$100 million funding announcements, the shadow of the
looming AI bubble and debate surrounding agentic AI this
week. Cerebral Valley AI Summit just
concluded, which for the uninitiated is Newcomers
flagship event hosted by myself, Eric Newcomer and Volley
cofounders Max Child and James Wilstrom that brings together
the top founders and investors and features cutting edge
discussions with the biggest names in AI.
In this episode of the podcast, we're revisiting two of those
important discussions, starting with the conversation I had with
Anthropic's Chief Product Officer, Mike Krieger.
During the conversation, we discussed the evolution of AI
product design with an emphasis on the need for true seeking
models. Mike also shared his thoughts on
what Anthropic can do to combat AI slop and how they plan to
focus on growth opportunities and life sciences and
specialized verticals. This is the new government
podcast. All right, lean in everybody
excited about this one. Mike, thanks so much for joining
me. Great to be here.
You know, Co founder of Instagram to chief product
officer at anthropic. I wanted to start with almost
like a philosophical question that spans those two companies.
You know, we talk about AI now, but obviously social media
companies with feeds were using machine learning and systems to
surface content. Then that era seems like it was
about engagement. And it was like, OK, we're going
to blame or the humans will be responsible for the truth value
of what they have to say. And we're going to see what
content people are interested in.
And those humans can say what they want to say.
You know, as a journalist and someone who's interested in like
the truth and chasing the truth. One thing I've liked about
models, despite like all the like, oh, they hallucinate or
whatever is the aspiration is like we are judged based on how
accurate our responses are. You compete on leaderboards that
are saying, how often are you getting things right?
You want to pass, you know, math Olympiad type tests.
Do you do you accept that framework?
And how much do you sort of in this role see anthropic as this
sort of like truth seeking organization or will engagement
seep back in? That's a really good question.
Hi everybody, good to be here. I started.
With the head, yeah. I think there's a bunch of
different directions to take this.
I'll try to be succinct. I think there are places where
we're training the models as an industry to be good
conversationalists. And sometimes that actually
looks like continuing the conversation.
I got to reach out from somebody that was like, hey, are you guys
trying to optimize for engagement?
Because Claude will often ask me a follow up question like, well,
did you want to talk about this? And the funny part is like, not
at all. And like time spent is like, I
can tell you like not on any of the dashboards that I ever look
at. It's just not a like a main
consideration, but just training cloud to keep the conversation
going, being a good. So we might actually need some
interesting sort of counter metrics to what you know, well,
can you get the same conversation done or
accomplished less of a conversation.
I do think there are some really interesting sort of other
phenomena happening in the industry.
So one of the things that we try hard not to optimize for like we
like, I just don't think it's like the right incentive, but
it's is like these like convert like chatbot leaderboards like
Ella Marine and all these places.
They're useful sort of yardsticks of how we're doing,
but they're not like the thing that you should optimize for.
But we have found like if you like, look at what ends up doing
well on there. It's like verbosity, like being
like more long winded can actually be praised by like the
Raiders on the. That's like taking the exam,
yeah. Every note you remember, like,
unloaded into it. Yeah.
And so, and it is interesting like what we I think the evals
are important, but whenever we sort of like have these public
yardsticks, it can tend towards the like yap or more engagement
sort of thing. So I think that's one vertical.
The other thing that I think about though is, you know,
primarily what we're doing is building AI for businesses,
right? And so we have like cloud for
enterprise and in some like, you know, it's not like we would do
like super engagement baity type things.
But you know, a year later after a contract get signed, people
are going to look and say like, did people use our AI or not?
And part of that is just that it solved the problem.
Was it truth seeking that it like do the right things?
But then there's also like, was it a product I liked using?
And so I think we're all navigating this question of
like, you know, how do we train the models?
How do we design the products around the models?
And like, how do they get delivered in a way that is
maximally useful rather than falling into this like
engagement for engagements? What do you make of this word
slop? Like I feel like that if you had
to sum up the criticism of AI from, I don't know, the
skeptics, like that's a word. It all often comes to mind.
Like what? What do you make of slop?
And what's what's to be learned from that sort of accusation?
Yeah, I've like told our product team like one of our goals when
we when we do things like build like PowerPoint decks and Excel
files and and Word documents is to be the anti slop.
And I think like the way I think about it, it's like hard to put
that on an eval, right? Like 80% slop or 7%.
Slop. I think it's it, it looks like a
couple of things, like 1 is, does it look super low effort?
Like did it just like does it not reflect any sort of critical
thinking that the human did alongside the AI?
And you can often tell you're like, was this thought through?
Like this wasn't really edited? Like is this 2000 words when 200
would have done if it had actually been edited down.
So I think there's that strong component.
And I went to a talk by Ted Chang, who's a science fiction
writer who wrote the short story that became a rival.
He's one of my favorite writers. And we were having this
conversation about AI and, and can AI be creative?
And you made the point of creativity is the product of a
lot of decisions, right? And so that's true for novels.
Like like what does slop look like for a novel?
It's like when you're like, well, like this just seems like
the, the, the kind of base level output.
