but look honestly if you zoom out even a little
bit all this stuff is so underhyped like you
can't you can't hype it enough right i mean it's
just like it's just amazing it's incredible this
technology works really well it affects so many
industries people are like maybe disappointed
that like it didn't solve every single one of
their business problems like right now and it's
like relax like obviously it will soon just wait
wait a month you know How did the best machine
learning practitioners get involved in the field?
What challenges have they faced? What has helped
them flourish? Let's ask them. Welcome to Learning
from Machine Learning. I'm your host, Seth Levine.
Hello and welcome to Learning from Machine Learning.
On this episode, we have a very special guest,
Lukas Biewald, the co -founder and CEO of Weights
and Biases. the host of one of my favorite podcasts,
Gradient Ascent, one of the earliest AI entrepreneurs
starting CrowdFlower Figure 8 back in 2007. Lucas,
it is such a pleasure to have you on the show.
Thank you so much. Thanks for having me. We're
going to get right into it. So what initially
attracted you to machine learning? I was always
interested in AI. generally, like, you know,
even when I was a kid, I think I really liked
to play games. And so I thought a lot, you know,
try to program, you know, my own kind of like,
rule based systems to, you know, to win it, like
connect four and stuff like that. So I was thinking
about that a lot. And, um, you know, my dad actually
read me that book, uh, Gertlischer Bach. And
I didn't understand much of it at all, but it
has these great parts where there's these sort
of stories. And so the stories are pretty compelling.
And then I think with that kid mind, I was like,
you know, AI does seem like humanity's last project
and kind of the most interesting thing you could
possibly work on. And then I went to Stanford
and I... I actually reached out cold to Daphne
Kohler, who was kind of a young professor there
at the time, and asked her what she did. She's
like, okay, take my class in machine learning.
The class was so much fun because it was kind
of the fun parts. And actually, the best part
of the class was this kind of reinforcement learning
piece where you train a fellow program through
reinforcement learning to... to get good at Othello.
And yeah, from that point I was hooked because
that's actually, Othello's a really satisfying
reinforcement learning project you could even
do back then where, you know, I kind of watched
that program, you know, over a few nights, basically
go from terrible where, you know, it couldn't
beat anyone to just like, you know, crushing
me every single time. And actually it's funny,
you know, I got way better at Othello playing
that game. I've actually never lost at Othello
since training that computer because the computer's
kind of teaching me how to play um towards the
end of it although i've never played like a i
don't know professional or like you know skilled
a fellow player but um but yeah i mean that that
was like the point where i got i got like you
know really really kind of hooked on it and then
it kind of went through like a sad part so i
you know i started doing research with um daphne
i kind of thought okay maybe you know i want
to be like a professor of this but you know this
is back in like 2004 2005 there really was not
a lot of stuff Um, working, I mean, Google was
like working really well, but a lot of that was
kind of like page rank and, um, you know, a lot
of the, the energy was around sort of like, you
know, ranking ads that didn't, you know, that
didn't feel like that satisfying. And I interviewed
like some hedge funds and, you know, I was like,
okay, like, I don't know if that's really like,
you know, what I wanted to dedicate, you know,
my life to. And I was actually thinking like,
okay, you know, label data is kind of driving
all the research, which is why I got into like
the data labeling business. So I thought, okay,
you know, like if we could create more label
data, we could create more interesting applications,
which is kind of like what I always, you know,
what's kind of the most fun part of machine learning.
It's like, you know, really seeing what it can
do. And man, I've like lost even what the question
was, but I'm no, you know, it's perfect. So that
leads you right into 2007. Founding your was
that that was your first company that was founded,
right? Yeah. You were way ahead of the game.
You were way ahead of the game. Yeah, although
you say I was one of the first AI entrepreneurs.
I don't think that's actually true. I mean, there's
been a long... People have been building AI companies
since the 70s, 80s. So I think the definition
of AI kind of changes. Once we get something
working, we no longer maybe consider it AI all
the time. Right. Yeah. But yeah, that was actually
sort of like an AI winter. It was also sort of
like an economic winter where there's... You
know, like Y Combinator just started, but if
you look at those early stuff, you know, those
are kind of much weirder ideas. And now I raised
my first round. It took a year to raise a round
on, um, $2 million, you know, valuation and we
had revenue. So it's really a different time.
Like Facebook was just starting to take off.
And, and, and so another thing that was funny
back then was I, I was basically, I was building
an AI labeling company, but I was kind of coached
to take AI, you know, out of the pitch. Cause
that. It was such an AI winner. Investors did
not want to invest in anything related to AI.
That just seemed like a bad science project.
That was a real sign of the times. It's funny
because what was happening actually behind the
scenes is these companies were starting to find
success. Our labeling product got some quick
traction with actually eBay was our first really
big customer and that really pulled us along.
Yeah, you mentioned a couple of really interesting
points that I've been talking about also recently,
like the moving goalposts of AI, right? Because
like once it works, it's it's just an algorithm.
It's like, I always think about computer vision.
And I think about like face detection and things
like that. I mean, those are just algorithms.
But if you thought, you know, whatever decades
before, if there would be a thing in your pocket
that can identify where your face was, right,
that would be obviously magical. Now we just
think about it like it's just a given. Like,
of course, that's how That's how things work.
So I guess the modern era of AI entrepreneurship,
I guess what I was saying early to the game,
there are still companies that are trying to
solve those same data labeling problems that
you were addressing in 2007. So you could really
kind of understand you had some inkling of something
amazing happening. I mean, think about it. That
was nearly nearly 20 years ago. And we're still
trying to solve these problems. Right. Yeah,
I usually ask like was there a moment that convinced
you that this was the field that you wanted to
build your career in? But it sounds like it was
pretty early on or was there something later
that happens that where you were like, yeah This
is definitely what I want to be doing No, I mean,
I think it was really early like I mean like
really like a little kid kind of thinking about
AI I think I remember somebody's dad I think
told me about like simulated annealing days to
call it which is kind of like a gradient descent
And he told me something was designed in that
way where they had the computer try different
weights and then gradually make the weights better.
It really just captured my imagination. I was
writing code. I was like, what if computers could
write this code? When you're a kid, it's not
obvious that facial recognition would be hard,
but multiplying two huge numbers together would
be easy. you kind of come to that, you know,
over time. And so I think I had like a lot of
optimism, which has sort of like slowly, you
know, crushed out of me through like, you know,
actually like trying to try to do it, you know,
from like, you know, 2003 to maybe like 2016.
Actually, it's funny. I was, I was a big Go player.
