spent a long time at Intel.

>> Yeah.

>> And uh

>> only 34 years.

>> 34 years.

>> Yeah.

>> Probably one of the greatest American

companies uh ever. And then absolutely

went off the rails and got absolutely

demolished by Nvidia, TSMC,

uh and I guess Apple to a certain

extent. So you had this incredible Intel

inside moment. We bought our computers

based on, you know, hey, the Pentium and

that sound

>> Intel inside, baby. Intel insideum.

And so, let's talk about how things went

wrong. What went right and then

>> how did it and and you were there for a

long time. You took a break and then you

came back. But there seemed to be have

been some critical mistakes that we can

learn from. So, let's just embrace it

and go right into it. tremendous success

as an American company coming back now I

think uh reasonably but when you when we

look back on it and we do our

post-mortem what were the mistakes a and

what would we change in terms of the

direction of that company

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>> Having spent so much of my life there,

you know, I view it. I joined when I was

18. I went through puberty at Intel,

right? I joke, right? I was just like,

you know, I am so early. uh Grove,

Noise, Bar uh Barrett, right? Uh and uh

uh you know, they they were the people I

grew up at, right? You know, so on. They

were my mentors. They were the people I

adored uh for it and they were deeply

technical.

>> Andy Grove,

>> Andy Grove, Gordon Moore, Bob, you know,

co-inventor, you know, these were deeply

technical leaders. I remember when I

joined the executive staff for the first

time, you know, there was, you know,

probably 15 of the 20 people that were

in the room were PhDs, right? You know,

it was just that technical. And, you

know, I view one of the things that went

off the rail was when it started to be

run by business people

>> as opposed to technical,

>> the bean counters, the finance people.

>> Yeah. And you know when I became uh CEO

uh in 2001 that was the first technical

leader in essentially 15 years

>> right you know associated with it you

know and if you have a business leader

who does he promote business leaders and

you know right you know so I think one

of the fundamental things is and you

know as you look at the great technology

companies uh today you know they're

deeply technical

>> and founderled typically

>> you know and even if they're not you

know Satcha is not a founder no right?

You know, Sundar is not a founder as

well, but they're deeply technical

individuals. And when you're making

these hardcore technical, you know,

decisions that affect billions of

dollars, you don't do that through a

spreadsheet. That's a lousy investment,

right? Unless the technology trends make

it the right investment. And I think

that's one of the fundamental things.

And obviously you know in the five years

uh five six years before I came back you

know Intel gave $100 billion to

shareholders.

>> Oh the dividends

>> and stock buybacks

>> a hundred. What I wouldn't have done for

another hundred billion dollar on the uh

what would you have done? You probably

would have made chips for iPhone which

Intel passed on. Yeah.

>> Yeah. you know, but you know, it hadn't

built a new factory in a decade when I

got there. It's like, you know, how can

you not be building? How could you not

buy EUV machines? You know, there's just

all of these things, you know, that you

would only do as a technologist because

the economics behind them by themselves

were not good.

>> So, you know, it's getting back to the

core of technology to me that was, you

know, the fundamental thing. You know,

you make good decisions, you make bad

decisions as leaders. Every business uh

does that uh as they go along but uh you

know fundamentally this is a technology

business and you need technologists

running technology uh that then hires

technologists that are sitting at the

staff that then hire the best

technologist you know you know

>> and take big swings at you know

categories that could matter in the

future like skating to where the puck's

going. If you look at Apple, they did

the same thing for the past 15 years,

buying back the stock, tremendous amount

of dividends. They're the largest holder

of capital of any company, I believe, to

this date. And what if what companies do

they buy? They buy little tiny

acquisitions on the margins. I think the

largest ones was was Beats cuz they

wanted to get inroads into, you know,

certain uh demographic segments like in

the Android space that they couldn't get

into. But my god, what a colossal waste

of time. Like you said, they could have

done so many amazing things. Tell me

about Steve Jobs

in 2008, 2009 deciding, I think we're

going to make our own silicon and that

impact because was that a covert product

project or did you guys know he was

doing that? Did he inform you?

>> Well, that seemed to be another one of

those forks in the road. Yeah,

>> you know, Steve was an incredible

leader. You know, he was also a ruthless

leader, right? You know, very difficult.

you know, read Walder Isaxson's book on

him uh as well. I had many many

conversations with Steve over the years

uh you know, for it. Um but you know,

when they moved to Intel and the

Centrino chip, it was a big deal.

>> Yeah.

>> Right. And they were putting

extraordinary demands on Intel. You

know, make the chip smaller, drive lower

power. They're demanding uh customer.

