Latest / AI for Founders with Ryan Estes / AI is quietly stealing your life’s work

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AI is quietly stealing your life’s work · AI for Founders with Ryan Estes

0:00
Your most valuable asset is being stolen right now and you
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handed the keys over yourself. Every document, every idea,
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every decade of hard won knowledge you've uploaded to
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ChatGPT, Gemini or Grok. That's not your data anymore.
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You just paid for the privilege of training someone else's
0:16
billion dollar model with your life's work.
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There's no free lunch. Read the terms of service.
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Now imagine flipping that. What if AI worked for you from
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your data on your machine, answering only from what you've
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built with 0 hallucinations and 0 leakage?
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A librarian who knows everything in your library and only your
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library? Let's say you've got 40 years of
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unstructured IP sitting on someone's laptop.
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That's worth nothing until it's interrogated.
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Courses, research, client work, voice memos, PDFs.
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No one's opened in a decade. That's a product waiting to be
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born. That's a course, a consulting
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offer a knowledge base your competitors literally cannot
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replicate because it only exists inside you.
0:57
The Moat isn't the technology. The Moat is your data.
1:00
So here's a question to sit with.
1:02
What knowledge lives trapped inside your business right now
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that only you can unlock? And what would it be worth if
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someone could finally ask it a question?
1:10
Welcome to AI for founders. I'm Ryan Estes.
1:13
This episode is for the founder who knows their ideas are worth
1:16
protecting, wants to understand how privacy first AI actually
1:19
works, and is ready to turn decades of buried knowledge into
1:23
an unfair competitive advantage. This one's a master class.
1:27
The man who's trained 75 plus graduate students is now
1:30
building an AI that finally serves the individual and not
1:33
the corporation. His name is Doctor Jonathan
1:35
Schaefer. The AI for
1:37
foundersnewsletter@aiforfounders.co is where the transcript lives,
1:41
The tools get named and the insights your competitor missed
1:43
get spelled out. Founders who read it ship
1:46
faster. Founders who don't are catching
1:48
up to the ones who do. So join us at AI for founders.co
1:52
and subscribe to the newsletter. If this episode made you think
1:55
differently about your own data, a five star review takes you 30
1:59
seconds and keeps the algorithm gods happy.
2:01
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2:22
That's Kitcaster by MO Burst. All right, Jonathan, welcome to
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the show. Thanks for joining me.
2:35
Pleasure. Thank you for having me.
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Absolutely, it was a is a real treat to kind of do some
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research on you get to know your work.
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And I, I want to talk about kind and, and some of your new
2:47
projects. But what really struck me was
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you were an early pioneer in kind of machine learning and
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what we call today AI. So maybe give us an introduction
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to your career and kind of the steps that's brought to you to
3:00
this point. Introduction to my career is
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very simple. I got turned on to computers
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when I was young, but I love playing chess, and I was a
3:08
competitive chess player. And then one day I decided, hey,
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it should be easy to write a computer program to play chess.
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That was in the late 1970s. I apologize.
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I'm just dating myself right now.
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And of course, it turned out to be much harder than I thought.
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And in many ways it's inspired me for the rest of my career
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because I've been able to combine my passion for games and
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playing games with my passion for computing science, and it's
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led to a wonderful career. Serendipitous too, because I
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mean, chess became kind of a benchmark for compute, right?
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It was, and interestingly enough, this week is the 30th
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anniversary of Deep Blue playing Garry Kasparov in the first
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match that they had in 1996. When Deep Blues shocked the
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world champion Garry Kasparov and won first game of the match,
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So to this week happens to be the a milestone of from history.
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Amazing. Do you still play?
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With a new company playing game? No, not possible.
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Not possible at all. Yeah, I I chess.com for a while.
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I was absolutely hooked, you know, and playing seven chess
4:33
games at the same time is is kind of a blast, you know, I
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learned a bunch about the sport. I love chess.com again.
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I just wish there was more time and once my company makes its
4:44
first billion dollars I will. I'll go back to play more chess.
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Let's talk about that. Let's talk about your company
4:51
and your current project. Give us a kind of a high view
4:54
introduction to and I believe it's Sincera and the the product
4:59
kind. So I've been, I've been in the
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AI business a long time and I've seen feast and famine, high and
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lows, AI winters and AI bonanzas.
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And over the decades, what has been accomplished in AI is
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amazing. In the past decade, 15 years,
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it's just been accelerating. In the last two to three years,
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it's been phenomenal. But along with the good comes
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the bad. There is so much potential for
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AI to do really good things for humanity.
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And I get very distressed when I pick up media and I see media
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stories talking about the negativity of AIAI is going to
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take over the world, AI is going to take away jobs, AI is is
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hallucinating or it's lying. AI data centers that are
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environmental impact. We can go on and on and on.
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And the message about AI and what it can do gets lost.
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There was a period of a few years where I gave a lot of
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public talks about AI and people love them.
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But when you they started asking the questions, they were all
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about the negativity of AI and not about the positivity.
