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How Chris Chambers Built Einstein Bridge After Surviving Business Challenges · The Inspired Stories Podcast

0:04
Welcome to another edition of the Inspired Stories podcast,
0:08
where leaders share their experiences so we can learn from
0:11
their successes and be inspired by how they've overcome
0:14
adversity. My name is Anthony Kotaspodi and
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today's guest is Chris Chambers. Chris is the founder CEO of the
0:22
Einstein Bridge. This is a revolutionary research
0:26
and innovation data as a service request platform where data,
0:32
innovation and research converge to drive unprecedented
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efficiency and sustainability. Their solution addresses
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critical data fragmentation challenges while providing
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organizations with powerful tools to unlike unlock insights
0:47
and accelerate innovation across industries.
0:50
More recently, his Research and Innovation Data as a Service
0:53
Company was accepted into the Peach Score Accelerator and was
0:58
selected to advance to the second round of Pepperdine's
1:01
2025 Most Fundable Companies. Chris has received co-author
1:07
credit for How to Survive and Thrive volume to sharing
1:11
inspiring stories from entrepreneurs and innovators.
1:14
He has earned recognition on the Presidents and Dean's List while
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achieving A4 Point OGPA in his MBA and is a recognized thought
1:23
leader, executive Dr. of Business Administration
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candidate at Pepperdine Graziadio Business School 2025
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Colorado Governor's Fellowship selectee and Advisory Council
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member of the Harvard Business Review.
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His forward thinking approach to Organization 6.1 and autonomous
1:43
operations has earned him numerous industry accolades,
1:47
including presentations at multiple Artificial Intelligence
1:50
Summits, nominations by Leading Technology conferences for
1:54
Outstanding Leadership and technology and Excellence in
1:57
Technology awards and selections for features by top business
2:01
publications, including CIO Views Magazine, Mirror Review,
2:06
CIO Outlook, the USA Leaders Magazine, and is a 2024 marquee
2:13
Who's Who in America honorary honored listee.
2:18
Now before we get into all that good stuff, today's episode is
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2:48
All right, back to our guest today, the founder and CEO of
2:50
the Einstein Bridge, Chris Chambers.
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I appreciate you making the time to Share your story today.
2:56
Absolutely. Thank you so much.
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Now look, if we have all this business thing aside, I need a
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spokesperson, I need APR person. I know who to come to.
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Thank you so much for the introduction.
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That's my pleasure. Let's jump right into it, Chris.
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So you've got your hands in a lot of different things right
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now. Kind of paint us a picture.
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What's the larger goal that Chris Chambers is driving
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towards? Absolutely.
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So it, it came about during my, my acceptance into a
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Pepperdine's doctoral program. One of the things I'm coming
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into the mid part of my MBA, I'm trying to figure out what's
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next. And I had never really thought
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about whether I was going to do a PhD or a doctorate, but it
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just becoming the natural, you know, next step after I set the
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goal of getting a four point O. So there was a accounting was
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that class. Like if I get past accounting, I
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knew I had it. I got a 95.
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I was like, Oh, it's over. Like, so, you know, I started
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thinking and did some research and I was trying to figure out
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really what is the fundamental difference between a PhD and a
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doctorate. And I came across Pepperdine
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University and they basically explained an MBA takes current
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knowledge, they're building emerging leaders into current
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companies. APHD takes theoretical.
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So they're pushing the boundaries of what people think
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is possible. A doctor, a doctorate in
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business administration is going to be creating practical
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knowledge for those leaders, those MBAs and other leaders to
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implement. That difference was fundamental.
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And I was like, wait a minute. So if you choose APHD, you're
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going to be engaged in theory, but it's not necessarily rooted
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in any practicality. If you take a doctorate, you're
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in practicality, but you're not pushing the boundaries of what's
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possible. Who synthesizes that and puts
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those two together? How do we accelerate the
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innovation curve? By meeting practical knowledge
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holders, theoretical, you know, pursuers and then organizational
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leaders and executors? That's the missing piece.
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Because what I saw in the market, and it was interesting,
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I was talking to a potential investor and he said, Chris, you
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know, the first thing I went on your LinkedIn profile, the first
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thing I thought was, oh, this guy's an academic.
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He's a reader. And it stunned me.
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I was like, and he was like, no, no offense, but academics
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typically aren't executioners. And it was at that point I
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realized I had to build the Einstein Bridge.
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OK. Before we get into Einstein
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Bridge, and I want to hear more about that, couple of things.
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First, be curious to hear about your recent transition from CEO
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to venture partner Iron Key Capital.
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Absolutely no. I, I absolutely love what, what
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Iron Key's doing out in the Web 3 and the AI space, especially
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around start-ups, seed stage start-ups.
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You, you know, if you're looking for an investment and you're
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looking to sustain an investment and hold it over a longer period
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of time, seed stage companies on average are outperforming even
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the S&P 500 and doing fairly well.
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You're talking about maybe a 14% return with the S&P 500, where
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if you have a seed stage, you know, diverse portfolio, you
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could be earning 25% returns. So it's definitely an area where
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you're, you're going to have some ripe opportunity to park
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assets and have them grow over a longer period of time.
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Moving in the venture partner for me was just the next
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evolutionary step. I came in as a strategy advisor
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was helping the, the CEO and founder kind of make it through
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a transitionary period. You know, anytime there's,
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there's a lot of growth there, there, there's a lot of room for
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a lot of noise, a lot of process, you know, just a lot
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of, of redundancy. And so I stepped in, in the CEO
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spot to help kind of eliminate that redundancy, rebrand a
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couple of their their assets to kind of lead the team towards a
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more unified cohesive goal. And then was happy to move into
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the Venture Partner after we got accepted at the Einstein Bridge
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into the Peach Score accelerator.
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So say more about the 25% returns that are possible with
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seed stage companies. As an outsider, I think of seed
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stage and I think of somebody who maybe the a company that's
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not generating, you know, profit net revenue yet where, where
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would those, those greater returns come from?
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Absolutely. I'm going to give you 3 examples
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of of seed stage investors. 1 is named Mike Marcolo.
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He was the seed stage founder or investor for Apple.
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People don't know that. If you watch the Steve Jobs, you
7:12
know, dramatization with Ashton Kutcher, he's the guy that
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showed up in that yellow Corvette when they were all
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outside in front of the home. He gave Apple the seed funding
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necessary. So he owned at one point 1/3 of
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Apple. Now, you know, remember Steve
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was fired and he came back. He got moved out of the board.
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He relinquished his shares. But that's one example. 1/3 of
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Apple today is worth a trillion dollars, right?
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But at that time, I think he wrote a check for $90,000.
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Seed stage company. Second one is David Mcquah.
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He's the seed stage funder of Microsoft.
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Now. I want you, You know, it's great
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right now. I'm using a PC.
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You're probably using an iPhone. You're like, yeah, Chris, those
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are the big, big, big, big names out there, right?
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But these companies weren't that at that time.
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They weren't what they are today.
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They were just companies in somebody's garage literally with
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Microsoft and and Apple. So David Marquard was the S seed
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stage funder of Microsoft and ultimately became a very
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successful venture capitalist. The last one is Peter Thiel and
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that's somebody, you know, I think a lot of people may
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recognize that name. Peter Thiel was the seed stage
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funder for Facebook. So he put 500,000 into Facebook.
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That stake when they went to their IPO is worth a billion
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dollars. The reason why seed stage
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companies end up making a lot of returns is because you get a lot
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of the company in the early stages.
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So in an early stage venture, you, you typically are betting
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on a couple of things. You're going to be either
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betting on the promise of the idea, meaning if you
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fundamentally believe, hey, if they actually can create this
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and bring it to market, it's going to have a lot of value.