Like I loved playing Go. I think one of the things
I liked about it was that computers are so bad
at it. And if you, you know, if you do play Go,
you kind of... Understand why like it's it's
like it's interesting. It's like it goes like
really designed for our brains to do it Well
in a super cool and satisfying way, but you know,
I remember when I was running I was running crowdflower
figure eight when alpha go be Lisa doll. Yeah
I mean, because I'm like a huge Go fan. I was
like, actually, you know, I was like watching
those games live and like, you know, watching
the commentary and really thinking about it.
And it was like, I actually had not really paid
attention to the advances in deep learning. Like
at that point, if anyone is ever like, hey, we
have a new algorithm that does things better.
You just have a huge skepticism because people
kept saying that, kept saying that it wasn't
true, wasn't true, especially in like the entrepreneurial
space. It's a good pitch, you know, but it's
actually like generally not true. So I was like
super skeptical that, you know, AlphaGo was going
to beat. uh, least at all. And then, you know,
when it did, I was just like, Oh my God, you
know, like this is like, that's kind of what
made me realize that, okay, I need to, um, you
know, kind of get more technical again. And I
was like, look, and that's like how I ended up,
you know, doing an unpaid internship at, at,
at opening up. This is like very brief and wasn't
on their payroll or anything, but I was like,
I just need to go somewhere where people are
doing the latest stuff and, and work with them
because I, you know, running a data label and
company, you work with like AI customers, but
you're not really doing. a lot of AI internally.
So I kind of was, it's funny. I was like, you
know, my thirties and kind of feeling bad about
myself. Like I had just become this like totally
like deeply non -technical person and just sort
of like a mediocre, you know, like entrepreneur
and like just didn't even really know like, you
know, what was kind of going on in the space.
And so I'm so happy that I did that. Cause I
think that like led to me. Getting a lot more
technical again and and like actually like getting
to enjoy This new era where like all the AI stuff
works and like things surprise you by how fast
they come rather than like how slow they come
Yeah Going back to the point that you were making
earlier, it's like back in whatever say the mid
2000s when you didn't want to say AI in your
pitch, thinking about what that's like now, where
every single entrepreneur, whether it is AI or
it's not AI, they'll just say it, it's become
more of like a marketing term. And then yeah,
the other point that you're making with Go, that's
like one of the main stories in the book Genius
Makers. And they talk about that like move, move.
Yeah, yeah. Yeah, where it was not intuitive
for a human to make that move, but it ended up
being the right move, which is just such an interesting
idea with AI in general, just like the ability
for it to pick up patterns, but it's more than
just picking up patterns, it's picking up patterns
that humans might not be able to detect, which,
yeah, unless you're kind of really in it, it's
hard to understand the power of it. And I'll
tell you, for the true Go aficionado, which I'm
not like a... I don't know. I don't play a lot
of Go these days, but I am pretty interested
in it. As a kid, I played it a ton of Go. And
I would always think, OK, what would God do?
You know what I mean? I wish I could know. Because
it feels so weird. The kind of optimal first
move is the 4 -4 point, or maybe the 4 -3 point,
which is just not the center of the board. It's
not the edge of the board. It's just like, OK.
And then it's debated. Is 4 -4 better or 4 -3
better? And all these openings, there's a lot
of tradition and like, You know, you're kind
of like, okay, how true is it? Like, and there
are people who come up with these like wild styles
and they'd like work pretty well. And I just
always was wondering, like, could we find out
in my lifetime, like what the best, you know,
moves are. And then when they trained it, like
the, the alpha zero thing where they trained
it with like not unprofessional games. I think
the one that beat least at all was mostly trained
on professional games and a little bit of self
play to get a little bit better, but it kind
of had a more. like a style that like came from
humans, but then the ones trained without any
human input are so interesting, both where they
find like, you know, kind of famous human openings
and like those, but also there were a few things
that were missed, like really like obvious things
that you could go back, you know, 20 years and
actually explain them to like any Go player.
And the whole world of Go players, like, you
know, like all these people just missed like
these like Choices that that AI found I think
it's like just so cool and and so profound Yeah,
I mean it a little bit happens in chess, but
I think I think go is like more weird Yeah, I
mean like I'm also kind of into chess. I'm less
proficient at it and know it less well. But it's
pretty fun watching, you know, the new chess
programs that are trained off of again from scratch.
And there's so much more aggressive than like
the deep blues and stuff. And that makes me feel
better about my unsound chess play. I can't speak
to my go, but chess for sure. I used to I mean,
I used to love playing chess. I wish I had more
time. And then I also think about like poker
and things like that and how much there is. Yes,
there is like the mathematical statistical statistically
correct, you know, move to make in every position.
But there's also the game that you're playing,
and there's the styles that you can have. So
it's interesting to think about that. And the
parallel that I'm thinking with, you know, frontier
models and LLMs and things like that is like
personas. And I'm thinking about that also almost
as like the style in which you want something
to be, you know, behaving in whatever environment
you're in with them. So it's interesting, dependent
on the context and dependent on like, you know,
what the what the prompt is, you could have two
very intelligent models, and they could respond,
you know, they could respond differently. Yeah,
well, but just making a different I mean, like,
so go, there's one best style. Oh, it is. Okay.
Oh, yeah. So like, you know, Because it's a full
information game like chess, right? So like there's
a best way This is best move at any point obviously
and so I think you know, it's just I think that
The models trained without human input now find
it, you know find a better More effective style
than the ones trained on on humans to sort of
reveal that you know humans do some suboptimal
Oh, I see what you're saying. I got you. I got
you I guess that the thing that I was thinking
about with chess was you could have the best
move or in poker, you could have the best move
given that you think somebody is going to then
respond to you thinking that they're going to
respond properly. But you never you don't know
exactly what they're going to respond. So in
chess, you could have a move where you're planning
one, two, three moves ahead. But, you know, some
of the best players are thinking multiple 10,
15, you know, 10, 15 moves ahead. Is there is
it like that with go also? Maybe. I don't know.
I guess I don't like to. Yeah, it's okay. To
play that way. I think generally relying on your
opponent to make a mistake is a dangerous mindset.
So what I'm talking about is the programs that
try to play optimally. I think chess programs
sometimes they try to train it to play like a
human and make mistakes like a human. And I'm
sure you could take advantage of human tendencies
more effectively and crush them even more thoroughly.
But I think these programs beat humans. 100 %
of the time, you know, and like by just playing
optimally, too. So, yeah, it's pretty amazing
how the how how fast that has happened. I mean,
like, I guess, you know, decades ago, people,
I guess there were a lot of different feelings
on the matter, but these sorts of games. Yeah,
the the machine, the machine, the machine is
going to the machine can win in all of these
now. So was it the idea of the magic behind gradient
descent, the ability for machines to get better
over time, that was the thing that kind of attracted
you to it? Or was it more than that? Well, in
a way, that's all there is, right? But I mean,
there's like what you draw from that, which is
like, if computers can program other computers,
then you can solve literally every problem that
humans face, right? I just think when you have
that recursive ability, Yeah, to automatically
prove algorithms. I think that's just the most
powerful technology that you could possibly build.