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How is AI gonna AI gonna transform the world?
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How's it gonna make my life better?
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Nobody talked about that. They always talked about the
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negativity. And when we talk about the last
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three years and it's, it's really only been 3 1/2 years, I
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guess since ChatGPT exploded on the scene and what November 9th,
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2022, that the conversation has changed.
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And the types of things that people are talking about AI are,
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are different, like hallucinations.
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And I've been working since 2017, an idea for a product
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about taking AI and bringing it, making it more personal,
7:06
bringing it to the individual and allowing them to realize
7:10
some of the benefits of AI. When I talked to people, they
7:13
were afraid to use AI. They didn't understand it.
7:18
They didn't want to get accounts like to, you know, go online and
7:21
created an account with an LLM provider.
7:25
They heard about hallucinations. They heard about AI plagiarizing
7:30
data, training on your data. They didn't want anything to do
7:34
with it. And I thought we really need to
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take something, bring it down to the individual level so that
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when in some way we can allow people to benefit from AI in a
7:44
risk freeway, a friendly way that allows them the benefits
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without the disadvantages. And that's been how kind has
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evolved. Although the idea started in
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2017, we only started building it in 2021.
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And so it's taken me a long time to get to this point where we've
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been. The product has evolved to the
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point where we have something that I'm actually very, very
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proud of and it it launched in the marketplace last week and
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the idea is to remove all the negative barriers of AI for the
8:21
individual. So kind, I'd like to call it a a
8:26
kinder form of AI because we've abstracted away the negativity.
8:30
It works on your data on your computer, it analyzes, it allows
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you to ask questions and and get answers.
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Now it's not quite a local LLM. It's not like you can go out and
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take open source software and put your own LLM on your
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desktop. It's more sophisticated than
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that. But by doing the putting in the
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infrastructure that we've put into kind, we can answer
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questions only from your data with no hallucinations, with no
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training, with no opportunity for a company to to plagiarize
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your data. If you think about it, your
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laptop is largely idle. And I confess, even my laptop
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these days is largely idle. And so we're bringing a lot of
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the AI computing that's out there from the data centers onto
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your laptop to ensure your privacy.
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You don't need an account other than, you know, purchasing kind.
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You don't have to know what a ChatGPT is or, or a clod or a
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Gemini. You just use kind.
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And behind the scenes, we do all the work.
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They the, the catch phrase I like to say is it's your data.
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You've curated the data, you've chosen the data and we curate
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the AI. We've chosen the AI that allows
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you to get the full value from your data.
9:56
Amazing. And congratulations on the
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launch. That's got to be just an
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incredible feeling, you know, getting something to the end
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zone. That's that's remarkable.
10:04
Well, I'll tell you a a little story about that because I've
10:09
been a professor for 40 years. So a lot of the things that have
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happened in the last, say, six months I've taught in
10:16
university. But of course, the academic
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view, creating a company, getting to the marketplace is
10:23
completely different than the real world scenario.
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And you know that very well. And so for the last, I don't
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know, 6-8 months, almost a year probably now in many ways, I've
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been getting a real world education that I never really
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appreciated was as as deep and challenging as it actually is.
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Because of the last year I've been, you know, hands on dealing
10:49
with all the financial, marketing, technical, user
10:55
interface, security, on and on and on issues that take you to
11:00
take you to the end zone. And it's a long and evolved
11:03
process. It's been a, it's been a blast.
11:06
We're having fun. That's the most important thing,
11:09
quite frankly. And we hope that there's success
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down the road. Yeah, you know, the, the, the
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tactical actual execution is, is a bear, you know, it really is.
11:19
But then there's also kind of the, the psycho emotional
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component of like kind of getting over all of those
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feelings and emotions. You know, often times just to,
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to simplify, it's like you're introducing your, your baby to
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the world and you want to make sure they think it's beautiful.
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How did you kind of overcome some of the psychological,
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emotional components? I've had to deal with those
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issues with a lot of my research and some previous companies that
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I either worked with or helped found.
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And so I this wasn't my first time around.
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And for me, it wasn't that hard because of my experience.
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I could wall it off. I could be almost dispassionate
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about it, which probably was a bad thing to do because you'd
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sort of pent up all the actual emotions which are inside, but
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got to keep this cool, even keel, unemotional facade and
12:24
just plow through all the things and get them done.
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When in fact, of course inside, although nobody except my wife
12:33
might see it, is is the turmoil and the elation that goes with
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starting a new company. Yeah, yeah.
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Was there any using any particular techniques you like
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going for walks? Are you a, a a gym guy?
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Like, how did you get out some of that angst?
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Wow, man, you, you, you read my mind.
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I do two things. One is I go to the gym every
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second day and I find that incredibly good for just
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clearing the mind and just venting frustration as I like to
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get things done and I want them done when I want them done.
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I want them done now. I want them done efficiently.
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I want them done right. And when you're dealing with the
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real world, these things don't happen sometimes.
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So the gym is just a great place to let off my frustration
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because I don't want to take it out on people.