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I, I have a gut feeling this is going to do very well.
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Or you're looking at the team and you're saying, you know
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what, this collective group of talent, they definitely can get
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their act together, especially if they're a line behind a
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mission. That secret sauce is when you
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have a great idea and a great team.
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And then of course, it's market timing, right?
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Great example of Microsoft Zune versus iPod.
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You know why 11 succeeded and 1 failed?
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Or Amazon phone, We love everything Amazon does, but we
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didn't like their phone for whatever reason, you know, so
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it's not to say that these ventures are not without risk,
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but ultimately because you're, you're getting an early think
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Shark Tank, right? I'll give you 150,000 for 20% of
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the company. The company's valued at let's
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say 500,000 will the next round of funding that company could be
9:30
valued at 5 million because they've made it to 1,000,000 in
9:33
annual revenue recurring. Your your stake just went up 5X.
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So we're talking, yeah, certainly higher risk, higher
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reward kind of longer term. Like you know, you get in early
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if you get in at the right venture, the returns can be
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really big kind of long term. So let's talk about your
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publication, How to Survive and Thrive volume to you're
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spotlighting stories of entrepreneurs.
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I'm curious, two things actually.
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First, let's start with how did the idea to to get involved with
10:05
this come about? OK, SO11 distinction here.
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That is actually the founder of Reebok, who is the author.
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And So what they do is they have this program called 1 Golden
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Nugget that they go out and they reach out to entrepreneurs like
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myself who want to share a golden nugget, something of a
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nugget of wisdom, a kernel that, that they want to share with
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virgin and entrepreneurs. And so I ended up on the 1
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golden nugget podcast and they really loved my story.
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My, my contribution was as you have to embrace the, the turn
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back moment. There's a dark, dark moment in
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almost every, if you really study successful people, I don't
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care who you would look at the Shark Tank guys.
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And yeah, there's a moment that you're going to, you're going to
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want to quit because everything is going wrong.
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I mean, everything is going wrong.
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And in that moment of darkness, there's a, there are two
10:58
options. You can guarantee that it will
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never happen because you quit, or you can step forward and the
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possibility of it happening remains.
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So you have to either bet on the certainty of never making it
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happen, or the possibility of it potentially happening.
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Which is greater for you? Say more about that darkness and
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maybe what you came to understand about that from some
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of the Shark Tank folks. So I, I mean, I've been studying
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success in terms of, you know, the motivational speakers, you
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too, they typically get a, a bad rap.
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But what was interesting is, is that I started studying success
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around 20/17/2018 when I, when I lopped off into
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entrepreneurship. And what I found out is that
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almost everybody had an incredibly difficult uprising to
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success. I mean, John Rockefeller had a
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moment where he almost lost everything.
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Andrew Carnegie almost lost every.
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I mean, you go, you read Napoleon Hill, Napoleon Hill's
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Think and grow rich, you're going to find incredible, almost
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cyclical, almost programmed like a computer program, nearly
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everybody. Elon Musk, Tesla and SpaceX both
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faced death early on in his tenure.
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There is a incredibly dark moment that I think it's
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programmed into the journey of success because when you push
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past that moment, what is given to you are riches and and
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resources beyond any person's wildest dream.
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But what you often hear, I don't care if it's Steve Harvey, I
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don't care who you're listening to.
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They will say I just never gave up.
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I just never gave up. Mark Cuban talked about the fact
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that he was down to $940 a funny story.
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OK, so, so he's really, he's, he really likes telling this story.
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So I'll, I'll shorten it. He, he was living in a six
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people to an apartment. He had this, this Nissan that he
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was driving with a hole in the floorboard.
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He had to put a quarter of oil in it to drive every 60 miles.
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And The funny thing was that he had a, he got a job as a
13:02
software salesperson and his boss, he loves to say this
13:05
Michael Hubecky. And he even spells it.
13:07
It's funny to hear. So Michael Hubecky is the owner
13:09
of the company. And Mark is like, look, I've got
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a $15,000 sale. It'll give me $1500 so I can get
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out of this rat hole apartment. He wanted to go, you know, so he
13:19
was like, I got to do that, but his job was to open the store.
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Lowest story short, he made the decision to go make the sale.
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Now he brings back a $15,000 check and gets fired.
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That's an incredibly dark moment.
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And what Mark did then was he went and found one of the guys
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who want his customers who liked him and just said, hey, look, I
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need $500. You know, basically sold him on
13:41
investing $500 to help him get the software.
13:44
And that became Micro Solutions. That then became broadcast
13:47
network and it became Dallas Mavericks and billions of
13:49
dollars. Daymond John on the show, he was
13:52
that he was down $1,000,000 in inventory.
13:55
He had like $500 in the bank and he said his mother, he was
13:59
working on his mother's house. He says his mother was like, I'm
14:02
going to take an ad in the newspaper.
14:04
He was like, mom, that's the stupidest idea of whatever.
14:07
She took an ad out on the newspaper, $1,000,000 in sales
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but low on cash. 20 investors reach out.
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Three of them he met with one of them became his mentor today.
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And you just go over and over the founder of kind same thing.
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He was like, I was down $1,000,000.
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I had almost like it was, you're talking about like, you know,
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basically days from death's door and they just kept going.
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And that's the cycle that you see across all successful
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people. So you made the comment it I get
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it almost seems like it's programmed into the path you you
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believe that like it's sort of hard, if not impossible to kind
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of get to the top of the heap without facing just death's
14:50
door. Like adversity, that seems
14:52
insurmountable. Well, I mean, if if you're a
14:55
James Brown fan, you you remember the song paid the calls
14:58
to be the balls, right? Paid the cost.
15:02
You have to pay that cost. You can take it in biblical
15:05
form. How do you get to King David?
15:07
Well, you start off in the field.
15:08
You have to slay your Goliath and then ultimately you've
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proven your work doesn't matter what area you're in.
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You know we can we can get it at the religious sense.
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We can get it at the societal sense, philosophical.
15:18
You can go to Greek mythology, Roman, I don't care where you
15:21
look. The pathway to success,
15:24
accolades, having your name recognized as you start off
15:27
insignificant, you decide you're going to take this
15:30
insurmountable challenge on and in this our case, we're
15:33
typically talking about entrepreneurship, which is
15:36
typically an insurmountable challenge and you push and you
15:39
push and you push and then you ultimately breakthrough, but not
15:43
before you almost die. Netflix is another one Jeff
15:47
Bezos faced this. It is like a program.
15:50
I don't know any other way to describe it, because if you're
15:53
just looking at it at basically, what are the probabilities that
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every successful person tells nearly an identical story?
16:00
It's got to be some level of programming because they all
16:02
start for different walks of life.
16:04
You know, my dad, since I was a little kid, always said if it
16:07
were easy, everybody'd be doing it.
16:09
And I think that's sort of a different spin on kind of the
16:12
message that you're saying is, I think to find success in
16:16
something, right? If it were easy, it'd be a very
16:20
crowded space and your success would get washed out.
16:25
So to find success in a very competitive world like this, it
16:29
takes struggle. It takes facing those hard
16:31
moments. And so I'm glad that we're we're
16:33
giving voice to this. And you're talking about, you
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know, multiple examples of, you know, household names, very
16:39
successful people that, you know, on the verge of throwing
16:44
in the towel, but but they couldn't because they had no
16:47
towel. They couldn't afford it, right?
16:49
And they just found a way to pick themselves back up and and
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keep pushing forward. Absolutely.
16:55
You know, the interesting thing is Mark, you even talked about
16:58
the fact that he had stolen the towel for Motel 6.
17:01
So that's funny you, you grabbed on the towel.