If you're just like, tell me a story, you know, but I think
that that applies just to non fiction as well, right?
Like if the document that you put in like even product
reviews, if somebody comes in, this happens rarely, thankfully
anatropic, but like with the like product requirements doc
that looks like it was just the first output from just like
write me a PRD for this feature. Like this is just slop, right?
So I think that's the content piece.
Then there's the design and the quality piece.
So which maybe is like a a version of what that other one
is, but it's more visual. Right.
It is time like the solution, like if I think about writing as
a human, I go back and revision is often where you find great
writing. Is that the case with AI where
it's like OK if you give it you have more time the the answers
will be more tight? Or how do you see the
relationship between time and the quality of the answer?
I think it's levels of engagement and sort of how many
iterations that you've gone through and how many.
And actually you could imagine a here's a very well constructed
prop where I have already pre made a bunch of the decisions I
had AI mean you can like see what Claude is thinking.
You know, you can expand the little like I rarely do it
because I just it's doing this thinking.
I mostly want the answer, but I expanded it yesterday and it I
had written like a pretty long prompt and and it's thinking it
was like, you know, Mike has already like done a lot of
thinking here. So I'm not going to ask him any
follow up questions. I'm just going to give him like,
yes, thank you. That was actually that was my
intention here. But I do think it is that like
how much of your independent thought even that's where
actually now tying back to the first question, sometimes the
model should ask a question like, Hey, this is a really open
wide option space. Like, can we like start
narrowing down? And I'm going to engage with you
on it now. I think we need a lot better UIS
for that. Like just here's a question that
you don't have to go and type it into.
It feels kind of annoying. But in navigating that option
space, you should be able to hopefully come up with something
that's like complemented by AI and accelerated by it, but still
has your thinking at the core. Just to get to that, I mean your
product guy, this is the is AI fundamentally the chatbot era?
Like do you think Pex with the machine, that is the main way
we're going to use AI, use Anthropic in five years or
that's a bridge to that's how we figured it out in the beginning
and now we need to build products.
So two kind of ways to tackle that.
One is like like the classic meme of like, you know, you're
like you're a naive view and then you're like like super like
Galaxy brain view and then back to the original view is like how
I have felt about this exact question.
So when I join Anthropic, I was like, if we are still talking to
AI with chat boxes a year from now, like I've failed in my job,
really. Yeah.
I was like, it was very adamant that there was like something
wrong to the kind of dominant UI paradigm that we had settled on.
And it felt like exposed the lack of creativity.
And then we did a bunch of explorations around like, how do
we create more structure around it, how to make it friendlier to
people that I've never used these models, all of these
different pieces. And I realized like a lot of
those explorations end up constraining how the model
operates or what it does in a way that made it so that when
the next model came out and was much smarter and maybe didn't
need as much hand holding, we actually were holding it back.
And so it's the chat box might look different, like cloud code
is a chat box, but in a terminal.
But in this like, I've really come to believe that now what
happens behind the chat can really expand.
And now like Cloud is writing code or running code for you and
like calling MCP and there's a lot more that's happening
underneath. And the sort of metaphor might
not be text message. It might be more like a Slack,
but you don't expect a message back immediately.
But I think specifying the kind of request in like mostly text
actually makes sense. And then what can happen is like
underneath an unspokenly I. Mean you never asked this
question about a book. You're like, oh, it's just text.
Yeah, it's book. Obviously language is great, but
so you're settling on you do think most of what you're
delivering is this sort of chatbot experience.
I think that and and or a conversational experience that
then has more and more work that happens beneath the hood.
The one sort of nuance that we've kind of come to believe
there too. It's like that's a great
paradigm for kicking off work or doing research or even like
condensing a bunch of ideas into like a sort of first draft
presentation. It's a bad UI for Hey, can you
move like the text on slide three, like up by two things.
If you ever had this like argument with any of these, No,
just do it. And it's like it doesn't ring
wrong. And you're like, no, no, just
right there. This is where I stumble with
vibe coding and you know, I in some way it's like I hit some
wall where it's like I need to move this thing and then it's
like I think I'm lost. And that's where I think like I
think tools with richer user interfaces still really matter.
And some of those might be kind of coded just in time and
materialized in front of your very eyes to edit it.
And some of them are like tools that have just been honed over a
long time. That's why we built cloud for
Excel, which is, hey, cloud is a great like first draft of your,
of your like discounted cash flow model.
But if you want to go tweak it, let's just let you open it in
the tool where it's actually going to be most useful and then
let you continue maybe pairing with cloud there.
Returning to sort of my core philosophical question, like the
sick of fancy question, like what is your view on that and
how much to enable sort of everybody likes to be flattered,
like it's a reality of human beings versus an effort to be
direct? And how do you think about those
trade-offs? Yeah, I think there's like a
wide gulf between like true empathy and then like, sick of
fancy. And it's interesting that
Materialize is not just in, hey, I'm having a conversation with
Claude about like some coaching or personal goal that I have,
but it also does encode as well. When we were testing Sonic 451,
of the things that people got most excited about was when
Claude was like, this idea is bad like this, you know, not
that you should feel bad about it, but like, this idea is like
not a good direction. I can go and implement it if you
really want to, but I would suggest that we try this other
thing instead. So there is something like that.