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I'll take it out on the exercise equipment or or, you know,
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running outside, whatever it takes to just let it off.
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And the second is saying, which is much more contemplative is
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the gym allows me to release energy, but taking the dog for
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walks allows me to regain and their energy.
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It's much more, it's relaxing, it's therapeutic, the mind gets
13:51
calmed. And when you come back from that
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experience, especially because for the dog, it's like the
13:57
highlight of the day. It's the most exciting thing,
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especially if there's a bit of running involved in it, is I
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find it inspiring for me and uplifting and puts me in exactly
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the right mood for me to move on to my next business related
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task. Love it.
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I'm exactly the same way. It's it's wild how like the
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different exercises elicit different emotions and sometimes
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that calm feeling is really fits the bill, but other times you
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want that charge, you know, lifting weights for me,
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especially heavyweight, it's like, I just feel great.
14:28
You know, it's, it's it's an elixir of joy and enthusiasm.
14:31
It's incredible. Now I want to talk a little bit
14:34
about kind. And particularly, it sounds like
14:37
you're addressing some pretty significant objections and
14:40
privacy seems to be the central one that maybe I would use grok,
14:44
but I don't want Elon Musk knowing it all my most intimate
14:47
details. So you, you create an LLM that
14:50
works locally on your machine and is, is not trained by your,
14:53
your data, but rather informed by your data.
14:56
Talk a little bit about the the the inputs that they're actually
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it's using and and maybe some use cases that people could get
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to know kind a little bit. OK, thank you for that question.
15:09
I have a massive amount of data on my laptop and it's all over
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the map. It's my life as a teacher, it's
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my life as a researcher. It's my life as a well going
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back as a chess player. I'm actually a collector.
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I also have lots of information about my family and it's all
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there. And some of it's been gathered
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for years, decades. I don't even know what's up,
15:34
what's there anymore. But what kind does is it allows
15:39
you to create a collection and what it is, is just a subset of
15:44
your file. So I can create a collection for
15:46
example, for my family and into that I can drag and drop all the
15:54
files to do with my family pictures, documents, you can
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have a few videos, things like that.
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And now I have this one data entity, which is a subset of
16:05
what's on my laptop, but it's all about my family.
16:10
And when you put those files into kind, it does this one time
16:13
analysis of the files. It does a deep AI analysis and
16:17
saves all that information on your laptop.
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And now you can ask questions and do things with it.
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So I can extract information about my family history and I
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find things that I just didn't know.
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I mean, the the data's there, but I've never gone through it.
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Yeah, it's really surprising. I collect things and I've been
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collecting for years and I don't know what I have in in the
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entirety, but I just hear all the PDF files on the catalogs
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and the videos and some podcasts that I've thrown in and all
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these pictures I've taken and I just throw them.
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And I have this one collection and all the data's there and
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it's all been analyzed and I can answer questions about it.
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And there's things in there that I've discovered that I didn't
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even know I had things in there that I didn't even know that I
17:12
should have known or things that I said.
17:14
Oh yeah, there's stuff there that I've just completely
17:18
forgotten about. The AI is only answering
17:25
questions based on the data that I give it.
17:27
It's not polluted by any external data.
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You're not getting hallucinations.
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If I ask a question about say, you know, do I have any books
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authored by so and so, it'll give me exactly what's in my
17:42
collection and it'll cite everything to the file or the
17:48
place in the file where that information is contained.
17:51
So it's completely fact based. There's no way you can
17:55
manipulate kinds so that it has egregious behavior.
18:02
So to me, it's like, I have this apprentice this, since it's my
18:08
collection and it's a collection of books, it's my librarian who
18:12
knows exactly everything that's in my library and only what's in
18:16
my library. And then of course, I use it for
18:21
my teaching and to understand things that I've never even
18:25
thought about. Like I've been teaching AI for a
18:27
long time. Here are all my AI courses.
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Can you tell me something about how my AI courses have evolved
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over time? And it's interesting to see what
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the AI comes up with in it. It's dispassionate, it's
18:42
impartial. It looks at all these courses
18:44
and, and does some analysis and comes back and says things that
18:48
surprise me. Hey, I never thought about it
18:51
that way. I'll give you one example.
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This is a very strange example for me.
18:56
I, I was, I teach a course in operating systems.
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OK, Yeah. All right.
19:02
So that's a geeky computing style and stuff, right.
19:06
And part of the course requirements are you have to
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teach about security and operating systems.
19:11
Computer security is a big topic, right?
19:14
I thought I was doing a great job until I threw it into the AI
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and discovered it says, hey, you know, you're what you talk about
19:22
security is actually a really small part of the course.
19:26
You're actually not fulfilling the mandate of the course.
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And I looked at it in horror and said, oh, come on, Of course
19:33
you're wrong. And I actually looked at the
19:36
material and said, boy, these last few years I've actually
19:40
haven't been talking about security as I should be.
