prompt and then the AI goes ahead and
builds software for you. This is
something that replet enables. This is
something I've tried. What are some of
the use cases that you're finding people
are actually uh having effective uh
approaches with this? Like where are the
places where people are doing this?
Well,
>> there's like broadly three use cases. Um
one is personal life, family life. Uh,
so you know, for example, like a lot of
people like to do health tracking. I'm
going to track my sleep. I'm going to
pull in data from my Fitbit. I'm going
to like have the AI sort of process that
data. I'm going to have this app on my
phone that I use every day. Or I'm going
to build an educational app for my kid
to learn math or reading. or we're going
to have like someone built like a chore
hero for their family to like, you know,
have an iPad on the wall and like here's
who's doing the most chores and
gamifying their family life. Uh you'd be
surprised how popular this use case is.
And so, you know, uh in the in the niche
that I've always been in, which is uh
like creator tools, uh that there's
always been this idea of personal
software, malleable software. By the
way, this goes to the early computing
history. So, you know, for example, like
um Apple had this piece of software
called uh Hyperard. Uh Hyperard allowed
anyone to make personal software. You
know, there's Vidual Basic. It's been
attempted so many times, but for the
first time now, anyone can make
software. So, there's a lot of there's a
class of personal software. We have a
mobile app and you can use that to make
software. It's the most fun thing to do
is sit down with your kids, five, six,
seven years old, and just brainstorm
games and make games with them. So,
that's one bucket.
>> Wait, before we go to the next bucket, I
want to ask you a question. So, um, does
this say something about the software
industry that the software industry just
hasn't served so many use cases or are
these use cases noneconomic
or is it possible that people will build
things for their family and then next
thing you know, they can serve that mass
market and it becomes a business? It is
certainly uh you know there's certainly
a market there and you can certainly
make a lot of money from that.
>> Okay. Because when I think about like
this concept and we're going to get to
jobs but this concept that AI is going
to take our jobs to me it's like wait
there's so much left to build. If you
think just about what we have today and
maintaining that maybe it will but
there's so much that software has not
yet touched that it seems to me that
there's more opportunity out there than
people are
>> just to touch on your earlier question
and you tell me how deep you want to go
because I can talk about this for hours
but when in the early computing pioneers
uh they all had this idea that um
computers are this the thing that makes
computers special uh is this idea of
program programing programmability right
the moment we had a program programmable
machine uh that was first invented by uh
vonoman
um and it's the same architecture that
we use today the thinking was oh anyone
can use a computer to program to solve
problems to to build applications and
all of that
it it didn't get mass um consumer
adoption
and the reason is because coding is is
hard And so you you had um the Xerox
Park as a research in Palo Alto Palo
Alto research center. They developed a
GUI. One day they they invite this uh up
and cominging entrepreneur called Steve
Jobs. Steve Jobs looks at desktops,
menus, items and and he's like he has
the Apple 2. Obviously Apple 2 is also
still command line. You can write some
basic uh and he's like okay this is the
key to get mass consumer adoption of
computers. And so he copies what Xerox
had and he builds it into the Mac and
obviously later Windows and Microsoft
copies uh UI and then suddenly you
computers are usable by anyone and this
is amazing now like billions of people
use computers and now we have phones
based on the same idea. What we lost is
this idea that anyone can program a
computer. So that's something I've been
passionate about all my life is like
computers should fundamentally be
programmable and there's been a lot of
different iterations with visual
programming. We had the no code low code
revolution that happened like maybe you
know 10 years ago. I would say it never
reached the full potential.
>> It was more of a buzzword than reality.
But now
>> I think I think it is is a multi-billion
dollar market for sure but it's not a
trillion dollar market. And I think this
idea of like anyone can make software is
such a massive market.
>> Okay. Okay. So, bucket number two,
>> uh, bucket number two tends to be
entrepreneurs. And so, everyone in the
world has ideas. Uh, people build so
much domain knowledge about whatever
their their field of work, right? Uh, I
was hearing a story today of an Uber
driver that is starting to make an app
with Replet and the app is about
logistics. He was a truck driver before
and so he had domain knowledge about how
to uh manage fleets for example but he
never was able to to make it into a
software because he didn't have the
skill maybe he didn't have the capital
to go you know commission a contractor
to do it and suddenly he can do it. So,
you know, pick anyone on the street uh
and they all in whatever industry
they're in, they realize that there's uh
uh a need for a piece of software or
technology that no one has built because
they don't have that deep domain
knowledge. Um so we see entrepreneurs
from all walks of life. uh one of uh our
favorite one we talked about it publicly
uh uh on on our rapid social media
channels a doctor from the UK that he's
like you know there's all these apps
around managing doctor uh patient
relationships but they never it's not
fully integrated so you know you have
Zach do you can go make an appointment
but you know how do you manage your
prescriptions uh can I track my patient
over time their progress can I get
information from their uh Wi-Fi
connected scale from their Fitbit from
you know and so he built this
comprehensive platform uh he got quoted
by an agency £100,000 and he built it
less than 200 uh you know British pounds
>> um
>> not 200,000 200
>> 200 200 you know pounds
>> so this is stuff that's being vibe coded
effectively prompt in I want to build
this software
>> and then Replet will go build it
>> yeah and this is now a startup and we've
had startups start on replet
multi-million dollar uh revenue run
rate. Some of them have raised at like a
half a billion dollar valuation. So we
have all the way from small
entrepreneurs to startup venture scale
entrepreneurs. But this is this gets me
really excited because
America has always been about
entrepreneurship and this is really what
attracted me to this country. But
actually if you look at the stats
entrepreneurship over time uh although
we hear about what's happening in in the
Bay Area and Silicon Valley there's all
startups every day but the rest of the
country actually uh you know new firm
creation has been going down over the
past hundred years there was an uptick
during co where everyone's sitting at
home it's like I started my business
>> right exactly which was great but that
actually we had a regression to the mean
and I think with AI we're going to see
that explode again so that's the second
bucket entrepreneurs One more bucket.
