-: Yeah, so perfect, Kevin,
thank you so much for joining today.
I'm really excited to just hop in
and have a discussion about AI
and you know, the project that you brought to my attention
that you're working on is pretty fascinating,
so, why don't you give a little bit of your background
and, you know, tell the students what you're all about.
-: Totally, that sounds good, excited to chat.
Maybe, to kick it off,
I'll give a quick background on myself
and then we can jump into Verified.
So I've spent my career building software products
and companies, both as a PM and as a founder.
The first one was a company called Kensho.
And Kensho is building AI tools for large banks,
back when AI was really NLP back in 2016, 2017.
And really exciting ride at that company.
What we did was we built a graph database
of four decades worth of events,
any type of event you can imagine,
say, Elon Musk tweeting about Tesla
or North Korea threatening some type of nuclear incursion.
And our goal was to build this massive dataset
that enabled a trader at, say, JP Morgan
to run a natural language query in literally a search box
and understand how certain events might correlate with
and ultimately impact certain assets
that you were trading on.
Encountered quite a few friction points at that time
because obviously at the time
LLMs weren't commercializable yet.
And so we were doing a lot of this work manually
and the responses that we were getting
were definitely not a natural speak and nearly as powerful,
but it was an exciting foray into what could be accomplished
with that scale of data
and with this combination of underneath the hood
datasets that are specialized and above the hood,
more natural intuitive interactions with that data.
So that company ended up getting acquired in 2018
by S&P Global for around 700 million.
From there I jumped to another company called Digits,
digits.com.
And at Digits we were building AI tools for SMBs.
For business owners to understand in real time
the health of their finances.
For example, say, you spent 30% more on transportation
than you did last month.
Why? What actually drove that change?
And how could you recalibrate your business expenditures
to hit your budgeting goals
and ultimately extend your runway,
which is critical for any SMB.
And so really exciting ride with that company.
We scaled through our series B
and series C most recently led by SoftBank
and it was a phenomenal foray
into really this financial dataset
and how AI and LLMs might impact that sphere.
From there I left
to co-found a Web3 FinTech company called Mural.
And at Mural we were doing cross-border payments
powered by Stablecoins.
We raised 6 million for that company
and it continues to scale as we speak.
And then last but not least,
I founded a venture capital fund called Predictive VC,
investing into early-stage companies,
primarily that incorporate AI in interesting and novel ways.
So if you're building something in AI,
feel free to give me a shout, happy to help however I can.
And that leads us to today.
Now I'm the co-founder and CEO of a company called Verified.
And Verified is designed for creators,
in particular, domain expert creators
to distribute and monetize their knowledge, harnessing AI.
We're really excited about the future
of both the creator economy and knowledge sharing,
as well as really how technologies,
like, LLMs, voice cloning, image animation
and more will really humanize AI
and enable knowledge sharing in a way
that hasn't been possible before.
So long-winded backstory, but excited to dive in.
-: Yeah, that's great my friend.
And it's really fascinating.
Before we get into Verified,
I wanna just chat briefly
about the very first endeavor you mentioned
about the bank filtering, 'cause when I started
to see the power of ChatGPT over this,
you know, last year, I'm thinking to myself,
"Man, what do the financial companies have
that are taking all the data from the stock market,
from the news and just synthesizing it,"
because I knew there's gotta be.
So, did that end up taking off?
I know you said, it was a pretty, it sounds like a big exit.
Is that just standard now across the board
for these kinda companies to pay for that kind of tool?
-: It's a great question and you know, it's,
especially in finance, the processes have been so archaic,
even into the late 2010s.
Before that I did a brief stint,
actually on a trading floor at JP Morgan
and went through the process
of literally compiling all of this data manually
into an Excel sheet.
And really your day to day as a trader
or equity researcher, even managing millions
or 10s of millions of dollars daily at a bank like that
is so and in many ways it continues to be quite manual.
An example in this case with JP was,
I was looking at the Latin American macro market
and in particular Argentina,
and our goal was to determine
how certain stimuli or certain variables
might affect the price of the peso.
And so we were looking at things like
soybean production, monthly
and actually collating that data into an Excel sheet
and then trying to extrapolate insights
from that Excel sheet
to actually then trade on millions or tens of millions of,
you know, dollars worth of, you know, certain assets.
So, yeah, it continues to be a quite a manual process,
but as of late, many of these banks,
especially more of the forward-thinking ones
have been more proactive in developing or adopting LLMs
and other types of technology
to really drive more predictive insights.