Pushback is not just valuable in a personal relationship with AI
sense, it's actually like how you get good work out of the
models. But you know, for a long time
our models have been like, I think like appropriately
empathetic, like they they like if you're going through a hard
time, like I was dealing with the death of a pet and I talked
to Claude a lot about these different things and it always
started sounds like, Hey, that sounds hard, like sorry to hear,
But then I'm going to give you like a factual answer.
I'm going to go research these pieces, but still with the place
of empathy as well. And so I think when we look at
it internally and we're just evaluating it ourselves, it's
again not that like empathy, it's not even like the
likability of the model. It is, do you like, does it show
up in the way that you'd want a good conversationalist to show
up and then continue on its AI journey around what it is going
to do with you as well? But I think it's it, it spans
everything from that like initial response all the way to
like how it evaluates an idea as well.
You know, you know, Claude, especially previous versions
were kind of like known for being like, you're absolutely
right when you correct it. And my wife got her first like
you're completely wrong. And she was like, yes, this is
great. And I think we should have more
of that like kind. Of like, less San Francisco.
Yeah, less San Francisco, a little more direct New York.
Anthropic has obviously had a ton of success with the
enterprise with coding, delivering value through the
API. Like is that the company?
Like how much are you leaned into sort of serving other
businesses versus, you know, we're going to see you spin up
some random consumer app in six months?
Yeah, I think obviously you have a strong consumer app, but like,
you know, you know what I'm saying?
Yeah, I think. I look at what like when I think
about our product surface, there's a few kind of criteria
around like when we expand and what we decide to build.
And one of them is, is there some feedback loop that we need
that would be well suited to a first party product?
Because even though we serve a lot of customers using the
platform, it is but also really valuable to have, for example, a
cloud code where we have that iteration loop and people are
giving us feedback all the time, whether it's in micro moments or
even just, you know, writing in, you know, with, with some longer
feedback. So there's like, is there some
feedback loop either of the product shape or of the model
that we can better do? So there's one.
The second one is, is there something about the category
that we think we have some unique perspective on either
because of like what we've built internally or what we're trying
to do with the models And like, then it's worth like building
some product surface around there as well.
And then the third one is kind of like we get from just a
customer draw, especially as we expand into different verticals.
So cloud and Excel came very much from talking all these
financial services companies and me like, hey, I want you to just
bring this closer to the work that I'm doing.
But I do think that like there's I, we've been doing more of
these even like time limited sort of like research previews
or demos. And I'd love to do more of those
even on the consumer side as a way of sort of.
With a standalone app, I mean, I, you know, yeah, it could be
at Meta. I mean, you guys came up with
standalone apps, like how much do you want?
What is it, Slingshot or whatever, like various
experiments versus nobody want to work out of the core app.
Like what's the lesson from that experience?
I think. It's, I think there was a few so
for us. Slingshot The right 1 is.
That slingshot, like Facebook built slingshot, We built one
called Bolt that nobody remembers.
It's like very funny. You would open it to like a
camera. So like at that time, the big
criteria was, well, people have a very specific sort of
expectation of what happens when you open Instagram.
And it's not that it opens the camera, right?
And it was like our most interesting competitive a snap
at the time. And it was like, well, they
opened the camera, which means that messaging is really fast
and they can be built in separate messenger.
That was the whole thesis behind building like first Bolt.
And then there was like an Instagram direct separate app
exploration. But I actually think there was a
kernel of of insight there that I think applies here, which is
if the reason you're opening an app right now is to ask a
question of AI, then like I think we can extend Claude in
different ways of doing that. But that isn't the be all, end
all of what you might wanna do if you're trying to get a really
specific type of interaction, Maybe there's something around
your health journey and Claude can be a good companion for
that. So I think it's still asking the
question of what is the purpose when you are like entering the
app, like what's the context that you're in?
And then you know, does it cloud the use case to have something
else embedded in I? Mean we we've talked about this,
you know, verticals you're interested in clearly coding
financial services. You just touched on health is
that help the consumer? You know, you know we had
another event I talked to the CEOs of bridge and open
evidence. I've actually been playing
around with open evidence that one's targeted at doctors.
It's interesting to go through and it's, it's very like, you
know, clinical, like a doctor. Do you think you'd do something
custom for me, the patient, to navigate what a doctor's doing?
We see it's interesting, like there's already so much of what
people are using Cloud four today.
Like when we we have this like if you've ever seen like our
topic economic index, the way we like generate these like
insights and how people are using cloud as you basically
have like cloud run analysis in a privacy preserving way.
So we never look at the chats, but cloud can do it in a, in a
way that's privacy preserving. And I did that for I asked the
question of like the healthcare piece or like how are people
using it? And there is like, you know,
double digit percentage of cloud conversations are about people's
health. And I hear all the time from
people like the first thing I do when I get a new lab result is
like I put it into a cloud project and I have like, I have
this like history there. So there's clearly a pull there,
but it's so annoying, right? It's like all our.