>> Third one is people at companies like
this one. Uh so actually I'll give you a
story from our HR department. Um we have
a small HR department. Replet is kind of
a lean team. We're 80 people. Um and so
we have a lot of these SAS tools. We pay
tens hundreds of thousands of dollars to
to do every specific kind of function.
And sometimes they don't really fit our
use case. We think it's they're too
expensive. Uh so this HR person had a
need for an orc chart software uh that
can visualize the ORC chart that can you
know add remove people maintain a
history can look back and see what
happened what changes it it did and went
on the market and saw that uh none of
the software captured that exact bespoke
use case where she wanted to connect it
to our uh kind of more uh other HRISH
systems or databases. is and they were
they all needed you know uh they were
all very expensive and needed a lot of
IT support. So she went into rapid and
built it vibe coded it in 3 days and so
that meant that we have a system that
exactly fits our use case and that also
meant that we're not paying 10 20
$30,000 a year for a piece of SAS
software and that's happening across the
board. We see companies saving hundreds
of thousands of dollars replacing SAS
software with built-in with internally
built software.
>> Now, do you need to be someone with some
technical background or some technical
knowhow to be able to do this? Well,
because I'll give you an example. I
mentioned to you before we start
recording, I opened a Replet account
this week. I wanted to build a simple
choose your own adventure game. I think
it was called History Havoc where you
can work your way through different
history scenarios. Um, but it just
didn't get to the point where I wanted
it to be.
>> How long did you work on it?
>> So, I spent about an hour on it. Not a
lot of time. And I also, full
disclosure, I'm just on your starter
plan. I'm not paying yet.
>> Yeah.
>> Um,
>> but I could I couldn't get it to work. I
also tried to build this story tracker.
>> Yeah.
>> And it wasn't able to crawl the web the
way that I hoped it would. So it still
seems like this to a lot of people that
this is something that is helpful if
you're technical, you want to make a
prototype, but these use cases that
you're giving seem to be full-blown
companies or working pieces of software.
So explain that disconnect.
>> I think it it requires grit. Obviously
there's like stocasticity and in the
machine learning models.
>> So explain what that is.
um uh the same prompts can
uh put you on a path of success based on
randomness that's happening inside the
GPUs.
Uh there's this parameter in large
language models called temperature and
temperature is literally like how random
is the sampling of the words coming out
of the of the LLM. So the LM the way it
works it you know you give it a piece of
text and it tries to complete the the
next word the next token as we call it
and the way it happens it generates um a
lot of candidates so you know the red
red fox you know jumped slept whatever
but like jumped is the top one you know
it's the highest probability one that
that h you know the model have seen it
occur after the sentence and millions of
of cases
uh but you know you have the sampler and
could be uh randomizing what it picks
and that that randomization
makes it more creative. Uh there's also
inherent random uh randomization inside
the like the Nvidia chips or the GPUs.
Mhm.
>> So this style of software is unlike the
software the classic software where
everything is discrete input output
machine learning models have inherent
randomness and that's a feature not a
bug that creates creativity right so uh
some people sometimes get on a on a bad
luck with with a rap obviously trying to
mitigate a lot of these problems but I
would say it's also requires grit like
the g the Aim you just described
professional programmers coding might
take them a two days thing on replet you
can do it in two three four hours but it
it would require a little bit of grit.
So it's not magic and the skills you
were talking about the technical skills
although they're not required you can
build them up over time and our
environment kind of shows some of these
features as you're working with it. Um,
and so I I I would I would suggest to
people that don't go into this thinking
you can just have prompt and have an
application pop up at the other end. I
would say at least set an afternoon uh
to to give it some good effort and try
to get like your first app in and once
you do that you just get addicted. So
there's vibe coding which is again
prompt and then you make an app and then
you can um refine it with more English
and then there's AI coding uh where you
could basically have AI you know
complete your code big autocomplete. So
what do you think the opportunity is in
vibe coding versus AI coding and where
do you think the energy is in the AI
industry today?
I gave the analogy of the the history of
computing and I think it's a um very
suitable analogy for a lot of what we're
talking about. Uh early on in computing
we had the mainframes. So the main
frames really big roomsized computers.
Uh IBM used to make them, large
corporations and governments use them in
universities, but every day people
didn't have access to them until Apple
created the Apple 2 and that was the
first mass consumer market uh computer.
And since then we've had Windows and all
these uh devices. The
mainframe was already serving the
professionals needs, but it wasn't
serving the consumer needs. Now if you
look at the market for PCs versus the
professional workstations, Sun micros
systemystems all of that which used to
be the case uh the PC not only was a
much bigger market eventually it
subsumed
the uh the more professional grade
software and this is this is called the
disruption theory. um you know a lot of
your audience that might be into
business history or or theory uh Klay
Christensen used to be I think a Harvard
business uh school professor and he
wrote this book called the innovator's
dilemma and the idea is that a lot of
technology start at the lower end and
because their mass market appeal they
onboard a lot more users and customers
and over time they reach certain
economies of scale and they subsume even
the upper end of the uh of the of the
market. Currently the upper end of the
market is what you were talking about
with AI coding tools, right? So um
there's like 30 million developers all
over the world, maybe a little more now.
Um those are professional developers
that went to computer science uh classes
in in in college. They were trained for
four or five years and now they're um
you know working at companies. If you
make those developers 20, 30, 40% more
productive, you get depending on, you
know, if you're company of the size of
Google, you like billions of dollars
worth of productivity, right? So the
market is really obvious there. You can
go apply it and and get get it, but it's
a zero sum market. If you look at
copilot which is Microsoft's product
which was the first to market versus
cursor which is the more modern kind of
AI coding uh IDE
as cursor is eating market share you can
see it is almost exactly proportional to
copilot uh declining in usage so that's
a sign of a zero sum market it is very
lucrative and there's a lot more growth
to be had there but it is not this
fundamentally
revolution that we can be going through
where it's anyone can make software.
