I think the magic of the deadline is that it forces you to focus. Look, I only have this much time. If I have this goal,
here's what I need to do to achieve it. And then everything which is not related to that, it just drops away completely. It's so beneficial to have some kind of meaningful deadline. It could be about customers. We need to ship this by a certain date. We need to actually get it done by then. That's why I was kind of able to achieve the things. Hi, I'm John Whaley, I'm the founder of Inception Studio and also founder of three cybersecurity companies. MOKA5, UNIFYID and Redcoat AI. I graduated from MIT with my master's and bachelor's in computer science,
and also from Stanford with my PhD in computer science. I'm also on the Stanford faculty
in the Computer Science department. Inception Studio is a very unique model. Unlike most accelerators, we're actually
a nonprofit accelerator, which means we don't take any equity in the companies. That's because we're focused on quality. We want to get the very best quality founders anywhere. We focus on the very earliest stages. We run these 72 hour retreat events,
where you go from just having a rough idea about what you might want to do,
and then meet up with co-founders and then form a team, and then you actually go
and and pitch the company at the end. So it's super intense, which is intentional. You're looking for co-founders. You want to know
how people operate under pressure. We've had quite a few success stories
that come through in the Inception so far. Andy Chou, who's he was previously founder
of a company called Coverity. He came to one of our inception events, the first one, in fact, at the end of that event, he had a team. He had a refined idea, he had a demo,
he had a pitch deck, and he had a set of investors who were all
interested in what he was doing. They ended up raising
a $10 million round seed round like led by Andreessen Horowitz. It's a very interesting area.
This was kind of like the genesis of idea. This idea like happened at the inception event. I got started with computers very young, like at five years old, my family had bought a computer. We didn't really have many games for the
computer, and so my option there was like, there used to be these magazines,
you would have the listings, like the code listings for games you could type in,
and then you could run the games. So that's how I got started. Back when I was five,
I was in kindergarten and I would just like sit there and type in all the code. And then of course, as you're typing this,
you're kind of learning these things. And then because I was like not satisfied
with just the games as they were, I wanted to be able to make modifications
to them and change things about them. And so like, I would have to basically
learn how to manipulate the code to get it to do what I wanted. That's that's how I got started. I've been doing computer science like ever
since I was like in high school. I was not a good student. I had all of my teachers
and my parents, everybody else. They were like, they said, oh,
you're going to go to MIT and you're going to struggle
and you're going to get C's and like, you're going to fail classes like that. My first sense that I was I'm actually good at this stuff is like there was a qualification round
for the USA Computer Olympiad. I had taken the AP Computer Science as
a sophomore, and I did well in the class and everything. And then my teacher there, she learned
about the USA Computer Olympiad. She's like, why don't you try this? And so other problems I had in the computer science class were like, pretty easy. Those problems they had on the
Computer Olympiad like these were hard. And there's one problem in particular
that was just I was really challenging, and I was just like,
I know there's a solution there. I just gotta figure this out. And then later on, I actually found out
that I was like, the only person in the US had had solved that one. I think there were six problems. And there was like one
of the problems that, like nobody else had solved except for me. So that was my first indication. That would be like,
maybe I am kind of good at these things. And then I kind of did
the competitive thing again. It was like, you have the format there
is you have three hours and you have to like basically program the solution
within a short period of time. In preparation for that,
I went to like the local bookstore, and this was actually the algorithms book
that was used in the MIT class 646. And so that was the main reason
that I went to MIT because of that book. There's so many amazing algorithms
and stuff in there. I love just reading about them
and trying them out. That was kind of like what opened my eyes
to like that whole side of computer science. I was very fortunate to get accepted
to MIT because my grades were not great, but I think because I had
that Computer Olympiad experience, I think based on that, then I got accepted to MIT. I don't think it's I had any kind
of natural like skill and can learn faster than kind of other people. I think I just like was much more dedicated to it because I had that deadline. I think the magic of the deadline
is that it forces you to focus. It's like I'm never able to finish
things unless there's a deadline. But like as soon as like I get that pressure, then it just clarifies everything. It's like, well, look, I only have
this much time if I have this goal, here's what I need to do to achieve it. And then everything which is like not related to that. It just drops away completely. So I think it's for me personally,
it's like very clarifying, just in terms of just allowing me to like really focus. That's why I was kind of able to achieve the things. Myself and like some of the other members
of my research group in the computer science department at Stanford, we met with Vinod Khosla, who is founder of Sun, and we talked with him,
and I think it was the next day we received like a term sheet from him. Effectively what he said was like, here's
$3 million, go build something cool. I was actually planning to be a professor. I had interviewed at a bunch
of faculty for tenure track, like faculty jobs that I didn't get
tenure track offers from either one. So I was at that kind of life stage
where, like, I was trying to figure out what to do next,
and then there's this opportunity. I was like, oh, we can found this company.