It sort of subsumes every other endeavor that
you might, you might try. Yeah, I kind of go
for a hard pivot into fast forwarding to founding
of weights and biases. What was the initial impetus
there? Well, you know, I think it was I think
in some ways for me personally, it was a reaction
to what I liked and didn't like around CrowdFlower
Figure 8. So I think I felt like CrowdFlower
Figure 8, it's a good business. I mean, Scale
AI has taken it and run really far with it. It's
a good business, but it might not be the perfect
business for me to be... You know, running like,
I think I'm a little more oriented towards wanting
to sell to developers, like wanting to make like
high quality products. And, and I think I also
just felt like a passion around it, like more
than like, this seemed like a good business models.
Like, you know, I really respect the people that
are building these AI models. I'd like to help
them. Like, I think like, you know, what I have
to offer is like kind of understanding like how
they work and like how to make their, um. their
work work better. You know, my, my co -founder
Sean and Chris and I, we were just really passionate
about like moving forward to the state of the
art of AI. We felt like what we had to offer
was good, um, developer tools. And we had been
pretty close to the GitHub founders and kind
of watch them, you know, be successful. We also,
I think one thing that was helpful to see was
the GitHub founders at first, everyone's like,
oh, developer tools. That's a small market. That's
a stupid thing to work on, but it's sort of like
developers became really powerful within the
organizations. Cause like what they're doing
is automating companies. I think what we were
thinking. with Weights and Biases is these AI
developers, even smaller market, it's like a
subset of developers, but they're even more powerful
within their organizations because they're automating
the automation. So let's get on their side. Let's
make stuff that's good for them. And that's going
to be a great thing to offer the world. Yeah,
definitely. So Weights and Biases was founded
back in 2018. So it was a different... machine
learning landscape then. I fell in love with
Weights and Biases back in 2019. I used it to
track experiments. I think I'm not sure if it
was even earlier, but definitely one of my first
one of my first big projects. I was like, I need
to figure out a better way than writing all of
my results down. And I want this to be automated.
I was like, oh, OK, I could just add this these
three lines of code. That sounds great. And now
what it has evolved into. Oh, my God. It's like.
So at the time, I was just, you know. logging
classification reports and confusion matrices.
Now I don't even know if it's like a standard
thing. I put like my visualizations. I have like
these data map plots that I put into weights
and biases. Yeah, I have a lot of fun with it.
I've told every machine learning person that
I know how much I how much I really weights and
biases has made my career so much better, like
just so much better. So thank you. Thank you.
Thanks for that. That kind of feels really good.
Yeah, definitely. I hope you hear that because
it's an amazing tool. Yeah, I guess maybe what
happens is people start taking it for granted,
but no, it has become ingrained in the way that
we do work, 100%. So yeah. But what I was going
to talk about or ask you is basically like, yeah,
the machine learning landscape has changed. 2018,
I think it was, I don't know what I was working
on a lot of just like classification stuff. And
now 2025, we have the LLM for the last like,
what, three years, I guess. It's a very different
landscape, the generative landscape. So how has
the vision evolved for Weights and Biases? Can
you talk like, yeah, just with the landscape
changing and... Yeah, just you touch upon that.
Yeah, I mean, the you know, the biggest change
by far to the landscape, I think, is that, you
know, when we started like. you'd mostly build
your own model and like fine tuning was like
some people would do it seemed like maybe a good
idea but it was actually kind of unusual and
now i think many many tasks you could just you
know use an off -the -shelf lm you could go to
open like if you want to translate something
you don't need to build a translation model you
can ask um opening literally just ask it to translate
something right um and so so many of the the
tasks now are just asking lms the right way and
then we have like these new even more exciting
tasks where it's like chaining the stuff together
using tools which you call like agentic systems
i think that's where um you know, most of the
interesting innovation is I do think deep seek
in particular cause the resurgence of people
wanting to do their own reinforcement learning.
So we are, you know, seeing actually more users
coming on board to like weights and biases, original
product and than ever, which is like really satisfying.
But, you know, it's like a different style of
thing, right? Your models are enormous. You're
typically, you're always running on like GPUs.
Often, you know a lot of GPUs running in a distributed
way like that's like kind of table stakes like
it was kind of exotic when we started like opening
I was the only one doing distributed training
for a long time on weights and biases and and
now I think you know any Anybody really trying
to do like meaningful scale is doing any distributed
training of some some variety So so yeah, so
look at what happened was we have this one kind
of product that does experiment tracking that
was really popular that we now call our models
product, just to give it a name. That's a WB
Python package you download. But we added a product
called Weave that's designed just for running
LLMs and kind of all the work that happens around
that. And the two are actually great together,
right? Because a lot of people, you want to build
a model, then you want to run it. But I think
that the number of people who would have a need
for a product like Weave that could track an
LLM that they got off the shelf is much bigger
than the number of people building AI models.
So I think what we actually saw was this democratization
of AI that everybody had talked about and where
anyone can really harness the power of AI. And
then, you know, they get all the kind of drawbacks
of AIT, right? Like, you know, just because you're
running a third party LM, it's like still non
-deterministic. You know, it's still like really
hard, you know, like to know how to test it.
Like, you know, you're never going to have a
hundred percent test coverage and you have a
non -deterministic, you know, LM underneath,
you know, your system. And you're going to have
to think a lot about like, okay, like what are
the metrics? I'm actually trying to optimize
here. How do I make them better? You know, what's
different is like, you know, people kind of coming
to that new without any machine learning training.
You know, they're, they're not as comfortable
with statistics as the average, you know, like
ML person, and they don't even necessarily in
the beginning, see the value and evaluations,
right? Cause it's kind of annoying to set up
evaluations. And you just wouldn't think of it.
You'd be like, ah, just I'll see if it's good.
And then I'll like, you know, ship it. And so
I think in the early days of waste and biases,
everyone, no one would be like, I shouldn't do
evaluations. And we're like, how do we do. Right.
I think with LMS, you kind of actually have to
convince people to do evaluations. I do think
there's a flip side of that though, like. I think
ML researchers had this tendency to just look
at aggregate statistics. And a lot of what our
product would try to do is get you to look at
individual examples and see what's happening,
because there's a lot there usually. And usually,
ML researchers don't do enough of that. I think
you see the flip side of that in this LLM world,
where people talk about testing by vibes. But
testing by vibes is actually trying the thing,
looking at some examples. It's a really good
thing to do. So now that happens enough, maybe
too much. And there's not enough to look at the
aggregate statistics because, you know, it's
like, Vibes will get you really far. Vibes is
really good at preventing you from doing crazy
things and having like these horrible, you know,
like deployments and stuff. You know, if you're
trying to like nudge inaccuracy higher, which
is kind of where every project ends up and you
want to get like from 69 % to 71 % accuracy to
73 % to whatever, that's where you actually really
need evaluations because you can't kind of make
incremental steady progress without, you know,
clear metrics that you're improving. Yeah, 100%.