>> Let me let me ask it this way. Uh I have
the stat here that Replet uh has
multiplied uh by its revenue by 10x in
less than 6 months to 100 million in
annual recurring revenue. So is that
growth vibe coding or is that growth AI
coding?
>> VIP coding.
>> Really?
>> Yeah.
And is are these vibe coding programs or
these bespoke programs that people are
building with prompts are they in
production or are they mostly hobbies
that people fool around with?
>> Depends on um the first bucket is is
more hobby personal life. Second bucket
entrepreneurs
as you know most startups die. So most
startup ideas don't make it to fruition.
the 10% of startups that or small
businesses that get off the ground, they
get the most value out of Replet. Uh and
some of them are in production now. Um
you know, I've talked about uh a lot of
these stories, but you know, for
example, we have this uh creator, his
name is John Cheney. Uh he's a serial
entrepreneur. Used to take him many
months and hundreds of thousands of
dollars to build applications, and now
he can spin up a business and get to
million-dollar run rates in in the
matter of of weeks. obviously he has
experience like he he knows the formula
of what it means to be an entrepreneur
but people can learn that over time and
in terms of the um enterprise um you
know we have for example Zillow the CEO
of Zillow recently on New York Times
Dealbook talked about how everyone at
Zillow is using Replet to accelerate
product innovation because product
innovation no longer depends on
engineers. you can have product managers
do the entire iteration getting user
feedback even without going to the
engineers. So it it just like increases
we have Dualingo
um a bunch of these customers that are
really focused on innovating building
their second third product uh that are
now using replet for for a lot of these
use cases.
>> So is the use case that you build like a
prototype and then you get some feedback
and then if everything works out well
then you build into the product with
your like core engineers. That's one
that's one use case. Okay.
>> Uh
>> that's interesting.
>> Yeah, that's one use case. It's really
great. It it rapidly improves uh you
know the the the time to market. The
second use case is operations and
internal tools. Uh so for examples like
um you know Sears Home Services really
old company uh employs people that go
and like fix homes. uh and they had an
operations team that uh wanted to build
a lot of AI tools and software for their
field workers to be able to manage their
work and their earnings and all that but
their software was like this
100-year-old cobalt programs and the
engineers were kind of busy kind of
migrating that and improving that. So
the operations team started using replet
to spin up these AI applications that
are deployed used in productions by uh
those field workers every day to manage
their day and and and kind of design the
optimal routes to how to maximize their
earnings uh per day. So the operations
type use cases tend to be deployed
running in production.
>> Okay. So just so I'm clear, you're are
you also facilitating AI coding or is it
mostly that you turned Replet into a
vibe coding company.
>> My mission has always been about how do
you enable people to do uh this magical
thing that is creating software. It's
one of the most magical exciting
experiences you would ever have and um I
I was a founding engineer at code
academy and before that I built open
source tools to do that. Code Academy
taught millions and millions of people
how to code and we changed you know a
lot of lives. So the DNA of Replet has
always been about how do you make
programming more accessible. It was it
had like a more developer bent at some
point but because Replet is sort of
batteries included platform. We give you
the database. We give you the
authentication. We give you the uh the
deployment. We give you the scalability.
We give you all of that out of the box.
You don't have to go anywhere else to do
any of that. It always meant that the
people that are getting the most out of
it tend to be they're not they're not
not professional programmers although
professional programmers do use it. I
would say like that's 20% of the use
cases.
>> And the question is then do the people
using Replet then come for uh the people
who are those professional programmers.
There was a funny thing that happened. I
watched uh you have a talk at the
Semaphore tech event in San Francisco a
couple months ago and I tweeted
something that you said that in one year
or 18 months uh companies might be able
to run themselves without engineers.
>> And then somebody responded to me uh
with this meme where they said founders
in public AI is writing 99% of our code.
In 6 months we won't need any engineers.
Founders in the DMs uh does anyone know
a good React developer? $30,000 bonus
and I will name my firstborn son after
you. So can you explain that disconnect
between this view that engineers are
going away and this
>> still like very intense demand for
engineers in the market?
>> I never made the point that engineers
would go away. I make the point that
entrepreneurs can start businesses
without needing engineers and that we
already see that we already see you know
I meet YC companies and uh Y Combinator
is the most uh prestigious startup
accelerator in the in the world Bay Area
and in the past Y Combinator would
encourage you to go get a technical
co-founder but like we said there's so
many people with amazing ideas that
don't have a technical co-ounder vender
and so they're starting to get into YC
and what they tell us is we're just
going to build this thing on replet
we're going to see how far we can get
and they often got get really really far
now if you're building a venture scale
company and you want to like get to
hundreds of millions of dollars of
revenue and you want to you become
billion 10 billion hundred billion
dollar company you're going to have to
hire engineers but if you're trying to
build um a company that uh creates a
really great living for you even you
know you can potentially get rich from
it
you
I think we're almost there where you can
do it on your own without any developers
and so when I'm talking I'm talking to
our audience
>> right
>> as opposed to I'm not I'm not talking to
Microsoft or or Facebook they're not
going to replace developers anymore view
on developer productivity is that
developers are
much more impactful than they used to be
because a single developer can be so
highly leveraged these days and so yes
you want to find the best developers and
we're expanding the team but the the the
scale that is at today we would be 10x
that the number of people if we're a SAS
company 5 years ago
>> wow
>> to reach $100 million in run rate um you
know five years ago on average you would
have like 500 a lot of companies will
have thousand people
>> how many do you have 80.
>> Wow.