And when he was no longer convinced that

we could continue to do that, you know,

he started the project,

>> right? You know, and if you remember

what was it, you know, you know, uh, P

semi, you know, they acquired some small

companies, started to build some

competency, but, you know, they did a

few little chips internally. It wasn't a

big deal and then the little chips got a

little bit bigger, you know, and Steve

was a master of this, you know, just

starting, you know, these small efforts

to build core competence inside the

company. Uh I remember when we had the

first conversation with Steve about uh

porting the uh operating system to the

Intel chip from the power chip that they

were running on before they moved to

Intel. And we were quite proud of the

silicon software competencies that we

had and compilers and operating systems,

you know. So Steve, we'll help you port

the operating system to the x86. And I

remember that Steve said, "I've been

working on that for the last four

releases."

>> He had been preparing the core

technologies inside of Apple for

something that might happen uh in the

future, you know, and he was already,

you know, to me, I just remember I was

just shocked. I, you know, I've ported

the last four releases to the x86. I

think we got this.

>> Yeah.

>> Right. You know, it was that kind of

thing. And that's how they got into the

semiconductor, you know, doing their own

semiconductor. Hm. I'm not sure I can

rely on Intel to be that much ahead of

the industry and I can start optimizing

the system design with the silicon

design as opposed to relying on one

that's been somewhat optimized for a

Windows environment versus an iOS

environment, you know, in their uh

operating system. And you know, it was

just, you know, uh, you know, it was

never that kind of thing. They sort of

said, you know, right, you failed as a

supplier. No, I can supply myself

better.

>> Yeah. and Jensen uh decides, hey, he's

going to go all in making these video

cards and talk about just incredible

uh serendipity that these happen to be

also very applicable for cryptocurrency

and running these AI jobs.

>> Yeah.

>> Was that luck or skill or combination of

both there? Well, you know, when you

think about that progression, you know,

Jensen, he was just building high

performance computers, you know,

throughput machines. You know, when we

were at the height of our strength on

CPUs, uh, at Intel, we sort of scoffed

at his machines. Yeah.

>> Right. You like, oh, that's a graphic

machine. You, you know, there's some

gamers who want to use that kind of

stuff, right? You know, it was always

the big CPU and those little GPUs. But

when they started to build a real

software stack, Yes. with it, right? You

know, sort of, okay, this CUDA thing and

SIMT as a technology, you know, uh, you

know, uh, multi-threading and so on. And

it just sort of kept getting a little

bit better and a little bit better and

it was a little bit jobslike in that

way. You know, we're just making it

better every release and it's becoming

more robust and all of a sudden, you

know, the crazy, you know, uh, Japanese

HPC guys said, "Hey, we could take those

graphics cards and maybe start using

them in HPC." H,

>> right? you know, and that was sort of

defining moment where it wasn't just

about doing graphics anymore. This was a

more computationally dense platform to

start attacking some of the world's most

interesting workloads. And I think

Jensen would agree that was a defining

moment and them sort of saying, "Oh,

these aren't just graphics cards

anymore. You know, these are

generalpurpose computing devices that

can start applying to these other uh

workloads." And you know AI was you know

had gone through what its fifth nuclear

winter by that point. We're just like

man you know you know this is never

going to matter right we're never going

to you know get the breakthroughs but

the community around it was continuing

to develop uh you know for it and the

CUDA software kept getting better uh

generation by generation and uh you know

I had a project at Intel Larabe right

where we were trying to take the x86 and

essentially do the same thing right you

know for it and you know in my first

departure from Intel the project was

killed a week after I left

>> huh

>> and the world would have been so much

different right I

>> I mean it really I think it's

illustrative of illustrative of what

continuous innovation taking some risks

and doing that fundamental research and

the compounding power of technology

because I think it was William Gibson

who said the street finds its own use

for technology like

Nvidia did not predict that this Bitcoin

project would take over and that this

would be the best way to do those

computations, nor did they anticipate, I

think, you know, that AI would take off,

but because it was the best solution,

the hacker community could kind of

figure that out. Well, as we wrap on the

Intel portion of your uh career, um,

okay, Apple Silicon, that's one. Uh, and

then you have Nvidia, and then you have

this Taiwanese company, uh, that starts

making, you know, really great at

fabricating the these chips. Um and

Intel missed that as well. Yeah. And and

maybe you could talk a little bit about

TSMC and their surging and we can even

get into a little bit of the the

politics of it now and then we'll get

into some of these AI chips and venture

investing. You know the thing with TSMC

was they started with a vision of

foundry

>> right you know they were going to become

the factory for the industry and again

these factories are so expensive 20

billion 30 billion and uh the

engineering and the continuous

investment required to do it and you

know it was a stunning you know vision

uh at that point in time Intel was IDM

as we called it the integrated design

and manufacturing you We never worked to

make our process and our factories

available for third parties

>> right you know it was always this thing

hey it's you know we do enough CPUs

oursel you know we reuse it for chipsets

and some of the other things that we're

doing but it was never standardized in a

way that it could be made available for

a broad ecosystem you know using PDKs

and all the design tools you know we did

a lot of our own EDA tools ourself you

know one of the projects that I started

early in my career was the foundations

of EDA, right? Uh as well, the first

place and route, you know, the first

standard cells, the first highle

description language, you know, it was

so proprietary and TSMC basically cut

that in half and says, I don't care

whose chip it is. I don't care what

you're designing, I'll be your

manufacturing partner. And at the time,

that was such a trivial piece of the

business, Intel didn't even care,

>> right? You know, so on. And then over

steady progress over a long period of

time and Apple as a customer driving

them to be could be become really

meaningful. You know obviously the world

changed and when I came back uh to uh

Intel in 2001 TSMC was producing 5x the

wafers of Intel.