So I guess a couple of things. Yeah, so I loved
When you guys came out with tables, I thought
that that was really cool, because I was able
to in weights and biases actually look and group
my data and find and see particular examples.
And I built and I've used weave. You know, we've
yeah, we've is so cool. Some of the things that
I really like about it is the tracing. So when
you have, say, like multiple LLM calls, you have,
you know, certain tracing. And what I really
like is that you can see where the what parts
of the code were touched with that area. And
I haven't really been able to get that sort of
detail anywhere else. So that's that's one of
the reasons why I like I like using weave. And
then, yeah, just to get into like evaluations,
I think people the hard thing is it's just it's
very hard to evaluate generative outputs. What
makes a summary, one summary better than another
summary, and you have to sort of think about
different criteria of what it's going to be on,
and then to go into your point. Yeah, like you
have to have a sufficient, you have to have a
robust evaluation system, because anytime you're
making any kinds of changes into the system,
whether it's prompt engineering, or whether it's
using a new model, whatever changing hyper parameter,
whatever you're doing, there are always trade
offs that happen. And if you don't have a robust
way of evaluating it going from something that
looks good. You know, like, oh, I think it's
good. I tried it out on 10 prompts. I tried it
out on 10 examples. It looks good compared to
like, oh no, like I need to get this performance
up from 75 to 85. Any change that I'm making,
I have to make sure. So what I've been doing
is I just keep building my evaluation set, right?
Just I get it done end to end and then I'll test
10 things and then I'll make sure it still performs
on those 10 things and it'll be 20, 30, 40. And
then like all of a sudden you're like, whoa,
I have like 150 things that I'm testing on this
thing. saying, all right, it's still not gonna
capture everything when I bring it to an actual
user uses it because you can't ever, you have
to continue to just kind of figure out how your
users are using your model. But at least I understand
there's no regressions in some of the capabilities
that I wanted it to have from the get -go. So
I think LLM observability is really hard. It's
a challenging problem and I... It's hard because
I know what it's like from your perspective,
what Weights and Biases does is a general tool
that can be applied to specific problems, which
I guess is a lot of software obviously, but particularly
for something where there's just... no real rule
book, there's no like, oh, like check the F1
score, you know, for like traditional machine
learning. It's not that simple. So it's hard
to know what to observe when it's unclear exactly
like what people want to be building with it.
So it must be a very, that must be very challenging.
I would say I'd say I don't know, how do you
deal with that? Like, where the goals of the
people using your product are evolving so fast?
Is that something that you think about? Totally.
Yeah. I mean, I think it's I mean, it's exciting.
It's like, you know, I think the, the entrepreneurial
gets like really excited. You know, I think it's,
you know, it, it kind of favors smaller companies
in a way, right? Like when you need to like really
iterate quickly. So I think like one of the funny
challenges that I find myself in is trying to,
you know, make like, um, you know, 250 plus person
company, you know, recently acquired by a thousand
person, you know, company, like just keep like,
you know, thank you. Thank you. Um, yeah. You
know, like I think we have to move really fast.
And we have to like listen really carefully to
what people are doing. But, you know, I think
that you don't also, also you don't have to solve
every problem, right? So like, you know, I think
we talk to our customers all the time and we
like look at their workflows and you see like,
okay, people want to make these like small sets.
They can quickly look at, you know, like LM is
a judge is like a really popular strategy. There's
different kind of patterns around that. There's
also like, you know, making sure that like users
can actually like, you know, put popular, the
feedback back into weave. And there's often like.
you know, sets of data where it's just like,
you can never like get this one wrong. Like we
never want like anything, you know, bad about
our brand or like, you know, we don't anything
like sexual in our, in our result, you know,
like, so you get, you get like what ends up happening,
I think in the same way that like, you know,
we'd kind of know that we were dealing with,
I think a serious like ML company when it'd have
like thousands of metrics, right? Cause he's
sort of like over time, you just get more and
more metrics that you're like tracking around
whatever you're doing. Cause it's like, in some
sense, you're always optimizing one number. But
nobody really thinks like that. You know, like
they, they want to see all the different things
that are happening. Cause they don't really know
that what they're exactly optimizing. So they
want to look like the same way as like with a
balance where it's like, you know, over time,
you know, there's all these different considerations
that you have to live. What means good. And so
you start adding more and more, um, you know,
evaluation sets. And so I think that's, that's,
that's where things move in. Like, you know,
we don't have to. do everything for you. We want
to do the most helpful stuff, but I think a lot
of people should be writing their own evals.
There's a lot of great third party open source
evals libraries that we try to support really
well. And then we also try to make it work easily
out of the box so people can at least get a taste
of the power of what we have. Very cool. We'll
go into this one. What are you most excited about
the future for Weights and Biases? Well, I think
we're now inside of a public... uh, company with
really big ambitions. And I think like, you know,
one thing that's exciting is that, you know,
they have, you know, they have a ton of resources
available to build, you know, lots of stuff.
So I think like, you know, we had to be really
careful about like, where are we like aimed our,
you know, resources, but we knew there's like
tons of other like steps in the, in AI workflow
that like, where we felt like, you know, we could
do a better job of, of building great stuff,
but you know, we also wanted to stay focused,
but I think now it's like, there's a bigger scope
here where kind of any any step in the AI workflow
is kind of like fair game or something like we
could build. That gets really exciting to be
able to build more stuff. And I think we obviously
don't want to only work with core. We've been
really careful about always saying that we're
going to work with every cloud. It's always going
to work with all the different infrastructure
providers out there. But it's kind of interesting
to actually see real life infrastructure and
hardware. Um, deeply cause it does show you,
there's like more stuff you could surface. Um,
that I think would like kind of help the end
user, especially the AI researcher. Like I think
we could do more to like, let them know like,
Hey, you know, weird things are happening in
your, in your hardware stack and your training
that you may want to take a look at. Um, we have
a conference coming up in a, in a couple of weeks
called fully connected. Um, and we're going to
announce a whole bunch of like integrations there
that that's kind of the tip of the iceberg of
what I'm, I'm excited about. Cool. Yeah, that's
exciting. We met last year at Fully Connected.