Okay. You know, it just makes me wonder
that as companies grow like this, what
the future is going to look like um from
the technical side and I'm curious, do
the folks who have technical abilities,
you know, let's say the economy expands
like this and everyone and their grandma
can build literally can build a company
using um AI tools. Do the technical
people then come in and sort of clean up
the problems? Are they your like cleanup
crew? I was reading this uh funny
article and uh publication called
Futurism. It says companies that tried
to save money with AI are now spending a
fortune hiring people to fix its
mistakes.
>> And it was about it wasn't about vibe
coding. It was actually about content
like content marketing where like your
your content marketing plan is just
filled with this like you know kind of
bland chatgpt generated copy and half
the time it says as an AI assistant this
is the message that I would use.
>> Google you'll see so many hits. Yeah.
>> So I am curious to to hear your
perspective on does does the technical
field end up becoming cleanup crews for
vibe coding gone wrong. Let me just tell
you where I think uh technical folks
have a um job security today. Um so I
think
if you're writing software for my Tesla,
I don't want you to be vibe coding. I
want you to write low-level verifiable
code. If you're writing code for uh
space shuttle, you're writing low-level
verifiable code. Um but also even I mean
those are life or death situations. So I
think we need we don't need VIP coding
there. We need more pre precision. Uh
but but even uh sort of large scale
platforms if you're building uh core
cloud component the storage or virtual
machine components on AWS or Google
cloud or Azure you want systems
engineers that understand distributed
systems understand how to uh create
failsafe systems at scale. So I think uh
engineers there have job security for
the foreseeable future right uh because
of the problem of stcasticity of these
models and and all of that you need you
need every line of code to be reviewed
and managed very carefully.
Uh now where I think AI is going to have
the most impact is on product and people
build building products they want to
iterate on it really quickly. they want
to um internal tools, people want to
replace all the mess of the SAS software
that we have today. Um so I think I
think that's happening. Now in terms of
the cleanup, I mean depends on where you
think AI is headed. Like do you think
that um AI is good at making software
but bad at maintaining it and it's going
to stay bad maintaining it, you know,
for the foreseeable future. If it's good
at making software, it must also be good
at refactoring software or testing
software, right? Actually, right now
it's pretty bad at testing software
because
there's this thing called reward
hacking. So, when you do reinforcement
learning uh over large models, uh you're
giving it a reward every time it does
the right thing. Reward hacking is the
way to so so the models become
incredibly goal focused. They want to
get that done, right? That's what RL
does. And often times what we see when
we try to get the models to test things,
it will uh start being corrupt in a way.
It will like change the test to fit the
mistakes it made or sometimes delete the
tests. It's really fascinating behavior
that uh actually Anthropic published
research on. So, but do you believe
that's going to be the case forever?
Obviously not. like I think over the the
next three or six months I I think we're
going to see uh machine learning models
being able to test and verify their
work.
>> Okay. So one of the biggest things that
this moment depends on is affordable
large language models coming from the
foundational companies. And that means,
you know, in layman speak, um if you're
going to want to build with AI code, uh
you have to actually have um the ability
to bring in models from an open AI or
anthropic that are going to generate
that code and not break the bank as you
do it. And we're still in this, you
know, fund VC funded or or investment
private market investment moment where
we don't really know the true cost of
these models. Um
>> meaning like the foundation model
companies might be losing money on those
and they are and the application
companies I don't think they on the
gross margin basis. I don't think they
are.
>> Right. But they're also training and
that's a lot of money. So they could
they're not profitable. They're losing
billions a year.
>> Of course. Yeah. Yeah.
>> And um there's been this thing that's
happened recently with I just want to
run it by you with both Replet and
Cursor where I think um end users have
seen pricing gone up. Um, Ed Zitron
wrote about this and I think it's a
pretty good piece talking about
effort-based pricing within Replet and
that is uh effectively a a different
pricing structure. We've seen um Replet
users talk about the fact that they're
actually paying a lot more for the same
services than they were previously. And
his theory is that OpenAI and Anthropic
found quiet ways to jack up their prices
for startups and we're beginning to see
the consequences cuz cursor had a
similar wrote
>> happen at Zitron.
>> Oh, okay.
>> Is that what's is that what's going on?
>> No, the prices haven't gone down and
that's the problem. So, we used to see
these, you know, we've seen token prices
come down 99% since uh since JPT, and
we've seen token prices come down
year-over-year. The thing that's a
little disturbing right now is that
token prices are not coming down. You
better believe that the unit economics
of the labs are getting better because
of economies of scale, because these
models are getting easier to optimize,
but they're actually um not reducing
prices. And so the concerning thing are
we reaching a steady state? Is there
price collusion? Is there uh now
oligopoly of few model companies that
are able to create these
state-of-the-art models and there's no p
downward pricing pressure right uh is
the are there investors
starting to demand better better
business fundamentals? I don't know
exactly what's happening. Um, we should
we should talk about the Chinese open
source models in a second because I
think that that will introduce an
interesting mix to to to this. Uh, but
it certainly is the case that we're not
seeing token prices go down. Um, the
reason the main reason we went to effort
based pricing
um is let me explain about effort based
pricing. So when we released replet
agent v1 version one uh of uh replet
agent would work for like 2 minutes at a
time. You would give it a message or go
try to do something for 2 minutes either
succeeds or fails you know gives you a
checkpoint uh commits the uh source code
and charges you 25 cents. Um and the the
reason it only worked for 2 minutes is
because the capabilities of the models
uh you know meant that it can only work
for that long.
Now models got better and we we we had
we knew that models are going to get
better and they're going to be able to
work for 10 15 minutes. And so with
version two of rapid agent started in
beta in in in February came out of beta
in April
the model would work for for 10 minutes
and so we can't charge 25 cents for like
a 10 minutes. So what we started to do
is came up with a heristics. Every nine
tool calls will do a checkpoint. And so
as it's working, you'll see it make a
checkpoint checkpoint check. That's a
hack, right? That often means that if
you make a small change that cost us 5
cents or whatever, you still you still
get 25 cents. But also, if you make a
big change, you might be costing us a
lot more than than what we charge you.
