00:00:00
spent a long time at Intel. >> Yeah. >> And uh >> only 34 years. >> 34 years. >> Yeah.
00:00:06
>> Probably one of the greatest American companies uh ever. And then absolutely went off the rails and got absolutely
00:00:16
demolished by Nvidia, TSMC, uh and I guess Apple to a certain extent. So you had this incredible Intel
00:00:26
inside moment. We bought our computers based on, you know, hey, the Pentium and that sound
00:00:33
>> Intel inside, baby. Intel insideum. And so, let's talk about how things went wrong. What went right and then
00:00:42
>> how did it and and you were there for a long time. You took a break and then you
00:00:47
came back. But there seemed to be have been some critical mistakes that we can learn from. So, let's just embrace it
00:00:54
and go right into it. tremendous success as an American company coming back now I
00:00:58
think uh reasonably but when you when we look back on it and we do our post-mortem what were the mistakes a and
00:01:07
what would we change in terms of the direction of that company >> if you were building a global financial
00:01:14
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00:01:26
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00:01:32
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00:01:38
Airwall built for the future. >> Having spent so much of my life there, you know, I view it. I joined when I was
00:01:45
18. I went through puberty at Intel, right? I joke, right? I was just like, you know, I am so early. uh Grove,
00:01:53
Noise, Bar uh Barrett, right? Uh and uh uh you know, they they were the people I
00:01:59
grew up at, right? You know, so on. They were my mentors. They were the people I
00:02:03
adored uh for it and they were deeply technical. >> Andy Grove, >> Andy Grove, Gordon Moore, Bob, you know,
00:02:11
co-inventor, you know, these were deeply technical leaders. I remember when I joined the executive staff for the first
00:02:17
time, you know, there was, you know, probably 15 of the 20 people that were in the room were PhDs, right? You know,
00:02:25
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
00:02:30
run by business people >> as opposed to technical, >> the bean counters, the finance people.
00:02:34
>> Yeah. And you know when I became uh CEO uh in 2001 that was the first technical
00:02:41
leader in essentially 15 years >> right you know associated with it you know and if you have a business leader
00:02:47
who does he promote business leaders and you know right you know so I think one of the fundamental things is and you
00:02:54
know as you look at the great technology companies uh today you know they're deeply technical
00:02:59
>> and founderled typically >> you know and even if they're not you know Satcha is not a founder no right?
00:03:04
You know, Sundar is not a founder as well, but they're deeply technical individuals. And when you're making
00:03:10
these hardcore technical, you know, decisions that affect billions of dollars, you don't do that through a
00:03:17
spreadsheet. That's a lousy investment, right? Unless the technology trends make
00:03:23
it the right investment. And I think that's one of the fundamental things. And obviously you know in the five years
00:03:29
uh five six years before I came back you know Intel gave $100 billion to shareholders.
00:03:36
>> Oh the dividends >> and stock buybacks >> a hundred. What I wouldn't have done for
00:03:42
another hundred billion dollar on the uh what would you have done? You probably would have made chips for iPhone which
00:03:51
Intel passed on. Yeah. >> Yeah. you know, but you know, it hadn't built a new factory in a decade when I
00:03:57
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
00:04:03
all of these things, you know, that you would only do as a technologist because the economics behind them by themselves
00:04:10
were not good. >> So, you know, it's getting back to the core of technology to me that was, you
00:04:16
know, the fundamental thing. You know, you make good decisions, you make bad decisions as leaders. Every business uh
00:04:22
does that uh as they go along but uh you know fundamentally this is a technology
00:04:27
business and you need technologists running technology uh that then hires technologists that are sitting at the
00:04:34
staff that then hire the best technologist you know you know >> and take big swings at you know
00:04:40
categories that could matter in the future like skating to where the puck's going. If you look at Apple, they did
00:04:45
the same thing for the past 15 years, buying back the stock, tremendous amount of dividends. They're the largest holder
00:04:52
of capital of any company, I believe, to this date. And what if what companies do
00:04:58
they buy? They buy little tiny acquisitions on the margins. I think the largest ones was was Beats cuz they
00:05:04
wanted to get inroads into, you know, certain uh demographic segments like in the Android space that they couldn't get
00:05:10
into. But my god, what a colossal waste of time. Like you said, they could have done so many amazing things. Tell me
00:05:16
about Steve Jobs in 2008, 2009 deciding, I think we're going to make our own silicon and that
00:05:23
impact because was that a covert product project or did you guys know he was doing that? Did he inform you?
00:05:30
>> Well, that seemed to be another one of those forks in the road. Yeah, >> you know, Steve was an incredible
00:05:35
leader. You know, he was also a ruthless leader, right? You know, very difficult.
00:05:39
you know, read Walder Isaxson's book on him uh as well. I had many many conversations with Steve over the years
00:05:46
uh you know, for it. Um but you know, when they moved to Intel and the Centrino chip, it was a big deal.