>> Wow.

>> Right. Not 10% more 5x.

>> Yeah. And all of a sudden that model of

foundry became the model of the

semiconductor industry with two

exceptions Intel and memory. you know,

memory design and manufacture, right,

for you know, that is uniquely

different. And obviously, you know,

we're seeing the, you know, $3 trillion

memory companies just extraordinary, you

know, and, you know, trillion dollar

foundry company uh in TSMC. You know,

the industry has said, I want a lot of

wafers. I want a lot of innovation of

different designs. I have a layer of

standardization and EDA tools. And the

world changed. And obviously as I came

back to Intel that was one of the core

thesis of the new strategy. Yeah. We

must become a foundry as well five to

one and now it's more like seven to one

in terms of wafers you know to TSMC to

and

>> are we going to be able to onshore that

obviously we had the chips act and just

give us broad strokes what you think is

going to happen here in terms of

obviously Taiwan is in play. Some people

in the administration believe um it's

going to happen the year after Trump's

out unless he takes his third term.

Other people believe like it was going

to happen as early as 27 uh or maybe

going into 28. So,

are we going to be able to replicate

that here in America in a reasonable

amount of time, or is this like truly

could be a cataclysmic event if,

you know, god forbid, China decides,

hey, we're going to blockade um Taiwan

and and the Taiwanese decide, yeah,

we're going to burn the fabs and we're

going to fly out all of the engineers

and ship them to America.

>> Well, there's a lot in that question,

you know. Do we have an hour to talk

about this question? Well, I mean we

have six minutes, but Okay. Um, yeah, do

the best you can.

>> Okay.

>> I want to talk also about the AI bubble.

>> So, super, you know, three things about

this super quick. You know, one is the

chips act is having benefit.

>> Yeah.

>> Right. You know, when we started the

chips act in, you know, in 2001 when I

came back, the US was building about 12%

of leading edge. Today, that number is

more like 18%.

>> Okay. You know, we're making progress.

It's not 50%. We have a long way to go,

right? You know, Intel is starting to be

a real foundry. Okay, that's real

progress. Uh, and TSMC's factories are

up and operating at scale, right? We

have Samsung and, you know, uh, as well.

But, you know, I say the Intel and the

TSMC progress. Okay, that's meaningful.

Now, let's make it ugly for a second.

Uh, the island of Taiwan has less than 3

weeks, a big article in the Wall Street

Journal two weeks ago on this, less than

3 weeks of energy reserves.

Wow. Okay, that should just put a chill

in everybody's spine, right? Because the

blockade after 3 weeks, the island

browns out. When you turn off a fab, it

doesn't come back on for 90 days, right?

The economic impact of a brown out of

Taiwan is greater than the Great

Depression, right? Uh in the world,

never do you need to do anything a shot

to be fired. You just need to say,

"Great, no energy for 3 weeks.

>> No oil." Yes.

>> Right. No LG, right? You know, that's

how the island run. That is scary, you

know, to me. We need more resilient

supply chains uh associated, you know,

with it. And I don't think this is an

alternative for the world because if it

really does become a risk, you know, and

I'm, you know, I, you know, I don't sit

in the situation room and get all the

data and so on, but let's remind each

other that I think China has blockaded

the Taiwan Straits seven times over the

last four years.

>> Yeah.

>> This isn't a theory.

>> No, no. They're running exercises.

They're being pernitious and

>> right

>> pretty provocative in terms of

>> Is that 2027? Is that 2030? Is that

2035? their intentions have been clear

over a sustained period of time. We need

more resilient uh supply chains, you

know, forward. So, something, you know,

I put a lot of my time and energy into

and we're making progress, but we need

to go faster, need to go more

meaningful.

>> Yeah. And let's talk a little bit about

the AI buildout. I mean, you watched the

PC revolution, servers, the internet.

These were all extraordinary buildouts.

And then this is the buildout to end all

buildouts. the amount of data centers,

the amount of chips, the amount of

inference needed.

Do you think it's a bubble? I think I've

heard you say like it's it's obviously a

bubble, but what what's the risk factor

here? That we build too much uh or that

the technology doesn't solve enough

problems and we are swimming in tokens?