17:04
But it, it really is it, it is, you know, entrepreneurship, it
17:07
gets glorified in our society. You know, you get your Instagram
17:11
reels, your TikTok reels, you know your YouTube reels, the
17:14
best 60 seconds or you know 90 seconds of somebody's life and
17:18
their overnight success is about a 20 year cycle.
17:22
If you really, you really think about it, you know, there are
17:25
some people who had rocket shots right away, but there's few and
17:28
far between. Overnight success looks like a
17:30
lot of failure, a lot of heartache, a lot of tears, a lot
17:34
of anxiety, a lot of sleepless nights over and over and over
17:39
and wondering if you made the right decision, especially if
17:42
you have to pivot. That's also, you know, very
17:44
terrifying moment. But if you pivot and then it
17:46
starts working and then something in the macro
17:48
environment, maybe it's COVID, maybe it's generative AI, we'll
17:51
get to that, that that disrupt your plan and you just have to
17:54
keep going. And, and so that's what success
17:58
looks like it, it typically is associated with your
18:02
possessions, your, your, your bank account and things of that
18:04
nature. But but true success starts with
18:07
the ability to manage your time and own it.
18:10
Have you worked hard enough to where you can own your time
18:12
regardless of how stressful it is?
18:14
Do you own your time? Can you control your destiny and
18:17
in and of itself, that success? Now, if you end up becoming
18:20
monetarily successful, it's because you've delivered
18:22
incredible value in some way. You know, I, this is great that
18:27
we're talking about this. We, we kind of get into this in
18:29
most of my interviews a little bit later.
18:32
But since we're here, I'm going to go ahead and ask you the
18:34
question. Chris, what's the hard thing
18:36
that you got through? How'd you get through it?
18:39
What'd you learn going through that process?
18:43
Oh, man, OK, so I've spent 16 years in corporate America and
18:48
moved up to a business engineer. You know, really thought I knew
18:51
my stuff, right? You know, I did to a certain
18:53
degree in corporate America because I'm being handed a
18:55
budget. Here's a $60 million P&L, manage
18:58
it correct. Got money, have a CapEx budget.
19:01
I can do that. Ready.
19:02
So 2018, you know, I'm early 30s.
19:05
I know it all now. I got it.
19:07
Oh yeah, let's go ahead and get out here and be an entrepreneur.
19:09
Let's build this business. The millions are flowing.
19:12
I've solved 10s of millions of dollars to help integrate
19:15
hundreds of millions of dollars in business units.
19:17
I got it. I know what to do.
19:20
Then I got out here and one of the things that stunned me right
19:24
away was that I had never struggled in my life to get a
19:27
job, meaning that if somebody had to define me, I could show
19:31
up with my CV regardless if I had experience in it or not.
19:34
They just like something in my background.
19:36
I could articulate my understanding of the problem set
19:38
in the industry and whatever else I had studied on.
19:41
And ultimately we get an offer. You get to the entrepreneurship
19:44
side. That's not the conversation
19:46
you're having at all. Not.
19:48
And it's stunned me because the very first idea I had was a
19:51
company called Prod Exec, short for Product Executive.
19:55
I'd spent my the last tale of my career in the last five years as
19:58
a product leader in multiple software organizations.
20:02
So I really knew the space ended up selling that concept, which
20:06
is now fractional. But I wasn't calling myself a
20:08
fractional back in 2018 was selling that built a portfolio
20:12
of about 10 startups. And there was a deal, some
20:15
equity stakes, some revenue shares, you know, some cash, but
20:18
a but a but a good mix. But they were all startups in
20:22
2018. Around 2019, I started making
20:25
money close to what I was making in corporate America.
20:27
I was feeling good right over this Q4.
20:29
I'm feeling good. OK, I'm going to tell you right
20:34
now the first mistake I made as an entrepreneur, I didn't
20:37
listen. I did not listen to my customer.
20:42
One of my customers was a company fit for trips.
20:45
Hey, Marcus, if you were going to might hike Mount Kilimanjaro
20:50
or the Swiss Alps or even the Rocky Mountains, let's say you
20:53
were going to his company helped you get ready for the terrain,
20:56
the elevation, the breathing that's necessary to be in high
20:59
elevation. This is Q4, this is around
21:02
November. His major source of customers
21:06
were tour operators worldwide. And in November, they had begun
21:10
telling us that on the on the eastern side of the world, that
21:15
there was some virus that was causing mass cancellations.
21:19
November of 2019. I had a four month head start
21:25
and it was over and over and over.
21:29
It was just, and we were stunned because now he's facing the
21:32
death of his business. We're stunned.
21:34
How quickly? So by the end of 2019, now you
21:37
know, I have AI, have a broad portfolio.
21:39
So I'm, I'm, I'm OK, right. But I'm trying to help Marcus
21:42
navigate this. And then COVID hits February
21:46
2020, the business closers shut down, eliminated all but one of
21:52
the startups in My Portfolio. I have.
21:56
I've truly never felt pain like that before.
21:59
I really never felt at anything before that, not not to that
22:03
degree. And I remember when I got off
22:07
the phone with my last founder, who was like, hey, man, I'm
22:10
going to have to find a job and I can't do this anymore.
22:13
And, you know, I told him I understood and respected it
22:17
because it was it was an incredibly difficult period, but
22:21
I realized I made some mistakes right then and there.
22:24
It was the most humbling moment ever because in hindsight, I can
22:29
tell you about the November phone calls that I had for four
22:34
months. The reason why I didn't pay
22:35
attention to that is because I violated a principle of business
22:38
PESTLE, political, economic, social, technological,
22:43
environmental, legal. That was an environmental
22:45
change. COVID was an environmental macro
22:48
phenomenon that was happening, was spreading like wildfire.
22:52
And I didn't have the maturity and also the knowledge necessary
22:57
to factor that into my decision making.
22:59
So I went back to school for a business degree so that I could
23:02
understand how to use macroeconomic trends along that
23:07
way. While I was pursuing my business
23:09
degree, I got an opportunity to purchase a company,
23:12
trulygen.com, off of Flippa. And what was interesting was
23:17
that, you know, Flippa, if you all don't know, is a place
23:19
whereyoucanbuy.com businesses, E com businesses, things of that
23:22
nature. So true Legion wasn't making any
23:25
money, but boy, I knew that brand.
23:27
True lead Gen. true lead generation.
23:31
Easy, the company was built to be like an Amazon of martech.
23:35
So you're thinking, you know, Google products constant
23:38
contact, you know, the big brands, but it's going to be
23:40
like Amazon for martech just come to true Legion and you can
23:43
get anything you need to DIY brand about 400,000 outbound
23:49
emails across 17 product verticals.
23:51
And again, we're talking about the, the product teams that are
23:53
the marketing teams that are building this collateral of
23:55
Google Constant contact. HubSpot had extraordinary open
24:00
rates and extraordinary click rates, but no conversions.
24:04
And it stunned me, right? Because I remember reading the
24:07
constant contact one, it was like a 73% open rate for the
24:11
constant contact themed campaign and like a 35% click rate.
24:16
I mean extraordinary. And I thought to myself right
24:19
then and there I was like, wait, I've seen this pattern before
24:22
three times in my career. I left being $1,000,000 producer
24:26
in terms of revenue for a smaller competitor of a Fortune
24:30
500 company and Bond we're talking about direct 1 to 1.
24:34
Ameriquest to 1st NLC. Bond went from doing 10 million
24:38
dollars, $11 million in nine months in sales.
24:41
First NLC comes, gets me, they ask me if I'll do the same thing
24:44
for them. They pay me, you know, and I'm
24:46
young, I'm like 19 at this point.
24:47
You get 4 extra money. I'm like that's a no brainer.
24:50
Nobody's picking up. Ameriquest is the most well
24:53
known mortgage company at that time.