00:05:53
>> Yeah. >> Right. And they were putting extraordinary demands on Intel. You know, make the chip smaller, drive lower
00:06:00
power. They're demanding uh customer. And when he was no longer convinced that we could continue to do that, you know,
00:06:09
he started the project, >> right? You know, and if you remember what was it, you know, you know, uh, P
00:06:15
semi, you know, they acquired some small companies, started to build some competency, but, you know, they did a
00:06:20
few little chips internally. It wasn't a big deal and then the little chips got a
00:06:24
little bit bigger, you know, and Steve was a master of this, you know, just starting, you know, these small efforts
00:06:30
to build core competence inside the company. Uh I remember when we had the first conversation with Steve about uh
00:06:37
porting the uh operating system to the Intel chip from the power chip that they were running on before they moved to
00:06:45
Intel. And we were quite proud of the silicon software competencies that we had and compilers and operating systems,
00:06:55
you know. So Steve, we'll help you port the operating system to the x86. And I remember that Steve said, "I've been
00:07:02
working on that for the last four releases." >> He had been preparing the core technologies inside of Apple for
00:07:11
something that might happen uh in the future, you know, and he was already, you know, to me, I just remember I was
00:07:16
just shocked. I, you know, I've ported the last four releases to the x86. I think we got this.
00:07:21
>> Yeah. >> Right. You know, it was that kind of thing. And that's how they got into the
00:07:24
semiconductor, you know, doing their own semiconductor. Hm. I'm not sure I can rely on Intel to be that much ahead of
00:07:31
the industry and I can start optimizing the system design with the silicon design as opposed to relying on one
00:07:38
that's been somewhat optimized for a Windows environment versus an iOS environment, you know, in their uh
00:07:44
operating system. And you know, it was just, you know, uh, you know, it was never that kind of thing. They sort of
00:07:51
said, you know, right, you failed as a supplier. No, I can supply myself better.
00:07:56
>> Yeah. and Jensen uh decides, hey, he's going to go all in making these video cards and talk about just incredible
00:08:06
uh serendipity that these happen to be also very applicable for cryptocurrency and running these AI jobs.
00:08:15
>> Yeah. >> Was that luck or skill or combination of both there? Well, you know, when you
00:08:21
think about that progression, you know, Jensen, he was just building high performance computers, you know,
00:08:27
throughput machines. You know, when we were at the height of our strength on CPUs, uh, at Intel, we sort of scoffed
00:08:35
at his machines. Yeah. >> Right. You like, oh, that's a graphic machine. You, you know, there's some
00:08:40
gamers who want to use that kind of stuff, right? You know, it was always the big CPU and those little GPUs. But
00:08:46
when they started to build a real software stack, Yes. with it, right? You know, sort of, okay, this CUDA thing and
00:08:52
SIMT as a technology, you know, uh, you know, uh, multi-threading and so on. And
00:08:58
it just sort of kept getting a little bit better and a little bit better and it was a little bit jobslike in that
00:09:03
way. You know, we're just making it better every release and it's becoming more robust and all of a sudden, you
00:09:10
know, the crazy, you know, uh, Japanese HPC guys said, "Hey, we could take those
00:09:15
graphics cards and maybe start using them in HPC." H, >> right? you know, and that was sort of
00:09:20
defining moment where it wasn't just about doing graphics anymore. This was a more computationally dense platform to
00:09:28
start attacking some of the world's most interesting workloads. And I think Jensen would agree that was a defining
00:09:34
moment and them sort of saying, "Oh, these aren't just graphics cards anymore. You know, these are
00:09:39
generalpurpose computing devices that can start applying to these other uh workloads." And you know AI was you know
00:09:47
had gone through what its fifth nuclear winter by that point. We're just like man you know you know this is never
00:09:53
going to matter right we're never going to you know get the breakthroughs but the community around it was continuing
00:09:58
to develop uh you know for it and the CUDA software kept getting better uh generation by generation and uh you know
00:10:07
I had a project at Intel Larabe right where we were trying to take the x86 and essentially do the same thing right you
00:10:14
know for it and you know in my first departure from Intel the project was killed a week after I left
00:10:20
>> huh >> and the world would have been so much different right I >> I mean it really I think it's
00:10:27
illustrative of illustrative of what continuous innovation taking some risks and doing that fundamental research and
00:10:36
the compounding power of technology because I think it was William Gibson who said the street finds its own use
00:10:40
for technology like Nvidia did not predict that this Bitcoin project would take over and that this
00:10:48
would be the best way to do those computations, nor did they anticipate, I think, you know, that AI would take off,
00:10:54
but because it was the best solution, the hacker community could kind of figure that out. Well, as we wrap on the
00:11:00
Intel portion of your uh career, um, okay, Apple Silicon, that's one. Uh, and then you have Nvidia, and then you have
00:11:09
this Taiwanese company, uh, that starts making, you know, really great at fabricating the these chips. Um and
00:11:19
Intel missed that as well. Yeah. And and maybe you could talk a little bit about
00:11:23
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
00:11:29
into some of these AI chips and venture investing. You know the thing with TSMC was they started with a vision of
00:11:36
foundry >> right you know they were going to become the factory for the industry and again
00:11:41
these factories are so expensive 20 billion 30 billion and uh the engineering and the continuous
00:11:49
investment required to do it and you know it was a stunning you know vision uh at that point in time Intel was IDM
00:11:57
as we called it the integrated design and manufacturing you We never worked to make our process and our factories
00:12:06
available for third parties >> right you know it was always this thing hey it's you know we do enough CPUs
00:12:12
oursel you know we reuse it for chipsets and some of the other things that we're
00:12:16
doing but it was never standardized in a way that it could be made available for
00:12:21
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
00:12:29
know one of the projects that I started early in my career was the foundations of EDA, right? Uh as well, the first
00:12:36
place and route, you know, the first standard cells, the first highle description language, you know, it was
00:12:42
so proprietary and TSMC basically cut that in half and says, I don't care whose chip it is. I don't care what
00:12:48
you're designing, I'll be your manufacturing partner. And at the time, that was such a trivial piece of the
00:12:55
business, Intel didn't even care, >> right? You know, so on. And then over steady progress over a long period of
00:13:02
time and Apple as a customer driving them to be could be become really meaningful. You know obviously the world
00:13:10
changed and when I came back uh to uh Intel in 2001 TSMC was producing 5x the wafers of Intel.