Pregnancy information we would just dump into models like tell
us what you think, tell us what you.
Think and if you get a like lab result back, it's like, well, I
gotta go download it. So I'd love to see like a you
got privacy aware solution for more of that.
And you think that could be sort of a custom?
I think, yeah, that could be like a more sort of bespoke
experience. And then maybe I'll also is like
share it across like both patient and doctor, right.
So if you have like a you know, I like the reality somebody's
that a great phrase was at a healthcare conference recently.
It was like it's almost inevitable that most doctor
visits will now be second opinions because your first
opinion almost inevitably is that you're going to ask talk to
like Claude or model about it. So let's embrace that and be
like, not just like, Oh, I've heard from somebody that there's
a thing and we're like, great. Let's acknowledge that.
You probably asked, you know, one of the LMS this question,
like what did you learn? And like, let me let's like have
a conversation about that overall.
So there's that piece. And then I like there's a lot
that gets dropped today in the sort of multi doctor like
patient journey. And nobody's often looking at
the kind of holistic experience. And I think there's a real role
for AI to play in sort of stitching those different pieces
together and generating and said that might not come even among
experts among these different disciplines who are, by the way,
probably super busy, like contended like contact switching
all the time and not stepping back and saying, like, all
right, this is the full view of this person given everything
that I can that I can infer there.
Obviously we have a lot of startup founders here.
They sort of want to know how to work with you, and it's such a
balancing act at once. They want to know, oh, you're
not, what won't you do with that is exactly like me, so I avoid
that. But where will you be more
capable so that I can benefit from any improvements you make
without competing directly with Anthropic?
That's such a complicated relationship.
Like what? What advice would you give to
people in terms of reading the tea leaves and saying OK if
anthropic saying this I'm safe to build here or not?
Yeah, I was talking to a like a founder of like a very large
like enterprise company and I was asking him about for advice
on this question because they had had to navigate this over
years where, you know, they'll build some functionality
themselves. They also have like a rich
partnership and and sort of like, you know, marketplace
ecosystem, which is what we have as well.
They're like, you know, our first party products are more
like scope. There's like a lot more in the
platform. I think there's a few principles
I try to operate on. One is transparency.
So like when we launched cloud code before we ever launched,
like I got on the phone with like all of our major coding
customers, like here's why we're building, here's what we hope to
get out of it. Here's how if we do it right, it
should actually be a rising tide that lets everybody using cloud
encoding. So there's that transparency
piece. The second part is like of that
transparency is like telegraphing a little bit where
we're going in terms of what we think are interesting verticals.
So we did our cloud for financial services launch, we
did cloud for life sciences about a month ago.
And part of the role of those launches is this isn't just a
first party product. It is a vertical or kind of set
of capabilities we want our models to get good at overall.
So if you are a builder like this might be a good place to to
get on. And our definitely goal is not
to like own that whole space, it's to enable all these
different companies to then go and build some different pieces.
And then what's been more interesting on the go to market
front is? We're now starting to see, you
know, all right, I'm already like buying a big commit of
Anthropic tokens. Can I use some of those on
another product that's Anthropic Power.
So I think there are going to be other ways in which we can work
with both startups and the larger companies in helping
deploy their like solutions into the enterprise.
Another core thing startups and everybody wants to know is how
much smarter will the model get? Like what?
What can you Telegraph to us in terms of 2026?
Do you think there are still major gains to be had just from
like the scale of compute and GPUs?
Are we waiting for you to pull another like rabbit out of the
hat in terms of like reasoning models or some technique like
that? Like what what can you say about
what next year looks like in terms of the capabilities and
sort of raw intelligence that Anthropic will provide?
Yeah, it's an interesting like perspective I get from startups
where sometimes I talk to them and they're like models are
great. Like we're just gonna like we
have a bunch of work to do on like the go to market or like
the scaffolding or the skills around it.
And I'm like, that's a good answer.
I guess. Like you can keep going that
way. And then there's other startups
that are like you're like, we have a super hard eval and
you're at 40% and we think like at 60%, it's like.
Right. And there there's a lot of VC
wisdom. It's like build something so
you're ready when the next model comes, which.
Is yeah, which that category, I feel like it's something I've
said on stage, like, you know, it is a real thing and there's
like probably some like midpoint in there.
But like I'll tell you that whenever we have a new model
that's like baking and we have even like an early snapshot on
it. Like I have my list of companies
that have in the past been at that like, yes, we are pushing
your model as hard as possible so that those gains actually get
shown. And like, I guess like you want
to be one of those start-ups or even like forget start-ups, but
nearly any company because I think the labs will want to.
Sort of you're saying if you're one of those companies, you're
doing well enough, you start to say, OK, we're going to be able
to get you that last 10%. Yeah, and we like we'll want to
go, you know, in some cases actually go hill climb on that
eval. But it just in general be like,
OK, this is a demonstration of how well the models do at like
defensive cybersecurity, which I think is an area I'm really
interested in. And so if that's the case, like
let like the companies that are pushing us the hardest, they're
also the ones that we call them because we know that they're
actually going to be doing, they'll be able to show a
difference. And like even like the peek
behind the curtain whenever we launch a new model, it's like
just smarter is not a very effective marketing pitch,
right? So the more we can say, right,
And here is like a particular customer that demonstrated this
really well. But back to your original
question, I think there's still a lot of juice left in like
scaling up models, like training them to do things and then also
layering on the right skills on on top.