So it was it was really out of whack. Um
now, uh that was a hack and we need to
move to a place where we're charging the
user proportional to what what how much
the model's working and the cost on us.
And we think that's the best way to
create a a long-term sustainable
business. And um when the when those two
things are aligned also opens up new
opportunities where when we uh do
optimizations, we're always optimizing.
We actually we actually had like 20%
optimization on on cost recently. We
pass it straight to the user because
because now cost and and price are are
are tracking with each other. Um what
happened with our community? The first
thing that happened is there was a
sticker shock. So you're used to seeing
25 cents every 10 tool calls and
suddenly you're seeing um $15, you know,
or $2 after 15 minutes of work. So
that's one. Uh two, it's true for some
users who are really advanced, the cost
have gone up with for them because the
projects are bigger, the contact size is
bigger, their their workloads are
bigger. Um but early on in the project,
it's actually cheaper. You mentioned
that you worked for an hour. You didn't
have to sign up for the uh the core paid
the paid.
>> We give free users $3. So you work for
an hour on $3.
>> Not bad.
>> Yes. So
>> it's cheaper than a developer.
>> It's cheaper than a developer for sure.
Um and and so uh that being said, we we
recognize that on advanced users, it is
now it's almost there's a tax as as you
go on. And so we we're trying to
optimize the context window and make
sure that advanced users are not getting
um you know more expensive experience.
The other thing that happened is we
introduced thinking mode, reasoning mode
and we introduced like high power mode.
Um and and people are enabling those and
sometimes they forget them enabled and
now we actually start to hide it under
advanced like don't enable those unless
you know what you're doing and you want
more power and there's like a 5x
multiplier on uh on so so a lot of
people are enabling those getting these
large checkpoints and we're like we put
out content we put out a video we put
out some documentation or blog post
here's when to use reasoning mode and
you should you should always have it on.
Um so just describing all of that that's
happening there's a there's a macro
trend in in the application space where
a lot of companies were subsidizing the
cost of of uh like a lot of companies
were paying money more money on thropic
and open eye than they they were making.
>> Was that were you doing that? We on V1,
no. On V2, yes, because the pricing
model was out of whack with how we're
charging. Um, actually the median cost
per checkpoint kind of went up only a
little bit. So on the lower end, we're
charging user less uh right now. Um, but
it used to be that on the lower end,
we're charging users more. On the upper
end, we're charging users uh less. So
now it's more proportional, more fair
for for both. Um, and so now we have
solid business fundamentals that allows
us to grow. And I've been talking about
how Replet has been my mission, my
passion for, you know, 8 8 nine years as
a company, 15 years as a like side
project and and a vision. And we're not
trying to, you know, rapidly expand
revenue while losing money in order to
to flip this company to sell it. you
know, we've seen all these acquisitions
or raised like the next big round. We're
really trying to build a business for
the long term and Replet is made of all
these different components. So, we have
cost not just on AI, we have cost of,
you know, traditional compute, CPUs,
storage, databases,
uh all of that stuff. So, kind of to
summarize, you know, I've talked a lot
about what was happening specifically in
Replet. I don't know what's happening in
cursor. Uh I think for sure that their
situation is like a little different
because they uh they their dynamics is I
think they actually did raise prices uh
for the uh you should talk to them but
but I think it's like a little different
dynamic than than what happened at
Replet. Um
to summarize there is a concerning trend
where token prices are not going down.
Uh, is that going to be the case for the
future? Because that sucks because we
want to be able to use more tokens to
create more intelligence to be able to
create better applications for users.
Is that going to be the trend forever?
Are we reaching a steady state? In
cloud, for example, we kind of reach a
steady state. When you have an monopoly,
there's no uh pricing pressure. But you
when you also have an oligopoly they
uh not intentionally without talking
start colluding
uh you know because it's it's like a
market dynamic where it's like if you
don't lower real price I'm not going to
lower my price. It's not in our sense of
as a whole because we own 25% each of
the market. Right.
>> Okay. I do want to ask you about
something that you didn't mention when
you looked at the different factors for
why prices might not be going down.
There might be investor pressure. Uh
there might have been this equilibrium
reached. Or is it possible that these
models have just gotten so big and
expensive to run that the fundamental
economics of AI are just not working? So
explain why.
>> Um uh you can surmise the bigness of the
models based on speed token token
throughput.
It's not perfect but but if you remember
GPT4.5
GPT4.5
uh was an experimental model from
OpenAI. It was the idea let's train
train a training parameter dense model
meaning it is not sparse meaning the all
the token all the neurons are activated
on every request and it was so slow it's
really hard to run these things. the new
models, even when they're big, they're
sparse models. They're called, mixture
of experts. So in every request, there's
a router layer that takes it to the
expert part of the circuit in order to
answer that question. So you know, there
are models with trillion parameters, but
any given request is 32 billion active.
And that's like a kind of small model.
Um, and what we're seeing based on speed
and things like that, it's actually
probably the models are getting more
efficient. I mean, Deepseek showed that
the models are getting more efficient
and if you know, Deepseek open source
was able to make it, you better believe
that the labs are also making more
efficient.
>> Okay, I do want to speak with you about
DeepS and Kimmy K2 and other Chinese
models. So, let's do that when we come
back from the break right after this.
>> Cool. And we're back here on Big
Technology Podcast with Amjad Msad, the
CEO of Replet, talking about all things
AI, code, vibe coding, and now let's
talk about these Chinese models. So,
this episode will air a couple weeks
after the emergence of Kimmy K2, but
we're talking about Kimmy K2, which is
another Chinese model. And of course,
this deepseek moment was a big moment
where we found out that this seeming uh
small hedge fund in China with some GPUs
was able to engineer a more efficient
model. That story will be debated about
what actually happened for a long time.
