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Fraser & Nabeel explore what it means to build great products in this new world of AI.
Two former founders, now VCs, have an off-the-cuff conversation with friends about the new AI products that are worth trying, emerging patterns, and how founders are navigating a world that’s changing every week.
Fraser is the former Head of Product at OpenAI, where he managed the teams that shipped ChatGPT and DALL-E, and is now an investor at Spark Capital. Nabeel is a former founder and CEO, now an investor at Spark, and has served on the boards of Discord, Postmates, Cruise, Descript, and Adept.
It's like your weekly dinner party on what's happening in artificial intelligence.
Nabeel Hyatt: technology tends to go
through ages of entropy and de entropy.
We all love, especially as engineers,
we love de entropy, we love simplifying
everything, cleaning it up getting
the signal from noise bringing it
all down into something that works.
Things that are trying to make a promise
of de entropy too quickly when all
of these LLMs are so new Just feel
incongruous to me when the goal is
solve the problem reliably and, and
we're still not at reliable solution.
Fraser Kelton: Boy, we wrestled with this
one, but that one feels really right.
It's going to get more complicated
in every direction because we are
not at the reliability required for
consistent value in many use cases.
And like, why bother adding abstractions
of simplicity if you say it's
still not going to be good enough?
Nabeel Hyatt: Yeah, exactly.
Fraser Kelton: lot easier for you to get
something than broken into production.
Nabeel Hyatt: That's
what, that's the headline.
Why are we making it easier to
get broken things into production?
Fraser Kelton: Oh, but what a teaser.
Nabeel Hyatt: I know, I know.
Hello everybody.
Welcome to Hallway Chat.
I'm Nabeel.
Fraser Kelton: Fraser.
Nabeel Hyatt: And we are here to
talk about what we've been talking
about in the world of AI mostly.
I, I didn't really know we were signing
up for this every week when we signed up.
They come fast, Fraser.
I felt like I just talked to you
on the hallway chat last week
about the launch of ChatGPT and
all of your stories around that.
But at the same time, there's like
a million things to also talk about.
So it is both, feels like these
C shows are coming all the time,
but also too much to talk about.
Fraser Kelton: I was thinking after
recording the last one, how nice it is to
be able to talk in depth with you about
these topics and just laugh and explore.
And so I'm good with it all.
The, you know, here, here's
something last week, I think I
said the line, where's Google.
And we had an answer.
Kind of, right?
We kind of had an answer.
Nabeel Hyatt: Yeah.
Oh, I loved, we had our AI dinner this
week, and we had somebody from the
Gemini team sitting at the dinner,
all night long, mouth shut, and I
am like spouting off and spitting
all kinds of stuff about Google.
And I don't know that I picked
up the smug look on his face.
Fraser Kelton: I
Nabeel Hyatt: yep, tomorrow morning
you're gonna see that Google's
got a little bit of a comeback.
Although I think it's a little
bit of a comeback, right?
So...
what is Gemini, Fraser?
Fraser Kelton: Gemini gets announced
in summer by Google, where they say,
we're training, A large language
model that's going to be amazing.
And has now become a little bit of a meme
because they have talked and talked about
what they're about to do and the rumors.
Nabeel Hyatt: their answer to
OpenAI and Anthropic and the others.
Yep.
Fraser Kelton: Yep, that's right.
And we're at that dinner, as you
say, and it's become a little bit
of a joke as like, where are they?
They've been talking about this
for months and nothing's here.
And then, boom, we wake
up the next morning.
I get a text from my friend
that says, surprise, and
they've shipped some of Gemini.
They haven't shipped the most capable
model and they shipped a lot of.
Demo videos which we'll come back to
and talk about a little bit, but they've
announced a something called Gemini Ultra,
which is, you can think of it as the
equivalent of GPT 4,, then they've shipped
Gemini, I don't know, the terminology is
crazy, Pro, Gemini Pro, and Gemini Ultra
is not available, Gemini Pro is available
as of the day of the launch, and that
is comparable in performance, at least
on the evals, the evaluations, to GPT 3.
5, and then they have What I think
is probably maybe one of the more
interesting things, they've then
distilled it all down to something
called Nano, which can run on, and is
running on the Pixel, which is a pretty
awesome thing for them to have done.
Worth calling out, Gemini Pro, the
mid tier model that's equivalent to 3.
5, is now live and integrated into
BARD, their ChatGPT like product.
And it's, you know, one year after
the fact that that's been rolled out
broadly by OpenAI, and so quite a
Nabeel Hyatt: I have to admit,
Fraser, If this is nothing else,
it reminded me that BARD exists.
It was the first time
we've even mentioned BARD.
Like that, that, how useful it is
in at least our workflows, right?
Fraser Kelton: it has
reminded me that it's a thing.
It still didn't encourage me to go use it.
Like, I'm not sure what this is
an answer to in terms of ChatGPT.
So, they now have a parity with the model?
Okay.
Right.
Okay.
Sure.
Nabeel Hyatt: We were texting and
I've got some WhatsApp groups that
were fiddling around and talking about
this when it launched in a Discord
group of AI engineers and so on.
And I got to say the evals of course
went from oh my god and then the
demo videos, oh my god, to pretty
quickly, hey, is this a bunch of BS?
Fraser Kelton: Yeah, that's right.
I think the first thing to call out
is I think the general level of
performance across Some eval benchmarks
give you a sense of where relative
performance of the model might be, right?
So GPT 4, far outperforms basically
anything up until the release of
Ultra, and you could then have probably
high confidence that it's going to
perform in a very different class
once you get it into production.
I think the thing that we can say is
without having actually played with it,
the evals suggest that Ultra, the large
one is directionally equivalent to GPT 4.
And That, that's something, right?
I think that the fact that we might
now have two GPT 4 type equivalent
models in, in market proves that
somebody else other than OpenAI can
do something on this magnitude and,
Nabeel Hyatt: not willing say that yet.
That's I'm not absolutely crap.
Like I'm willing to say that
soon and, and, but, but,
Fraser Kelton: Yeah, that's fair.
, Nabeel Hyatt: the MMLU benchmarks are
comparing a five shot reported GPT 4
benchmark to a 32 shot, I think it was.
If I remember correctly, UltraReport.
It's just not, those are
not comparable at all.
Frankly, the stuff that everybody would
probably normally out of the box use
this product for, the average consumer,
those all, at least the evals we can
see, seem comparable and seem fine.
So let's not overstate some kind
of eval problem where there isn't
one, least in this specific case.
But, the kind of like general
math cases in particular seemed
a little cooked and unfortunate.
And I think, frankly, when they're
speaking to a highly technical audience,
I'm not sure why they were doing that.