19:43
And so I learned something about my data and I let me point out
19:48
that operating system course is being a sort of a watershed in,
19:52
in my understanding of AI and how people use it is because a
19:57
student came to me and the student said here on this exam,
20:02
you marked a question wrong. You, you took off points and I
20:06
said, well, OK, that's you got the wrong answer.
20:10
I'm sorry, I can't give you the marks.
20:12
And he said, oh, but I went out on the Internet and they gave me
20:15
this answer. And that's the answer that I put
20:17
into this question. And I said, well, that's great
20:22
that you found some place on the Internet that had that answer.
20:25
But that's not the right answer, and it's not the answer that I
20:28
taught you in this course. And he was extremely angry at me
20:32
because, of course, the Internet was authoritative.
20:36
Well, first lesson of anybody who's using the Internet, Yeah,
20:41
it's caveat emptor. You need to know the information
20:45
that you're getting out there and how good it is.
20:48
And you just can't trust AI to give you 100% accurate
20:53
information there. We all know there's a lot of
20:55
wrong, misleading, biased, perhaps data out there.
21:00
And so that really drove home the need for KIND to eliminate
21:05
the hallucinations. When I teach a course now and
21:08
use Kind, the students have access to all of my course
21:11
material. They'll only get answers from my
21:13
course material, not from the Internet.
21:18
So it's been an evolution that brought me to the point where I
21:26
really appreciated the fact that I could take data, put it into a
21:31
program that would guarantee that it would answer questions
21:35
only using the data that I wanted it to use.
21:42
Love it. It's amazing.
21:43
Couple of things come to mind. One right now I'm very
21:46
interested in caches of data that are unstructured and and no
21:50
nowhere else unstructured IP. So I've been like looking at,
21:55
you know, manufacturing and maybe more traditional
21:58
businesses that might still be pre digital, you know, so like
22:01
their data is like literally papers that are in a room
22:04
somewhere. I feel like the, the, the
22:07
potential to create products and revenue around that is
22:10
extraordinary. Now what I'm thinking about is
22:13
what about the unstructured IP that everybody has, which is
22:17
your 40 years of your career that's locked inside your, your,
22:24
your laptop that now you're adding structure to, you can
22:27
arrange that data and nobody else has it.
22:30
So, you know, for you, you know, I can't help myself looking for
22:33
revenue opportunities. But if you wanted to do online
22:35
courses, you could like literally talk to your data with
22:38
40 years of history, what worked, what didn't, what did
22:42
students really like? What are what's the the core of
22:45
what I'm trying to get AT and start to arrange products like
22:48
that and nobody else can because it's like literally your data.
22:55
Everybody is unique. This is all about giving agency
23:00
to the individual. You want to understand your
23:04
data. It's your blood and sweat that
23:06
you've put into it, perhaps creating it or perhaps
23:09
collecting it. And the only other alternative
23:15
is to take all that data, put it into a cloud based provider and
23:20
get them to do some analysis. And that's a a viable solution.
23:24
Some people want to do that, but for me, it's my data, it's
23:28
private. There are things in my data that
23:30
I don't want a cloud provider using for training.
23:37
You know, I know authors who are extremely upset because their
23:41
books have been put into the cloud by somebody and they can
23:44
point to places where these these large language models
23:48
literally almost quote from the book without citation.
23:51
Of course I have personal information.
23:54
I don't want to see that in the Internet.
23:56
I don't want to see other people having access to that
23:58
information. It's private.
24:01
We need to bring AI back to its roots, which is fundamentally AI
24:05
is meant to benefit the individual.
24:08
All the attention in industry is these wonderful corporate
24:11
applications that are going to empower businesses so that they
24:17
can improve efficiencies and reduce hiring so they can save
24:23
money. Well, that's great, but
24:25
fundamentally, AI is meant to serve the 8 billion people in
24:30
this world, not just the big corporations.
24:33
So we believe in rooting it where AI really should be, which
24:39
is with the individual. I think you're really on the,
24:41
the cutting edge here too, because this is a big
24:43
conversation. You know, for years data
24:45
companies have, you know, capitalized on our data, you
24:51
know, and they, they provide us with entertainment or whatever
24:53
in exchange. I don't think it's necessarily
24:55
unfair, but as some people's data is more valuable than
24:59
others and I think having the option for privacy or having the
25:03
option to to rent that data, data out to companies is going
25:06
to be huge. You know, kind of securing micro
25:09
payments that people do want to access your data then OK, cool.
25:13
And we can negotiate a price, you know, so having and securing
25:16
that data now to be able to take advantage of, you know, the
25:21
incredible power of LLMS, but maintain, you know, your, your
25:25
IP is really important right now.
25:27
Because I do really believe that like, I'm not alone in looking
25:30
for these unique data sets, these unique, the IP and unique
25:33
knowledge bases that probably only exist at the individual
25:37
level. And like that will be the, the
25:39
next massive wave of training data.
25:42
But, you know, while they're still working out how we can do
25:45
crypto payments through the browser so that I make sure I'm,
25:48
I'm compensated, I can keep it close to the chest, You know
25:51
what I mean? Understood.