Vinod Khosla is going to back it. And like that was it's like, yeah,
of course I'm going to do that. So that was my first company.
MOKA5. Basically a MOKA5, what we're trying to build was we call it
the next generation computing utility. And like we envisioned the world
where you wouldn't have to worry about maintaining software and maintaining your
operating system and patching and updates and antivirus and things like that. This is like back in the early 2000. It's like, oh,
I wanted a piece of software. It used to be like, you would go to the store and you would like, buy a CD and you'd install the software on your computer. And we envisioned the world
where you would be able to call up your service provider and like use providing your computing service. Say, hey, I want Photoshop. And they would like turn on a switch, and
then suddenly you would have office and it would be a new icon in your desktop. And like we naively believed
that internet companies and these kind of like cable company, your DSL,
like they're going to want to do this, they're going to get lock in there. They're going to have
they're going to have all your files. It's providing a great service. And like everyone's going
to want to do this. It was like, no, okay. That's not. (We) ignored
the kind of reality of the world. The place where we actually started to get
traction was when we realized that there's an opportunity on the business side where
there's all these companies that wanted to provide computing environments, but
people wanted to use their own computers. And the big breakthrough for us
was the release of the MacBook air. It was like the sexy new computer
and like everybody wanted one. But these companies were like,
oh, well, we're a windows shop, we're a Microsoft shop. All the business phone was BlackBerry,
and they had all the features, all the enterprise features. And then iPhone came along
and then suddenly people wanted iPhone, and they wanted to bring these into
the companies and use them in the company. I was like, well, it doesn't support MDM
and all the other things that you need. Well, we don't care
like we want to use it. And so then there's an opportunity there. So we rode that opportunity
like the Mac and the enterprise, the bring your own device like all of that. And so that was like when we started to do
well is where we kind of really began to focus on that problem. That was the opportunity that existed in the market. And then we started to get some early traction there. And our first really big customer was Goldman Sachs. We had to do some like crazy
sweetheart deal to like get them. I was like not very much money for an
unlimited license of all you can eat. They can use us in perpetuity. That's the type of deal that you have
to do when you're an early stage startup, because as soon as you got Goldman Sachs,
suddenly there's ten other banks that are all interested because like Goldman Sachs
had a great reputation for cybersecurity. And like all of that, it's like, oh,
if you pass their bar, we're now interested in you. But we've made a lot of missteps along the way. There's a time period where we ended
up hiring kind of a CEO that was more of a consumer type, not really enterprise. We end up burning a bunch of money
through that and like just not really having anything to show for it that kind of later on just ended up kind of compounding over time. And so it gets harder and harder. As you hit a series C and series D,
it becomes harder to be successful there. We kind of went through multiple CEOs
and it was also this is enterprise sales, enterprise deals. It's like elephant hunting. It's either you're going to like kill
the elephant, in which case we got some big huge like multi-million dollar deal. And it's just like, hey,
we blow out our numbers or that Po doesn't come in in time. And we also like hiring a bunch of really
expensive sales inside sales people. And it's a difficult game, especially
early on when you are trying to do big enterprise deals as a small company. Some of the big lessons
we learned from MOKA5. Number one, just because somebody
is willing to give you money doesn't mean it's a good idea
or it's like worth your time. In this case, it was like Vinod came and
he's like, oh, I want to give you money. I was like, oh, for no, it's famous.
He's on the Midas List. This must be a great idea. Actually, there was not exactly true
because your time is worth more than their money. Another big lesson is like, really
put a lot of thought and consideration about who you have as investors. Investor may be nice to you, but when they
put in a lot of money in their company, there's a lot of expectations there. Push comes to shove,
they may not be kind of fully aligned with what the interests of the founders are. So again, being kind of careful about
who you have involved in the company, your first 20, like your founders
and your first 20 employees, they determine like, what is the DNA for
and the gene pool for your company. Those first 20 employees should be
very intentional about who we are hiring. Like what are the skills like for the ones
that like the ones that are going to stick around there? They're going to have a huge impact
on the future of your company. So like, and what kind of company
do you want to build? Is it like customer focused?