00:13:18
>> Wow. >> Right. Not 10% more 5x. >> Yeah. And all of a sudden that model of foundry became the model of the
00:13:27
semiconductor industry with two exceptions Intel and memory. you know, memory design and manufacture, right,
00:13:35
for you know, that is uniquely different. And obviously, you know, we're seeing the, you know, $3 trillion
00:13:41
memory companies just extraordinary, you know, and, you know, trillion dollar foundry company uh in TSMC. You know,
00:13:48
the industry has said, I want a lot of wafers. I want a lot of innovation of different designs. I have a layer of
00:13:55
standardization and EDA tools. And the world changed. And obviously as I came back to Intel that was one of the core
00:14:01
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
00:14:07
in terms of wafers you know to TSMC to and >> are we going to be able to onshore that
00:14:13
obviously we had the chips act and just give us broad strokes what you think is going to happen here in terms of
00:14:19
obviously Taiwan is in play. Some people in the administration believe um it's going to happen the year after Trump's
00:14:27
out unless he takes his third term. Other people believe like it was going to happen as early as 27 uh or maybe
00:14:35
going into 28. So, are we going to be able to replicate that here in America in a reasonable
00:14:42
amount of time, or is this like truly could be a cataclysmic event if, you know, god forbid, China decides,
00:14:49
hey, we're going to blockade um Taiwan and and the Taiwanese decide, yeah, we're going to burn the fabs and we're
00:14:56
going to fly out all of the engineers and ship them to America. >> Well, there's a lot in that question,
00:15:02
you know. Do we have an hour to talk about this question? Well, I mean we have six minutes, but Okay. Um, yeah, do
00:15:08
the best you can. >> Okay. >> I want to talk also about the AI bubble. >> So, super, you know, three things about
00:15:14
this super quick. You know, one is the chips act is having benefit. >> Yeah. >> Right. You know, when we started the
00:15:20
chips act in, you know, in 2001 when I came back, the US was building about 12% of leading edge. Today, that number is
00:15:27
more like 18%. >> Okay. You know, we're making progress. It's not 50%. We have a long way to go,
00:15:34
right? You know, Intel is starting to be a real foundry. Okay, that's real progress. Uh, and TSMC's factories are
00:15:41
up and operating at scale, right? We have Samsung and, you know, uh, as well. But, you know, I say the Intel and the
00:15:48
TSMC progress. Okay, that's meaningful. Now, let's make it ugly for a second. Uh, the island of Taiwan has less than 3
00:15:56
weeks, a big article in the Wall Street Journal two weeks ago on this, less than
00:16:00
3 weeks of energy reserves. Wow. Okay, that should just put a chill in everybody's spine, right? Because the
00:16:11
blockade after 3 weeks, the island browns out. When you turn off a fab, it doesn't come back on for 90 days, right?
00:16:20
The economic impact of a brown out of Taiwan is greater than the Great Depression, right? Uh in the world,
00:16:27
never do you need to do anything a shot to be fired. You just need to say, "Great, no energy for 3 weeks.
00:16:33
>> No oil." Yes. >> Right. No LG, right? You know, that's how the island run. That is scary, you
00:16:39
know, to me. We need more resilient supply chains uh associated, you know, with it. And I don't think this is an
00:16:46
alternative for the world because if it really does become a risk, you know, and
00:16:50
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
00:16:56
other that I think China has blockaded the Taiwan Straits seven times over the last four years.
00:17:02
>> Yeah. >> This isn't a theory. >> No, no. They're running exercises. They're being pernitious and
00:17:08
>> right >> pretty provocative in terms of >> Is that 2027? Is that 2030? Is that
00:17:12
2035? their intentions have been clear over a sustained period of time. We need more resilient uh supply chains, you
00:17:20
know, forward. So, something, you know, I put a lot of my time and energy into and we're making progress, but we need
00:17:25
to go faster, need to go more meaningful. >> Yeah. And let's talk a little bit about
00:17:29
the AI buildout. I mean, you watched the PC revolution, servers, the internet. These were all extraordinary buildouts.