Like, I, I don't, I don't
Fraser Kelton: Yeah, yeah, that,
Nabeel Hyatt: it's not them.
It just felt like a hiding the ball when
clearly even with code, like the code
generation at a Gemini is quite good.
I just don't understand
why they even did it.
Fraser Kelton: I think if you strip
it away and you look at a comparable
measure of five prompt, five examples
in the prompt, GPT 4 outperforms
it, but it only outperforms it
by a couple of percentage points.
But again, like, I think that
once we get our hands on it, my
guess is that we will see that
this is directionally similar ish.
Nabeel Hyatt: let's finish on this rant
and then I actually want to talk about
the thing I really loved about Gemini.
The part that I think was unfortunate and
I hope no startup takes away from is that
everybody gets excited because there's
an announcement from Google that there's
finally Gemini out and within a few
hours, it just dawns on everybody that,
okay, Gemini is not really here because
it's just pro, we don't have an exact
release date still, this, this graph of
evals is, Cooked in a couple places and
the rest of them are still comparing to
GPT 4 back in March eval tests and only
beat them by three five percent and since
then GPT 4 has gotten a lot better and
By the way, evals don't really matter.
So like that's the negative side the
positive side is So many companies
absolutely fail to show their
product in action in unique and novel
ways that pull the heartstrings.
And I think they, if you haven't, if you
guys are listening to this on the podcast
and you haven't yet watched the Gemini,
Demo videos go on YouTube, you should
take a look there's some wonderful craft
work that is not too pretentious and not
too overblown, but just, in fact, it's
very clean and simple, except for the
YouTuber Roper, Roper video, that's kind
of overblown, but the rest of it is very
simple, good demos of showing various ways
that this product can be put into use that
an average consumer, which might be the
aim of this announcement, is much more
the average consumer You know, or Google
stockholder then it is aimed at engineers.
I mean, didn't you love those demo videos?
You watch them, right?
Fraser Kelton: Oh, yeah, but
I, I don't know if You're
getting me riled up here, man.
I had shared the one demo video where
they show it, the three cup technique
where there's one ball underneath the
cup and then they shuffle the cups around
and it tells them which cup it's under.
Because this, It's a multi modal
model that has been trained from
the start for multi modality.
So, it's accounting for text and image
and, and video and, and audio right
from the, the start of pre training
steps, rather than, you know, kind
of bridging that in after the fact.
And this, this demo video is one of
the best demo videos I've ever seen.
Nabeel Hyatt: Yep.
Fraser Kelton: And then, and then
it comes out that it's all fake!
Nabeel Hyatt: Right.
Fraser Kelton: So in the
demo video, go watch it.
It's, it's remarkable.
There's a a set of hands and some
cups and a ball and the demo says,
okay, now I'm going to put the
cup under here and move it around.
Where is it?
And in, in real time, the Gemini
voice comes back and says, the cups,
I don't know, under the left side,
and the man lifts up the cup on the
left and sure enough, there it is.
Nabeel Hyatt: Right.
Fraser Kelton: And then, People
have discovered shortly thereafter
the fact that this is basically the
equivalent of like a simulated scene
where they had to prompt Engineer
along the way that said, just turns
out to be they fixed it in post.
Nabeel Hyatt: Yeah, that's really
Fraser Kelton: Now, I will compare that,
I will, yeah, I will compare that to
Greg's demo of GPT 4, which was all live.
without any editing and in real time.
And that is the, I think that is the
way that you introduce your products.
It's brave.
It's brave, right?
You're doing it live.
It could fail.
And you, you're owning it because you have
so much confidence in what you've built.
Nabeel Hyatt: uh, I don't,
I can't entirely disagree.
You know, it is as much as I love
the product demo, what I would
have loved was a demo like that
and then a how to behind it.
You know, I think it's okay to make
things that are somewhat polished and
beautiful, but it would be great if it if
it turned out that they reveal the covers.
And by the way, that's no implication on
Fraser Kelton: think, I think,
I think Paula should be.
Nabeel Hyatt: separate
Fraser Kelton: Yeah, yeah.
Polished and beautiful is good, but I
think it has to be grounded in reality.
This is a case where they edited
across two different dimensions, and
people came away with a dramatically
different perspective of what
it is that's actually happening.
And so I think it's unfair to not
allow anybody to use the product, and
then introduce it with a demo video
that basically obfuscates the truth
from two different perspectives.
That's just weird.
I think that this is An excellent
moment for everybody at Google because
they've shipped, or at least they've
partially shipped and I think that
you, they've taken the first step.
No, they've taken the first step.
No, no, no, no, no.
Like, listen, this, the
race was set off a year ago.
They, they did this in a year.
foR a company of their size, this
is, this is not to be, scoffed at.
Think about all of the complexities.
They had to live through smashing
together brain and deep mind.
They had to go and find, like, the
path through all the bureaucracy and
politics to get an aggregate amount of
compute required to be able to do this.
They had to solve all of the
different challenges, both
technically and politically,
within the organization to do this.
And it's out.
And I think that, that itself is
something that should be respected.
And, we can squabble over the evals
and stuff, and the proof will be
when we actually get to use it.
But it looks, it looks directionally good.
And that's something.
You know, I, I feel like I also
did a good job playing your role.
You usually are the one who's
clairvoyant in, in many respects.
And at that dinner, My guess was that
Google was going to come roaring back.
Nabeel Hyatt: your quote.
Fraser Kelton: that.
Yep.
Because they are The best at, at the
technical pieces that have to come
together for training a model like this.
And if you look at some of the stats,
I forget what the stats called, but
for basically the measure of efficiency
when they were training Ultra, I think
they reached some level of like 90%, 97
percent efficiency in the utilization
of their hardware when training Ultra,
which is just a remarkable achievement.
And this is the area where we
should expect them to be great.
And I think they have shown
that they can be great, if not,
you know, on a year's delay.
And then I think the real challenge for
them is going to be how they bring the
great technical piece into their two
products that are now the two front war.
BARD, as you laughed earlier, is a
thing, and then the second one is they're
going to have to find the right way to
integrate these technologies into search.
And that's going to be an
excruciatingly hard challenge because
it's orthogonal to the business
model that is search historically.
Nabeel Hyatt: Look, I, I, I, I say if I
were, I'm not to speak for Demi or Eli
or anybody else over the Google team and
what they're doing, I'm sure they know
a lot more about how to do this than
we do, but, but I do think their role
or their way to fit if I were trying
to navigate this space and I was Google
was to take almost an Apple approach
to this given their scale and size.
And what I mean by that is I, I always
joke people think of Apple as innovative,
and I think of Apple as a last mover
advantage, not first advantage company.