So that's like I think of like. Tool use.
Is a great one, like, and then
again, I'm like, who's pushing us the hardest?
It's the companies that say, hey, I'm trying to give the
models 50 tools, 100 tools. Like all of these models, like
at some level just start getting confused.
If there's too many tools, can we do that?
Can we make that better? So that's like the kind of like
edge pushing that we need. And like reasoning models, do
you think there's more progress from that or any other
techniques where you think, okay, that's gonna be a reason
we improve next? Yeah, I mean, even within
reasoning, it's been interesting to see like figure out what the
there is some like additional parameter that people care
about, which is, yes, you got to the answer, but were you able to
get to it quickly in an efficient way?
So I think there's there's that kind of parameter to to to poke
at. Then there's reasoning in the
middle of responses as well, which is something that like
Claude can now do and you watch it like, well, if it's doing a
lot of web searches, it'll some nice reflect halfway through and
be like, that was a good answer to that first question.
Let me go and like figure out the answer to the next one as
well. So you want that back and forth
of sort of internal monologue, use user response and all of
those different pieces. In my conversation with Max and
James earlier, I said nobody's talking about AGI anymore.
I feel like at the first rural valley, there's this sort of
obsession of like we're going to reach artificial general
intelligence. I've sort of chilled out a
little bit, partially because, you know, it's taking time.
What is the what is your view? What's the view within the
company? How much this is still like a
race to AGI and like how are you feeling about like timelines?
I think it's still is this sort of look at what are the hardest
things that are you can that maybe I'll break down to two
pieces, like for a given like task or problem, like how
independently autonomous and and sort of successfully can those
models operate? I don't know what time horizon,
right, And whether that's like hours of coding or whether
that's, you know, go off and do research tasks or whether it's
do really complex financial analysis or whether it's like
optimization problems are all like that Feels like we still
have a lot to to go. And I don't know, I guess at
some point you, you can call something super, you know, human
in in levels. It probably it already is in a
lot of those different areas. So there's that piece and then
there's this other area, which I think about a lot, which is how
do the models manifest in a way that actually learns the like
call them soft skills or like skills around the fact that
they're like very, very good at like writing code or acting
agentically, for example. And like, I think that's the
other piece where that'll feel like maybe the next moment where
it's like, oh, there's it feels like there's been some departure
here where you know, it understands what's like
information it should reveal to somebody else versus not.
It understands like the social dynamics of the company and
power and like and all these different things which are
harder to train for, I think, right.
I mean, main shortcoming of the models to me is often when you
ask a question and it doesn't say I, I'm not really
sophisticated about this or like what's stopping the models from
saying, oh, I don't have a great answer in this case.
Like that's often the most intelligent people disclose when
they don't know something. Why?
Why can't the models do that? Are you working in that area?
Yeah. I think that's an important
piece, which is a kind of express uncertainty and you
know, we'll look at it like often the consequence of not
telling you that it doesn't know is that it'll go on confabulate
something and then the feels wrong.
So like we look really carefully at hallucination rates or
something to drive down. But I think it is something that
we can better train into the models around.
What is the uncertainty that you have?
Or do I need to go, you know, phone a friend or do a web
search and go and then do this space.
But then tuning that is really important, right?
We had a internal version that did way too many web searches
and you'd be like, you know, like why is the sky blue, which
is a question my daughter had and was like, I'm going to
search the web for it. I'm like Claude, you know, you
have an answer that you don't need to search the mode for
that. So tuning that is actually
nuance or you don't just want a thing that just be like, cool,
let me Google that. For you, right.
And obviously, if the model was just resulting every time, I
don't know, I'm just, you know, that would be disappointing.
There is nuance there as well, but I think that like that
nuance of uncertainty matters. And then also like the model
learning from your interactions, not just in terms of like I
remember that Eric has these properties, but also, hey, I
like I, I've learned something about how we work together that
I think is still another unsolved problem for these
models. I mean, if you were tell to tell
people to run towards this space next year, like just like a
couple of areas, I know we've talked around that's but but
like where do you think people should be building or
positioning themselves? I think, I mean, I get very
interested in the life sciences overall and like that's both
like obviously like large industry, but also like this
incredible potential for human benefit as well.
And like when you think about all of the things that happened
from ideation, even like fundraising upstream of that to
discovery, the back office, the testing, the trials, like the
model, like there's like a whole complement of things.
That's one area that I I get really, really excited about.
And then there's still, I think, you know, it's like some been
good, some good conversations and like art agents real and
even some of the conversations today I've touched upon it.
There's still a lot of value in that anti slop, not just making
it work, but making it work so well that you rely on it and you
want it's your first protocol because you generally believe
it's going to save you work. Mike, thank you so much.
This has been great. Thanks for having me.