25:54
My data is worth something to me.
25:56
It may not be worth anything to the rest of the world.
25:58
I don't care. But it's personal and therefore
26:05
I don't want it out there. And you know, just going back to
26:08
your point, there is no free lunch.
26:13
There are a lot of sites where you can upload your data and do
26:17
some wonderful things with AI. And, and if that's what you want
26:19
to do, that's perfectly fine. But in the terms of agreement,
26:23
you have to understand that these companies don't really do
26:26
it for free. They get something out of it and
26:28
it typically it might be access to your data and that may mean
26:31
that the data gets used, quoted, trained on, maybe even sold.
26:38
And OK, maybe that's all right for some people, but I think for
26:43
the average person it's probably not OK.
26:45
So personal agency, I think, is important.
26:49
I agree. Now I've got a really random
26:51
idea I got working right here of a potential project that maybe
26:56
kind's the perfect tool for it. You want to hear it?
26:58
Absolutely. All right, so I, I love to walk
27:02
too. We moved recently to the sticks
27:05
outside of Denver S about 15 miles.
27:07
But we lived in north Denver for a long time next to Sloan's
27:11
Lake. So we're always going to the
27:12
park and and riding bikes when my kids were growing up and
27:15
walking was a big part of what we do.
27:17
And so, you know, they're 17 and 18 now.
27:20
So let's call it for 18 years, I spent, I did a lot of time on
27:23
sidewalks. And in that time I kind of
27:26
became obsessed with the concrete men of the old dusty
27:33
Denver, maybe for the last, in some cases, 100 years.
27:36
Because just about every antique sidewalk in Denver has a
27:41
concrete stamp and it has the date and it has their name.
27:46
And so in all the neighborhoods of Denver, I've been cataloguing
27:49
these concrete men by taking pictures of their stamps.
27:52
And there's one man I want to give him a shout out right now.
27:56
The absolute legendary concrete man of Denver is John Sandoval.
28:02
This guy, for what looks like at least 50 years, had to have done
28:06
a significant portion of the concrete sidewalks in Denver,
28:09
Co. His stamp is everywhere.
28:11
Now, he might not have been the only guy, but he had to be the
28:14
best because, you know, it's probably a ratio of five to one
28:19
of old concrete stamps. And you know, it's John
28:22
Sandoval. So I have this unbelievable
28:25
cache of pictures of John Sandoval and these other
28:29
concrete men that literally it's like I've always envisioned kind
28:33
of creating like a, a map of Denver where you can see all the
28:36
different concrete stamps, you know, in various places.
28:40
If for nothing else, just for a, a, a great gift for my kids
28:44
because probably they're the only kids ever who are get
28:48
really excited about seeing concrete stamps.
28:51
You know, maybe kind is the tool to make this.
28:54
Let's just say that that is the most unique use case I've ever
28:58
heard. So that's that's really cool, so
29:01
to speak. Yeah, I, you know, and now
29:05
everywhere I go, I'm like eyeballing for those concrete
29:08
stamps. Not even in Denver.
29:09
I'm like, I wonder if they got a John Sandoval in San Diego, you
29:13
know? The the interesting thing
29:15
though, you're taking pictures of all of these, right?
29:18
Yeah. So it's got the metadata right.
29:20
So it's got not, it's got the metadata maybe that can be
29:25
automatically superimposed on a map so that you could see it.
29:28
Also, have the stance changed over time?
29:30
Maybe there's some trends there? Definitely.
29:34
And they're dated, so you can kind of see the way
29:36
neighborhoods grew and, and were built and, you know, yeah.
29:41
So I mean it, what you've got is the foundational data for
29:45
building something that's really an interesting history of 1
29:51
aspect of, of the city of Denver and how it grew you.
29:55
You might, you might sort of assume that putting in the
29:57
concrete is is reflects, you know, the growth of wealth in in
30:04
Denver in that important areas would get paved with concrete
30:10
sidewalks earlier than outlying areas.
30:13
So you can probably see the spread of the city and how
30:18
urbanization changed over time. So there might be a lot of
30:21
interesting results that could come out of it, especially if
30:25
you combine other kinds of data. Well, maybe there could be a
30:28
cool Perry Mason moment. Like I'm just imagining there's
30:31
a court scene and somebody has a neck brace on and they're
30:35
testifying that that the the the concrete was damaged and that
30:39
like he slipped and fell and somebody's got to pay.
30:42
And the defense attorney is like, I'd like to call Ryan
30:45
Estes to to the stand. And I come up there and they're
30:48
like, oh, no. And then I can testify.
30:50
I'm the sole person that can talk about John Sandoval's
30:55
handiwork and that he would never allow such a thing to
30:58
happen. And that the gavel comes down.
31:00
Case closed, case dismissed. Maybe that could happen.
31:04
You know, Ryan, you just dated yourself.
31:06
I know who Perry Mason is, but I once used Perry Mason in one of
31:09
my lectures and people gave up. They just got all these blank
31:15
stares at me. So sorry.