They have had a few moments in their
life where they have been very early,
but in many ways it's taking the
things that are already out there,
that are already somewhat proven.
And then making them so polished
and so well thought through that
you just, they feel like they
fit into your life immediately.
And, you know, they were not the first
to release little notification widgets
on a smartphone that was Android.
They're not the first to do
wireless charging that was Android.
Go way back, they, they, they took a lot
of their early ideas from Xerox PARC.
And so if Google wants to the game
of being last, because it's really
gonna work and work reliably,
there is a game to be played there.
Because I don't think OpenAI
wants to play that game, to be
honest, and you can't play both.
I think right now, in many ways,
OpenAI Playing closer to the Android
or Samsung, if we're going to use
smartphone analogy model where they are
riding the front edge of development.
It drives them crazy if somebody else gets
something out new ahead of them and they
want to play the front edge of the game.
I think both can be successful
strategies as long as the thing that
Google eventually releases As you get
to Ultra is worth the time and energy.
That's the, you know, the, like,
it's worth weight that that's the
thing that will be left to find out.
Fraser Kelton: We shall see.
We shall see.
Nabeel Hyatt: look, not, it's hard.
Of course, the cup example is tough.
You know, these, these
prompts are hard to shape.
It's hard to get the little alien
inside my computer to understand
that I'm playing a Cup game.
Fraser Kelton: Issue with the cup thing is
that they imply that they lead the viewer
to believe that there's zero prompting.
It's not that prompting's hard.
The way that the video is presented
suggests that there's zero prompting and
that there's this real time multimodal
model watching you and, and the cups
and inferring with real reasoning
and there's somewhat complex prompting
happening at each step behind the
scenes, which is what I think has,
has caused everybody to be really
disappointed in, in a decision to do that.
Nabeel Hyatt: The last thing I'd say
on Gemini, is that, is a lot of this
consternation would have been solved.
If they would have released APIs for
developers to build with at the same time.
And I think, I think we've, supposedly
gonna come out December 13th.
I don't know if Ultra's
gonna be involved in that.
But in a world of AI movement that's
five, seven days from now, I mean
op open AI fires a CEO and goes
through a, a coup attempt then gets
back a CEO in that time period.
Like a lot, a lot happens in five days.
Fraser Kelton: lot happens in five days.
Nabeel Hyatt: and so, like, I'm sure
this was PR oriented, they wanted
people to watch a Mark Roper video and
so on and so forth before developers
had control of the narrative.
But it's really unfortunate on,
because I think it creates a sense
of doubt when it shouldn't be.
It should just be high fives,
hand clapping, and playground.
And I think that was a little bit
of a PR mishap, but we'll, we'll
see what happens in, in seven days.
Fraser Kelton: Yep.
Yep.
And anyway, prompt, prompting is hard.
We, we talked last time about efforts in
ChatGPT to simplify the complexity and
ambiguity of prompting specifically with
DALI, where they want to take the, the
three words that somebody who's unfamiliar
or lazy with their, their directions wants
to do and how, if you're a power user
such as yourself, it's just suboptimal.
Nabeel Hyatt: Yeah, it's,
Fraser Kelton: What, what
Nabeel Hyatt: I saw my great example
of that this week because I'm going
to keep banging the drum that I think
Prompt Engineering is a real skill and
will be a career for quite some time
and that actually Prompt Engineering
is going to become more of a language.
Before it eventually gets abstracted out,
but our ability to totally abstract it
out while we're still trying to figure
out what these non deterministic models
can actually do is very, is very far
away, maybe years and years away before
we can build these systems on top of them.
I got handed a wonderful
example of this today, I sent
it your way which is Anthropic.
Which we are investors in by disc
full disclosure to everybody.
Anthropic has a competitive model
to OpenAI and Gemini called Claude.
And there is a well known research
problem and execution problem
in these long context windows.
of AI where I'm asking it to, for
instance, look at an entire PDF or look
at a long chat and find some phrase
or find some word inside of that, did
Sam talk about the beach or not, or
what's the best cooking technique?
And it turns out that if it's mentioned
in the beginning of a doc, Or at the
end of a doc every model, all of these
LLMs show that they can find information
at the beginning of a doc and at
the end of the doc faster and more
reliably than in the middle of the doc.
The middle, it's the missing middle
it just sometimes misses stuff.
Well, this has been a quote unquote
known thing, of which people have been
trying to do all kinds of different
engineering techniques chunking the
data into smaller bits, and then
there's like comparison evals against
different models at different times,
and how they perform on the missing
middle, and so on and so forth.
And then it turns out that Claude
releases a paper today called Claude 2.
1 Prompting, that says, well,
what did it say, Fraser?
What's the crazy, deep Engineering
technique that, that scientists
have figured out in order to finally
unlock moving from 23 percent
missing middle accuracy up to 97
percent missing middle accuracy.
Fraser Kelton: mean, they add a
line to the prompt that says here
is the most relevant sentence in the
context, which basically nudges the
prompt to go and pull out the relevant
sentence for the question at hand.
And that's the bump,
Nabeel Hyatt: Yeah.
I mean, that's insane.
This afternoon, I'm going to do
some work and figure out whether
this works in GPT 4 as well.
I didn't have a chance to come up,
but, but it'd be really interesting.
If both of the results, don't you
think, Fraser, would be interesting?
Like, if that phrasing does work in
GPT 4 as well then it's like, oh, you
just figured out a new incantation,
kind of like we found out that if
you, you tell a model you're going
to tip it to do something, I'll give
you 20 if you answer this question.
They, it tends to perform better in
that question, even though, of course,
you're not giving the model 20.
Another crazy incantation.
And then if, so one, if it worked,
that's interesting, and it tells us
more, a little tip into the language
of how to use these models for large
context windows, which is particularly
valuable for Claude, because it
has such a large context window.
You can just put lots and
lots of text in there.
If it doesn't work in other models,
that's even more interesting, right?
Now, for all these companies that are
trying to say, don't worry, I'm building
middleware dev tools that let you switch
in and out models arbitrarily, with
the, like, like they're all the same.
That they're not.
Fraser Kelton: I would be so
surprised if they're the same today.
And the difference is only
going to grow over time.
There's a whole bunch of
different things going on here.
This is a quote unquote eval
called Needle in the Haystack.
And I think that, yet again, this is
a situation where the eval doesn't
measure anything proximately close
to what happens in, in production
for people who are building products,
right, is if you, if you insert into
the middle of some financial set of
documents, a single sentence that
says Dolores Park is the best place
to have a drink in San Francisco and
then the model can't find it, right.
I'm not sure that that is reflective
of any real world problem that
people are trying to solve with this.