What what worries you about what you're

seeing now? the valuations of these

companies has gotten quite

extraordinary. And you know, if they

build too much and they spend too much

money and they don't make enough money,

well, based on your experience with

running a company, a public one, that's

a lot of tension on it. When you don't

make as much money as you're spending,

people tend to fall out of love with

these stocks. Yeah.

Well, I do think there, you know, there

there is a silver lining here that

guarantees we don't get too far ahead of

oursel in terms of bubble, you know, and

that is energy capacity.

>> Right.

>> Right. You know, energy capacity in the

world is expanding four 5%. You know, in

the US we had a decade at 1%. Right. You

know, I mean, it's just hideous what we

did to our energy grid, you know, over

about a decade and a half. But now

that's getting built out. But

essentially, nobody's going to build and

buy GPUs and build data centers if they

don't have energy.

>> So essentially, you have an upper bound

on how aggressive and how hyped and

bubbled that we get. So I take a lot of

soloulless in that.

>> Yeah.

>> Right. You know, for it because what

then is the incremental value of a token

and if it's a measure of intelligence,

it's somewhat infinite, right? You know,

in the sense if I have more

intelligence, I will do, you know,

better supply chain. I will do better

finance. I will do more, you know,

efficient logistics. I will, you know,

all of those things. So to me, the the

potential value that we unleash in a

token economic world is somewhat

infinite, right? And particularly with

labor shortages and so on that we see

right in uh developed countries, I am an

optimist, you know, that we're in a

couple of decade buildout.

>> Wow.

>> Right. Not a couple of years, a couple

of decades. One of the big objectives

I've said is that I have to make AI

10,000x

better,

>> right? You know, it's way too expensive

today. you know, we want to drop, you

know, by five orders of magnitude the

cost per token, you know, the energy,

you know, per token so that we really do

have Jevans law that we just explode the

access to AI, right, in much more

economic uh ways,

>> which it does seem like Jevans uh

paradox has been at play over the last

year, like, oh my lord, these tokens are

so cheap and the tools are getting so

good. Yeah, I'm just going to start

using these tools all day long until the

bill comes in and you're like, "Okay,

yeah, maybe I need to get some ROI out

of this." But you do have these

incredible companies, Cerebrris, Grock,

etc. making inference

>> dematrix

silicon and so you know, and you know,

if we accomplish right, you know, these

orders of magnitude improving and token

economics, availability, reduction in

energy costs associated with it. You

know, we just have a fantastic couple of

decades in front of us. There has not

been a time in human history where it's

been better to be a technologist than

the one we're in right now. We will

solve chemistry. We will solve language.

We will, you know, invent new materials,

re, you know, new forms of, you know,

uh, interaction, you know, uh, killing

cancer, right? Lifting people out of

poverty. There is not a better time to

be alive than the one that we're in

right now. And as technologists, we get

to sit in the driver's seat of it.

>> Pretty amazing. and you're investing uh

and that's your passion. Now what do you

think of these valuations? It's quite

seems a you know if you live through the

dotcom bubble we did see a disconnect

there. These companies slightly

different. We just had 11 labs up 600

million in revenue. Lovable I think

they're at five or 600 million. So

that's quite different than the do

speculation. Yeah.

>> Yeah. Well fundamentally we have real

revenues you know real margins coming

out of these businesses as well. You

know that said anytime the multiples get

too high okay some corrections you know

and to me periodic corrections that keep

the multiple you know earnings multiples

and you know so on in reasonable things

is good because this will not be a

smooth curve you know I'm predicting two

decades of goodness and there's going to

be lots of disruptions along the way

it's not going to be a smooth curve and

every time we have one of those

corrections say thank you right we're

not letting the bubble get ahead of

itself right you know hey we had the SAS

apocalypse there's going to be other

apocalypses on that journey when when

industries get impacted by the

capabilities that will be unleashed and

that's even before it gets exciting and

what I call the trinity of computing

classical computing AI computing and

quantum computing and when those three

come together okay that's when things

get really exciting

>> quantum's been about 5 years away for 25

years um when is it actually going to do

anything meaningful

>> this decade this decade. So by 2030,

>> yep,

>> it'll be meaningful. What should we

expect in terms of its impact in 2030?

Like

>> you know, you're going to be able to

start doing things that cannot be

computed today.

>> You know, chemistry, you know, biology,

there will be things that can't be

computed today. You know, some of the

easy things will be some of like the

logistics where I will compute the best

answer to get this thing to you, right?

>> Traveling salesman problem,

>> right? you know all of a sudden all of

those problems uh obviously it's

probably going to be you know 2020 2032

2033 when we solve you know things like

encryption right you know where you know

you'll have the fundamental Qday you

know kind of implications but this

decade we will see quantum supremacy uh

results across multiple industries you

know we know how to build cubits we know

how to error correct cubits we now have

algorithmics right against uh quantum

and you know now it's just about

engineering scale.

>> Who's going to win?