00:17:37
And then this is the buildout to end all buildouts. the amount of data centers, the amount of chips, the amount of
00:17:44
inference needed. Do you think it's a bubble? I think I've heard you say like it's it's obviously a
00:17:50
bubble, but what what's the risk factor here? That we build too much uh or that the technology doesn't solve enough
00:17:59
problems and we are swimming in tokens? What what worries you about what you're seeing now? the valuations of these
00:18:06
companies has gotten quite extraordinary. And you know, if they build too much and they spend too much
00:18:12
money and they don't make enough money, well, based on your experience with running a company, a public one, that's
00:18:20
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
00:18:26
these stocks. Yeah. Well, I do think there, you know, there there is a silver lining here that
00:18:33
guarantees we don't get too far ahead of oursel in terms of bubble, you know, and
00:18:39
that is energy capacity. >> Right. >> Right. You know, energy capacity in the world is expanding four 5%. You know, in
00:18:46
the US we had a decade at 1%. Right. You know, I mean, it's just hideous what we
00:18:51
did to our energy grid, you know, over about a decade and a half. But now that's getting built out. But
00:18:57
essentially, nobody's going to build and buy GPUs and build data centers if they
00:19:01
don't have energy. >> So essentially, you have an upper bound on how aggressive and how hyped and
00:19:07
bubbled that we get. So I take a lot of soloulless in that. >> Yeah. >> Right. You know, for it because what
00:19:13
then is the incremental value of a token and if it's a measure of intelligence, it's somewhat infinite, right? You know,
00:19:20
in the sense if I have more intelligence, I will do, you know, better supply chain. I will do better
00:19:26
finance. I will do more, you know, efficient logistics. I will, you know, all of those things. So to me, the the
00:19:33
potential value that we unleash in a token economic world is somewhat infinite, right? And particularly with
00:19:40
labor shortages and so on that we see right in uh developed countries, I am an optimist, you know, that we're in a
00:19:48
couple of decade buildout. >> Wow. >> Right. Not a couple of years, a couple of decades. One of the big objectives
00:19:55
I've said is that I have to make AI 10,000x better, >> right? You know, it's way too expensive
00:20:02
today. you know, we want to drop, you know, by five orders of magnitude the cost per token, you know, the energy,
00:20:08
you know, per token so that we really do have Jevans law that we just explode the
00:20:12
access to AI, right, in much more economic uh ways, >> which it does seem like Jevans uh
00:20:19
paradox has been at play over the last year, like, oh my lord, these tokens are so cheap and the tools are getting so
00:20:25
good. Yeah, I'm just going to start using these tools all day long until the bill comes in and you're like, "Okay,
00:20:30
yeah, maybe I need to get some ROI out of this." But you do have these incredible companies, Cerebrris, Grock,
00:20:36
etc. making inference >> dematrix silicon and so you know, and you know, if we accomplish right, you know, these
00:20:43
orders of magnitude improving and token economics, availability, reduction in energy costs associated with it. You
00:20:50
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
00:20:57
been better to be a technologist than the one we're in right now. We will solve chemistry. We will solve language.
00:21:03
We will, you know, invent new materials, re, you know, new forms of, you know, uh, interaction, you know, uh, killing
00:21:11
cancer, right? Lifting people out of poverty. There is not a better time to be alive than the one that we're in
00:21:17
right now. And as technologists, we get to sit in the driver's seat of it. >> Pretty amazing. and you're investing uh
00:21:23
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
00:21:31
dotcom bubble we did see a disconnect there. These companies slightly different. We just had 11 labs up 600
00:21:38
million in revenue. Lovable I think they're at five or 600 million. So that's quite different than the do
00:21:44
speculation. Yeah. >> Yeah. Well fundamentally we have real revenues you know real margins coming
00:21:49
out of these businesses as well. You know that said anytime the multiples get too high okay some corrections you know
00:21:55
and to me periodic corrections that keep the multiple you know earnings multiples
00:22:00
and you know so on in reasonable things is good because this will not be a smooth curve you know I'm predicting two
00:22:06
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
00:22:12
every time we have one of those corrections say thank you right we're not letting the bubble get ahead of
00:22:16
itself right you know hey we had the SAS apocalypse there's going to be other apocalypses on that journey when when
00:22:23
industries get impacted by the capabilities that will be unleashed and that's even before it gets exciting and
00:22:30
what I call the trinity of computing classical computing AI computing and quantum computing and when those three
00:22:37
come together okay that's when things get really exciting >> quantum's been about 5 years away for 25
00:22:44
years um when is it actually going to do anything meaningful >> this decade this decade. So by 2030,
00:22:51
>> yep, >> it'll be meaningful. What should we expect in terms of its impact in 2030?
00:22:56
Like >> you know, you're going to be able to start doing things that cannot be computed today.
00:23:01
>> You know, chemistry, you know, biology, there will be things that can't be computed today. You know, some of the
00:23:06
easy things will be some of like the logistics where I will compute the best answer to get this thing to you, right?