The other thing that is so interesting
here is the Anthropic model they
hypothesized performed poorly when
people were running it through that,
that eval because they've trained their
models to cut down on inaccuracies
specifically for these types of use cases.
Right.
And so they basically have trained the
model to say, okay, if something feels
completely orthogonal from the rest of
the documents, it's probably not something
that's, that's important and or accurate.
It's probably not even accurate.
So, so just ignore that.
Right.
And then the eval is basically testing for
the model performance to do exactly that.
Nabeel Hyatt: Yeah, but I want to get back
to the point that I wanted to make, which
this is six words that you put into a
prompt if you were trying to do long text
retrieval, text from a long context window
that, that does boost performance and.
I don't know, it just, it tells
me how naive we are collectively
about how to use these models.
Um, Emma, who's an AI hacker in
residence for us, she did a benchmark
on some internal tools that she was
using on Glaive versus GPT and she
found that without prompt engineering,
Glaive did better than GPT 4 probably
because it's trained only on highly
quality synthetic data and so on and
so forth, but that if you added the
sentence, You're a well known historian
to the prompt for both Glaive and
GPT that then 4 suddenly did better.
And it's just another good testament
to you just need to find the magic five
incantation words to suddenly make your
business be able to move into prod.
That's
Fraser Kelton: that is so crazy.
They could just even try to internalize
that in the brittleness of these models.
You're going to have, you are,
you're a well known historian and
then it finally outperforms it.
I don't know how this gets solved,
like, other than at the system level.
Nabeel Hyatt: But, in the very early
days of video games, you worked at the
assembly level to make a video game.
In the early days of computer
graphics, before we got to
engines, we had to work in code.
And we will eventually get
to lots of GUIs and engines.
And we've actually talked before
about how prompt engineering is not
how every average user to use these
products and people are bad at English.
But at the same time, if you need
performance, you need to be at bare
metal as close to the model as possible
for probably a little while until
everything really, really works.
And at that point, when it's automatic
when we've made our 50th first person
shooter, That's in production and making
hundreds of millions of dollars a year.
Then we can talk about making an engine
for making first person shooters.
And when we get in game parlance,
you get Unreal and and you get
Unity and so on and so forth.
But it feels like we are still in,
you know, how was Pac Man built?
Fraser Kelton: I don't, I don't want
to open up this can of worms, but
don't you think that is a measure of.
The model's capabilities
not being strong enough.
Nabeel Hyatt: We know that.
Just like in, like, early programming
days, you were wrangling with
the amount of memory on computer.
You've got a thousand twenty four
bytes of memory, and you're just
trying to make a spreadsheet work in
this tiny little bit of memory, and
you need every little squeezing bit of
thing just to make it operate, right?
And isn't about speed the way it often
was back then but it's still about
whether the job can be done well or not.
And, and yeah, we'll need to be very
close to bare metal, until all these
things run perfectly all the time, and be
about efficiency and cost and abstraction
Fraser Kelton: what Yep
Nabeel Hyatt: the rest of that stuff.
If five words for your specific use
case going to increase performance,
then I don't know if I'm Fidelity or
Procter Gamble or Figma, or InstaWork
or another startup , like, I don't know
that I'm willing to take the future of
my business's effectiveness in AI, which
could twist and turn on five words,
Fraser Kelton: right.
Yeah.
Nabeel Hyatt: And who's going
to figure out those five words
for your specific business?
It's certainly not going to be
some random middleware company.
It's going to be you because you
care about your company and you've
hacked away at it or had a prompt
engineer who's hacking away at it.
You've really worked it to try and
figure out how to wrangle this alien
to do the work that you want it to do.
Fraser Kelton: The point here is that
the brittleness of these models today
across different use cases suggests that
you're going to want to have people,
quote unquote, like, working at the metal.
Yep.
Nabeel Hyatt: analogy I would use is,
in the really early days of the web,
there was almost immediately
A bunch of WYSIWYG web page
developer software companies.
There were 30 startups that were like, you
don't have to learn CSS and HTML just use
our little product, and you can get your
web page out without tweaking it at all.
And, you know, if we fast
forward 10 years, of course,
there's many of those companies.
Today, there's Squarespace and
webflow, a bunch of these companies
that are helping everybody from a
restaurant up the street all the
way to complex enterprise websites.
But in the early days, As a good example,
prior to CSS, the way that you laid
things out on a webpage, so the way I got
something to show up on the right hand
side of a webpage versus the left hand
side of a webpage, was to use a kludge
which is to build a table, kind of like
a spreadsheet on that webpage in HTML,
and then, in one of the cells on the
right hand put my logo so it's on the
right, and then make the cells of that
spreadsheet And it's a, for me, it feels
like we are way more in that land than
we are in, in WYSIWYG abstraction land.
And so the whole first wave,
the whole first couple of years.
Of WYSIWYG website builder companies all
went out of business very, very quickly.
What, what happened there?
If we're going to use that
analogy, what happened there?
WhAt would be the business, if you
wanted to help a million companies
build their first LLM applications.
And the contention is that it's
not the time to build the square
space of the space, uh, which I'm
not, by the way, you know, this
is us just chatting on a podcast
.
A founder could walk in tomorrow.
And pitch and pitch the most beautiful
wonder idea for Squarespace for AI
and, and just prove you totally wrong.
And that's the joy of this process,
Fraser Kelton: That's,
that's what this rule so fun.
Nabeel Hyatt: Yeah, exactly.
So strong, strong convictions,
really loosely held.
But, actually, do you
believe in my analogy?
Do you think that's an apt analogy
or do you think I'm full of it?
Fraser Kelton: No, I don't
think you're full of it.
So, if I understand what's happened in the
Anthropic case, it is The way that they
have tried to nudge the model to improve
performance has then resulted in some
wonky behavior that you can then nudge it
over that hurdle with five magic words.
And what does that say to me?
That, that says to me that
there's probably a solution that
happens at the system level.
If you think about how this may mature
why would they want their customers
to ever have to think about that?
They'll, they'll find ways to absorb
the solution or abstract the solution
for use cases where it makes sense.
Nabeel Hyatt: Yeah, but I
don't have time for that.
I'm a founder that wants
first mover advantage.
Or, My boss me that I need to have an
AI strategy and I need to, I need to
launch next month and it can't, it's
got to get out of demo land cause I've
got an earnings report next quarter.
Fraser Kelton: This, this is
why that that person is having.
Random success.
Sometimes they're succeeding,
sometimes they're failing.
And sometimes they come back to
the drawing board with an entirely
new approach one month later.
We've seen that a lot.
Nabeel Hyatt: That's very true.
I do wonder.