24:56
Insight to GHA Technologies. Insight is a Fortune 500 company
25:00
who had just implemented the the most technologically advanced
25:03
stadium at that time, the Arizona Cardinals.
25:07
Robert Half to Genuine and that was the last time I needed to
25:10
see the pattern. Robert Half is the most well
25:12
respected or one of the most well respected companies in
25:14
America. Genuine was a smaller
25:17
competitor. All three times they offered me
25:19
more to do the same amount of production, but I didn't
25:22
recognize something until later in life.
25:25
What was that? When I saw the brand, it was
25:28
brand. When I saw the constant contact
25:32
performance but no conversions, I was like the constant contact
25:34
makes money. I just literally just googled
25:36
how much annual revenue constant contact made divided by 365 then
25:40
divided that by 24. I was like, if I spend an hour
25:42
reading this and trying to figure it out, constant contact
25:45
has made X number of dollars since then.
25:47
So it's clearly not constant contacts issue it's true
25:50
legions. And I was like, OK, I'm in
25:52
trouble, right? So I purchased the brand and I
25:55
paid a premium for it. I'm trying to figure it out.
25:58
And I'm getting to the broader, you know, the broader point of
26:01
the of the struggle because I'd already lost a business
26:03
portfolio to COVID. So in this particular case, I
26:07
was like, well, I automated my business development position at
26:10
Robert Half in 2010 and I made, you know, millions of dollars in
26:13
gross margin for the company. Staffing industry really
26:16
respects Robert Half's name. So here's what I'll do.
26:18
I'll find the best in class LinkedIn automation tool.
26:20
I'll socialize that story when I sit down with the people.
26:23
Number one, I already got you because you came through
26:26
automation. So I've proved automation works.
26:28
This is 2022. Now all I have to do is explain
26:32
to you what I did for Robert Half and how I did it.
26:36
End up closing 60 customers over the next 12 months.
26:38
The asset started at a valuation range between 7000 to 32,000.
26:43
And by February of 2023, I got excited because I was tracking
26:46
the founder path and the valuation had risen to 787,000
26:51
to 2.5 million. So there's like 4 buying
26:53
categories. So I was like, I couldn't
26:57
believe I was like this is you. You don't get that kind of
27:00
appreciation for anything other than California real estate,
27:02
right? So I'm a California kid.
27:04
It's time to get ready to sell. But I had found the path do an
27:07
assessment of the business. And one of the risk I had was
27:11
that it was a month to month business.
27:13
So I prided myself on being able to retain people by selling them
27:16
every month, right? Because you could just walk
27:17
away. So I had to close my customer
27:20
every. I felt good about that.
27:21
But business was an extraordinary risk.
27:24
OK, let's get to it. I didn't see ChatGPT coming at
27:28
all. That's again another violation
27:31
of PESTLE as the technological. To be fair, Chris, you're saying
27:35
I didn't see ChatGPT coming, I didn't see COVID coming.
27:39
I mean, put yourself in the same bucket as billions of other
27:43
people, right? Like it's nobody's got the
27:46
crystal ball to, you know, hey, yeah, this virus, there were
27:50
some stories about it, but nobody knew what it was going to
27:52
become. So I'm just saying be a little
27:54
easier on yourself. But but please continue with the
27:56
story. Well, hold on.
27:58
OK, OK, I'll give you, I'll give you COVID, but there was the
28:01
Spanish flu, right? So, so let me give you, let me
28:03
give you the ChatGPT thing. In 2013, I was in an oil and gas
28:07
consulting engagement with Saudi Aramco, some reservoir
28:10
engineers. They were describing 4 key
28:12
indicators off of a well and they wanted to stream those into
28:15
a big data set so we can figure out how to reduce the well
28:17
health or the well maintenance costs and extend well life.
28:21
I recommended that we use those four key indicators, big data,
28:24
advanced analytics and IBM Watson who had just smoked
28:28
humans on Jeopardy. IBM Watson was the first
28:31
generative AI application out and I was in 2013.
28:35
So I knew the technology was there.
28:38
What I didn't realize when I say I missed it, ChatGPT is, is that
28:41
what my business was built on was my ability to understand
28:46
nearly instantly any industry or any market, any position.
28:51
So really what it boils down to was, is that I was really good
28:54
at crafting high impact messaging that resonated with
28:58
anybody. You can be a bioinformatics and
29:01
I go from bioinformatics to construction.
29:03
You go from construction to Salesforce CR.
29:05
It didn't really matter what the industry was.
29:06
I know how to speak to people at a fundamental level and my
29:10
messaging was high performing. But then when ChatGPT shows up,
29:15
it's me in a box. Why would you pay me a premium
29:20
for my ability to to craft high performing messaging?
29:24
And if you could pay $20 to something that reads the same?
29:29
See the biggest thing is, and we're learning it later, but it
29:31
read the same, right? So I'm verbose.
29:33
I was delivering verbosity in automation before anybody else
29:37
was because if you haven't noticed by now, I'm verbose by
29:40
nature. This is truly who I am.
29:43
So I was giving people the ability to take my verbosity and
29:47
deliver it to their customer, basically a blurb of an insight
29:50
of how well I understood the market, or in their case, how
29:52
well they understood the market so they could get a, a meeting
29:55
when ChatGPT comes along. And within two weeks of, of the
30:01
mid part of February, I heard this often and this I'm
30:04
generating for one person 1400 replies in four months.
30:07
They were like, you know, Chris, you know, we, we're just and it
30:11
was the, the, IT was very, I could tell I was like, Oh, you
30:14
discovered ChatGPT. And so they one by one, they
30:17
would end their, their term with me over and over and over and
30:20
over. And there it was.
30:23
It's painful because I didn't, yeah, I didn't set the business
30:26
up and I was trying to get to, to 5 million.
30:29
But but in that I'll, I'll tell you what, what, what dawned on
30:33
me, the reason why I was tracking and founder path has
30:38
nothing to do with the ego side. Yeah, I'm pretty proud that I
30:41
delivered like a 10,000% return. What I was trying to do was
30:45
figure out how do I scale this? How do I find 10 other people?
30:50
I can do that too. Because if you can go from 7
30:52
seven thousand 32,000 to 787,002.5 million with one
30:57
person, if you do it with 10, look how big the pot can grow.
31:03
And so I went back to school for my MBA because I really wanted
31:07
to understand leadership principles.
31:10
And so in that moment, I decided I'm never going to do the solo
31:14
solopreneur thing again. No more.
31:17
You know, I've got to do the Jackson 5.
31:19
It's, it's really hard to be Michael Jackson out here, right?
31:21
So I got to get to the Jackson 5 and be Michael and the five,
31:24
right? Because that's how everybody
31:25
fell in love with it. You know, the solo act comes
31:27
later. And so right now I'm, I'm
31:30
building four teams out of the rubble of those first two
31:34
failures. And it's actually really fun.
31:38
OK, so is this a good time to segue into the Einstein Bridge?
31:42
Sure. I mean, yeah, we can.
31:44
We can go there wherever you'd like to go.
31:45
Yeah, I mean, I think this is kind of your your passion at the
31:49
moment. This is, you know, where you're
31:51
putting a lot of your energy. So kind of break this down into
31:54
simple terms for us. I explain sort of the the
31:57
impetus behind the idea and and what it is that you're doing
32:00
there. Absolutely, absolutely.
32:02
So I come out of my MBA or midway through and I remember so
32:07
accounting was was that one class.
32:09
Now, if I got past accounting, I was going to make it happen and
32:12
I shot for four point O not because I think anybody cared
32:15
about it, but it's because you, you have to set goals.