That's a great conference. Yeah. In terms of,
you know, joining a larger company and, you know,
understanding more of the integration and scaling
and things like that. So, yeah, you're thinking
there's just a more diverse problem set or more
areas where you could kind of help your end users
or your understanding things better. Yeah, like
what excites you about that? Well, I think it's
like, you know, we go head to head against like
Amazon. Amazon, you know, we're friendly with
Amazon, but they have a competing product for
like most of the stuff. that we do, and mostly
their product is garbage. I don't even know if
they would like, if you privately meet these
people, they'll tell you that they think the
weights and biases product is better. Boy, I'm
not making any friends with this comment, but
honestly, I think we've picked off a few things
that done them really well. I think at an executive
level though, people want to buy a complete solution.
No developer wants that, but for some reason...
I shouldn't say for some reason. I understand
why. Like executives and companies kind of want
to buy like one complete solution, have it be
like cohesive and sell it. And I think like,
you know, it's kind of a danger for weights and
biases, the product, right? Like, you know, people
would use like MLflow because they're like a
Databricks shop and they're just like, look,
I wish I could use weights and biases, but like,
you know, Databricks comes with like MLflow for
free and my boss is saying I have to use it,
you know? Like, I think there's a lot of power
immediately. in bringing these two companies
together, like CoreWeave and Weights and Biases,
now we can offer more complete solutions. CoreWeave
is also incredibly fast, like revenue growth,
far faster than us because of this amazing demand
for hardware and they execute really well. And
so we can also use some of their resources to
build a more complete solution, which is probably
where the world is ultimately going. So I think
we could have gone on Um, alone, but it's not
free. You're always kind of like looking for
ways to, to like get an edge because it's these
markets tend to be like winner take all or winner
take most. And so like, you know, I'm pretty
focused on like, okay, how do we like completely
dominate this market? Like not end up in some
sort of niche, um, category. Yeah. Sort of transitioning
into the entrepreneurship, uh, parts of things.
It's interesting thinking about. tech solutions
and how there are many solutions obviously for
many problems that's like, you know, the exciting
thing about being in this in any, you know, market,
but Basically, like Weights and Biases has a
lot of competitors. There's open source competitors.
There's other third parties. There's complete
companies around it. There are companies where
they're just doing things. I mean, you know,
you spoke about Amazon a little bit. It's like,
yeah, like Amazon has their own foundation models,
too. You know, but like, I don't know a single
person that's using them. And they also invested
in other foundation models. So they have these
huge, huge incentives. But to go back to the
other point, it's There's an interesting thing.
If you think about all the problems that are
flowing down, you try to get a bucket and you
try to find that bucket that you're going to
capture a lot of the problems that you're getting.
But then at a certain point, if you're a company
that's buying these vendors, it's like, well,
how many buckets am I going to have? I want one
solution that's going to solve all of these things.
And then sometimes you might not be able to focus
narrowly on the problem. I know you definitely
think somewhat about this, but I'm curious, like,
what's your take on this whole there being many
solutions for things and like tech consolidation?
Yeah, like you sort of touched upon it, but do
you have you do you think about that in the industry
that you're in? Yeah, totally. I mean, I think
what Marcus tend to do is like when there's some
like new problem and you need new capabilities,
that tends to favor smaller. companies. So you
get this kind of Cambrian explosion of different
options and the sort of biggest ones tend to
consolidate. And so I think we're kind of seeing
consolidation of some stuff. I think there's
a time when MLOps is all the rage. You don't
hear about that anymore. I think that's definitely
kind of consolidating market. But then there's
also a lot of new like LM, you know, functionality
around like, I don't know, like, like routing,
like, you know, how many companies need like
a router today? Like, probably not a lot, like,
will they need a router in the future? Probably
most will want one, right? So that's like a really
interesting, you know, category of thing. And
there's a bunch of companies there. And, you
know, I think There'll probably be more. And
then over time, there'll be less, right? Because
maybe that's not really a standalone business.
I'm not sure. Maybe the ones that are doing it
will expand into other categories. I think that's
just life and technology. But I think with AI
and the speed of innovation, it just happens
way faster than it used to. Yeah. I think I'm
stealing a question that you asked your guests.
What's a free idea? You asked that, right? What's
an idea if you had any time in the world that
you think... Yeah, it's funny. I feel like I
ask it more in a non -commercial context where
I'm actually just curious. Because I always think,
if I'm not doing this, what would I really do?
It's a good question to ask yourself what's interesting
out there. In terms of startup ideas, Man, it's
like, I don't know. It just seems like such a
fun time to be an entrepreneur right now. Because
I feel like, I mean, this is not a secret, but
you take this into any application and it just
still blows people's minds. People just do not
connect the dots between LLM generally and LLM
applied to a particular problem people have.
And so I'm just seeing all this. You know success
in that enthusiasm. I think people kind of think
they're late Like my friends will talk to me
be like, oh am I too late to this? No, man, you're
like still early It's amazing, you know, just
like right like I think lms applied to anything.
It's probably probably like a good idea honestly,
um, you know, I don't know like I think it's
like good to pick ideas that you kind of care
about um, I mean Again, i'm not telling anybody
the things that they probably don't know but
it's like um The progress in robotics right now
is unbelievably exciting. That's obviously going
to be this huge thing. Again, I think you might
think you're late, but you're early if you're
looking into that. That's what I'm intrigued
by. When you're doing a company, you really don't
have time for anything else. I know. The potential
for robotics is just too exciting. I mean, being
able to combine sort of all of the power of LLMs
along with, you know, computer vision and all
that, I think that's a really exciting, really,
really exciting field. I'm so curious to see
what's going to be coming out in the next couple
years and what people are currently working on.
Zooming out to just like kind of machine learning
in general and you know, maybe we touched upon
it Maybe we didn't but what do you think is an
important question that you believe remains unanswered
in machine learning? I mean, there's a lot I
think like I Don't know what then I'm like kind
of like intrigued by is like you know if you
ran if you ran back like history like a thousand
times, you know, like how much of, you know,
what we do around like a transformer architecture
would be consistent and how much is a sort of
product of like, you know, how we, how we do
it. Like I think like, you know, there's just
not a lot of like appetite for experimenting
with stuff and people don't tend to write it
down when they get just the same result. Like
I feel like. There used to be LSTMs, GRUs, all
these different architectures. I even remember
trying to program these things by hand and getting
it a little bit wrong, and it's still fine. So
I wonder a lot about what are the core bits of
this architecture that really matter. And then,
of course, we now design our chips around this
transformer architecture, so it's still locked
in. You wouldn't want to change the aspects of
it. But I think that's maybe an intellectual...