If Procter and Gamble and, and Fidelity
and JPMorgan and every other company
is trying to figure out how to use AI.
If I just think about the web, the
web analogy for a second, and you
don't want to overstretch any analogy
of course, but the really effective
companies in that first wave for
helping to bring everybody onto the
web were kind of a mixture of tools
companies slash consulting companies.
Fraser Kelton: Yeah,
Nabeel Hyatt: It was scient and viant
and Razorfish that, you go pay them
hundreds of thousands of dollars
and they would build time magazine.
com for the first time, these
kind of mixture of design agency,
software engineering, and then
they ended up with internal tool
stacks that they knew how to use.
I think there's an analogy to
Fraser Kelton: Oh, hell, yeah,
I mean, yeah.
There's a reason why OpenAI has, I
forget, I'm going to get the names
wrong here, but has a Keystone
partnership with Bain and Anthropic
has a Keystone partnership with BCG.
Is these are footsie things to bring
into the enterprise, as we've seen.
Five words makes the difference between
something that looks horrible and
something that would be delightful in
production, and there has to be people
who can help you navigate that, uh, as the
world is changing underneath your feet.
Three months.
No.
Nabeel Hyatt: Well, the contention
is right that, Razorfish and Scient
and Vine were net new org, yes, they
were consulting organizations that
rhyme with Bain in the way that they
actually but Bain is old school.
Are there really great AI
implementation engineers waiting
at Bain to take you out to market?
Absolutely not, I would guess.
I, I suspected it's a net, that there's
an opportunity for a net new company to be
filled with people who like to implement,
who will help take these tools, which seem
maybe very easy to stand up very quickly.
I can just go to a prompt and type
things in, but I think are probably
more complicated and people will find
are more complicated than they think.
To actually implement and get live.
And that's why I like the HTML analogy.
It's incredibly simple to
build your first HTML page.
But then, and it feels
like anyone can do it.
But actually trying to
run the NewYorkTimes.
com you know, is another whole
order of magnitude more difficult.
And especially in the early days
where people didn't really know
web and how to do web development.
You needed a set of people that were
your launch team and stood up the
internet, you know, website by website.
I think there's a little bit of that
that probably goes on and I just
don't think it's going to be McKinsey
or Bain or the folks that have,
Really very little of this specific
type of DNA, but I could be wrong.
Fraser Kelton: Yeah.
People who did it back in the day for
transitioning people onto the internet.
Did they do it through just specialized
know how, or did they build tools
and platforms that allowed them to,
to simplify the task for others?
Nabeel Hyatt: Like anything you start
out making a thing and then you're
like, once you've done it two or three
times, engineers can't help themselves.
And so you start to build efficient
Fraser Kelton: is it, but that a, so, but
are we back to this is, there actually
is a middleware company, like a tool
that's going to start from a consultancy
type perspective and then get built out?
And then is your, your issue
with the tool startups?
Just the fact that they're not
going to market appropriately.
Nabeel Hyatt: That's a good pushback.
It might be.
I mean, we'll, none of us know, we'll see
how this all plays out, but yeah, maybe
the right way, it's not what VCs want.
Hey, why don't you hire more
implementation engineers?
It's not what VC on a panel would be.
They'd be like, no, no humans.
The AI should write itself.
But for where we are on the technology
side it might be that the right answer
for the next 12 to 18 months is.
You have a whole bunch of
implementation engineers that are
script monkey that know all of the
unique folklore about how to wrangle
these models in the right direction.
So you're still selling your tool
set, but you're selling your tool set
along with a handful of implementation
engineers and a maintenance contract.
And I know that that, that breaks a lot
of the purity software that we would
all love for engineering to be, but
it might be the right thing for, for
this particular stage that we're in.
Fraser Kelton: Could be . You know,
going back to the start of the API,
there were two people, a guy named
Boris and a guy named Andrew at OpenAI
who were prompt wizards, like they
just knew how to, to construct and
orchestrate these things in a way.
And that's what, that's what they did.
They ran around to the implementations
that seemed most interesting and then
helped them sand off the rough edges
to see if it was a path to production.
And in many cases, They could nudge them
there, whereas as few, few people could.
Nabeel Hyatt: Boris is a
great name for a startup.
Fraser Kelton: Yeah, he is remarkable.
He himself could be a startup.
So you don't think that these things
get abstracted the other way, where
they get pulled down into the actual
model level, and that people aren't
interacting with any of this above that.
And, and it kind of ties back
to the Gemini thing, right?
Nabeel Hyatt: Oh, I think
that's a very good point.
Very likely that that happens in parallel.
And, technology tends to go through
ages of entropy and de entropy.
We all love, especially as engineers,
we love de entropy, we love simplifying
everything, cleaning it up getting rid
of the noise from signal bringing it
all down into something that works.
But when things are not working fully
you can't jump three steps ahead, you
have to go through a phase of entropy.
It's why I don't get nervous
about One more model launching
or one more startup launching.
We need as many shots on goal
and bets to move this technology
forward as quickly as possible.
Things that are trying to make a promise
of de entropy too quickly Just feel
incongruous to me when the goal is
solve the problem reliably and, and
we're still not at reliable solution.
And so my is that reliable solution
is going to get way more complicated
before it's going to get easier.
Fraser Kelton: Boy, we wrestled with this
one, but that one feels really right.
It's going to get more complicated
in every direction because we are
not at the reliability required for
consistent value in many use cases.
And like, why bother adding abstractions
of simplicity if you say it's
still not going to be good enough?
Nabeel Hyatt: Yeah, exactly.
Fraser Kelton: lot easier for you to get
something than broken into production.
Nabeel Hyatt: That's
what, that's the headline.
Why are we making it easier to
get broken things into production?
or, we could just fix it
with marketing Frazier.
Fraser Kelton: No, man, this is like,
I'm listening to you talk about Gemini
and then like nudge me and I don't, they,
they misrepresented what the product is.
Nabeel Hyatt: you, you're
not reacting to the evals.
You're reacting to the demo video.
Fraser Kelton: That's right.
Actually, I don't even care
that much about the evals.
I think it's more interesting to
consider that all of these models are
going to have different tricks ranging
from those five words that Anthropic
had to do all the way up to like Q
star with test time compute type stuff.
The thing that bothers me is video.
And I just thought about
what the equivalent is.
Remember like a decade ago Apple
started marketing their new cameras
by showing you the output of the
iPhone camera when they announced it.
And then I don't know
whether it was Samsung or LG.
And when they announced it, they shared
photos from DSLRs and they silently
just wanted people to infer that
that was the image quality that was
coming from the phone, and then people
discovered within an hour that it was a
digital, like, SLR that took the photo.