32:18
One of the things that that's really interesting about the
32:20
physical world in a gym or or the academic world is if you set
32:24
a goal, you can achieve it through just sheer work.
32:27
Now in the professional world, it doesn't work that way at all.
32:29
It doesn't matter. You can work hard, hard, hard,
32:32
hard, hard, hard and never make your goal, never get promoted
32:34
because maybe your boss doesn't like you or maybe your
32:36
organization, that there's a lot of reasons why you can't reach
32:40
your goal in the professional world through sheer hard work.
32:43
It's a lot of luck, a lot of networking.
32:46
But in my MBA, I was at the point where where I wanted to
32:51
know what was next. And I googled what is the
32:54
difference between a PhD and a doctorate.
32:57
And Pepperdine University came up and they had a blurb on their
33:01
on their website, their Business School website.
33:03
That was like something to the effect of MBAs are are trained
33:08
to be leaders in the current business world.
33:11
PhDs do research in the theoretical and doctors create
33:15
practical usable knowledge today.
33:19
And I didn't think anything of it at the time.
33:21
I, I, I at that point was knee deep in kind of building an
33:25
advisory firm. So, so I wasn't really thinking
33:28
too deeply about it. And lo and behold, you know, for
33:31
both I do a lot of blogging and micro blogging on LinkedIn and
33:36
out of nowhere. And I mean, you know, maybe,
33:38
maybe it was out of nowhere. I don't think so, right?
33:41
We, we all got machines and technology that can track you
33:43
down to your IP level. An academic advisor for
33:47
Pepperdine ends up in my inbox. It's like, hey, Chris, would you
33:51
like to explore the executive doctorate at Pepperdine?
33:56
I googled them and was like, whoa, yes, yes, I want to right.
34:01
I was just honored for the opportunity.
34:03
And so so I get into the admissions process.
34:06
I make it through phase one in the phase two, Phase 2 is an
34:10
interview. And, and I'll never forget we're
34:12
we're going over, you know, how to narrow down.
34:14
I tend to boil the ocean a bit. We're going to get to the moon.
34:17
It's like, yeah, Chris, but you got to create the metals, you
34:18
got to create the rocket fuel like that gets at the steps.
34:21
So they they narrow it down and help me kind of distill down my
34:24
autonomous operations concept into something practical.
34:28
And I remember the question that closed off the interview.
34:32
It was just like, OK, Chris, this is great.
34:34
Love your research proposal. Where are you going to get the
34:37
data? And right when he said that, it
34:39
didn't even take me like a fraction of a second.
34:41
I was like, I'm going to build a research and innovation to
34:43
advisory company, the data company, and I'm going to build
34:46
a company to do it. And at that moment, because I'm
34:49
doing research around automation with operations managers and
34:53
leaders, that I was like, oh, what if I built a bridge between
34:58
the people, the doers and the knowers?
35:01
But they're two types of knowers.
35:03
There's the doctorate, the practical builder, the
35:06
theoretical PhD. When you intersect those with
35:10
the doer, that collaboration and collision, if you think of it
35:13
like a, you know, an atomic bomb is an explosion of innovation.
35:18
And that's what we're building today.
35:21
And so where is it today? What's what stage?
35:24
So I'm in the process of solidifying my leadership team.
35:27
I've got a really strong group behind me.
35:30
I'm in building the first prototype should be done within
35:34
the next two, maybe 2 1/2 weeks. I've gotten some interest from
35:38
investors. I've been accepted into
35:40
accelerator. I got another offer for an
35:42
accelerator. It's really one of those
35:45
companies. I'm at the, I'm at the precede
35:46
stage, right. So precede is you have a really
35:48
good idea or you have a good idea and you're, you're
35:51
socializing it to the point people want to get in early.
35:54
Seed stage is really when you've gotten a little bit of traction
35:56
where you've grabbed a couple of clients, things of that nature.
35:58
So I'm in the process of, of bridging the gap between C
36:02
precede and C by going out and getting clients.
36:05
I, I can, you know, explain the theoretical or the principle,
36:09
you know, with the best of them and get people to understand,
36:11
you know what I'm looking for. If you look at product diffusion
36:14
curve, it's about 2.5% of the market is innovators.
36:18
You know, that's the traditional Jeffrey Morris crossing the
36:20
chasm diffusion curve. And then another 12.5% of what
36:24
they would call early adopters, they get in early.
36:26
So it's about 15% of any market that will really get behind the
36:30
principle. But the innovators, the people
36:32
that will make that jump are about 2.5%, which is why we went
36:36
research. Research and development is a
36:39
tax deductible thing for a business to engage in.
36:43
And So what that ends up doing is it moves us out of the bucket
36:46
of consulting and the types of engagements that we can get into
36:49
our research and development, which ultimately could be tax
36:52
deductible. But I'm not a tax professional.
36:54
That's not tax advice. Right.
36:56
On. What lessons can you take from
36:59
some of your previous ventures in lead Gen. etcetera and parlay
37:05
into what you're doing now to kind of try to get some of those
37:07
early customers? I think it's really, it's really
37:11
not building for yourself in each of the, in each of the two
37:15
previous ventures, while I had moderate success, if you you
37:19
want to call it that, they were all built for me to be a one
37:22
person Accenture. That was my goal when I came out
37:25
of when I came out of corporate America.
37:26
It's like I'm going to be a one person Accenture and I made that
37:29
goal. I had no idea the flaw in that
37:32
goal until I went through it and and I think that's really the
37:36
biggest thing. You know, the other thing is, is
37:38
that those previous ventures also gave me a a lot of lot of
37:43
content, right? I can tell the story
37:45
authentically and and it has helped me as I socialize a story
37:50
to get fairly well recognized for some of the things that I've
37:53
been doing, you know, from an innovation in that perspective.
37:57
And so it's helped me gain a little bit of notoriety.
38:00
And and with that, you know, and I'll share this with you pre
38:03
show, but I'll share it with people.
38:05
I was driving to Colorado from Houston.
38:08
It's about 16 hour drive. I decided I've never watched
38:11
Shark Tank a day in my life. And we're talking about just
38:14
this past Wednesday last week. Never watched it decide I was
38:17
going to play it for 16 hours. And one of the things that that
38:21
kind of humored me a bit was that Mark Cuban and Kevin
38:25
O'Leary, Now, Kevin, he had just sold a $4.2 billion valuated
38:30
software company, right? So Mark Cuban's a famous
38:32
billionaire. They're kind of bickering.
38:34
And Mark Cuban goes, yeah, man, money's all the same.
38:36
But your name and my name, it's not the same.
38:38
And if you know Kevin O'leary's Mr. Wonderful.
38:41
And I, I thought very, very deeply about what he was saying.
38:45
And what he was basically saying is, is that there are a couple
38:48
of ways to do it in life. But one of the ways that you
38:50
really get out there is if your personal brand is incredibly
38:54
well respected and well known, it is that one of the things
38:57
that allows for you to command opportunity for others.
39:01
And that's what Mark Cuban does. And that's what I'm doing now by
39:04
by going about the business of building the Einstein Bridge.
39:09
A comment you made to me before we went live with our interview
39:13
here is that you're hacking the machines.
39:16
What do you mean by that? So I it's not a coincidence that
39:21
we came into contact. So I'll tell the story really
39:24
quick. I have always been somebody who
39:28
who wrote to the three thousandth character and like I,
39:30
I know it's a three thousandth character limit.
39:31
Like I just write all the way to it.
39:33
Sometimes I have to chop, you know, I'm going to get to that,
39:36
that very last character, whether everybody reads it or
39:38
not. I'm only looking for a certain
39:41
type of reader. Here's what I didn't realize at
39:44
a certain point in life, in probably the last three years,
39:49
the readers are machine learning.