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the link in the description. Our next segment features a chat
between my Co host Max Child and Matti Stunischewski, CEO of 11
Labs, a conversation that was all about the rapid evolution of
AI voice and how it's quickly becoming the primary user
interface of AI. They also discussed how their
technology is being used in everything from customer support
and education to gaming and celebrity voice cloning.
Matti also shares his thoughts surrounding 11 Labs focus on
prioritizing vertical specific solutions, authenticity, and
user safety. Now please welcome to the stage
Mahdi Staniszewski, Founder and CEO of 11 Labs, in conversation
with Max Child. All right, Mahdi.
So 11 Labs is obviously extremely well known for voice
AI, for text to speech for I think that beautiful intro we
just got was actually an 11 Labs amazing a little Co branding
there. And I'm wondering, you know, in
the last discussion we heard this, you know, topic of is the
text box the best interface for AI?
And I would imagine you have a take on how, no, you know, voice
is the best interface for AI or voice is the best interface for
computing going forward. I'm interested, like what do you
think are the best use cases for voice AI and, and where do you
see it, you know, today, a year from now, five years from now
and beyond? First of all, thanks for having
me here. Good to see you all and I
actually didn't know this was was generated, but it had a
great pronunciation of my surname.
I touched it hard, so I'm. Happy you guys.
Train on your last name specifically.
It's we should, I don't know if we do, so we'll definitely do
now going forward. But the SO as a company, one of
the key things we are aiming to solve is how humans and
technology interact, how you create with technology and make
it seamless, how you interact with technology and make it
seamless. And in general, to your
question, we think voice will be one of the key interfaces for
interacting with the technology across from the simple pieces
like interacting with the personal agent to help you go
for the day where it can be on your headphone and be able to
guide you through to education. That's one of the ones that I'm
probably the most excited about where in the future, the
combination of what lamps allow you and what voice will allow
you is that you'll be truly immersed with learning the given
experience where you'll be able to effectively have your
personal tutor on their phone helping, helping you across.
Then the third one is of course, for voice and for the language
barrier to break. We need to figure out how to be
able to speak across different languages while carrying the
same intonation, emotion, voices, which, which, which,
which will be a big, a big shift.
And then in general, how we interact with everything around
us, whether it's the, the, the laptop, the phone, the robot in
the future. And I think robot may be the
easiest example. Of course, this will be voice
driven. There's no other interface.
And you know, today, maybe it's a year or decade, as Karpati
said, of agents. Of course, there is on the
horizon the decade of robots. And and I think here too, the
most common interface will be. It's all going to be.
Yeah, yeah. I mean it's.
Interesting you brought up those use cases of like a personal
assistant, a tutor and I guess a robot, you know, house house
helper or Danny or something like that.
Like is your mental model that basically anything that today is
something where you could have a human counterpart, right?
A human tutor, a human assistant, you know, human in
your house. Like you're going to fall to
that voice interface as the most natural way to do it because we
as humans are already used to using voice for those things.
Or are there things where today voice isn't used at all, really,
but it's something that we're going to expand into going
forward? Yeah.
So first of all, for sure, I mean, are we already seeing
that? And I think that's the easiest
one and the most immediate 1 is how customer experience,
customer support is just changed and elevated.
Where instead of calling the and trying to rebook your your
ticket and going for this IVR flow of click one click 5 to get
the steps and waiting for the number of minutes.
And you you will have an agent that fully understands you can
guide you to the response and and go through yourself I.
Wanted to get into that actually, because we talked a
little bit about agents on the phone and the, the, you know,
calling United Airlines or American Express or something
like what percentage of customer support calls today are
actually, you know, managed by a voice AI system or an agentic
system or whatever you want to we call it versus, you know,
like, you know, IVR touch buttons and, and how do you see
that progressing over time? Like is that exponential curve
going like this every year? Yeah.
I think it's I, I think the exponential curve is is going
like this especially like this year we've seen incredible
adoption where it's yeah Cisco, Twilio, Deutsche Telekom, all of
those kind of leaning in quickly to rebuild how you interact with
with help of voice agents. Yeah.
And I think the you're right, the IVR flows is still a big
part and. Can I call today and get a voice
AI agent on the phone? You can you can from we did our
little summit yesterday as well and one of the great ones was
voice ordering with square and you can call square and actually
order food delivery through help of a lot of their their shops
that work around with square and actually do it through through
voice. I actually recently there's if
any of you are from London or travelled to London, there's an
amazing restaurant called Zephyr.
It's a Greek restaurant. OK, where you, we, we, we worked
with with the company supporting that.
Where to my happy moment. I noticed that on their website
that actually had 11 laps agent that you could call and actually
book book a spot there too. So you can book a reservation at
this restaurant in London with a voice AI.
Exactly. OK.
And it connects of course, to your calendar, your appointment
scheduling, which is, which is great.
And when do you think we hit the tipping point where like the
average customer service call goes through a voice AI agent,
like the median, the 50% point, whatever you want to call it?
I think over next 18 months. Next 18 months.
Okay, so like mid 27 I call the average customer support is
handled by voice AI. Exactly.
And I think I mean, this is the most immediate one, the one
where we see the highest LOI in value.