That feels exactly what happened here.
And I'm sure that the demo with a little
bit of rough edges that they would have
had if they had shown us the prompt steps
in between and the wait for an inference
to occur still would have been a magical
moment and people would have lost their
minds, but because we feel misled, it
erodes our trust and we feel betrayed,
which is a very funny thing to say.
This reminds me of A moment that
has surprised me, and there's a
lesson here broadly for founders,
and it's not just, you know, be
honest in your marketing material.
I knew when I was a founder that the
common wisdom was just be completely
upfront with VCs because they have seen
so many pitches that they can sniff out
when something doesn't sound correct.
I will tell you in a pitch a couple
of months ago you may not remember it.
There was one moment where you paused, you
raised an eyebrow, you asked one question.
And it was, it was not an aggressive
question, but it, it pulled the
first thread that got to the truth.
And my sense is, in that case, if
he had just been up front We reach a
slightly different outcome versus having
to pull that thread and discover that
there was a little bit of, of deception
in how he was presenting things.
Nabeel Hyatt: Oh, well, to be clear, he
was trying to put a gloss on everything.
And I do remember exactly that
meeting the company had gone through
a pivot, some founder breakup y
stuff, you know, just a lot of change.
And I think.
And had been around for a little bit,
all things we didn't know when that
founder came in to present, but we
take first meetings all week long,
like, if we're not good at reading
people and figuring out what's really
happened, you can't do this job.
And meanwhile, the most important part
of this job is Establishing whether the
other person across the hall is authentic
and you can trust them, because you're
going to be on a long journey together.
And so, before it's a good business
model or it's an amazing product or it's
somebody you want to work with because
you love the intellectual banter and you
think they're going to be a great leader
or whatever else is going to get you
excited about this startup, you can't do
it if you don't think they're all being
authentic and real and honest with you.
And so, Yeah, that was a founder who
had clearly gone through some stuff,
Fraser Kelton: we don't care if
they've gone through stuff, right?
Of course.
that's part of the
Nabeel Hyatt: we would love if gone
through stuff, like, you learn some
lessons, just own it, and tell the
story about how your, you started
thinking it was this other thing
and you were just wrong, or, Bye.
You moved to this town and it was
the wrong town because there was
just a bunch of fly by nights.
You took this founder on board, but they
were just a ne'er do well, so you had
to get rid of them or just whatever it
happens to be that, that you went through.
You just want your learned insights.
And I think way too often people
want to tell a glossy story about how
everything's up and to the right and
you got to get on board right now.
Because this round is, oh, the other trick
is like this round is closing in two days.
This stuff, like create senses of urgency.
None of that stuff, all
of that stuff just hurts.
If you have a slow fundraising
process, first of all,
people probably already know.
Just say, I think they're all dumb.
This is the reason I think you are
going to be smarter than all of them.
You can try and go to their ego.
So I'm not saying you
don't try to storytell.
I'm just saying you have
to know how to do it with.
Being yourself and being authentic
to the journey that you've been on.
Fraser Kelton: Yep.
How many times have we seen somebody,
oh, okay, the deal's coming together
in two days, you have to move quickly,
and then we're like, okay, well
then this is not the deal for us.
And then all of a sudden you see
them try to backtrack fairly quickly.
Well listen, we really like you, so maybe
we can give you a couple extra days.
And you're like, alright
Nabeel Hyatt: I had the exact opposite
thing happen to me last week where I had
a founder email in and I passed over email
and I wrote up, but I wrote like a good
little paragraph about why, like this is
the thing that these are the reasons that
I'm not sure you're going to be there.
And I've gotten, mostly you get
crickets, they're going to move on,
which is totally understandable, right?
The second thing you get is defensive,
angry feedback that I'm just dumb.
Which I'm not sure exactly what
that sales tactic is, but whatever.
I got back from that
founder you might be right.
Here are the things that I
think I've worked through.
To try and prove what you're saying
wrong, and then gave a couple of little
notes of the other things they tried
that don't come out, of course, in
the one paragraph pitch of the other
versions of that business over time,
the struggles they've had, and so forth.
I mean, I got on a Zoom on that
person, like, three hours later.
Fraser Kelton: Yep.
Nabeel Hyatt: I was like, Oh, you're
Fraser Kelton: get it.
I get it.
Nabeel Hyatt: this business.
And you're authentically
trying to engage with me on it.
And you're not combative about it.
You're just having a
conversation about it.
Like, awesome.
And I, you know, didn't turn
into an investment that day.
It may in the future we'll
see, but I certainly hold that
founder in really high regard.
Fraser Kelton: I get it.
It was amazing to see, some depth
of experience such that you just,
you knew based on two sentences
that something wasn't right.
And it just reinforced what I had
been told when I was a founder.
Don't bother, right?
Nabeel Hyatt: It's similar when
you're pitching and you don't,
you're trying to gloss over the
particular risks or problem with your
startup, the old real estate trick.
That Realtors use is when they
show you a house they list all
the wonderful things and then, and
then they're doing the walkthrough.
They talk about the one thing that's
the problem with this house and what
they're trying to do focus your time
and energy on the one thing so you
don't think of the 30 other things.
That's very different from
authentically having a conversation
with an investor about your business.
But similarly, these are
early stage startups.
There's no way nothing is
wrong with your business.
Fraser Kelton: Yeah.
Nabeel Hyatt: And so you might as
well talk about the things that you
think are really risky or are broken
or that you haven't figured out yet.
Because the right investor is going to
be the person that's going to be like,
I don't think those are real risks, or
I'm willing to take on that risk, or
like, I think you can solve that risk,
and that's, that's the right way to
have the conversation about the path.
Nobody expects these things to be
totally finished and that's a very,
Fraser Kelton: sure.
Yeah.
Somebody internally here said
that the quickest way to a no
is when there is no risk, right?
Because that's not a venture business.
That's, that's not for us.
Nabeel Hyatt: When a founder feels like
They know the problems they think they've
kind of solved and the areas where they're
self reflective and self aware enough to
realize they've got a lot of work to do.
And you can have an open and honest
conversation about doing that work..
Fraser Kelton: Mm hmm.
I also think the challenge here is
that every firm is different, right?
And so, whenever anybody has
shown us a demo, you see almost
everybody in the room lean forward.
And when people have had stilted
presentation pitch mode, everybody's,
you know, kind of in lean back.
Nabeel Hyatt: At Spark, we like
demos, we like talking product, and
that's, you know, but you're right,
that's not how it is at every shop.
That's not that's not how
lots of investors operate.
Fraser Kelton: that would
probably be the challenge here is
everybody operates differently.
That's where you have an opportunity in
these moments to find the person that
you want to be with for a long time.