Um, but let me ask you one influence of
deep sea question and then we'll get
into the others and Kim K2.
So you mentioned before the break that
western models have um taken after
deepseek. So, do you think they learned
what DeepS did and sort of put those new
innovations into play in their own
models or was that coming anyway?
>> From what you've what we've seen from
the Twitter sphere is that it seemed
like there were some surprises uh cuz
researchers just talk a lot. It seems
like there were some fundamental
innovations from the deepseek models
that that weren't known in the in the
West.
>> But have they implemented those now? And
that's probably why we're getting more
>> models like um
>> yes I I'm sure like the models are
getting more powerful without going
getting slower.
>> All right. So tell me about Kimmy K2.
When Anthropic came out with uh Sonnet
Claude 3.5 that was a u fundamental
shift in the industry where uh the
models got a lot better at coding and
suddenly instead of making small
snippets of change uh sonnet could could
generate entire files and enabled things
like cursor composer where it's it was a
start of vibe coding where you can put
in a prompt and generate entire files
and all of that or generate large edits.
Then uh sonnet uh 3.5 v2 was the first
model. It was a computer use model was
the first model where you could sense
that there's agentic true agentic
behavior. I don't know what they did.
They cracked RL whatever happened there.
You can give a model a VM and it can
give it
>> virtual machine
>> virtual machine. You can give it an
objective
>> and it can sleuth around in the virtual
machine look at the files do run some
commands and
>> um and then write a program test it and
um and then solve solve a problem. that
experience there's a benchmark called
SweetBench software engineering uh bench
um and you start seeing the score going
up dramatically. I don't know I think we
were at like 10%
last year and now we're at like 70% and
80%. 80%
>> world class coding. Um it's such it's
the the interesting thing about sweet
bench is not just coding because there
are other benchmark that that just do
like the code generation right sweet
bench I think the harder thing about it
is the agentic workflow is writing the
code testing it running commands finding
files understanding files and this this
stuff was like a huge
um jump that happened with with sonnet
uh 3.5 v2 then 3.7 then 4.0 and they've,
you know, kudos to to Anthropic. They've
been able to make create a lead that
hasn't been bridged by the other labs.
Gemini is getting there on the Agentic
stuff, but I would say OpenAI kind of
lagged behind. Uh 03 has some
interesting Aenteic capabilities,
especially around deep research, but it
it it hasn't been uh as good as the
other models on this agentic stuff. I
mean they did some interesting stuff
with codeex. I don't know if those
models are are in the API but everyone
is using uh claude for the agent coding
experience. The interesting thing about
Kimmy K2 I would say is they c caught up
not to clot sonic 4.0 perhaps clot sonic
3.7 at least that's the vibes right now
um before the other labs.
>> Wow.
You know I think that's really under
reportported. Again this is vibes.
Everyone's starting trying to figure it
out. But it looks like it has a really
good sweep bench. It is doing 65 on
sweep bench. Sonnet is 72 72. If you do
sampling, which is you for every step
you ask the model to generate any number
of solutions, you can get up to 72%. It
can be competitive with with sonnet.
>> And this is with export controls.
>> Yes. And I think in the paper uh they
talk about
the solution is scaling uh reinforcement
learning. We also saw that with Gro 4.
Gro 4 spent as much on reinforcement
learning as they spent on on
pre-training which is unheard of. But
even but that's an important point
because with that big spend on
reinforcement learning Grock is a
competitive model but they spent a m
billions billions hundreds of millions
on RL
>> I don't know
>> which is this goal setting form of
training and it's not like it's a new
category so it shows there are some
limits and
>> Xi is an amazing team uh and they
they've been able to achieve so much in
so little time but it's also well known
in the industry that they're computer
inefficient they're so comput
that that they're throwing computer at
the other the problem in many ways.
Yeah.
>> So what is the significance that Kimmy
K2 is now as good as some of these
anthropic models? a small research lab.
I think the rumor is like the 200
people. Again, there's expert controls
as well. Uh was able to figure out um
how to catch up to near state-of-the-art
agentic coding models before
big western labs that are highly
capitalized, a lot a lot more
researchers was able to. And does that
mean then that they can undercut them on
price or
>> So let's see.
>> Right. So let's see.
>> Are you going to integrate Kim and K2?
And
>> we're looking at it. We're looking at
it. There's a lot of
>> So far we're impressed.
>> Okay.
>> So far we're very impressed.
>> So I mean look these things sometimes
they overfit to certain things and I
would say it's like requires a month
from the entire community to kind of
like really have consensus over like
whether the model is really great. Um
and similarly with rock for I think a
lot of people are playing with it. Um
but but my sense is that it is good
enough and again the economics are so
good that
you can expend more tokens
uh to get more intelligence.
Though um it is not at the frontier but
it is near frontier but given that it's
cheap and fast enough you can spend more
tokens that that creates some more
interesting potential for us to create
new capabilities in our platform because
it is cheap and fast.
>> How much cheaper is it than the
anthropic models? And I um am bad at
this, but like I would say I don't 1/4th
maybe. Um
>> Oh yeah, that's that's on the official
API,
>> perhaps more even. I I forgot. Maybe you
can look it up after the show.
>> We're going to have to re this show is
going to come it will come a couple
weeks after we record, but we'll have to
release this segment early because
that's
>> Yeah.
>> astonishing. I want one more question
about anthropic. Uh I can vibe code and
claude. uh and do it all the time and um
they also have this claude code product
where people are you know writing
prompts getting code are they your
competitor long term or how do you see
them on that front because that's the
question is eventually do the labs just
subsume everything else that's built on
top of it
>> I think the question is is for them
right like you should ask I know you're
going to talk to to to Dario you should
ask the question
>> listeners viewers this will air a week
after Daario but I'm about to after this
go in and speak with him. So,
>> you might see this question a week
earlier.