>> Well, obviously I'm a SI quantum guy,

right? Since that's one of our portfolio

companies. But the thing that you're

seeing is that you now have like four,

five, six modalities of quantum that are

demonstrating pretty good results,

right? You know, across trapped ions,

across, you know, photonic uh

approaches, spin uh approaches. So, you

now say modality is not an issue. Error

correction's been proven uh across them.

And you know, I think the race will be

on and my prediction is meaningful

results before 2030.

>> Wow. You realize that's about 40 months

from now.

>> Yeah. Okay. Meaningful results. Thanks

so much, Pat, for sharing all this

incredible uh

>> information and knowledge. Great to see

him.

>> Very good.

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>> Oika is one of my favorite founders.

He's the founder of Lovable. Why do I

love this founder? Uh well he's built a

product that people are addicted to

primarily Anton the people who work for

me and I uh love talking to you because

as a founder you have a northstar you're

incredibly laser focused on enabling

anyone to build great software. Yeah

it's the mission of the company. I'm

paraphrasing here but

>> essentially that's the mission of

lovable

>> mission I talk about empowering humans.

empowering humans

>> and the first gap is to build a product.

>> The second gap is to build a business

around that product, right?

>> And now at everyone at Lovable, we're

we're working on both of these two gaps,

right?

>> The first one, we got very far. We're

seeing a million new projects built

every single week on the

>> incredible. And on the on the second

one, we're investing a lot in making it

easier to run your business and to get

people to care, people to discover what

you build and the entire business of

what you're whatever you're doing as a

small business. As if you're a large

business, we're also getting a lot of

traction. Um, and uh we're actually

seeing as a proof of that more than 700

million visits to the applications every

month. So every month there's um extreme

growth in in the surface area of of the

entire

more than 50 million apps built on the

platform to date.

>> How many years has lovable been in

market or how many months now?

>> 20 months since 20. Yeah. And and again

we're seeing people who are first-time

founders. We're seeing enterprise

leaders move much faster together with

their teams on this platform that has a

lot of opinionated pieces in how you

should uh create software and how to

operate that software and how the

different applications in your company

connect to each other over time. So

that's what why we're seeing so much

growth also on the enterprise side which

where where we actually growing fastest

right now. This is really interesting

because 10 years ago, uh, people were

doing wizzywig software. Um, what was

the name for it? Before Vive, no code,

low code. Yes. And when I saw that 10

years ago in my incubator, you know,

every 20th company, somebody would come

in who was an MBA or not a developer and

they had vibecoded something and um not

vibe coded, they had no coded and they

were using these different software

platforms and the software didn't look

good. It didn't work perfectly well. It

was slow, but the promise was there. And

I guess it took LLMs and this new

intelligence

to make actually good software. So maybe

you could talk a little bit about

who is the customer because developers

uh do developers use lovable or is it

the other 95% of society that are your

customers? How do you think about who

your ideal customer profile is?

>> Yeah, we're seeing people use Lovable

both with a technical background. about

20% are technical or some type of

engineer and they they love that we're

quite opinionated. We put all the best

practices into how the software is

architected and we make it seamless to

um with one prompt get payment set up in

a very secure way and do things like run

security scans after every change even

now in the background monitoring the

projects. So it's actually quite

appreciated by the engineers in the

technical community. Um also because

it's a great bridge

>> from the nontechnical people which is

four out of five are are nontechnical uh

and they're building uh often first to

figure out what is the right thing to

build

>> which is where lovable has always been

exceptionally good and uh now what we're

seeing is that people are running

businesses making more than million

dollars of revenue on the on this

platform. So it's it's this we're

building for everyone. It's this entire

spectrum. And what's what's exciting to

see is often that if someone who

discovers lovable from their colleagues

at a large company, they go out and then

run a side hustle and some of those

those idols hustles really work. They

make hundreds of thousands of dollars

and then they become a founder after

that. So there's this crosspollination

from both.

>> Yeah. And this is like the really

interesting thing about vibe coding. If

we were sitting here last year, people

would look at it and say it's a great

way to make a mockup. like you said, a

great way to think about product and

maybe create wireframes or a workable

prototype. All of that's out the window

now. The whole concept of building

wireframes and building a mockup, well,

you can just go right to building the

product in a day or two days. And what

people I think don't appreciate about

what you're doing at Lovable is after

you've made a product that you're proud

of and that has some product market fit,

there are many more steps that are

required. You mentioned payments, you

mentioned security, uh making sure that

the data isn't lost or that it's not

leaked.