Right?
So there, there will, there are
different founders who appreciate
different types of techniques too.
Nabeel Hyatt: It can feel from the
fundraising side, and I certainly felt
it as a founder, like, I just want
somebody to give me a first term sheet.
I'm just trying to raise
capital, whoever it can be.
But that's a little bit like in today's
age, like applying to college by just
saying, I, I really love your school
because it's a great school for learning.
And it's, that's not a great
way to get into college.
I had an admissions person at NYU tell
me that they, the admissions people
there, they always do a thing where they
cover up, why do you want to go to NYU?
And if the answer is you could put in
Columbia instead of NYU the answer, then
that's not the person for NYU, right?
Fraser Kelton: Yep.
Yep.
Nabeel Hyatt: know, it's, if
it's, I love the opportunity for
internships in the dynamic city
and, you know, stuff like that.
It's like, that's not really about NYU.
That's about New York.
so I think similarly when fundraising
The thing I got to in the latter
half of my third, fourth startup in
fundraising was, um, I'm going to pitch
the way I want to pitch, not the way
my founder friends tell me to pitch.
And I'm going pitch in a way that is
authentically me and the way that I
want to talk about how I want to raise,
run this company, the culture I want
to build, the problems my startup has.
I'm just going to lay it
on the table authentically.
And then the job isn't to find.
50 term sheets.
The job is to find one or two term sheets.
If I, if I can pitch the way I want
to pitch my business I'll get lots
of strong no's, but one strong yes.
And lots of strong no's, but one or
two or three strong yes's, it is ten
times more valuable than a bunch of meh.
This seemed okay, because those
don't lead to board seats and checks
and people who are going to join
your cause for the next ten years.
Fraser Kelton: So having listened to
that and then having a moment to reflect.
The thing that I would do differently
that I think would have a material impact.
is to have a very authentic opening
as to why I was excited to have
this conversation with this specific
person in this specific firm.
You and I had the joy of sitting
with that founder a couple of months
ago now, who said, I'm excited
at the prospect of working with
Spark because you have a history of
supporting founders doing brave things.
And I know that that worked.
On you and I because we independently
said it with other people after the fact,
Nabeel Hyatt: good
Fraser Kelton: And it was
gr it was great sales.
It, but it was, well, just like
any great sales, it was authentic
and it, and it resonated, right?
Nabeel Hyatt: and you could feel it in
the tone of when they were saying that,
it was something they were really feeling.
What do you do when you're
pitching, X, Y, Z, fund?
That you don't really know why,
why you're talking to them.
You just, you can't figure out
the most amazing, there's no
obvious amazing reason why you're
talking to them in the first place.
Fraser Kelton: Why are you wasting
either of your party's time?
Is the first one.
If you can't put in 15 minutes of thought
and research and come up with one reason,
then why are you talking to that person?
Nabeel Hyatt: There are people that like,
there are CEOs that like to build very
long spreadsheets of the 40 people that
they're going to go through and talk to.
And look, there are times where
fundraising is really CEOs who It was
the 38th person in the Excel spreadsheet
that you got to that raised the round.
Personally, I have had rounds where
I have had to do that in the past.
But I think that is different
from being casual about it.
And I think that's what talking to.
Long term relationships, these are
big decisions for the person on the
other side and they can feel it when
when the work hasn't been put in.
And so, I know for a lot of founders
raising money can feel like a quote
unquote distraction and I want to
get back to quote unquote work.
And I've always really hated that
phrasing because You know, getting
rid of a board member is like 10
times harder than getting divorced.
Like, you're, you're recruiting
somebody that is going to be
you for a really long time.
Like, you should put the time and
effort in the same way that you would
to recruit a CTO or anybody else.
Fraser Kelton: Oh, for sure, right?
It is, it's a byproduct of COVID
maybe where it became speed dating
and the whole community went crazy,
but the idea that you would sign
up for this level of intense.
Camaraderie without having a an
investment seems rather silly.
You know, I got good news for you.
I got good news for you.
Nabeel Hyatt: What's up?
Fraser Kelton: I googled superpowered.
Nabeel Hyatt: Mm hmm.
Fraser Kelton: And I'm, I'm reading
an article and we'll come back
to the product, but I wanna, I
want to, I wanna live with you.
The company says they are not
shutting down the initial product,
Nabeel Hyatt: Yes!
Fraser Kelton: All right.
Let's take a step back.
Tell us about superpowered
and why you're over the moon.
Mm,
Nabeel Hyatt: so, this is a great
segue into product of the week I have
been trying to record most of my life
on a daily basis more and more of my
life, and try and summarize it and make
it searchable and so forth the super
powered started out as kind of like
meeting bot helper company actually
prior to GPT and then post ChatGPT,
they turn it into an AI note taker
for your Zoom meetings or your Google
Meet meetings and so on and so forth.
Now if that sounds like 30 other startups,
that is because there are like 30 other
startups that are also aI note taking
startups, folks like Fireflys, and I
think Gong does this for salespeople.
And you could just go open the Zoom
app store and take a look through.
And by the way, Zoom itself has
natively launched summarization
as well to take notes while
you're inside of your meetings.
And so the question is, why am
I excited about superpowered not
dying when all these things exist?
Everyone's going to launch a
version of a product, and they're
all going to be noisy, but the real
question is, who's done it right?
And at least in my personal view,
I've tried all of these products.
And none of them are good enough that
I would ever use them week after week
after week, except for SuperPowered.
Fraser Kelton: What,
what is super powered?
Nabeel Hyatt: You mean,
what does the product do?
Fraser Kelton: You just said that it's
all of the small things that they've
done right that make it stand out from a
different AI, like transcription service.
Like, isn't it, don't you just want
it to do reliable transcription?
Nabeel Hyatt: No.
First of all, nobody wants to look
at the transcription of any meeting.
thEre is no way That I want to
actually look through all of the
ridiculous things that I talk about
every single day, word by word.
What you really want is summarization,
and what you really want is action items.
And the execution on that
summarization and the execution on
those action items is what matters.
And it just turns out that
there's actually wildly
variant execution on that job.
The particularly two problems that I
have With most of the other products
that do summarization are first,
they run inside of Zoom as an app.
And I don't want Zoom
having control over it.
I want my desktop to have control over it.
And so, SuperPowered is a
desktop app, not a Zoom app.
That's the first that matters a lot.
Fraser Kelton: that's a big difference.
Nabeel Hyatt: and it allows
them, in particularly, to add
new interfaces, new Chrome.
They can iterate on it,
like, 50 times faster.
than trying to be one button on the
toolbar on the bottom of, of Zoom.