Question that I I wonder about a lot and I think
and there's been an explosion of Research and
reinforcement learning right now, which I think
is totally makes sense, but I'm having trouble
Comprehending I'm talking way like this is sort
of trying to like understand right now. It's
like what what about these different? Like reward
functions make them effective or not. I Don't
know. I'm confused. Maybe somebody understands
it. But yeah, I'm more confused these days than
I was in the past Yeah, we have a mutual friend
that would understand. John Schulman would understand
proximal policy optimization. But anyway, going
back to the other point, Transformers, and I
think about it too, I think about this with everything
in life also, is it just a local maximum or is
it a global, you know, or, you know, is it a
local minimum or is it a global? Like, is this
the most optimal? And then I just was speaking
about it, but also like, yeah, like, The 2017
architecture is not the 2025 architecture and
that's because the bet was placed and the bet
was placed and the infrastructure was put into
place and the and the chips were made and the
ecosystems were created and then you start iterating
on something and if you make a choice and you
iterated on on it for eight years and You start
to solve all of those problems that people were
having it's like you're gonna get it to a place
where it's really good and then we are seeing
that we're that we're probably going to hit a
ceiling but I think that already it's unlocked
so much it's unlocked so much and just like what
going to your previous answer just applying LLMs
anywhere is there's you're going to be you're
going to be able to you're going to be able to
get value so it's going to be very interesting
to see sort of what what happens this is another
one that I like to ask how do you view the gap
between the hype and the reality of AI? Yeah,
okay, so like hype is tricky because I live in
San Francisco and I Don't really know like the
level of hype Like out in the world like it seems
like very like uneven like I'm always kind of
surprised that like resonates with my friends
You know another in other Geos and and not But
look honestly if you zoom out even a little bit
all this stuff is so underhyped Like you can't
you can't hype it enough. I mean, it's just like
it's just amazing, you know, like it's incredible
This technology works really well. It affects
so many industries. People are maybe disappointed
that it didn't solve every single one of their
business problems right now. And it's like, relax.
Obviously, it will soon. Just wait. Just wait.
Wait a month. Yeah, exactly. There'll be a new
model next month. I can do it. I mean, one of
the exciting things, I don't know if you've had
a chance. Have you had a chance to play with
any of the AI assisted coding like cursor or
things like that? Yeah, of course. Come on. Yeah.
If you're not doing that, what's wrong with you?
I honestly like when I come just an engineer.
Yeah. Yeah. Yeah. I mean, I think there's like,
I think cursor code is like so much fun. Yeah,
I think that. I think that really is a really
well -done product. Sorry, I mean, Cloud Code.
Oh, Cloud Code. Yeah, Cloud Code, I think it's
really delightful. I think Cursor is also really
well -done. I mean, Windsurf. I don't know, I
like them all, honestly. I think they change
so fast and they're just awesome. It's funny,
I'm coming into... You know core weave and I'm
and they like, you know that for them like kubernetes.
They like know it so well, and I'm just like
Like they're like, oh here's some machines, but
it's just like a kubernetes cluster. I'm like,
oh man Like I never learned how to do this, you
know, and so Yeah, I started asking like cursor
like hey just like to play my stuff like in this
kubernetes cluster And it's just sort of like
okay, you know, like has a couple questions and
then it's just like firing off jobs I'm like,
yeah, that's amazing. I love it. Yeah, and then
meanwhile my daughter, you know, it's me is like
Claude code together To just like vibe code like
weird games, you know, and and I was like, you
know, I tried with cloud code. I mean, this is
like going to be so dated in a few months because
everyone knows. But I was like, hey, like, can
you like deploy this so my daughter can show
it to her friends? You know, and it's just like,
oh, yeah, make a Netlify account and I'll like
put the app in there for you. And it really did.
Like one, you know, like what's good is so cool.
It's so cool. Yeah. Yeah. I find myself like.
anyone in my company that wants to work on something
or like has an idea, I'm just like, can you just
download like, can you just download cursor?
It's not that simple. You need to know how to
code to be in a code editor like you do, like
you still kind of do. But there are other tools
that are out there. What is Lovable? I don't
know, my dad uses Lovable. He was making these
crazy games and showing them to me. And yeah,
I mean, like, yeah, Lovable is great. I mean,
there's other ones too, but that's the one that
he was. Yeah. Replet has one zero V zero. There's
so many and I'm sure in like, you know, a couple
of weeks, there'll be another there'll be another
dozen that'll be doing something that's mind
boggling. It's it's it's amazing. I mean, the
nature of work is changing. I just there are
so many people that view that look at the negatives
of it, but there are so many positives to it.
Also, the democratization, like you were talking
about earlier, many more people can do many more
things. And it's just it's so it's so much. It's
such an exciting time. It's it's such an exciting
time to be in. Yeah. What what's your daughter
deploy? No, it's in some links. I mean, we're
we're just five. So the mind of a five year old
is like a weird. weird place, you know, so yeah,
but that's so cool that she was able to do it.
Very cool. Where do you see things in three years
or five years? What's your take on like software
developers? Do you think that there's going to
be like you think there's really going to be
a real hit to software developers? Or do you
think the nature of work is going to change?
What's your take on there? Look, I don't have
any special insight. I do think like one thing
that I'm seeing that's kind of I've been thinking
about a lot and this is it seems like You might
have thought that this sort of be like more of
a democratization of Code and certainly that's
true, right? It seems like non developers can
like make stuff But it seems like the more dominant
effect is it's making the best developers even
more productive Than not the best developers
like I don't know when you like meet the teams
at some of these companies like, you know cursor
You know kodium or madness or something? It's
just like it seems like a small number of like
incredibly hard -working effective people and
so that's like sort of seems like the winning
Strategy like it sort of seems like everything
we do kind of increases Inequality like unfortunately
and that and that it seems like that's what's
what's happening, you know right now and it's
very hard to predict like I think like if you
told me It would be able to automate a lot of
code tasks. I think I would have thought, you
know, the upshot of that would be like, wow,
now I can like hire like more developers, be
even, you know, more effective. But I'm not so
sure that that's like the right thing to do right
now. Like even with, you know, with what we're
seeing, like it might be, you know, now like
really small teams or even like one person can
be more effective. I definitely think like. you
know, engineers have like more of a product mindset
can be more effective. Cause the way a prop,
probably just kind of like a prompt engineer,
you know, for fresh years, right? So, um, it
seems like it seems like moving up the, the sort
of stack is probably, you know, probably, probably
like favors that. Um, but yeah, I mean, um, I
don't know. I think about it with like my kids
too. It's like, okay, like should they, Like
learn to code and stuff like I'm kind of like
okay like learn to code as we do today If it's
like fun for you in the same way that I think
like woodworking projects are like sometimes
fun, right? Maybe we'll you know, we'll have
like artisanal You know libraries like charming
bugs Yeah That's funny. No, it's a couple of
good points there. I mean, well, I'll start with
the last one. It's like, you know, like I had
to like build a clock at one point in my life.
And no, I never need to know how to actually
build a clock, but I learned so much doing that.