That's changed dramatically over the

last 12 months. Yeah,

>> very much so. So um I would say many

engineers they don't look at the code

they don't write code anymore and that

means that you don't need to be an

engineer to create software right um but

the the thing that lovable does for any

anyone also the non-technical people is

that it it um takes uh creates a

structure for the architecture of the

software that you build and it makes

sure that you don't go off a cliff um

and that things like setting up payments

emails things like getting discovered by

other AI chat engines and uh by Google

search those things are kind of taken

care of. So you don't have to know how

all these things work in the details you

trust you can trust the platform to take

care care of data security connecting to

other tools that you might be using in a

secure way and and that's really where

um us being opinionated from day one and

being focused on making this for the

99%. It's a it's a vast market right

from the from day one is what made us

very successful.

>> Yeah. And I can tell you internally I

gave my team all the different tools

they could possibly want to use and

somebody had started with lovable. I

think I told you the story when you were

on this week in startups a year ago like

and they made some interesting websites

and they were trying to make an

internet. They couldn't quite get it

done. Then I had some people who started

using you know cursor or clawed code.

They started vibe coding stuff but they

couldn't finish the product. And then

people tried to solve some problems with

codework. I really like perplexity

computer. And then my team came to me

and for one of our projects I was

talking to you about founder university

our pre-acelerator. They wanted to make

an internet.

Um, now this is something I would have

never okayed because it would have cost

$500,000

10 years ago to make it and we don't

have that kind of budget. You know, we

would rather put that towards the

founders in the program and getting more

people into the program. And in 4 to 8

hours, they made the whole internet and

they made a bunch of things I hadn't

asked for. And it was the person running

the um this founder university who made

it and she did it on her own without

uh permission in lovable. I said, "Whoa,

where how did you build this?" She said,

"Loveable." I was like, "Oh, we still

have lovable." And they're like, she's

like, "I just put it on my corporate

card." To your point, she made it. Now

that software is driving the program and

the reason people do the

uh the the program in their country, we

have it in Saudi and in Japan is because

it has economic impact.

>> So I said, "Hey, I have an idea. Can you

make for me an economic impact of the 50

companies that are in the program?"

She asked Lovable to do it. I gave her

some, you know, prompting, human

prompting boss to now it has the

economic impact in there and it

considered, you know, with our

prompting, well, how many people work at

each company? What are they paying

taxes? How much do they rent their home

for? What is their average salary? And

it built something that I would have

never been able to afford to build. And

lovable is 50 bucks a month, I think. I

don't know how much you charge, but it's

far too little. Like

>> $50 a month, I think.

>> Yeah. That's if you're on a business

plan. Yeah. It starts at 25.

>> Yeah. So, uh, the economic impact of

what you're building is I would equate

for what you built to us, it would have

cost me $500,000 2 years ago. It was

built in 4 hours by an employee, which

if you just put employees at 50, 60,

whatever, $70, uh, plus the cost of your

software, it got made for less than

$2,000

>> in a year. It's extraordinary.

I I'd love to hear more about the

progress of the of the internet.

Anything that you asked for that you

want to forward directed to me.

>> Uh well, right now, you know, my concern

was security and making sure that data

didn't leak and they talked to your team

and they went through it and

>> it's secure. So, we feel good about it.

>> Well, look, um I'm now asking people who

do penetration testing to say, I want

you to compare all the tools. Yeah. and

uh make sure that there's a all the work

that we're doing that's not visible on

security and trust. Yeah, there's a lot

a lot a lot of things um where we we

invest and spend money on that every

also free users get a lot of security

scanning running in the background that

that actually um translates to something

that security experts can can see. And

>> a year ago we were at mock-ups. Now

we're at functionality and secure and

super viable for deployment. Where will

you be in a year?

>> Yeah. So, what we're seeing is that

there's a gap in build being able to

build the product, right? And and you

built an entire internet on the

platform. That's great. Um, what we've

done since then is to have a new product

line basically the hosting part which is

both the AI and you know all the normal

hosting and that product line has been

going faster than the building uh thing.

I mention

>> AWS competitor.

>> It it lets you it lets you run all your

software and then we're working with

companies like AWS under the hood as

well. But but what you also want to have

is um to use lovable we're seeing by our

customers as an AI co-founder,

>> a partner that you talk to about

everything in your business. And if

you're running your apps, your tools are

on the platform, then just talking to

Lovable has access to all the data that

you might want to know about your about

your company, how it's doing. So, we're

we're working with some of our customers

in pre-release to give them access to a

co-founder that works for you even when

you're sleeping

>> and comes back to you in the morning and

says like, "Here are some strategic

directions you could go. here's some

optimizations you can do go in terms of

growing your business faster serving

your customers better uh faster uh and

and and that's um that evolution towards

operation and intelligence for towards

driving towards outcome

>> for your business

to build the software but you stay to

build the business

>> yes to operate your business and um what

we're already doing I've been doing for

a very long time is to compound from

everything we're learning every time

lovable makes a mistake. Uh it goes to a

gentic system with our engineers in it

improving it. That compounding

intelligence is of course applicable to

our our customers, our users running

their business on our platform as well.