00:23:14
>> Traveling salesman problem, >> right? you know all of a sudden all of those problems uh obviously it's
00:23:18
probably going to be you know 2020 2032 2033 when we solve you know things like encryption right you know where you know
00:23:26
you'll have the fundamental Qday you know kind of implications but this decade we will see quantum supremacy uh
00:23:33
results across multiple industries you know we know how to build cubits we know how to error correct cubits we now have
00:23:40
algorithmics right against uh quantum and you know now it's just about engineering scale.
00:23:46
>> Who's going to win? >> Well, obviously I'm a SI quantum guy, right? Since that's one of our portfolio
00:23:51
companies. But the thing that you're seeing is that you now have like four, five, six modalities of quantum that are
00:24:00
demonstrating pretty good results, right? You know, across trapped ions, across, you know, photonic uh
00:24:06
approaches, spin uh approaches. So, you now say modality is not an issue. Error correction's been proven uh across them.
00:24:14
And you know, I think the race will be on and my prediction is meaningful results before 2030.
00:24:20
>> Wow. You realize that's about 40 months from now. >> Yeah. Okay. Meaningful results. Thanks
00:24:25
so much, Pat, for sharing all this incredible uh >> information and knowledge. Great to see
00:24:30
him. >> Very good. >> Your most valuable conversations rarely happen at a desk. The hallway sink, the
00:24:38
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00:25:00
>> 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
00:25:08
product that people are addicted to primarily Anton the people who work for me and I uh love talking to you because
00:25:16
as a founder you have a northstar you're incredibly laser focused on enabling anyone to build great software. Yeah
00:25:25
it's the mission of the company. I'm paraphrasing here but >> essentially that's the mission of
00:25:29
lovable >> mission I talk about empowering humans. empowering humans >> and the first gap is to build a product.
00:25:36
>> The second gap is to build a business around that product, right? >> And now at everyone at Lovable, we're
00:25:41
we're working on both of these two gaps, right? >> The first one, we got very far. We're
00:25:46
seeing a million new projects built every single week on the >> incredible. And on the on the second
00:25:51
one, we're investing a lot in making it easier to run your business and to get people to care, people to discover what
00:25:58
you build and the entire business of what you're whatever you're doing as a small business. As if you're a large
00:26:03
business, we're also getting a lot of traction. Um, and uh we're actually seeing as a proof of that more than 700
00:26:12
million visits to the applications every month. So every month there's um extreme
00:26:17
growth in in the surface area of of the entire more than 50 million apps built on the
00:26:23
platform to date. >> How many years has lovable been in market or how many months now?
00:26:28
>> 20 months since 20. Yeah. And and again we're seeing people who are first-time
00:26:33
founders. We're seeing enterprise leaders move much faster together with their teams on this platform that has a
00:26:40
lot of opinionated pieces in how you should uh create software and how to operate that software and how the
00:26:48
different applications in your company connect to each other over time. So that's what why we're seeing so much
00:26:53
growth also on the enterprise side which where where we actually growing fastest
00:26:57
right now. This is really interesting because 10 years ago, uh, people were doing wizzywig software. Um, what was
00:27:05
the name for it? Before Vive, no code, low code. Yes. And when I saw that 10 years ago in my incubator, you know,
00:27:14
every 20th company, somebody would come in who was an MBA or not a developer and
00:27:19
they had vibecoded something and um not vibe coded, they had no coded and they were using these different software
00:27:26
platforms and the software didn't look good. It didn't work perfectly well. It was slow, but the promise was there. And
00:27:35
I guess it took LLMs and this new intelligence to make actually good software. So maybe
00:27:42
you could talk a little bit about who is the customer because developers uh do developers use lovable or is it
00:27:53
the other 95% of society that are your customers? How do you think about who your ideal customer profile is?
00:27:59
>> Yeah, we're seeing people use Lovable both with a technical background. about
00:28:04
20% are technical or some type of engineer and they they love that we're quite opinionated. We put all the best
00:28:11
practices into how the software is architected and we make it seamless to um with one prompt get payment set up in
00:28:18
a very secure way and do things like run security scans after every change even now in the background monitoring the
00:28:26
projects. So it's actually quite appreciated by the engineers in the technical community. Um also because
00:28:33
it's a great bridge >> from the nontechnical people which is four out of five are are nontechnical uh
00:28:39
and they're building uh often first to figure out what is the right thing to build
00:28:44
>> which is where lovable has always been exceptionally good and uh now what we're
00:28:49
seeing is that people are running businesses making more than million dollars of revenue on the on this
00:28:56
platform. So it's it's this we're building for everyone. It's this entire spectrum. And what's what's exciting to
00:29:01
see is often that if someone who discovers lovable from their colleagues at a large company, they go out and then
00:29:07
run a side hustle and some of those those idols hustles really work. They make hundreds of thousands of dollars
00:29:13
and then they become a founder after that. So there's this crosspollination from both.