What kind of clock did you build? It was like
at camp and it was like a wooden it was like
woodworking and then we put like the motor in
and it was able it was able to do it yeah it
was it was pretty cool I remember like staining
the wood and you know cutting it and sanding
it and doing all that stuff like no like do I
ever need to do that stuff but it's like it's
like being able to do it I think there's like
um One of my friends in college always used to
talk about this time that he was working as a
farmer, basically, and how much it changed the
way that he just thought about every like everything
like what it takes to maintain a garden or a
crop a set of crops. It's like, those are the
lessons that are very important that you need
when you're doing anything that has any anything
that's not just like a one time thing right like
which code very much is it's something that evolves
over time so being able to maintain and take
care of your code is is something that that's
that's very important And then you also raise
a really interesting point. It's like, yes, there
is the democratization, but there's also the
concentration of power. You know, whether you
believe in like the 10 X engineer or not, but
whatever, a very highly effective engineer that's
then able to automate more of their work is just
going to become extremely, extremely effective.
Doesn't everyone believe in the 10 X engineer?
Like, how could you not? Like, if you actually
work with engineers, I think that's like. I mean.
I don't know, I just didn't want to assume that
you did, because some people are very much like,
I don't know, I've seen a lot of polarity on
that. Really? Yeah, some people really don't
and then some people really are. Maybe you're
right, maybe I'm just not talking to the right
people. I think that if you know engineers, you
know that there really are people that - I can't
imagine working with engineers enough. No, 100%.
No, there are people that are just capable of
doing that are capable of architecting things
that just other people are not capable of doing
and make an impact that's just much larger on
things 100%. Yeah. But yeah, so I just didn't
want to assume that that's what you thought too.
I'm moving into like advice and things like that.
I'm going to switch this one. What advice would
you give yourself when you were starting your
career? Yeah, I think the most important advice
I'd give myself is like, you know Lucas like
you think you're late, but you're early like
I just always felt bad like I you know, I Like
it's funny. Like I I graduated, you know, like
2005 and I got an offer to go to Google and I
think I didn't take it honestly cuz I was like
all my friends went there like You know, it's
like, it must be like late. You know, I think,
yeah, this is like my whole career is always
like feeling bad that I was like late to stuff,
but actually like, you know, I was like, um,
like early to everything. And so, um, yeah, I
don't know. Maybe that would make me just feel
better about my career advice is tricky, right?
Yeah. What advice would I give myself? It's probably
something I just think about more. That's a good
one though. And I think another thing. That I
think another thing that I realized later Is
it carving out like huge blocks of time to stay
technical? I kind of felt like bad about that
actually when I was like first You know for like
a long time I felt like it was sort of like I
you know I had these coaches like executive coaches
like if you go off for a week You need to like
come back with like something you produced and
like a message to go like you can't just like
Disappear for a week, and that's like you know
fuck it like I disappear for a week Like if like,
cause I would go in like paternity leave and
I would come back with like so many ideas and
like you can't really concentrate on paternity
leave, you know, but it's like, I think like
saying technical, it's going to like be like
lots of ideas. Actually it was, I met the new
relic CEO and he was running a public company
at the time. And he was like, you know, I spend
one week a month just like basically by myself
or with the smaller people just like writing
code. And that like, it was pretty inspiring.
And I think another thing it's like, you know,
I think when I I think for a long time, people
have this stupid idea that you can have these
executives that don't stay technical or able
to do the IC work. Because management is its
own skill. And so you should hire good managers,
not good ICs. And I just think that's so stupid.
How many times do you have to see you hire someone
who seems like a good manager, but they can't
do the individual work and watch them fail? Before
I said, give me a fucking break. Like, you know,
like, you know, if you're going to like work
for me, you better be able to do the IC job.
And like, I do not know how the company's function
without that mindset. I'm just like baffled.
I thought maybe it was just like me, but I actually
just think there's like a lot of bad executives
that just kind of like keep their head down and
somehow, you know, keep like shuffling around
and like, you see like VPs of engineering that
are pissed if you like ask them to like code
in an interview. It's come on. I'd be like. Excited
if you give me like a like a leak code Google
code interview I might not be like awesome at
it, but I would be like pumped Yeah Corvish has
done that when they when they hired me Go back
go back to the due diligence and tell me you
want you want me code See you better be able
to do the leak code I love it. I love it Yeah,
it's it's huge. I mean getting shit done I mean
like that's what it comes down to is You can
talk about it and you can plan and you can do
whatever you want But it's like are you able
to actually execute and are you able to actually
get this done? And the amounts that you can get
done in a short amount of focus time is like
is unbelievable So you better have people around
you that are able to do you know do do things?
I think that in what I love about startups is
that you don't have the time, you don't have
the fact, you don't have that, you can't, you
won't survive, right? If you're someone that
can't execute, if you're somebody that's not
able to get the job done, it's just like you
don't have a, you just can't really have a role
at a startup. That's what I love about entrepreneurship.
Yeah, and thank you for the real answer. That
was great. I like that a lot. So getting into
just, you know, this is learning from machine
learning. So I have to ask the question. What
is a career in machine learning taught you about
life? Well, I think like one perspective that
I have from the machine or whether you think
about a lot like when you do machine learning.
This might not be true now. I think most of my
machine learning was done like building and fine
tuning models. This is not like the new, uh,
you know, engineer stuff. But like, you know,
I think when you're like training models, you,
you kind of don't know, like when you should
stop, like when something's like not working,
like when you have, you know, an idea. So I think
a lot about, okay, like when do we like pull
the plug, you know, on this thing that I'm doing,
like how much information do I like really need,
you know? Um, and I think another thing is like
the, the feedback loops are kind of like your
unit of um, your unit of work. So I think like
the faster you're like getting feedback and the
more feedback that you're getting, I'm sort of
obsessed with getting, you know, feedback, whatever
that means, you know, for whatever I'm doing,
kind of getting that like quickly to try to get,
you know, better, whatever that thing is. I think
that kind of comes from doing machine learning
where you're just trying to get like the information
on like, is this working or not? So I can like
move on to the next experiment as fast as I can.
Yeah, that's interest that that's interesting
so going to the first one where you're saying
like knowing when to stop are you particularly
talking about like you're training a model and
you're Iterating on it and you're like when is
it good enough to go into production or like?
Like I was thinking of it's like okay. You have
a model There's usually like an infinite number
of things you could try to make it better like
get more training data different types of hyper
parameters different architecture, you know,
all these different things, you know, and you're
kind of like, okay, like, what should I try?
And then when should I like move on to trying
like the next thing? And I think that's, I mean,
it's funny, I was starting to say, I think that's
like effected my life, but I think only the fact
that I think about that a lot kind of comes from
in machine learning, like think about a lot.