>> Is software going to become

100% bespoke even like the internal

tools. I was looking at Slack and our

bill for Slack even on the highest

version is maybe $10,000 a year. It's

not a lot of money. It's well worth it.

But I was starting to think, well, maybe

I should vibe code my own Slack so it's

integrated into everything we do at a

deeper level. So how do you think the f

what do you think the future will look

like in terms of some of these

you know uh foundational pieces of

software that every startup every

enterprise uses Salesforce HubSpot

Slack

uh the Google suite Microsoft Office

will bespoke software

start to replace those do you believe

>> I I like this question let let me ask

answer it but I'll just give you a story

about someone I recently heard who's

going on this journey. They're quite

advanced. So,

>> NAD, he works at a pretty large company

in the US, Nursa, and uh he came to our

platform because he wanted to build out

a new product lines, nurse study for

educating more nurses, right? And and he

built out all the admin tools for the

program, the scheduling for the nurses

getting getting their licenses and their

certification management. and he was

able to build that into a product and to

take it to market because they have they

have had all that access to nurses

wanting their certification. What he

also did was he took it into the back

office internally and they've now

replaced more than 10 tools that they

had bespoke applications and um I think

in terms of your question you can do

that for multiple reasons. In their case

they're saving more than a million

dollars per year,

>> right?

>> So that's that's huge, right? But it's

also the case that in some cases you

have specific requirements where the

tools that you've been using to date

they aren't suited for those requirement

exactly and in those cases I think yes

>> you will have more more bespoke

solutions. Yeah,

>> but we're I also expect us to see that

lovable continues to interoperate with

all of those tools. And uh I'm not sure

if you tried this if if you ask for

connecting to anything in the Google

suite or now to anything in the

Microsoft suite or or Slack lovable

guides you through all the steps to do

that in a way where you can get a a very

good overview of exactly how the data

flows which is of course very important

that you don't give access to the wrong

person to the wrong data and you can

continue to use Salesforce um HubSpot

and all the tools that you kind of like

to use under the hood but with a bespoke

interface on top of H how have these new

frontier models they're in some ways

competitive but in some ways you can use

them to power lovable. So how do you

think about the competition with them

opensource

a and the future of lovable because

people have announced that lovable's

dead every 6 months since you started

and then every 6 months you go from 100

to 200 to 300 I think you're at 400

million in revenue something crazy. We

we we reached 500 in May.

>> Okay. Growth is a phenomenal.

>> So you're dying again by another 100

million in annual revenue.

>> Exactly.

>> So but underneath the hood you're using

some of these.

>> Yeah. Let me explain. Yeah. So we've

always had this strategy that we do

whatever is best for our customers. And

in terms of the intelligence that means

that we're using multiple models. And so

if you ask Lovable now, it's actually

routed to the model that's most suitable

to whatever you want to do. And that's

both the commercial frontier models. So

from multiple vendors

>> and increasingly it's open weight models

where our team when whenever it's get

gets routed to an to our own model that

model becomes more intelligent for our

agent harness. Yeah. especially on the

mistakes that it might be making in some

cases on which which tool to call, which

integration to create and how to guide

you through uh success for your

business.

>> Right. So, you're all in on open source.

You believe that's the future of

Lovable. I I'm reading into it. So we

have multiple partnerships and we're

investing heavily to be close with those

partners and it's the big the big labs

and it's also to make sure that um we

get the fastest performance at the

lowest cost for our customers when we

know that we can do that with our own

models

>> right

>> and uh we have a really really strong

research team up in Stockholm who is

working on what's called post training

so and we're applying all the best

practices to do that and scaling up that

team uh quite significantly since we

also believe it's a it's a part of the

European ecosystem to have that

capability in Europe specifically.

>> Are you doing or are you using any of

the data labeling data training

companies to help you understand the

most common businesses and build that

proprietary data? So, so what we're

doing is that we're looking at um the

mistakes that any of the models do right

now and then we we prioritize them by

what drives most impact for our

customers and then we make the models we

create data sets or we um we do did

something called reinforcement learning

specifically for the problems where the

frontier models are making mistakes for

us right now and um we have this

enormous token distribution right from

um a million new products being built

every every single

You're burning a lot of tokens.

>> We are. Yes. And that's and that's a lot

of signals for making the system both

the agent harness

>> and um what we've been refining over the

last two years which is the skills that

we have have this like internal type of

skills that the agent knows when to

remember the facts from our software

engineers that know how to build really

really good software. We're modifying

both of those on every every single

week.

>> It makes total sense. And somebody told

me some companies are doing token

dumping. They're, you know, selling $100

worth of tokens for $50. Um, you know,

basically they become token resellers in

some ways and they're money losing

businesses. You have to you're money

you're profitable I believe now or close

to it. Um, we we always monitor our

margins, but again um we're doing what's

best for our customers and that means

that often means more intelligence. So

we're not we're not looking at oh let's

use a we've never had the decision to

say let's use a cheaper model here if

it's measurably worse for our customers

and we can measure that what's best for

>> but are they is it unlimited for the 50

or you have caps now

>> we have caps

overages and caps are people starting to

hit them

>> yeah our customers definitely hit caps

and then you can top up you can have a

we have multiple subscription tiers

>> what number I'm just curious like what

percentage of people need to top up.