Is it like all about product? Is it like technology? Those are all determined
by like what is the DNA of the company. And that's determined
by the people who are there early. Through my experience through at
MOKA5, I just kind of fell into that role. I was doing things that was kind of not authentic. I wasn't particularly passionate about that problem, and I was doing a lot of things where it's like, oh,
I felt like this is good for my career. In retrospect, it was like it put everything in perspective for me about what was actually important in life. The company was kind of like less
important at that point, and we ended up at a point where it's like,
hey, I was like, leave the company. And then things really just crumbled
from from there. And they ended up like fire
selling the company and everything. I started my second company called UNIFYID In UNIFYID, we saw this problem
of identity and authentication. How do you identify yourself in the age of computers? This is often a password. I come up with some secret
and I tell you that secret. And then that's how you know that it's me. By the way, I'm not very good at coming
up with a good secrets because, like, I'm going to reuse them all over the place. What if we say that we're not going to
require the user to do anything different? They just be themselves. Is there is it possible to, like,
authenticate them in that context? And the answer is actually yes, because
there's a lot that makes us unique things like the way that you walk and the way
that you hold your phone and the way that you type, you combine them all together,
the preponderance of the evidence. And it's like, this is almost certainly
the right person at that point. So that was a world where I envisioned. At UNIFYID we told a really big story, winning at a bunch
of pitch competitions because of this. Like we were runner up
at a TechCrunch disrupt. We ended up winning at RSA. Based on that, we ended up raising
a huge series, a $20 million, and it's like, okay, now we have the money. Now let's go in and pursue that goal. And then it took us a long time
to actually get to the point where it's like the technology reached to the point
where it's like we were able to achieve the things that we wanted to achieve. In retrospect, like we would have been better off biting off a much smaller chunk
and having that tighter deadline and like that specter of like, hey,
we don't have four years of runway here. We have like 18 months because it
forces a lot of discipline there. And UNIFYID we kind of ended up having
a too broad of a vision. You know, we didn't achieve our full potential there. There's all these like very interesting intermediate points where we could have spent two months and built something that would have gotten us revenue right away and solved customer problems right away. All that changed when I did UNIFYID. Honestly, I just became much more open
and authentic and like honest with people. Curious thing happened when that happened. Whereas like, people started to react
in a much more positive way. And then it was like, oh, it turns
out people like respond to authenticity and like when you're honest about things
and they can sense that. And then like, naturally, people
like more people want to follow you. It was like, this is a company we want to build. This is the way we want to do things. But it was like very honest
and authentic to ourselves. And then a lot of things that were I'd
struggled with in the past became a lot easier just in terms of like things
like for leadership, fundraising, even, or inspiring people and that sort of thing. We eventually got acquired back in 2021.
So right before our series B. I was kind of at a point in my life where
I was like, what do I want to do next? I'm very much a startup person. I like the kind of early stage I knew
that I wanted to do something in the large language model and generative
AI space, and the AI in general, and machine learning like to do
that kind of gate analysis and the other type of stuff we're doing, we're using a lot of sophisticated machine learning algorithms and everything. So from that I was familiar with transformers and like everything that was happening in the LLM space,
as soon as GPT three came out, I knew that there was a huge, very interesting
commercial opportunities from LLMs. It was began to explore and look around
like try to come up with some ideas. Honestly, it was not making
a lot of progress on that because it was me sitting alone in my bedroom
just thinking about problems. I had no deadlines. I had no pressure. My life was just too comfortable. Time was just passing by
and this was back in 2022. GPT 3 had come out. There's kind of murmurings
of like other things. GPT 4, there's going to be coming out soon. I need to make more progress here.
This is not good. I know what I needed, number one, I needed
to get away from everyone and like, just remove myself from all my distractions. And so I needed to go away somewhere
for some short period of time just to be able to like, focus. Number two is I needed to be surrounded
by other really smart people. Most importantly is I needed a deadline. I go to Hackmit pretty much every year
and a tree hacks and Cal hacks. I love the kind of hackathon vibe and
scene, just like that kind of intensity that you have and like the for the fact
it just forces you to focus. So like it's just sort to kind
of recreate that type of feeling. And so we went on Vrbo, like the vacation
rental site rented out this house in Lodi, which is like middle of nowhere. And I just started calling
people up and saying, we have this place in the countryside. Do you want to, like, go out there
and just brainstorm and hack and just try to build a startup? And the topic area was large language
models and generative AI. And so this was in November 2022. The event happened about ten days
before ChatGPT came out. So like the timing was was perfect. And then out of that first event,
actually five companies formed, which is really exciting. The reason the event was so good
is because we the quality of the people there was really high. We want to run some more of these events,
but we thought about like, what kind of structure should this have? Like, and I've seen things like
Y Combinator and TechStars and just a bunch of others like these type
of accelerator type of programs. They all had a common failure mode there,
which was start off pretty good, and then they would try to scale and they
would scale at the expense of quality, and then they would not get as good founders. The quality dropped. And so like there was like an adverse
selection problem there where it's like the best people would choose not to go. And it started just like eventual downward
spiral in terms of the reputation. And so it's like, well, why don't
we just do this in a different way? Why don't we make it a nonprofit? We just want to run these events
and put really good people together. When you're a nonprofit,
a lot of these questions just kind of melt away about like, well, who owns the IP? Our goal is not to make a profit
off of, like, these early stage founders. That's very short sighted view. The genesis of the next billion
dollar or 10 billion or $100 billion company is like at that event. That is the actual value.