And it's like, it's hard, you never really get
that feedback on if you like cut that experiment
short fast enough. And I think different than
like more like statistical practices, you kind
of need to make these decisions. within perfect
information, right? Which is more like life.
You know, like, I think like medical stuff, I
felt very familiar having like medical issues
where I'm trying to look at a bunch of studies
that don't kind of line up and none of them are
like seem totally statistically significant,
but yet you have to make a decision about your
health that's important. That felt like a very
familiar situation to me from machine learning
where, you know, you've done a bunch of tests,
none of them are perfect. Probably there are
some bugs in your code one time, so it's questionable
if you can trust it. So you have 30 messy data
points, and then you have to decide what information
to collect next or what to try next. I don't
know. I think that's a common situation in life.
I think machine learning gives you a lot of practice
handling that. But I'm not sure I would claim
to be good at it. I just, I don't know. I think
I just, you know, you counter that a lot to get
that perspective. And you know, like running
a company, it's like, okay, you know, you try
some like new growth. Like, it's funny, our head
of growth, it was actually, she was a machine
learning person. And it was like interesting
to watch her. You're kind of thinking that it
was like - Lavanya? Lavanya, yeah, yeah. So her
growth stuff felt like machine learning was,
okay, we're going to run these like 10 experiments.
you know we're going to try to like cut them
off with like imperfect information get the like
11th experiment in there it kind of made me realize
it's actually a really similar thing to trying
to find the best um you know ml model for some
task yeah that's what i was going to say is that
it while in machine learning, you're dealing
with uncertainty, also in entrepreneurship, you're
dealing with uncertainty and making decisions,
yeah, with imperfect information. And you can
just make the best decision, you know, in that
moment. And then going into your your other point,
yeah, about feedback, it's, yeah, I mean, it's
it's crucial being able to collect the right
feedback, being able to understand how people
are actually using the thing that you created,
and then trying to incorporate that back into
the model, whether it's some Sometimes it's like,
yeah, obviously, you could change the data set
that you train the model on, or you could change
the way that the user interacts with your model
and things like that. That's great. Is there
anything else that you'd want to share about
that? Well, I don't know. I guess I was thinking
about you had an earlier question about how do
you... like what advice would you give yourself?
And I think like one of the funny things that
I've experienced in life is that you get a lot
of advice that's like really good and you just
don't like do it enough, you know, and sort of
like over time you like learn to just like do
that like obvious thing that you'd obviously
like tell yourself just like even to a more bigger
degree than um than you're getting. It's like
I feel like asking customers for feedback is
such a funny one where like You know, just no
one, no entrepreneur does it enough. And it's
like, it's not the only thing, right? Like, you
know, like, I mean, just sort of blindly doing
what customers want is kind of a stupid way,
you know, to operate. But I think like understanding
customers deeply and asking them like what they're
thinking and like, you know, like what they would
want. It's like, I really think that that, I
think all my co -founders, we really, like what
I think about like all my co -founders, my two
other co -founders, we all really had in common
was. We've really felt like super bad when customers
were unhappy and we like worried about it a lot.
And it would, it really like hurt our feelings.
You know what I mean? And so like, we would,
I feel like we all kind of just like maybe a
little bit of like an anxious style. Like we'd
be like asking her feedback. Cause I think we'd
be just like worrying like, okay, like, is there
some problem? You know, like why aren't you using
our product? And I think like getting that feedback
from customers like all the time. you know, just
like really, really helped us be successful.
And it's so basic, but it's like, I just, I know
I like watch a lot of entrepreneurs like not
do that. And I think even in my first company,
I probably didn't do it enough. Um, honestly,
as I'm talking about this, probably should do
it even more, um, you know, now, but it's funny
because everyone knows you're supposed to do
that, but I think it's just, it's like pretty
annoying to do it. It's like a little bit stressful,
you know, um, you know, we just don't do it enough.
Yeah, well, you make a really good point in Well,
you made many good points, but the the point
around like you don't just blindly take customer
feedback and then apply it. It's the step of
deeply understanding the pain and the problem
and the need of your customers to then inform
your roadmap and then to inform the decisions
that the decisions that you're making. And I
think that that's where the challenge is, is
that you can get tons of feedback from your customers.
But the hard thing is to really have the empathy
to put yourself in the shoes of your customers
to understand what their problems are what the
incentives that they're dealing with are. I mean,
I know that from my perspective, I think about
the problems that I'm trying to solve, and they're
often not the problems that my people who are
going to pay for the software that I'm creating
are going to are dealing with. And it's that
gap that makes that such a hard, such a hard,
such a hard thing to do. Yeah, totally. Lucas,
this has been like just like unbelievable. I
said to you offline, but you're one of the people
that I wanted to get on this podcast for so long.
Yeah, it's it's incredible. Maybe you think that
you were late or whatever. You were early to
the game. You know, you've had such an incredible
career to successful exits, not exits, but you
know what I mean in terms of your your startups.
It's it's really, yeah, it's really admirable.
It's it's it's really admirable. And also just
the type of person that you are. It was so amazing
getting to know you a little bit at Fully Connected
and just how real you are as a person and not
the usual CEO. And I really just appreciate you,
so many of the things that you're doing, the
incredible software that you're doing, but just
the force that you are in this field. So thank
you so much. It's really amazing to have the
opportunity to talk with you. No, thanks, Ed.
Really appreciate it. Yeah. Is there anywhere
where listeners can learn more about you? Well,
just go to WMB .com. But if it's before Fully
Connected, then they should come to Fully Connected.
We'd love to have your listeners at our conference.
It's a great conference. I'm in New York. I don't
know if I'll be able to make the trip to California
this year, but I'm sure I'll be there again in
the future. I would encourage people to come.
Lucas, man, what a pleasure. Thank you so much.
I really appreciate your time. Thanks, man. On
this episode of Learning from Machine Learning,
I had the privilege of speaking with Lucas Biewald,
co -founder and CEO of Weights and Biases. We
trace this journey from programming games as
a kid to building one of the most essential tools
in AI development today. Lucas's career demonstrates
that conviction often matters more than consensus.
From surviving the AI winter in the mid -2000s
to the AlphaGo moment that changed everything.
Lucas reminds us that we're still early in humanity's
most transformative project. He challenges conventional
leadership wisdom and shares his unwavering belief
that executives must stay technical. He explains
bluntly, if you're going to work for me, you
better be able to do the IC job. Most importantly,
Lucas's philosophy that feedback loops are your
unit of work transforms how we approach both
machine learning and life. His advice to his
younger self cuts through common doubts. You
think you're late, but you're early. In a world
racing towards progress by any means necessary,
this reminder couldn't be more relevant. Thank
you for listening. Be sure to subscribe and share
with a friend or colleague. Until next time,
keep on learning.