31:18
I got to use another apology. You're going to have to look it
31:21
up on kind to find out who Perry Mason is.
31:24
Speaking of kind you got you launched a week ago.
31:27
So I'm very interested how you're approaching go to market
31:30
where you're finding your first customers.
31:32
Well, maybe design partners you use to see who potential
31:37
customers are just kind of the whole go to map market strategy.
31:41
Good question. So although I've been involved
31:44
with other start-ups, kind is the first one that it really
31:49
sort of quarterbacked myself and very early on I have to admit
31:56
that there are things that I just didn't know.
31:59
I'm on the technology side and I can I can see a good idea and
32:03
appreciate it. But on the business side, I
32:05
needed help and we wanted to go to market and we needed to do
32:13
some of the foundational work, which meant engaging a group to
32:17
do market surveys for us, bringing in the data and doing
32:20
the analysis and working with them.
32:23
And then once we had that and understood where we wanted to go
32:26
with it, then working with companies, professional
32:31
companies who understood the whole ecosystem of marketing,
32:36
you know, podcasting, printed media, Tiktok, LinkedIn, all the
32:43
social media sites. You know, media has changed so
32:47
dramatically and it's not my area of expertise.
32:50
And I think one of the most important things for me, and I
32:54
suspect for most founders, it's just knowing what you don't know
32:57
and spending the appropriate money to bring in that
33:00
expertise. And so that's that's what we
33:02
did. Yeah.
33:04
Well, it does. It does come with an expense
33:07
which is largely you find yourself on these podcasts.
33:10
But it's a pretty sophisticated maneuver because now in 2026,
33:14
kind of the founder LED initiatives really are are what
33:18
kind of gets the biggest ROI, you know, the biggest push.
33:22
So as you're kind of contemplating and you know what,
33:25
you're also not alone. I would say probably 7080% of
33:28
the folks that I'm talking to on the podcast and beyond are
33:31
technical founders, you know, so they're kind of reluctantly
33:34
thrown onto the stage, so to speak.
33:37
So, you know, as being kind of a, a behind the scenes guy.
33:40
What was the the feeling like or how did you kind of deal with
33:43
like all right, Will I guess I got to get an X account start
33:47
doing podcasting. Not going to get the X account.
33:51
I'm going to bring in the professionals who know where the
33:55
right places are to be podcasting, who the right people
33:58
to talk to, what the right places to let to put placed ads
34:03
or to where the right places to document user experiences, where
34:10
the right tools for synthesizing all the data and understanding
34:16
where the markets are, who's buying, who's not buying.
34:20
And it again, it's part of the education experience.
34:26
And you know, one, if the company is successful, we'll
34:29
bring some of that in house. But there are professionals out
34:32
there who know this better than I and I'm in a hurry.
34:36
I want to get things done. And so, yes, it comes with a
34:38
price, but I'd rather bring in people on day one who are
34:42
professionals. I can honestly say in my first
34:46
company, which goes back to 1995, we didn't know what we
34:51
were doing and we made lots of mistakes.
34:54
And part of it was thinking that we did know what we were doing.
34:59
And so it can't repeat those mistakes.
35:05
Yes, it cost a bit more money to bring in professionals, but I
35:09
can tell you right now having gone through several months of
35:13
working with professionals, what a difference it makes.
35:15
It's it's night and day. And so the bank balance is a
35:20
little lower than I would like, but I think the results are
35:24
going to be impressive and so I'm delighted.
35:27
You bet, you know, but you're not completely devoid of
35:30
marketing expertise. I mean, after all, 40 years as a
35:32
professor, you're, you're not, you're not shy.
35:36
No, I'm not shy, but my wife has extensive experience there in,
35:40
in that area and she has experience in on Wall Street
35:47
with banks, with in academia as well.
35:51
So and I defer to her for a lot of a lot of the day-to-day
35:56
decisions and with her help, we brought in the professionals.
35:59
Amazing. OK, it's that time in the
36:02
podcast where we're about to enter the Quantum round.
36:05
Are you ready? Do.
36:09
You have a particular life motto or quote that that is
36:13
inspirational or gives you a little bit of momentum when
36:15
you're down. Actually, the answer is no.
36:25
I'm rarely down. I'm actually quite an optimistic
36:28
person and if I'm down it's it's time to take a walk with the
36:33
dog. Yeah, yeah, Dogs are like joy
36:36
dispensaries, right? When I was young I was something
36:41
happened and I was extremely upset and the neighborhood dog
36:45
came by, sat beside me, licked me and just patiently listed,
36:51
well you know this 8 year old kid poured out his heart.
36:55
And so ever since then, whenever I'm down, just need a good dog
37:00
or a cat to to talk to. Dogs are much better than cats,
37:04
by the way, and that's what gets me through.
37:10
Amazing. I'm a cat guy so I take that
37:11
highly personal but. We have cats.
37:15
Steering you up, At least not mine.