I think a big challenge
for banks in particular is data privacy.
And obviously, when you have a bank
that has proprietary data,
a big risk is whether you want to
or whether it's secure to actually devolve that data
to a mass market LLM, like, OpenAI for instance.
-: Hmm and how do you see staying on the business side
of things before we kinda go into the individual,
how do you see really the future
of different business sectors, finance, real estate?
Do you see them all having their own trained LLMs
that they're, you know, I'm just curious to see your thought
on that business-wise and how that's gonna play out
in the next five years.
-: So, I think we're gonna see different LLMs being developed
for different use cases.
I think they're going to have our mainstream LLMs,
take ChatGPT for instance,
that are the dominant market leader
for a majority of consumer use cases.
But I think when it comes to the business,
take finance for instance, the element of security
and data privacy is really top of mind.
So we're going to see really, you know, on-prem
or siloed LLMs being developed for very specific use cases
for certain enterprises.
And even today what we're seeing is a lot of enterprises
actually banning or preventing the use
of certain types of more commercializable LLMs,
really for the sake of data privacy
to ensure there's that healthy separation.
-: Mm-hmm and so will these companies have their own,
basically their own GPT model
that's trained on just finance information
and is siloed off, where you put in the information,
you go through the process,
but it's not really speaking directly,
connected to the internet in the way
that when you go on a ChatGPT, it's open
and the data is widespread.
-: Totally, I think we'll see a combination
of internally developed LLMs, as well as an attempt
by more of the mainstream companies like OpenAI
to build more guardrails and make themselves more friendly
to these enterprise use cases.
So I think there's gonna be this healthy push and pull
between both sides.
And obviously, the trade-off right
is sheer number of parameters
and sheer computational power actually
to derive insights for specific use cases.
I think in some use cases you might not need
the sheer number of parameters that, say,
OpenAI as a platform can provide
and that's maybe okay depending on the business use case,
with the trade-off being that security.
-: Right. Right.
And then moving away from the business side,
what do you think for the individual this means,
I mean, we've seen the widespread effect
of stuff, like, ChatGPT, it getting banned from schools,
you know, different issues happening,
more around guardrails and security
and dialing back the power of it
by making it more safe and less biased.
But what about the positive impact
for, you know, individuals, whether it's a musician
or an artist or a photographer?
How do you really see that playing out
over the next few years?
-: Hmm, you know what's really exciting
and I think given the nature
of what we're building with Verified,
we've spent a lot of time talking
with a variety of different domain experts
and by domain experts,
I mean people like you or me
that are specialized in certain industries.
It could be anything from cooking to dating
to parenting to health and wellness and more.
And each of these professionals
have been proactively looking to adopt AI
into their daily lives.
I think there's a healthy dose of skepticism,
but for the most part
folks have actually been proactively trying
to learn how to adopt AI
and I think obviously the elephant in the room is ChatGPT
as a baseline mechanism to quickly conduct research,
for instance, maybe new ideas with what to focus on next
or sending emails for instance, more copy related work.
But I think each of these folks is also,
in general from what we've seen,
searching for the next wave of use cases
and how they can even more intelligently adopt AI.
I think that's the missing link at the moment,
that we're all waiting to see really what happens next.
-: Hmm, so you're saying that the technology is there
for a lot of benefit,
but people in different professions are looking for,
"Well, how the heck do I use it essentially?"
-: Yeah, they have the baseline right,
in platforms like ChatGPT
and in general, people are enthusiastic
to try it out at least.
But as we're seeing really with ChatGPT engagement,
it's actually been declining in many cases.
And I think the question
that's on a lot of people's minds is, what's next?
And beyond this perhaps sterile or generic interface
that enables 'em to access some sort of,
you know, fact checker or, an idea generator,
what does the next wave of AI look like
for these consumer use cases that makes their life easier?
-: Yeah, yeah.
And so let's start talking about Verified
and let's try to do it in a way that is,
like, interesting, educational for the students
to have at least a view of where things are going.
'Cause the reason I really like when you guys approached me
and we started talking is
because I was toying around
with all these different AI tools
and I was like, "Man, there is this kind of missing link
of having basically your own sidekick."
I see that eventually,
we'll all have the same way we have an iPhone,
we'll have our own mini Siri, mini Alexa.
I don't like using those as examples,
'cause I don't ever, I literally don't think that those,
those are still in v0.0 beta to me
'cause they don't work very well.