39:52
They're algorithms. Because as I continue to tell my
39:56
story, all of a sudden it'd be near instantaneous.
40:00
Like I'll write an article about some innovation that I did back
40:03
in my back in my, my, my lifetime.
40:05
And then I'll get an e-mail that says you have been selected for.
40:09
And then I remember when the Harvard Business Review selected
40:11
me and I was like, that was new. We live in a world of machines
40:16
and everything is machine learning.
40:18
The machines are listening and they're reading everything that
40:21
we do. And so one of the things that I
40:23
found out is that if you really can write well, if you can write
40:28
and you know, one thing is you have to have the background,
40:31
right? Like GPT.
40:32
You can't GPT your way to 17 years in the field.
40:35
Like, you have to have been there and done it and walked the
40:37
war stories. But if you're if you're diligent
40:40
about it, the universe will find you.
40:42
And it's because of machine learning.
40:45
What about machine learning and AI would the average person find
40:51
surprising? What?
40:52
What aren't we getting or seeing that is either already happening
40:57
or just around the corner? OK, so for the average person
41:02
right now, AI begins with ChatGPT.
41:05
In November of 2022, there was a chat bot, a virtual
41:11
psychotherapist named Eliza, produced by Stanford and MIT in
41:18
1967. Artificial intelligence is over
41:22
70 years old, expert learning systems.
41:27
IBM Watson. Many people don't know about IBM
41:29
Watson because it's too expensive.
41:31
IBM Watson. If you go and you Google IBM
41:34
Watson and Jeopardy, what you will find is in a general
41:38
knowledge competition where the rules are, you get a statement
41:41
and respond. The answer is in the form of a
41:43
question. AI in 2011 destroyed the best
41:47
humans. Even Ken Jenning, the host
41:49
today, he destroyed them. IBM Watson was like 77,000.
41:53
The humans Brad, Brad Rutter and Ken Jenning were like 21,000.
41:58
They just could not keep up with it with IBM Watson at all.
42:02
That was in 2011. ChatGPT comes up and because
42:06
it's democratized, people think it's the birth of AI.
42:09
It's not. It's the latest in a long line
42:13
of applications. Machine learning, Netflix.
42:17
Do you remember when Netflix used to just give you the top,
42:20
the top recommendation? We're talking about early in the
42:22
Netflix days. That was machine learning.
42:24
That's how that's how Facebook knows what ads to show you.
42:29
That's how you know Amazon knows what products to present to you.
42:32
That's how that's how Netflix knows what movies to present to
42:34
you. Machine learning is an old
42:36
technology. We use it all the time.
42:40
And so these two technologies are are core.
42:43
It's it's niche in novel to a certain segment of the market,
42:50
the broader market, because in the product diffusion curve, you
42:54
have to cross the chasm. Crossing the chasm is a literal
42:56
gap between the early, the innovators, the early adopters,
43:00
which are about 15% of the market.
43:02
So thank people who worked in D tech, neural networks, you know,
43:05
data science, machine learning, AI, those people, they
43:09
understood the technology. Crossing the chasm is when you
43:12
have the general market being fully aware, the early majority
43:16
34%, the late majority 34%, and then the laggards at the very
43:20
end, the final 50. Where do you see it going?
43:25
What what's coming in the next, call it six months that you're
43:29
kind of in tune with that is going to blow our minds?
43:33
I think, I think we're hitting a point now where where a Gentek
43:37
AI, AI that can perform on its own, it is really going to start
43:41
changing the way we understand how work is going to be
43:45
performed. It's one of the fundamental
43:46
cores of my concept autonomous operations.
43:50
In 2010, during a programming class, I came up with the
43:53
concept that you could integrate several technologies together.
43:56
At this point it was robotic process automation, workflow
43:59
automation, and cognitive automation.
44:02
And ultimately that you would be able to simulate human activity
44:06
and invert Karito's principle, the 8021.
44:09
And So what we're going to be seeing a lot of over the next
44:13
six months is really the the push for Agentic to get smarter,
44:16
to get broader, to get deeper. And ultimately the capabilities
44:21
of LLMS, while they seem to be close to peaking that peak plus
44:26
Agentic AI plus other innovative ideas are going to spawn an
44:30
explosion of business models and explosion of, of innovations
44:34
that we really had never seen before.
44:36
We're talking about now being able to innovate in days and
44:38
weeks instead of months and years.
44:42
Chris, can you give a practical example for, I don't know, your
44:47
average business owner, business executive where they might be
44:51
able to utilize an AI agent today in their business?
44:57
Absolutely. I, I think one of the things is
44:58
really, if you're going to use AI, you definitely want to have
45:02
it connected to the external market.
45:05
One of the hardest things for businesses is, is you have the
45:08
day-to-day demands of the business all day, every day.
45:11
There's not a day you're in, you're in business that you're
45:13
not dealing with the day-to-day in that I'm not the only
45:18
business that messed up in in lifetime and missed an economic
45:23
external. Lehman Brothers at $68 billion.
45:27
We didn't see the simultaneous collapse of the subprime
45:30
mortgage mortgage market, which had an extraordinary impact and
45:33
led to the 4th largest corporate bankruptcy in the world at this
45:36
point, possibly the largest. Those are the types of things
45:40
that if you can get AI and agentic AI to really be focused
45:44
on the external market. So you can see, I mean, just
45:47
imagine being a marketing and advertising agency in November
45:51
of 2022 and you didn't pay attention to the release of
45:54
ChatGPT. Trust me, by the middle of 2023,
45:57
you knew exactly who it was because it was eating through
46:00
all of your profits and your customers.
46:03
So I think agentic AI would would be very well suited for
46:08
business owners and executives to connect to the external
46:10
environment that they operate within and to get intelligence
46:14
from the external environment. That'd be something that you can
46:16
make strategic pivots on fairly quickly, rather than hitting the
46:20
iceberg and hoping you can see in the whole.
46:22
So let's say I've got like an SEO marketing agency or you
46:25
know, I do PPC for companies. What would an agent possibly be
46:29
doing for me in the background that's helping my business or my
46:32
clients? It's really understanding how
46:35
websites need to be built today. One of the things that I, I came
46:39
across reading an article that says, you know, we, we used to
46:42
build for humans. Now we have to build for
46:43
machines. And I've actually touched on
46:45
that. And so one of the thing an agent
46:48
would be doing in the background for, for a business like that is
46:51
really understanding. How do you how do you now build
46:54
for the the AI models that are out representing the humans,
46:58
right? It's a lot of machines talking
46:59
to machines. And so an agent can go out into
47:02
the marketplace and understand what are the best trends in
47:05
terms of SEO now that machine learning is a dominant portion
47:10
of the market or in a paper click campaign, what are the
47:13
highest performing paper click campaigns in the industry and
47:15
why? Those are the types of things
47:17
that you can get an answer to. And if you can get it summarized
47:20
right, it's one thing to come across an article is like it's a
47:22
10 minute read or do you really have 10 minutes?
47:24
We used to, right, when it was newspapers, but we don't really
47:27
have 10 minutes anymore. So those are the types of things
47:30
that if you were running an SEO or pay per click company and you
47:35
were using a Gentek AI, it's really about being able to stay
47:38
in front of the trends. And also, if an algorithm
47:40
shifts, this happened. So you're typically going to
47:42
have people who are very expressive in terms of, of
47:45
monitoring algorithm. Think Reddit, you know, those
47:48
types of pages where people are really, really deep into
47:50
understanding how algorithms work on the social platforms.
47:53
Well, you need something to be able to call and summarize what
47:55
they, what they're actually saying, right?
47:57
And so think, you know, micro blogs and blogs like all of this
48:00
information is out there, but can you really get a core
48:03
distillation of what am I supposed to do with it?