37:20
Lifetime of academia. If you were entering college
37:24
today as a freshman, what would you study?
37:27
Wow, that's a tough one. If you asked me a few years ago,
37:32
I would have said computer science just because there was
37:34
so exciting and so much fun. I think computing science is in
37:38
flux right now and I don't know where it's going to go.
37:43
There was still be a need for computing professionals, but the
37:48
education I received, I think is is is changing dramatically.
37:55
If I was going to school right now, I wouldn't want to go into
38:01
archaeology. Yeah.
38:04
I, ever since I was a kid, I, I just love going to
38:07
archaeological sites and, and understanding ancient history.
38:11
And many of the trips that I've gone on to have gone on have
38:15
literally been to places where I knew that I could go and rummage
38:19
through ruins and read about history.
38:21
So I love history and and more importantly than that, I Love
38:25
Actually getting hands on, seeing the real places and and
38:29
being able to recreate history in my mind at the actual
38:34
locations. Love it.
38:36
What? What periods?
38:38
In what places? Well, interestingly enough, a
38:42
variety. I've certainly been to Italy and
38:46
Greece and the Middle East and the Middle East is amazing
38:51
because they're just so much there and so much history.
38:55
But I'm from Canada and we last year we took a trip through the
39:02
Northwest Passage and over to Greenland.
39:05
And so seeing some of the old historical sites, the, the Inuit
39:09
sites, the, the early cultures in the Arctic open my eyes to
39:15
other kinds of archaeology that are more recent than say, you
39:20
know, biblical archaeology, but are equally fascinating and fun.
39:24
So I'm in my element when I'm around old things.
39:29
Yeah, I love that, you know, and I I really relate to that too.
39:32
I'm kind of a Old West buff, but really that's kind of like
39:37
coalesced in the last few years have really kind of the the
39:39
corridor all the way from South America all the way up to Canada
39:44
kind of, I'd say east of the Rocky Mountains.
39:46
So not necessarily like all the Mississippi cultures, but like
39:50
there was like large connected trade routes.
39:53
I mean, I should say loosely large, you know, so I'm just the
39:57
same way, you know, the, the place I go hunting is was
40:00
traditionally like the ute's summer hunting grounds.
40:06
And you know, when I was probably in 4th grade, you know,
40:08
we found like an Arrowhead and it like absolutely melted my
40:12
brain. So now I've been going really
40:14
hard on the Toltecs and the Aztecs in Mexico and like how
40:18
they were connected to the Apaches and the Anasazi's and
40:21
like all these like old antique cultures.
40:24
But yeah, I really shared that that would be be amazing.
40:27
And that's, it's an interesting answer to the question, which is
40:29
to say that like I was advising my kids the exact same thing.
40:33
It's like you're gonna study computer science there.
40:35
That's what you're going to call it for until about 3 years ago.
40:38
And then I was like, OK, I don't think that is the path.
40:41
So now truly learning how to think, I think also learning how
40:45
to be to trust your passions and run towards it till it kills you
40:50
is, is a great advice. So it's like, if I really loved
40:53
archaeology, like, why not study that now because AI is moving so
40:57
fast. There's you know, what you're
40:59
learning today is irrelevant in in six months.
41:02
Yes and also AI isn't about to go to these archaeological sites
41:06
and start digging the humans still there.
41:09
They're doing the digging to find the artifacts.
41:11
So human is still in the loop there.
41:13
But your point about passion is is critical.
41:18
Sadly, I've met a lot of students in computing science
41:21
over the years who were in there because in the profession,
41:24
because that's where they thought the jobs where that's
41:27
where they thought the money was, but the passion wasn't
41:30
there. And inevitably they went on to a
41:32
career that was mediocre and they were unhappy.
41:36
And so the most important thing and the the advice every student
41:39
needs is, is to follow your heart.
41:42
Do what interests you. If you're not passionate about a
41:45
subject, then you need to move on to something else.
41:48
You know, your whole life ahead of you.
41:51
Choose passion over anything. I love it, I love it.
41:54
Maybe you can give us some insights to this generation as
41:57
you see them coming up, like how they're different.
42:00
What are their strengths right now that you think they're
42:02
they're going to be able to really give the the rest of us
42:05
as they're kind of coming into maturity?
42:11
At the risk of embarrassing myself, I'll I'll say the
42:14
following. When I was a young student, I
42:17
thought I was a hotshot. Everything came easily.
42:20
I learned fast. I was good at what I was
42:26
interested in, maybe not so good at things I wasn't interested
42:29
in, but I did well. I did extremely well, and those
42:35
skills translated into a successful career.
42:40
But over the years, as I've taught generations of students,
42:45
in my opinion, the students today are amazing.
42:51
Yeah. The top ones are so much better
42:55
than I was at that age way back when.
43:00
And what's scary to me about these students is they actually
43:04
don't know how good they are. And I, I've always felt it's
43:08
part of my role to tell these students how good they are, to
43:11
instill them with a sense of confidence and awareness of
43:14
their abilities. They're smart.