They're so addicted to it that they're

blowing past the the

>> so from the lowest subscription tier.

>> Yeah.

>> Um I I think it's the

>> uh m

it's something like 60% of our customers

I think

>> I'm hearing that more and more often

that people are willing to pay the

overages because they're getting so much

value. And I think that's the future of

the business is people are looking at it

going like I am. Well, if I'm paying

$600 and if you token max to 6,000 a

year, but this is a $500,000 piece of

software, I don't care. I'm still paying

somewhere between.1%

and 1% of what I would have paid 3 years

ago. Who cares?

>> Go for it. Um, so

>> yeah, what we're seeing is everything is

about moving moving fast these days and

and AI more AI usually lets you move

much faster. So the spend is usually

worth it.

>> Do your customers a final question for

you because I'm starting to see this now

where multiple people in the

organization try to solve the same

software problem and they're competing

with each other. So like this internet

I'm talking about, we built one for

Japan.

>> Yeah.

>> But somebody built the US one. So now I

have two pieces of software. So I said

to the two different people

or do we have did you guys fork each

other's code or they're like no we just

built two different lovable projects.

And I'm like is that the right thing to

do because you went faster and I had two

swings at bat two different intelligent

brilliant people making their version of

the software.

>> But you would never have done that

>> in the previous way of building

software. You would have one track of

software and you would be building

Franken software where you'd be trying

to get all the needs into it from the

two different groups. I Yeah, I I'm

actually a huge fan of very rapid

experimentation and I I have a story

where for a while I worked at a a place

called CERN where they do particle

physics. It's it's pretty here in

Europe, right? Uh and that's where I was

introduced to this concept of

co-opetition where they have two

actually quite isolated teams working on

the same um particle accelerator but

different places on it and then they

don't share the results until they

publish and that way they uh they can

kind of over time learn what's working

best in the different organizations but

you don't get stuck in a local minimum

and it's you know free markets work

extremely well because of competition

and they they they do that in academia

as well and now since the engineering is

less of the bottleneck. It's more the

question of what is the right thing to

build. I think it's a great thing to

have if you have the sufficiently many

humans right to do to try to attempt

solving the same problem in different

ways. And then if you do that on

lovable, what I like to do is I I take I

bring up a new project or one of the

projects and I I say, "Hey, can you go

and just check out this other one and

take this these three things that I

really like and and bring them bring

them over here and maybe even run an a

split test, run an experiment to see if

it's if it's improves improving the

metrics for for our customers we're

trying to serve.

>> Did you see somebody used Fable to build

Fortnite

>> and uh

>> I've seen the 3D some of the 3D games?"

Yeah.

>> Yeah. What is your take on, you know,

this latest version from Anthropic

Fable? I know they're a part or I assume

they're a partner. I don't know that.

>> Yeah, we use Fable as well as one of the

models.

>> What do you think of it in terms of

compared to the last generation faster,

better, both?

>> Yeah. Is it a massive step function?

Yeah.

>> What I've seen is that it can in the

first attempt create very sophisticated

things that look really good. Then when

as you're evolving right it's it's still

the same thing where you as a human you

have to think you often should be

planning together with your agent about

what is the right thing to do and and

that's more of that's again more of the

bottleneck uh whereas more intelligence

is on some tasks it's great yeah like it

creates really beautiful things 3D games

for example but on figuring what to what

to build figuring out figuring out what

are the right strategic directions or

experiments you should run to improve

outcomes for your business. That's um uh

that's not changing as fast is the

humans knowing how to use the tool to

get and to plug in all the right data to

be able to take the right decisions for

taking your product forward and to take

your business forward.

>> Um listen, I love the product, but even

more than I love the product and you as

a founder, I love the outcome. The

outcome for business is extraordinary.

So, anybody who's listening, Lovable is

absolutely worth your time. Don't wait.

Just put it on your corporate card and

start building. That's my message. Just

start building with Lovable. It's an

incredible product. And uh

congratulations on being reborn six

times cuz every 6 months you add 100

million in revenue it seems. And then

everybody says Lovable's dead because

the new foundation model is so good. But

you keep studying your customer and and

you keep somehow surviving and thriving.

So congratulations as an entrepreneur.

Thank you so much, Jason. I enjoyed that

chat. I hope you enjoy the rest of your

stay here in Paris.

>> It's pretty great. And the Palace of

Versailles is so impressive, huh? Uh

someday we'll be building this with

lovable and optimist robots. I

>> I'm looking forward to it. I'm going all

in.

I'm going all in.