48:07
That's where I'll say agentic AI.
48:09
And so how could a small to mid sized business go about creating
48:14
their own AI agent? So a couple of ways to do that.
48:20
Number one, it always boils down to why are you doing it that
48:23
way? What do you really need to
48:25
solve, right? Because if you're going to build
48:29
something proprietary in house, it's going to be fairly
48:31
expensive. And one of the big things you're
48:33
going to run into is where you're going to get the data.
48:35
Like that's, that's one of the questions people.
48:37
Or what state is your data in? So you may say, OK, if we've got
48:41
$1,000,000, let's say you're, you're a decent sized SMB and
48:44
$1,000,000. If we're going to set aside and
48:45
build this agentic AI solution, then you get into it and you
48:48
realize you're going to have to park another 500,000 to 750,000
48:52
for data cleaning for it, right? Because your data is not
48:56
necessarily usable in a way that can actually power an SLM small
48:59
language model or a large language model.
49:01
So what it really boils down to is what are you trying to
49:04
accomplish? And does it require your, your
49:07
company data proprietary or, or trade secret type knowledge?
49:11
Or does it, does it require maybe guidance, maybe scenarios,
49:17
right. And you can go through those
49:18
scenarios and then that spurs the innovation of the trade
49:21
secret holders in the organization.
49:24
It's always a gift and a curse. But if you are going to go down
49:28
the path of building your your first model, you're going to
49:32
have to start with the data. And So what you want to do is
49:34
you want to have a consultation with somebody who really
49:37
understands how data is structured and whether or not
49:39
your data is in a form that's usable for an AI model or a
49:43
gentic AI. So when we're talking about
49:46
these AI agents, like I think tell me if I'm right or wrong
49:49
here, with the $20 version of Open AI ChatGPT, you can create
49:55
your own agents that will do XY or Z for you.
49:59
Is this what we're talking about or are are you referencing
50:03
something that's kind of operating at a different scale?
50:05
Well, I, I think it just depends on what you're again, what
50:08
you're trying to solve, right? So I'll use open AI.
50:11
So one of the things that that open AI does an incredible job
50:15
of, but it's also to our detriment is it shows us the
50:18
wonders of of generative AI. But I'll break it down in real
50:23
math terms. So I was doing some research on
50:26
the AI models when they first came out, trying to understand
50:28
the difference between the models across Claude, you know,
50:32
Gemini ChatGPT. And I asked one of the models, I
50:35
said I always hear the term 175 billion parameters.
50:40
Can you equate that to me in terms of volumes of
50:42
encyclopedias worth of information, just so I can
50:46
understand human equivalency? So sure, Chris.
50:49
What that means is that I have access to 52,500,000 volumes of
50:54
encyclopedias worth of information that's open AI and
51:00
all the work they've been doing. Open AI has been around a very
51:04
long time building that knowledge base.
51:09
That is not how an AI project will start at a company who
51:14
wants to build a proprietary model.
51:17
And that's the delta. And So what it boils down to is
51:21
do you need the information now or do you need the competitive
51:25
edge? Those are the that's really the
51:27
line of demarcation. If you're looking for a
51:29
competitive edge, then you're talking about creating an AI
51:32
model that expands your organizational capabilities.
51:35
If you're looking for the answer now, as long as you're not
51:38
exposing company data, you generally can use an
51:41
out-of-the-box GPT or Gemini. So if I want the competitive
51:46
edge, what's the process? Am I hiring a special kind of
51:50
developer to build this, you know, internal tool for us that
51:54
we can feed our data in and it's kind of, you know, firewalled
51:58
off or how does this work? You start with a use case.
52:01
You you, you first want to define what the use case of that
52:05
AI model is. The second thing you want to do
52:08
figure out if people have had success with that use case.
52:11
So you Google that use case and see if there are other companies
52:14
that have implemented had success with that.
52:16
Ultimately, what you'll find is if there is success, there's
52:19
also successful implementers of that model, right?
52:22
Because it's typically in a case study form.
52:24
Then you figure out who those who those implementers are and
52:26
you reach out to them to see whether or not they're inside of
52:28
your budget. But you want to define a use
52:31
case or use cases that you believe are ultimately going to
52:34
be successful and figure out if there are case studies of
52:37
successful implementations. One of the, the biggest
52:40
challenges with, with artificial intelligence implementations is
52:44
that 80% of them never reached their intended goal.
52:47
And, and the reason for that is because it, it's more about the
52:50
technology than the use case. And if you start at the use case
52:53
and, and really understand what are we trying to achieve as a
52:56
business? And is it necessary for us to
52:59
achieve it in the way that it that we're we're proposing it?
53:02
Who else has achieved this and who did they use?
53:06
Then you'll ultimately find more success.
53:08
Chris, if you had to recommend one resource, a book, a course,
53:13
a podcast for someone to level up and these things that we're
53:18
talking about with AI, machine learning, what, what would that
53:21
be? Man, I got to give it to Big
53:23
Blue now. And let me kind of, if you're a
53:27
scientist at heart, I love IBM. They, they're a pioneer in the
53:32
space. They've been doing it the
53:33
longest. They've built the the best
53:36
systems. They're, they're fame, they're
53:38
claiming the fame of the many that they built an AI model that
53:41
beat a grand champion of chess, right.
53:44
So being able to think ahead, IBM is a, a wealth of resources
53:49
in terms of really understanding what these, what these
53:52
technologies are and where they have had success.
53:56
You know, not everybody can afford an IBM in terms of the
53:59
implementation partner. But I, I would say that IB Ms.
54:03
the gold standard in, in any industry around the advanced
54:07
technologies that hold the most patents.
54:09
They, you know, they're, they're deep into the space.
54:11
So I would start with IBM if you are, if you're looking for like
54:16
a less sciency way and you just really you want to have some fun
54:19
and really understand it, I would look, I would YouTube Mark
54:22
Cuban and AI. He, he does a very good job of
54:25
explaining AI, relating the concepts in a way where where
54:31
you really get to kind of some of the baselines of what AI is
54:34
possible. I I like him to be able to kind
54:37
of broker the conversation if you don't want it at a science
54:39
level. Any particular daily habits or
54:44
rituals that you go through, Chris, that kind of help you
54:48
either get started in the day, keep you on track, keep you
54:51
centered? Yes, So as soon as I wake up,
54:55
usually the very first thing I do definitely take organic
54:58
lion's mane for the brain. It's one of the the best
55:01
supplements for cognitive ability.
55:04
Alpha GPC is another one. And then I get to the gym and
55:08
during my gym time, all I listen to is motivational speeches or
55:13
the stories of success. This is where I, I spend my
55:17
time. I coat my mind.
55:19
I try to get at least two hours of of mind coding before I let
55:23
any outside external influence of any kind into my sphere.
55:27
So I don't check my e-mail, I don't check my text messages, I
55:30
don't check anything. I just get to that.
55:32
I get, you know, get my brain stimulated with the, with the,
55:35
the organic ingredients and I get to the gym, I get the right
55:39
coating so that when I get into the flow of the day, you know,
55:43
because once the day starts flowing, it starts tumbling.
55:46
And then at probably about the midpoint in the day.
55:49
So Ed Mylett is famous for this, that he, he has compressed time
55:53
into 3-6 hour days so that, you know, it's like 6:00 AM to noon,
55:57
noon to six PM, 6:00 PM to midnight.
55:59
Those are so he's like, I have three days in the span of your
56:03
one day. And it actually does make some
56:05
sense when you think about the speed at which we can get
56:07
information. I no longer have to drive to the
56:09
library to get a book, which took time.
56:11
Now I can just get the answer in you a millisecond.
56:13
So I fundamentally accept it and I've applied.