>> Yeah. So, look, we're we're uh committed
to our relationship with Anthropic.
They're a great company to work with. Uh
we have a great partnership. Um and it
it's not like we uh we didn't anticipate
them wanting to build products in
addition to to the models. Every model
company is building products right now.
uh the thing that they're going to have
to manage is their their pricing. If
they're if they're going to compete by
undercutting everyone on price, they're
going to destroy the ecosystem,
right? Uh I think Replet right now is um
uh has the advantage of this platform
that we built over eight years that it's
going to take a lot of blood, sweat and
tears to build and also the user
experience that is focused on on on that
sort of non-technical user and um like
we really care about this this idea of
empowerment. Right now, cloud code is is
used by developers and loved by
developers and I think they're competing
head-to-head with cursor windsurf and
those kind of products. Um whether
they're going to move into our space
again, you should you should ask them
about that. But I think a more
interesting question um how
how uh how do they want to nurture the
ecosystem
um versus just go go and because they
can compete on price they can steamroll
everyone
>> right I mean cloud code is the max
package is $200 a month and you see
developers getting thousands of dollars
of API value out of that
>> not you must notice this
>> yeah not good for the eosystem system.
>> I don't think so.
>> Why?
>> Because
again, you're you're competing on on
price, not how good the product is. And
there's a uh there's a price at which
maybe the quality doesn't matter as much
as how many tokens I'm getting. Although
Cloud Code is a really good product, but
but then, you know, Cursor, no matter
how good they make the product, they're
still going to be more expensive and at
a disadvantage. And and people are like,
well, you know, I really like cursor,
but like I can get 10x more value out of
clot code and so the marginal uh gain in
product quality will not matter as much,
>> right?
>> And that will that will destroy the
ecosystem.
>> Fascinating. I mean, I think that this
question is just one small question or
one version of a big question we're
going to be asking as these AI models
get bigger and better and more
intelligent. So I want to I want to
spend the rest of our time talking about
some philosophical questions if that's
okay with you.
>> Sure.
>> Um there's this idea that um the AI
research houses want to use the code
that they generate to sort of or these
coding applications to speed up the
development of the next model um and
compress the time it takes to get better
models. People call it an intelligence
explosion or things of that nature. Do
you see that as feasible and is that
something we should want?
>> So, uh you should think about what are
the limiting factors to uh the next
version of a model. What are the uh
bottlenecks? Where's that innovation
need needs to happen? I can think of a
few few areas. Um one is is research. Uh
so this is algorithmic
uh research like figuring out the next
algorithm next improvement in and um in
in in in training algorithm and in
inference algorithm whatever it is. Uh
and then systems engineering these
training runs are massive that requires
a lot of interesting distributed uh
systems engineering. Um will
uh AI coding help with AI research on
the margins? Perhaps they can like they
can spin up Python notebooks faster.
Uh I don't think it's that impactful
like the models can't do AI research can
come up with ideas and test them really
quickly. Will it help with distributed
systems?
Perhaps it is not as impactful right now
on writing Rust code or C or go whatever
as it is on JavaScript and Python and
higher level languages. And like I said,
it requires a little more precision and
uh better system design to and that the
bottleneck to really good distributed
systems is is design and not like the
the amount of number of codes you can
generate which is more true on the
product side. On the product side,
you're just you need to generate tons of
CSS and JavaScript and try a lot of
things and delete a lot of things and
iterate and do AB tests and all of that
stuff. So like volume of code is
important there. I would say on the on
the backend distributed systems I don't
think volumes of code is. So I'm
reasoning in real time now and I guess
my answer would be I don't think
>> it's going to have anything more uh than
uh you know uh
you know marginal improvement on on
speed to to the next model.
>> All right. I guess that makes me rest a
little easier then. Um, by the way, just
on a uh, you know, you speak with a lot
of people in the AI industry. Of all the
economic activity in the AI industry
today, how much of it do you think is
code?
>> Just someone uh, someone actually made
that slide that's been going around and
I think it was something like 1.1
billion of ARR is in the uh, AI coding
and VI coding space.
>> Okay. So, it's actually kind of small
compared to like the total
>> well so revenue.
>> Yeah. So, Anthropic has four$4 billion,
>> right?
uh let's say
>> yeah $4 billion ARR let's say um they
have also have their own products their
own coding products I don't know let's
say 1.5 billion is is off of that is is
AI coding it's substantial but it is not
the entire thing
>> okay
>> uh but but then you have $10 billion of
AR on on on on open AI side and that's
more consumer
>> now on the rush to artificial general
intelligence which we've talked a little
bit about um do you think Silicon Valley
is the one that should sort of possess
this or be the one that controls it. I
mean, there it's an interesting place.
There's a lot of kooky ideas here and it
seems like if this is possible, it's
going to be something that's controlled
by or owned by one or more of the labs
here. Is that good? assuming it'll
happen and assuming one company will
reach there first and have some kind of
advantage or monopoly over AGI which I'm
not entirely sure I agree with these
assumption but if you want to make if
you want me to make these assumption and
then answer the question I'd be happy to
but I just want to make it clear that
>> yeah let's make those assumptions okay I
know there's a lot of things that need
to happen in order to get there
>> yeah I might have some fundamental
disagreement with this assumptions but
let's
>> talk through the disagreement I I don't
think AGI is any point in time for one.