Because guess what? You know, maybe you get a chance
to invest early or be an advisor or otherwise develop those relationships. That's where their actual value is. That's where you're going to get, you
know, the thousand X returns and stuff. That was the genesis of Inception Studio. AI space is moving really quickly. The fundamentals don't really change
about there's certain fundamentals about I'm building a company. I'm starting a company about like
who is the customer? What is the need that like you're
actually solving for them. How do you reach that customer?
What is your kind of differentiation? How were you ten times better
than what they otherwise have? If your solution is, hey, we take
this amazing thing called GPT 4, and then we just kind of wrap it up in a
nicer packaging for somebody else to use. That is not really
a great sustainable business there. But if you're solving a real customer
problem and you actually have differentiation there, and it could be differentiation in terms of access
to the data that you have that's really unique. It could be unique insights that you have
or can even be things about, like how you bring the thing to market,
like in terms of I know, like all of the people who are in this space and I've sold to them before and they all trust me, and so I can bring this to market
better than anybody else possibly can. That can be your differentiation there. And like, you can still be successful. And don't be fooled by the fact
that there are businesses that exist that have raised a whole bunch of money. That's all they are fundamentally. Those are not what you want to be spending your time on. Yeah, sometimes people get lucky
or they have new businesses for now. But even major ones like with Sora
and like there's other kind of things that OpenAI is releasing. These are like some well funded
companies are now it's like what happens with Pika Labs once that like, you know,
it's like Sora is released. It's like, oh, that's a real question there. They're gonna have to keep
innovating and end up doing something else because it just becomes really,
really hard to compete there. And I think there's I mean, there's
two parts to this game and one is like, I'll call it like the big boys game. You're building huge foundational models
like the differentiation there is, like how many GPUs can I have and what kind
of access to data I can have and like. That is a game that is
very hard to compete with as a startup unless you are very well funded. This is why you need to like raise
these billion dollar rounds as like Anthropic and Mistral and those ones,
they're trying to play that game with the Googles and the OpenAI's
and the Microsofts and the others. That's a hard game to play,
because whether you win or not is like determined by your access to data
and your access to compute. And like, you're how deep your pockets are
and you can always like outspend and stuff and then but there's a whole kind
of ecosystem that's like, that's not that there's great viable businesses there. Like we can have like a lot of differentiation where you're kind of being AI native, like you're actually fully
embracing AI in your company, and there's a huge, massive appetite for this. If you look at AI budgets maybe 3 or 4 years ago and like just globally like IT spend, how much of it
spend on artificial intelligence? I think it like doesn't
even register on the pie chart. It's like less than 1% now. It's huge. A huge proportion of the money
that is being spent is on AI because there's so much promise. It's really unlocked with ChatGPT
and like, just like opened people's eyes about what the capabilities
of these large language models are. And like this generative AI
and agents and everything else. It's not only like there's hype around it,
but there's also like real capital behind it as well. And companies are spending like
real money, like huge amounts of money to basically develop AI solutions that
use AI. So if you can be in that space and you can position yourselves
as one of these AI companies, there's a massive opportunities there. We're in the midst
of this economic downturn. There's like layoffs happening
and a bunch of other stuff. Economy is not great,
like rising interest rates and all this. It's a challenging environment. If you look back to kind of 2008,
2009, 2010, like so many great companies are formed like during that time. Same thing is going to happen here. Some will be like the kind of opening eyes
and like building like these, like like huge foundational model things. Other ones are going to be around
particular verticals, like using generative AI for finance or generative AI
for legal or generative AI for marketing. It's going to end up touching all
these different areas, and there's going to be some clear winners there.