56:16
So around like the four or 5:00 mark, I'll take a moment.
56:21
And I was trying to take, you know, stock of the day.
56:24
You know, what have I accomplished?
56:25
What do I need to get done? And ultimately take about two
56:29
hours to read. Right now I'm in design thinking
56:32
and innovation. It is really a core book of mine
56:36
that I'm really holding on to. And then, of course, think and
56:40
grow rich. You know, you can never go wrong
56:42
with that as a master principle, you know, get my 2 hours of
56:45
reading and then around the 7:00 mark, you, you get back after it
56:49
and you really just try to push that, that pedal to the metal
56:52
home because this is a full contact sport.
56:55
It's 24 by 7. You know, there's when you
56:58
achieve work, work life balance, it's because you, you, you've
57:02
balanced your bank portfolio, right?
57:04
That's when you really achieve work life balance.
57:06
So you work till you get it. And then once you get it, you
57:09
remember the days that you had to work so hard to get.
57:12
I like the especially the morning part of that.
57:14
I'm curious, where do you find the motivational speeches or the
57:18
success stories to listen to? I love so Evan Carmichael is a
57:22
really good resource that he aggregates them.
57:26
You know your 10 rules of success.
57:28
You you can find it on Michael Jordan, you can find it on Kobe
57:31
Bryant. You know, you name the business
57:33
persona Bill Gates and Steve Jobs.
57:35
He's really, really good aggregator.
57:37
Ben Lionel Scott is another one. He's he's got really you use
57:41
like a Evan Carmichael when you want to understand principles,
57:44
right? They're going to be communicated
57:45
to you in a very serene direct mode.
57:48
You use a Ben Lionel Scott when you want to get motivated
57:50
because his his video is going to get you running through a
57:52
wall. Motive versity is another one
57:55
that are really, really good. And these are aggregators of
57:58
motivational speeches. They're really good.
58:01
And then ultimately, you know, YouTube has been a phenomenal
58:05
source of information. So I can find anything on
58:08
anything I need to know, right? So Ted talks, you know, Stanford
58:11
lectures, you know, MIT, like whatever I need.
58:13
So I would say that that that YouTube is a great resource for
58:17
that. But ultimately, if you find a
58:20
great podcast across Spotify, you know, I love Neil deGrasse
58:23
Tyson. So you know, those types of
58:26
Spotify is a good resource as well.
58:28
But I I prefer you to. OK, Chris, let's say you and I
58:32
reconnect in a year and you're celebrating one thing.
58:36
What is that one thing? Oh, that I broke through for
58:41
others. The whole purpose behind the
58:44
Einstein Bridge, you know, I kind of ran over it.
58:46
So I'll give you a little bit more of what it meant to build
58:49
the bridge is doctorate and pH DS.
58:54
Once they complete their research and they've got their
58:56
credentials, they still have to go out and monetize.
58:59
And that's typically not what they've been taught to do.
59:02
They've been taught to research. They've been taught to grow.
59:04
So there's a challenge with the academics.
59:07
And then there's also push back in the doer side of the market,
59:10
right? The executioners who are like,
59:12
well, all you do is practice theory.
59:14
And So what I wanted to do is create the largest decentralized
59:18
network of researchers who can all contribute to an operations
59:22
database, the operations data repository that combines their
59:27
ability to contribute from a research perspective.
59:30
We use a blockchain so that we can have proof of work.
59:33
So anybody who contributed to a particular data set that ends up
59:37
being consumed by the doers gets paid for their contributions.
59:41
And So what I wanted to do was create a way for the researchers
59:45
of the world to be able to profit off of their research,
59:49
off of their capabilities. And so part of the Einstein
59:52
Bridge is building that, that, that basically that economic
59:56
model, that ecosystem where PhDs and your doctorate level
59:59
researchers can come in and they can contribute to a knowledge
1:00:02
base. We commercialize that knowledge
1:00:04
base, we get it sold or get it licensed.
1:00:06
And they they share in the profits because the blockchain
1:00:08
Ledger shows that they were part of the contribution of that data
1:00:12
set. And then it just becomes a
1:00:14
circular economy where I've got my researchers creating.
1:00:17
Information and in the middle, of course, is an artificial
1:00:19
intelligence agentic AI and other technologies that are
1:00:23
helping my doers do more. And so that that's really the
1:00:27
the big thing. So I think a year from now what
1:00:30
I would be celebrating was the fact that I that I created a lot
1:00:32
of opportunity for doctorates, pH, DS, operations leaders and
1:00:36
managers. I like it.
1:00:39
Chris, I've just got one more question for you.
1:00:40
But before I ask it, I want to let people know the best way to
1:00:42
get in touch with you or follow your story.
1:00:44
What would that be? I'm pretty heavy on LinkedIn.
1:00:47
LinkedIn is is you know, it's one central source of truth
1:00:50
typically on a human. So you can check me out Chris
1:00:54
Chambers comma MBA. I should be one of the one of
1:00:57
the only, but if you need an additional keyword, check.
1:01:00
Pepperdine. OK, great.
1:01:02
And we'll include a link in the show notes for everybody.
1:01:04
But last question for you, Chris.
1:01:06
Looking ahead, do you foresee a major shift in how venture
1:01:10
capital firms are going to collaborate with AI driven
1:01:13
organizations? I think there's there's still a
1:01:17
lot of buzz and a lot of excitement.
1:01:19
One of the things that the low and no code movements have done
1:01:21
is it made it very easy for almost anybody with an idea to
1:01:25
get that idea to market. It used to be you used to have
1:01:27
to have a technical Co founder. You had to have some coding
1:01:29
capabilities and if you didn't have it, you couldn't get in.
1:01:33
And so I could see venture capital continuing to put money
1:01:36
behind AI companies because lo and no code will give more
1:01:42
ideas. Birthright at the end of the
1:01:44
day, AI and computer science, whatever, they're all mediums of
1:01:48
delivering value, right? Value hubs value customer value
1:01:52
all around whether or not there's a value consumer and is
1:01:55
there a value hub and you know Amazon consumer Amazon
1:01:59
marketplace, right that and in between that is a lot of
1:02:02
technology. So this is going to be an
1:02:05
incredible time that venture capitalists are going to be
1:02:07
looking for the idea creators in the world to leverage AI because
1:02:11
people just love the term AI, right?
1:02:14
You don't really even have to be a true AI company.
1:02:17
You can just have AI in your, in your company name or something
1:02:20
about it being AI driven and people love that.
1:02:23
And so the other thing AI is going to do is because of the
1:02:26
speed at which these these machines move, we're going to
1:02:29
see an extraordinarily fast shifting marketplace, right?
1:02:33
So venture capitalists are going to have to look at, at their
1:02:36
portfolios and rebalance them often because there's, there's
1:02:40
always somebody coming out with a new AI thing, right?
1:02:42
And and that's one of the core reasons why my autonomous
1:02:46
operations concept, generative AI and agentic AI are one of
1:02:50
eight technologies that we are integrating together to power
1:02:53
the operations of a business. Operations being product
1:02:56
operations, marketing, operations technology
1:02:59
operations, business operations, finance operations, customer
1:03:03
service. There's a lot of operational
1:03:04
components to a business. So we didn't want to centralized
1:03:07
around one particular technology rather than it be a set of
1:03:11
components because not every department, not every use case
1:03:14
needs generative AI or agentic. Chris, it's been fascinating
1:03:19
conversations. I want to be the first one to
1:03:21
thank you for sharing both your time and your story with us
1:03:24
today. I really appreciate it.
1:03:26
Thank you so much for having me, have a great day.
1:03:28
Folks, that's a wrap on another episode of the Inspired Stories
1:03:31
podcast. Thanks for learning with us
1:03:33
today.