43:17
They're fast, they're creative, and their technology literacy by
43:25
and large is amazing. And I'm not talking about TikTok
43:29
and, and social media posting and this, that and the other
43:34
thing, when I see the students in the classroom, and not just
43:38
in computing science, in other scenarios as well, they're just
43:46
sharp. They have much greater breadth
43:50
and depth of knowledge in areas that I never would have had or
43:55
could have had. You know, when I was growing up,
43:58
the source of knowledge was going to the physical library
44:01
and taking out physical books. And now everything is at your
44:04
fingertips. And these students are
44:06
incredibly knowledgeable. I've had some philosophical
44:10
students discussions with students that are, in my view,
44:14
just amazing for people who are so young and therefore, like
44:20
relatively inexperienced. But the Internet has opened up
44:24
so much knowledge to these students and they're absorbing
44:27
it without even necessarily knowing it.
44:31
And the skills that they've acquired, particularly in
44:34
computing science and in particular in the last three
44:37
years with the, the AI tools that they can use for coding,
44:44
it's impressive to see what they can do.
44:46
And I for 1:00 AM, you know, I follow with many of these
44:50
students that I know and I am just delighted when I see the
44:53
success that they they have. And it gives you hope for the
44:59
future that so many smart, talented people are out there.
45:04
And the kind of future that I hope they're going to create is
45:08
is exciting. Me too, I love it.
45:13
Kind of wrapping up here, what's a book you think is so valuable
45:16
to read you'd buy it for someone to read it?
45:22
Well, it's an odd book and it's not the kind of book that people
45:29
are going to run out and read, but I I recommended it many
45:32
times. My passion, one of my passions,
45:40
is the history of exploration, geographic exploration.
45:45
When I was young, I became fascinated with geographic
45:47
exploration, like traveling around the world or exploring
45:51
Africa. And in particular, because I'm
45:53
Canada, I got interested in the Arctic and Antarctic regions,
45:57
the polar regions. Very odd for a kid to get
46:01
interested in that. But in many ways it shaped my
46:04
life because I moved from geographic exploration into
46:08
scientific exploration. That's what I am as a scientist.
46:10
I'm a, I'm an explorer of new technologies.
46:14
Anyway, there's a famous story of true story, The Race to the
46:19
South Pole, Robert Falcon Scott from Britain and Roald Amundsen
46:23
from Norway. And they raced to get to the
46:26
South Pole first. Amundsen won by about a month
46:30
and Scott then on the way back from the South Pole, dejected
46:35
because he had lost the race to the South Pole, didn't quite
46:39
make it home. And he and his colleagues ended
46:41
up freezing to death and not making it.
46:45
And they're, they're martyrs, they're heroes in Great Britain.
46:50
But then you read Roald Amundsen's book that he wrote
46:54
about going to the South Pole, and you discover that this guy
46:58
was so methodical. He thought out everything well
47:03
in advance. It was so carefully planned.
47:08
And then you compare it with what Robert Falcon Scott did,
47:12
which was orderly thought out and some of the stuff was
47:16
seated, the pants and the consequences are are evident.
47:21
Amundsen made this amazing multi 1000 kilometer trip in the
47:27
brutal conditions, came back, everybody alive and well.
47:32
Scott goes there, terrible deprivations and ultimately
47:40
death. And the contrast is is is just
47:44
shocking. Anyway, the book I recommend is
47:46
is the South Pole by Roald Amundsen.
47:49
It's an understated book, but when you read it through, you
47:52
just see how somebody, just because of who he is and and
48:02
understanding the risks and dangers that he's about to take
48:06
undertake just sinks through every possible scenario plans in
48:11
advance. And the end result is he has a
48:13
completely problem free, effortless yet extremely
48:19
dangerous trip to the South Pole and back.
48:22
It's an amazing accomplishment and it's a model of how you
48:27
should tackle many things in life like business.
48:30
It all comes down to preparation, and it's a
48:33
brilliant book on teaching you the lessons of how to prepare
48:38
for any anything that involves lots of risk.
48:44
Right, well, I got to check it out.
48:45
You know, those so early Mountaineers are, you know, when
48:50
I'm, you know, freezing and cold with gore Tex and goose down
48:54
and, you know, leather boots that are bulletproof.
48:59
You know, I always think of those guys doing it with in
49:01
cotton and, you know, denim. What an incredible book.
49:04
I'll definitely have to check that out.
49:06
Jonathan, this has been absolutely extraordinary.
49:08
I'm very grateful for your time. If people want to check out, and
49:11
I insist they do, it's SYNSIRA. sincera.com will take your kind.
49:19
I will link it in the show notes so you can click there and check
49:22
out the product. Congratulations on your launch.
49:25
If people want to connect with you online, what's the best
49:28
place that they can do that? Just e-mail me
49:31
jonathan@sincera.com. That's it.
49:34
We'll make sure that's in the show notes as well.
49:36
Jonathan, thanks so much for joining me.
49:38
Thank you Ryan was a real pleasure talking to you.