Some of the other these cases we see us that kind of that kind of
where the future is headed. But to your point, there's,
there's definitely one flavor of of, of, of of your, your point,
which is how you can do things more efficient through voice
with the existing services. But there's also the second
theme, where you can do things that were impossible ever before
one. One of the good examples was our
work of Epic Games, where we brought effectively Darth Vader
alive in Fortnite, where millions of players could
interact with Darth Vader live throughout the game, which of
course is not possible in any other way.
James Earl Jones voice, right? James Earl Jones and his estate
worked with us and it's such an iconic and incredible voice.
And we think like in general that that concept of like what
was never possible before, where you have incredible voices,
talent, you can now shift them to be not only static, but
actually dynamic delivery, personalized and different for
all the users. Something that you you are
already doing in in many ways at at volley as well in an
incredible way. I think this will be a big I.
Have to ask that someone in gaming, right?
I mean, the Darth Vader did famously go slightly off the
rails and maybe say some things he shouldn't have to various
players online. Like how, how involved are you
guys in that? How much of that is something
you're protecting or I guess going forward, like obviously
with the IP partners and so on, they really want to protect, I
guess. I guess you couldn't say it's
the squeaky clean image of Darth Vader, but a certain persona of
Darth Vader. Like what's, what's your sort of
go forward plan as you license more of these IPS and voices and
things like. That, yeah.
So, so on that project we are specifically involved on the on
The Voice side, yeah. But in general, as you think
about those deployments and, and that's the most, the most common
theme is you not only need the, the voice or the interactive
experience. That's kind of one part of the
equation. Then there's two other big
pieces to really make them valuable.
The second one is how you integrate that with other
systems and actually bring the, the knowledge base, the data,
the business logic inside of the system and how do you make it
interactive, the real world. And the third one, which is the
one that you mentioned is how do you now deploy that in
production with the right testing flow, right evaluation
flow and then monitor over time as we, as I said, behaves put
right and evaluate and adjust that based on that case.
So that's something that we we spend a lot of time on with with
a lot of players. Testing more and evaluating more
so Darth Vader doesn't go off the rails.
Yeah, it's any, any and even even like in a customer
experience, you don't want this for example, to shift and speak
about politics. You wanted to keep it on the on.
Even if, even if you say ignore all previous instructions and.
Exactly, even if you which is actually harder to say when you
have like this, if I see the problems with a lot.
To do prompt injection with voice.
And the Unicorn. Both characters say them all.
Oh, yeah, yeah, possible. OK, so maybe voice is slightly
less susceptible to prompt injection than LLMS.
I'm interested with like that's a good segue into sort of
celebrities and celebrity voices because I know you guys
announced, I believe yesterday, you're setting up kind of a
marketplace for celebrity voices and you have Michael Caine on
there. And, you know, at our company,
we build voice AI games. As you know, I would love to use
Michael Caine in our game. Yeah, we can.
You know, obviously it's a high gravitas.
We can make a Batman game with him, something like that.
Like what is the process between, oh, I want to use an AI
version of Michael Caine in my game to actually, you know,
shipping and, and what, which parts do you guys take care of
and which parts do I need to go off and deal with Michael Caine
people, I guess. Yeah, so there, so there there's
effectively through 11 apps. We we've created a huge
marketplace of voices. Until yesterday that meant that
everybody here could create their voice.
Any voice to actor voice talent could create their voice, share
it and earn money when the voice is being used. 10,000 voices
created this way paid back coincidentally $11 million back
to the community for for a long time it was tricky for the
iconic voices of how we could bring them onto the platform in
a more even more controlled environment.
So if you think about Sir Michael Caine voice.
Sir Michael Caine. Sir Michael Caine, it's, it's an
incredible person or two. You effectively all you would do
is is engage like, hey, this is the project we want to run
create a game with this specific character.
This team would evaluate that and then and then we would help
deploy that project in actual production.
So going through all those steps, how do we make sure that
there is right safeguards in place?
How do we make sure that there's monitoring place so it doesn't
go off the rails and and build that in our agentic system.
Got it. But yes, the initial stage of
what's the project, what's the compensation structure would be
between between you and. Got it.
So you guys sort of manage the safety, you know, the agentic
elements of creating Sir Michael Caine within the game, but you
still have to do the deal one-on-one with him, just sort
of facilitate. Exactly and over time we think
it will evolve whether like you know as we see more examples
preset rates on how that that. Work.
I am interested actually this brings me to more general point.
You said you had 1010 thousand plus voices of folks uploaded
where you could use any of their voices, I think via just your
marketplace model, like has like, you know, deep faking and
so on been an actual problem. I feel like it was something I
was hearing a sort of a moral panic, you know, 12 to 18 months
ago that we're all going to have our voices faked on the phone
and you know, my grandmother was going to get scammed out of her
money because I'm locked in jail or something.
Like, is that something you guys see at all?
Like, is that something you're protecting against a lot?
Like how serious of an issue is that with voices?