It also means a user doesn't have
to ask corporate overlords whether
they will approve this, this
app to run their infrastructure.
Which I think is a real thing
we've got to think about in AI.
A quick aside, I was talking to my
friend who works at Amazon and he said,
you know, we talk in these podcasts
of all these wonderful products and he
goes, he just, he's like, just remind
you what's going on in real life.
Every time he even goes to use ChatGPT,
Amazon internally puts up this big
prompt that yells at him and says,
listen, just so you know, if you put
any confidential information into this
product, we will come and kill you.
He can't have things scraping his
email to summarize them properly.
You can't have products that are
helping arrange meetings through AI,
Amazon's not getting that stuff happen.
They're on lockdown.
Oh.
And anyway, so, but super powered
runs on your desktop, that's the first
thing, and so I have control over
its use not my corporate overlords.
And then the second thing and again, it
kind of maybe goes back to this previous
conversation about just understanding
where we are on the entropy curve, is
that they let you edit the prompts.
So they have, they have meeting types.
So for instance, if I'm meeting
a new company, I have a meeting
type called New Company.
And then when I meet with the founders we
work with, that's called Founders, right?
And, and the notes I to take away,
the takeaway from each of these types
of meetings is remarkably different.
And of course have prompts that they put
in there that are starter prompts for the
noob who doesn't know what they're doing.
But inevitably, I probably for
90 percent of people today, Like,
something's wrong about that.
It says something in there that I
want that's not right for me, and it
lets you open up the prompt, edit the
prompt, and get what you want out of
it, which is the difference between
something that is kind of like, meh and
okay, and it like gave me a couple of
interesting summarization topics and
titles, versus I feel like an active
participant in making this thing work.
Fraser Kelton: Don't you think that
this is also maybe why they're pivoting
away from it in the sense that This
is a really hard problem, right?
I was just thinking that the diversity
of meetings that people have and then
the preferences of workflows across
those different types of meetings means
that there's like an explosion in Quote
unquote, getting this to work well.
Nabeel Hyatt: I think there's two
points there worth touching on.
The first of which is that,
look, of course it's a problem.
It's a problem.
That there are lots of different use
cases in meetings, and it's a problem that
this is a really busy market with lots of
competition, so it's hard to stick out.
iF a startup doesn't want to solve
problems, then what are they doing?
Like, I, I do worry sometimes that we,
we try and avoid all of the risk in
our startups when problems existing
out in the world is why startups have
a chance to exist in the first place.
So you have to pick your
proper problems, but,
Fraser Kelton: Huh.
Nabeel Hyatt: but yeah, let's, but let's
spend a little time figuring out if I'm
a startup, how to solve this problem,
because if we can solve it, then it's
perfectly obvious for the 18 or 20
other AI meeting note companies that
they have not solved this problem yet.
So if I have a
Fraser Kelton: Mm
Nabeel Hyatt: then suddenly I can
explain that breakthrough very
clearly to my customers, and I now
have an advantage in the market.
Fraser Kelton: Hmm.
Nabeel Hyatt: So,
Fraser Kelton: Good, good point.
That's fair.
Nabeel Hyatt: and then my second point is
somewhat related, but I think it's also
back to the entropy, de entropy thing.
If you, if you honestly think that
we are still at the point where we're
trying to make all these AI products
work, then just accept the idea that
this is AI products are early adopter
products for right now, and they will
not be early adopter products, only
idiots, crazy people, only crazy people
like you or me might try and play with.
An early adopter across every
single vertical and horizontal,
every single week, to see how all
of these tools are developing.
But early adopter doesn't just
mean nerd in, in It means that for
somebody, this problem is so acute
Fraser Kelton: Mm hmm.
Nabeel Hyatt: will be an early adopter
to try and figure out the solution.
And that early adopter customer will
help you find the solution with you if
you give them the tools to work on it.
And as an, as an example, you know,
Adept, which is where an investor in,
is an action, they create an action
transformer model, and they've released
a workflow tool for building your own
little webpage navigator to take actions
on a webpage and do little workflows.
Now, the model itself is not the large
model they'll be launching relatively
soon, so it's an earlier model, and
the workflow tool itself is, let's
be honest, like, kind of hard to use,
clearly an R& D product and definitely
not a late adopter product that I
would give my mother or father, right?
But
Fraser Kelton: mm hmm,
Nabeel Hyatt: for the people who,
which Those workflows are really,
really acute problems in their lives.
They are going to trudge through it.
And then you will learn with your customer
versus in some R& D lab somewhere where
your assumptions about your customer
are wrong, which is the right way to
build when you're early in a market.
Fraser Kelton: mm hmm, the challenges that
exist today are, you know, normal in terms
of trying to figure out how to solve a
large, meaningful problem and that it's
a shame, uh, if that's the reason why
a group who had a little bit of an edge
on it is, is likely to not be investing
too actively into trying to solve it.
Nabeel Hyatt: And look, we don't
know the superpowered AI founders.
I don't know where they are in
funding or their traction or their
progress or what excites them
and gets them up in the morning.
I'm just a consumer of the product . But
all I know is that everybody here should
go to SuperPowered AI and give them
money so that they stay in business,
so that I can keep using product.
Fraser Kelton: You know, I
just skimmed the article.
It says that it's hard to
differentiate in this type of a
market to have sustained growth.
thEy are profitable, and so
they hope to find somebody who
will just continue to run it.
But they're pivoting to become an API
provider for anybody to create a natural
sounding voice based AI assistant.
Nabeel Hyatt: That is also a busy space
of course, but, you know, it's also
very possible that's just a problem that
they are more excited about solving.
And will, they will through the
hard difficulties of that particular
problem with more verve and with
more passion than they have for
meeting notes, which is fine.
Fraser Kelton: you know, yeah, people
don't often talk about that, right?
Is you go through a pivot and you're
doing it for a lot of You know,
logical reasons, but you might either
pivot into or away from an idea
that you actually care deeply about.
Nabeel Hyatt: Yeah, it's not a short road.
Yeah let's be done for today.
I
Fraser Kelton: do it
Nabeel Hyatt: think we went
through some good stuff.
Go download SuperPowered.
Give it a try.
We'd love to hear from
you on that product.
I'll also add there's a couple
other products that have launched
that are allowing you to do live
AI drawing if you wanna try one.
Leonardo AI launched a pretty good
live canvas feature where you can draw
on one side through a prompt and it
redraws it on the right hand side.
If you want to give it a shot
and then we will see you all.
doing one next week, Fraser?
Fraser Kelton: I don't know.
Let's see.
We'll see in the future.
We, we're not sure.
Nabeel Hyatt: We'll see
you all in the future.
Bye
Fraser Kelton: See ya.