00:29:17
>> Yeah. And this is like the really interesting thing about vibe coding. If we were sitting here last year, people
00:29:21
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
00:29:27
maybe create wireframes or a workable prototype. All of that's out the window now. The whole concept of building
00:29:36
wireframes and building a mockup, well, you can just go right to building the product in a day or two days. And what
00:29:46
people I think don't appreciate about what you're doing at Lovable is after you've made a product that you're proud
00:29:52
of and that has some product market fit, there are many more steps that are required. You mentioned payments, you
00:30:00
mentioned security, uh making sure that the data isn't lost or that it's not leaked.
00:30:06
That's changed dramatically over the last 12 months. Yeah, >> very much so. So um I would say many
00:30:12
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
00:30:19
engineer to create software right um but the the thing that lovable does for any
00:30:24
anyone also the non-technical people is that it it um takes uh creates a structure for the architecture of the
00:30:31
software that you build and it makes sure that you don't go off a cliff um and that things like setting up payments
00:30:38
emails things like getting discovered by other AI chat engines and uh by Google search those things are kind of taken
00:30:47
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
00:30:53
care care of data security connecting to other tools that you might be using in a
00:30:59
secure way and and that's really where um us being opinionated from day one and being focused on making this for the
00:31:06
99%. It's a it's a vast market right from the from day one is what made us very successful.
00:31:12
>> Yeah. And I can tell you internally I gave my team all the different tools they could possibly want to use and
00:31:19
somebody had started with lovable. I think I told you the story when you were on this week in startups a year ago like
00:31:25
and they made some interesting websites and they were trying to make an internet. They couldn't quite get it
00:31:31
done. Then I had some people who started using you know cursor or clawed code. They started vibe coding stuff but they
00:31:39
couldn't finish the product. And then people tried to solve some problems with codework. I really like perplexity
00:31:45
computer. And then my team came to me and for one of our projects I was talking to you about founder university
00:31:52
our pre-acelerator. They wanted to make an internet. Um, now this is something I would have
00:31:58
never okayed because it would have cost $500,000 10 years ago to make it and we don't
00:32:04
have that kind of budget. You know, we would rather put that towards the founders in the program and getting more
00:32:09
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
00:32:16
asked for. And it was the person running the um this founder university who made
00:32:23
it and she did it on her own without uh permission in lovable. I said, "Whoa, where how did you build this?" She said,
00:32:30
"Loveable." I was like, "Oh, we still have lovable." And they're like, she's like, "I just put it on my corporate
00:32:36
card." To your point, she made it. Now that software is driving the program and the reason people do the
00:32:48
uh the the program in their country, we have it in Saudi and in Japan is because
00:32:52
it has economic impact. >> So I said, "Hey, I have an idea. Can you make for me an economic impact of the 50
00:32:59
companies that are in the program?" She asked Lovable to do it. I gave her some, you know, prompting, human
00:33:07
prompting boss to now it has the economic impact in there and it considered, you know, with our
00:33:13
prompting, well, how many people work at each company? What are they paying taxes? How much do they rent their home
00:33:19
for? What is their average salary? And it built something that I would have never been able to afford to build. And
00:33:25
lovable is 50 bucks a month, I think. I don't know how much you charge, but it's
00:33:30
far too little. Like >> $50 a month, I think. >> Yeah. That's if you're on a business
00:33:35
plan. Yeah. It starts at 25. >> Yeah. So, uh, the economic impact of what you're building is I would equate
00:33:44
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
00:33:51
if you just put employees at 50, 60, whatever, $70, uh, plus the cost of your software, it got made for less than
00:33:58
$2,000 >> in a year. It's extraordinary. I I'd love to hear more about the progress of the of the internet.
00:34:06
Anything that you asked for that you want to forward directed to me. >> Uh well, right now, you know, my concern
00:34:13
was security and making sure that data didn't leak and they talked to your team and they went through it and
00:34:20
>> it's secure. So, we feel good about it. >> Well, look, um I'm now asking people who
00:34:27
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
00:34:33
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
00:34:41
invest and spend money on that every also free users get a lot of security scanning running in the background that
00:34:47
that actually um translates to something that security experts can can see. And >> a year ago we were at mock-ups. Now
00:34:55
we're at functionality and secure and super viable for deployment. Where will you be in a year?
00:35:03
>> Yeah. So, what we're seeing is that there's a gap in build being able to build the product, right? And and you
00:35:09
built an entire internet on the platform. That's great. Um, what we've done since then is to have a new product
00:35:16
line basically the hosting part which is both the AI and you know all the normal
00:35:20
hosting and that product line has been going faster than the building uh thing. I mention
00:35:25
>> AWS competitor. >> It it lets you it lets you run all your software and then we're working with
00:35:31
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
00:35:40
customers as an AI co-founder, >> a partner that you talk to about everything in your business. And if
00:35:45
you're running your apps, your tools are on the platform, then just talking to Lovable has access to all the data that
00:35:53
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
00:36:02
in pre-release to give them access to a co-founder that works for you even when you're sleeping
00:36:07
>> and comes back to you in the morning and says like, "Here are some strategic directions you could go. here's some
00:36:13
optimizations you can do go in terms of growing your business faster serving your customers better uh faster uh and
00:36:20
and and that's um that evolution towards operation and intelligence for towards driving towards outcome
00:36:28
>> for your business to build the software but you stay to build the business >> yes to operate your business and um what
00:36:36
we're already doing I've been doing for a very long time is to compound from everything we're learning every time
00:36:42
lovable makes a mistake. Uh it goes to a gentic system with our engineers in it improving it. That compounding
00:36:48
intelligence is of course applicable to our our customers, our users running their business on our platform as well.