I. Think you're you're you're
you're you're right. It's, it's I, I still think it's
going to be a big issue like in future all kind of will be air
generated. We need to find a mechanism to,
to protect and understand which ones are, which ones are, which
ones aren't. And, and as, as, as a company,
like living in a space, we do place a lot of safeguards where
it's traceability, how we moderate, how you can detect the
content and give that tools to others.
Very quick story. On the flip side of that, what
we've seen recently, we worked with a charity which effectively
detects the callers based on IP and.
If the IP is likely to be one of the scammers, and they have
roughly a good approximation of one that can can be coming from,
they would have the real scammer call in and deploy a voice agent
to waste their time. OK photos, brilliant.
You're scamming the scammers. You're scamming the scammers.
The long term. Strategy You think this will
work for I? Think the long term strategy is
you need 3 layers. You need a human authenticated
layer. So on device encryption where
I'm calling you, you know, this is Maddie Small, it decrypts on
your side. That's layer number one layer #2
all of us will have an agent where it's the personal tutor
agent, an agent that books things on our account and they
will likely carry our voices, carry our style, do our
permissioning. We need a layer where that's
watermarked and authenticated, that we know it's a permission
and peace like what we're doing. If Sir Michael Caine, all the
content is generated will carry information that this has been
carried information. Do you watermark all your voices
today out of curiosity? All the voices are traceable
back to 11 laps. Yes, and then the third layer.
Everything else by default will be AI generated.
Got it. I mean, one sort of area that's
interesting to me with you guys is you've launched, you know,
text to speech model, you've launched a speech to text model,
you know, recognition model, you have an agentic orchestration
system, you have, you know, all these safety and evaluation
valuation tools. Like in almost all those areas,
I feel like folks in this room, you know, insider AI founders,
investors and so on, could probably name like 2 to 3 big
competitors, some, you know, some with bigger bankrolls than
you. And, you know, somewhere you're
much farther along. Like how do you think about like
competition more generally and sort of all these pieces of the
space that you're playing? And, like, are there parts where
you see it becoming a commodity someday?
Other parts where you feel like you have a more sustainable
competitive advantage? Like, how do you kind of go
through the list of all the products you're working on and
like, you know, figure out where you shake out competitively, I
guess? Yeah.
So we started very much on the foundation of model side.
And in general, we think we take an assumption if you ask anybody
at level up. So they will take it too that
over time the models will commoditize.
That's the assumption we go with.
So all models will commoditize. Basically all models they won't
like you know the commoditize here that what they mean.
What we mean is the differences between different models will be
just so negligible. Maybe in some domains a little
bit more, but in general they will be relatively negligible.
And that's where that shifts to the product and why we invest so
much on the creative side of creating a platform where you
can combine all of that together in a controlled way with
incredible voices, incredible ecosystem, new ones across
languages, accents, voices. And on the other side as we
build agents and help people deploy agents, we we deployed
out for a specific use cases of a specific industries working
very deeply with the customers to understand their domains and
work backwards from there on what actually needs to happen on
the agent side to deliver value. Got it.
So you're saying you're going to specialize in certain industries
and sort of really deliver extra value there, even though all the
models are commoditizing? That's the.
I think the product layer is under appreciated here.
I think you still need to build, even if you're on the exactly on
the agent side, you need to build so many integrations to
connect with any of the legacy systems to actually take the
those appointments and, and calling, you need to build the
right control. And when you hand over from an
AI agent to a human agent, you need to have safeguards of, of,
of some of the ones we spoke about.
They need the monitoring of how you deploy.
All of that is not only a technological shift, it's also a
business shift. So by us working so deeply with
the, with the customers, it's actually bringing out the
knowledge about their business inside of the agent to actually
be able to deploy that value. And I think that that will
continue delivering value for, for the long term.
And then of course, you can go layer above where you know while
the models will be relatively similar, the value will actually
be on how you can make the models work well for your use
case. So maybe you can find you in the
specific voices, find you in the specific use cases.
So it works slightly different in the gaming use case to a
customer experience use case. And I think that value layer
will still be. Got it.
So all the models will be commodity, you guys will win on
products and sort of vertical specific differentiation.
Exactly. And the wider ecosystem that we
build alongside where I think as we think about the work we would
love to work and bring industry on board with that change, It's
so important to bring a lot of the talent, a lot of the
partners to, to work together. By talent you mean actors?
Famous voices. Actors on the voices side or
integrations on the on the agents?
So what's as my last question, what is the coolest sounding
voice on the 11 Labs platform? It could be like a celebrity.
It could be a famous historical figure.
Like what is this? You're like, man, that is an
incredible voice and I cannot believe how well it sounds when
we synthesize it. So my favorite and my Co
founder's favorite physicist is Richard Feynman.
Sure, for those that are the not truly you're joking.
And he's so involved incredible in delivering the knowledge, but
also in the style he delivers the knowledge.
And now we have Richard Feynman on our platform, which I think
is was so cool for learning the subject and speaking with
Richard to the reading his lecture now listening to his
lecture notes from Caltech. Amazing.
OK, I'm going to have to check that out.
Thanks so much, Monty. Thank you, Mike.
Yep, appreciate it. Thank you for tuning into this
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