Uh and I think there's going to be right
now the uh the distance between any lab
is is just an order of few months on
anything that really matters. You know,
between 01 preview and and deepseek was
like two three months. um between I mean
the biggest one was was this Kimmy K2
one that we just talked about that that
was like maybe nine months or something
like that but it's still sub one year
>> um and so whomever reaches AGI first
they're not going to go into
intelligence explosion and and just like
suddenly you know super intelligence
gets born people you know other labs
will like catch up really quickly and
and then you know there's going to be a
lot a lot of models I don't think it's
going to look that different from the
ecosystem that we have today and if you
assume that AGI will actually have an
impact on model development through
research and speed of development then
everyone will get the benefit of that as
well and so actually you might have get
even more competition once you once you
have AGI so I don't think it's going to
be a monolith
>> okay but if it is
>> okay if it is uh is it would I want
Silicon Valley uh I guess it's like a
moral uh Yeah, philosophical
>> philosophical question. I wouldn't want
any human being to we're all fallible.
That's why markets work. That's why um
that's how a human society evolved over
time. It is, you know, Darwinian
evolution and uh free market capitalism.
It's all based on competition and um and
and and the idea that like one system
would be this this monolith controlled
by one human being. We've seen disasters
and massive human suffering happen when
there's this top-down sort of leviathan
type thing whether it is uh in in in
Soviet Russia with with all the deaths
that happened there or or um in uh China
or whatever. And oftentimes
like in in as I understand it in the in
the Soviet era they they had this kooky
idea about uh evolution I think what was
it called? Um lenko lucenism or
something.
>> I'm not familiar but I'd love to hear
the explanation.
>> Yeah. So basically they had they thought
that evolution is this bourgeoa idea.
You know communism has this this idea is
like anything that's you know high class
bourgeoa is like wrong. And so this had
this ideological view on how evolution
works or should work that led them to do
agriculture in the wrong way and led to
famine and and that sort of thing. And
like um and so uh often times they do
kill people and cause mass suffering,
mass poverty. uh even if they don't
intend even if like outside of the
gulaks and all the other uh oppressive
uh explicitly oppressive system those
systems are inefficient because they
they have these wrong ideas and there's
no competitive pressure to have better
ideas
um and so that's fundamentally
broken
um static system that doesn't improve
like competitive systems and I think if
we or have a super intelligent
uh monolith controlled by a single
company or single human being. Uh it's
bad. It's fundamentally really bad. I
agree. All right, last question for you.
Uh we're seeing a lot more AI love bots
uh come out. Is that a good thing or a
bad thing that people are going to fall
in love with AI more often?
>> It's a bad thing. um like app priority
bad thing like um the
the the reason humanity grew and and and
flourished and all of that is because we
have babies
and uh anything that that you know you
know takes away from that uh especially
given the fertility rate is so low right
now is is is is will will potentially
lead to really massive problems
Especially since capitalism is based on
large uh middle class consumerism like
the the the the the current
instantiation of how the economy work
requires that uh requires taxpayers to
fund social security and like elder care
and and all of that. The welfare state
is based on this large young population.
And when that starts to collapse, you're
going to have, you know, massive
instability in these in these systems.
So even if you know, humanity doesn't go
extinct like like Elon would say,
although Elon is is is the first person
to create a really interesting mass
market companion, I think, right now,
>> interesting is a fun word for it.
>> Um, it looks like it's really
compelling. I see people, you know,
right now talking about on X so much and
looks
>> it's got some work. Yeah. But it is
these type of things are going to
definitely become real partners to
people. U people when this technology
has been bad or hardly workable have
gotten married to them
>> right
>> before LLMs. So it's going to happen
again and and in greater numbers.
>> Hey the question is
I wrote this uh I used to do like more
creative creative writing. Um I I wrote
this uh this uh essay on the hyperreal.
So I think it is like French
postmodernist
um
theorists like Bulgiard uh wrote about
this this concept of the hyper
hyperreal. Uh and the idea is like we
have reality like you and I are
interacting right now and then you have
media created realities and the reason
sometimes it is hyper real it is more
intense than reality itself and more
enticing than reality itself. So uh you
know even in real things you know for
example um when you when you get a when
you eat like a I don't know Twinkie or
something like that like fatty salty
sweetie kind of a snack it is like it it
is not like a piece of chicken or beef
or whatever it is uh it is this
hyperreal thing. It like hyperengages
your senses and it makes you addicted to
it. And similarly, social media is
hyperreal in a sense that I can go get
go there and get get a lot of social
interaction, tweet something, get
hundreds of likes, and it's like much
easier than going out in the wild and
like uh finding 100 people that could
like me,
>> right? And so we have these um
technologies that are and the market
around it that that is bootstrapped to
make us addicted uh uh because they're
so so much more enticing and loweffort
than uh the reality that we know and and
experience dayto-day.
And I I think that that is a huge danger
uh for for the existence and evolution
and and longevity of of human
civilization.
Uh and uh and I think it is uh you know
I talked about how good free markets
are, how important how how competition
is important. This is one thing that
capitalism is so
um adversarial to humans at right and
and so I don't have a solution for it. I
think in the past the solution was
religion for example in like Islam you
can't depict humans or animals in art.
That's why in Islam the the art uh
became more uh geometric and if you go
go you know visit like the mosques or
whatever they have like all this
geometry that or like calligraphy that's
really interesting um and I think part
of the the idea there is is uh is is I
think the hyper real like if the the
ultimate expression of a um a something
so enticing is a virtual being like
we're we're seeing right now. And I'm
not saying like, you know, Islam had
like the the foresight or whatever, but
I think it's, you know, religions used
to have this built-in mechanism to
protect against these predatory
um
sort of um consumer products. Uh and I I
I wouldn't know how to solve it in the
future, but perhaps it is it is
potentially societal,
maybe governmental. I'm always kind of
skeptical of that or um or religious uh
kind of protection.
>> We're going to need something.
>> Yeah.
>> So, Lord help us.
>> I'm Chad. Great to see you. Thanks so
much for coming on the show.
>> My pleasure. All right, everybody. Thank
you so much for listening and watching.
We'll be back on Friday to break down
the week's news. Until then, we'll see
you next time on Big Technology Podcast.