00:36:56
>> Is software going to become 100% bespoke even like the internal tools. I was looking at Slack and our
00:37:06
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.
00:37:13
But I was starting to think, well, maybe I should vibe code my own Slack so it's
00:37:18
integrated into everything we do at a deeper level. So how do you think the f what do you think the future will look
00:37:24
like in terms of some of these you know uh foundational pieces of software that every startup every
00:37:32
enterprise uses Salesforce HubSpot Slack uh the Google suite Microsoft Office will bespoke software
00:37:44
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
00:37:51
about someone I recently heard who's going on this journey. They're quite advanced. So,
00:37:56
>> NAD, he works at a pretty large company in the US, Nursa, and uh he came to our
00:38:02
platform because he wanted to build out a new product lines, nurse study for educating more nurses, right? And and he
00:38:09
built out all the admin tools for the program, the scheduling for the nurses getting getting their licenses and their
00:38:16
certification management. and he was able to build that into a product and to take it to market because they have they
00:38:22
have had all that access to nurses wanting their certification. What he also did was he took it into the back
00:38:29
office internally and they've now replaced more than 10 tools that they had bespoke applications and um I think
00:38:38
in terms of your question you can do that for multiple reasons. In their case they're saving more than a million
00:38:43
dollars per year, >> right? >> So that's that's huge, right? But it's also the case that in some cases you
00:38:50
have specific requirements where the tools that you've been using to date they aren't suited for those requirement
00:38:56
exactly and in those cases I think yes >> you will have more more bespoke solutions. Yeah,
00:39:02
>> but we're I also expect us to see that lovable continues to interoperate with
00:39:09
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
00:39:16
suite or now to anything in the Microsoft suite or or Slack lovable guides you through all the steps to do
00:39:22
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
00:39:29
that you don't give access to the wrong person to the wrong data and you can continue to use Salesforce um HubSpot
00:39:35
and all the tools that you kind of like to use under the hood but with a bespoke
00:39:39
interface on top of H how have these new frontier models they're in some ways competitive but in some ways you can use
00:39:48
them to power lovable. So how do you think about the competition with them opensource
00:39:55
a and the future of lovable because people have announced that lovable's dead every 6 months since you started
00:40:02
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
00:40:09
we we reached 500 in May. >> Okay. Growth is a phenomenal. >> So you're dying again by another 100
00:40:16
million in annual revenue. >> Exactly. >> So but underneath the hood you're using
00:40:21
some of these. >> Yeah. Let me explain. Yeah. So we've always had this strategy that we do
00:40:26
whatever is best for our customers. And in terms of the intelligence that means that we're using multiple models. And so
00:40:35
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
00:40:42
both the commercial frontier models. So from multiple vendors >> and increasingly it's open weight models
00:40:50
where our team when whenever it's get gets routed to an to our own model that model becomes more intelligent for our
00:40:57
agent harness. Yeah. especially on the mistakes that it might be making in some cases on which which tool to call, which
00:41:04
integration to create and how to guide you through uh success for your business.
00:41:09
>> 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
00:41:17
have multiple partnerships and we're investing heavily to be close with those partners and it's the big the big labs
00:41:22
and it's also to make sure that um we get the fastest performance at the lowest cost for our customers when we
00:41:31
know that we can do that with our own models >> right >> and uh we have a really really strong
00:41:36
research team up in Stockholm who is working on what's called post training so and we're applying all the best
00:41:42
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
00:41:49
European ecosystem to have that capability in Europe specifically. >> Are you doing or are you using any of
00:41:55
the data labeling data training companies to help you understand the most common businesses and build that
00:42:03
proprietary data? So, so what we're doing is that we're looking at um the mistakes that any of the models do right
00:42:10
now and then we we prioritize them by what drives most impact for our customers and then we make the models we
00:42:17
create data sets or we um we do did something called reinforcement learning specifically for the problems where the
00:42:23
frontier models are making mistakes for us right now and um we have this enormous token distribution right from
00:42:31
um a million new products being built every every single You're burning a lot of tokens.
00:42:36
>> We are. Yes. And that's and that's a lot of signals for making the system both
00:42:41
the agent harness >> and um what we've been refining over the last two years which is the skills that
00:42:49
we have have this like internal type of skills that the agent knows when to remember the facts from our software
00:42:55
engineers that know how to build really really good software. We're modifying both of those on every every single
00:43:00
week. >> It makes total sense. And somebody told me some companies are doing token
00:43:05
dumping. They're, you know, selling $100 worth of tokens for $50. Um, you know, basically they become token resellers in
00:43:14
some ways and they're money losing businesses. You have to you're money you're profitable I believe now or close
00:43:21
to it. Um, we we always monitor our margins, but again um we're doing what's best for our customers and that means
00:43:28
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
00:43:34
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
00:43:41
>> but are they is it unlimited for the 50 or you have caps now >> we have caps overages and caps are people starting to
00:43:48
hit them >> yeah our customers definitely hit caps and then you can top up you can have a
00:43:54
we have multiple subscription tiers >> what number I'm just curious like what percentage of people need to top up.
00:43:59
They're so addicted to it that they're blowing past the the >> so from the lowest subscription tier.
00:44:05
>> Yeah. >> Um I I think it's the >> uh m it's something like 60% of our customers
00:44:12
I think >> I'm hearing that more and more often that people are willing to pay the
00:44:17
overages because they're getting so much value. And I think that's the future of
00:44:21
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
00:44:30
year, but this is a $500,000 piece of software, I don't care. I'm still paying somewhere between.1%
00:44:38
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
00:44:47
about moving moving fast these days and and AI more AI usually lets you move much faster. So the spend is usually
00:44:54
worth it. >> Do your customers a final question for you because I'm starting to see this now
00:45:00
where multiple people in the organization try to solve the same software problem and they're competing
00:45:06
with each other. So like this internet I'm talking about, we built one for Japan.
00:45:11
>> Yeah. >> But somebody built the US one. So now I have two pieces of software. So I said
00:45:15
to the two different people or do we have did you guys fork each other's code or they're like no we just
00:45:22
built two different lovable projects. And I'm like is that the right thing to do because you went faster and I had two
00:45:30
swings at bat two different intelligent brilliant people making their version of
00:45:35
the software. >> But you would never have done that >> in the previous way of building
00:45:40
software. You would have one track of software and you would be building Franken software where you'd be trying
00:45:46
to get all the needs into it from the two different groups. I Yeah, I I'm actually a huge fan of very rapid
00:45:53
experimentation and I I have a story where for a while I worked at a a place called CERN where they do particle
00:46:01
physics. It's it's pretty here in Europe, right? Uh and that's where I was introduced to this concept of
00:46:07
co-opetition where they have two actually quite isolated teams working on the same um particle accelerator but
00:46:15
different places on it and then they don't share the results until they publish and that way they uh they can
00:46:22
kind of over time learn what's working best in the different organizations but you don't get stuck in a local minimum
00:46:27
and it's you know free markets work extremely well because of competition and they they they do that in academia
00:46:31
as well and now since the engineering is less of the bottleneck. It's more the question of what is the right thing to
00:46:37
build. I think it's a great thing to have if you have the sufficiently many humans right to do to try to attempt
00:46:45
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
00:46:50
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
00:46:55
take this these three things that I really like and and bring them bring them over here and maybe even run an a
00:47:02
split test, run an experiment to see if it's if it's improves improving the metrics for for our customers we're
00:47:07
trying to serve. >> Did you see somebody used Fable to build Fortnite >> and uh >> I've seen the 3D some of the 3D games?"
00:47:14
Yeah. >> Yeah. What is your take on, you know, this latest version from Anthropic
00:47:19
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
00:47:24
models. >> What do you think of it in terms of compared to the last generation faster,
00:47:30
better, both? >> Yeah. Is it a massive step function? Yeah. >> What I've seen is that it can in the
00:47:38
first attempt create very sophisticated things that look really good. Then when as you're evolving right it's it's still
00:47:45
the same thing where you as a human you have to think you often should be planning together with your agent about
00:47:52
what is the right thing to do and and that's more of that's again more of the bottleneck uh whereas more intelligence
00:47:58
is on some tasks it's great yeah like it creates really beautiful things 3D games
00:48:03
for example but on figuring what to what to build figuring out figuring out what
00:48:08
are the right strategic directions or experiments you should run to improve outcomes for your business. That's um uh
00:48:15
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
00:48:22
be able to take the right decisions for taking your product forward and to take your business forward.
00:48:26
>> Um listen, I love the product, but even more than I love the product and you as
00:48:31
a founder, I love the outcome. The outcome for business is extraordinary. So, anybody who's listening, Lovable is
00:48:39
absolutely worth your time. Don't wait. Just put it on your corporate card and start building. That's my message. Just
00:48:45
start building with Lovable. It's an incredible product. And uh congratulations on being reborn six
00:48:53
times cuz every 6 months you add 100 million in revenue it seems. And then everybody says Lovable's dead because
00:49:00
the new foundation model is so good. But you keep studying your customer and and
00:49:05
you keep somehow surviving and thriving. So congratulations as an entrepreneur. Thank you so much, Jason. I enjoyed that
00:49:12
chat. I hope you enjoy the rest of your stay here in Paris. >> It's pretty great. And the Palace of
00:49:16
Versailles is so impressive, huh? Uh someday we'll be building this with lovable and optimist robots. I
00:49:22
>> I'm looking forward to it. I'm going all in. I'm going all in.