Latest / AI for Founders with Ryan Estes / Are you building a team of AI passengers coasting toward irrelevance?

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Are you building a team of AI passengers coasting toward irrelevance? · AI for Founders with Ryan Estes

0:00
Stop hiring AI passengers. They're dead weight.
0:03
If your team says well this is what AI thinks, fire them.
0:07
Here's why. 2 billion people use AI every day, but they won't pay
0:10
a dime for it. That means enterprise has to
0:13
foot the entire bill, and right now they're hemorrhaging cash on
0:16
$30.00 a month seats that nobody's actually using.
0:20
The brutal truth? CEO spend a year bragging about
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being AI first while only 10% of employees touch the tools.
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The rest are paralyzed, terrified.
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They're automating themselves into unemployment.
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But the smartest founders aren't just deploying AI, they're
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building companies where 32 people do the work of 45.
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The secret weapon? They demand AI drivers, not AI
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passengers. No cutting and pasting, no AI
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said to do this. You own the decision, you own
0:48
the conviction. Because when you hit turbulence
0:51
and you will get a rally behind, well, the chatbot thought it was
0:54
a good idea. So ask yourself, are you
0:56
building a team of AI drivers who own their outcomes or AI
1:00
passengers coasting towards relevance?
1:03
Welcome to AI for founders. I'm Ryan Estes, you just learned
1:06
the enterprise adoption playbook from Gregory Shove, 7 times
1:10
founder, 250 million in exits and the guy solving billion
1:14
dollar question. How do you get big companies to
1:17
actually use AI? You can get the full playbook at
1:20
AI for founders.co.weeklyinsightsthathaveyourcompetitorswonderinghowyoureshipping.threetimesfastersubscribebeforeyourcompetitiondoesthatsthenewsletter@aiforfounders.co.
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That's Kickcaster by MO Burst. All right, Greg, welcome to the
2:18
show. Thank you so much for joining
2:19
me. Thank you, Ryan for the
2:21
invitation. Glad to be here.
2:23
It's my pleasure and there's a lot of ways this conversation is
2:26
going to go, but I I saw a great entry point because I saw you
2:29
did an episode. I listened to some of it with
2:32
Code story, Noah's a buddy of mine and one thing stuck out.
2:36
It said it talked to you're an enthusiast for Argentinian BBQ.
2:43
Yeah, I am. I just love cooking over wood.
2:47
I just think, you know, I love fire.
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I love the smell of fire and wood.
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And so my last, my last couple of is not the current one.
2:55
I just moved to San Francisco, but the last two or three houses
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I've built these Argentinian grills.
3:02
And yeah, you know, I just think it's great to, it attracts
3:05
people and you hang around the fire and and it just beats
3:09
propane. Yeah, I'm with you, man.
3:11
I'm a big griller smoker, you know, I love it.
3:14
Mrs. Estes is like, when's dinner?
3:16
I was like meats out at 11, you know, like what are we talking
3:19
about? Get some appetizers.
3:22
We'll be OK. But I, I wasn't aware
3:24
Argentinian BBQ. So this is like if I'm used to
3:27
like, I like Texas BBQ, you know, and so you've got the big
3:30
drums and you know, it's, it's all condensed.
3:33
Like, is it, is it correct to assume the Argentinian BBQ is
3:36
like it's open? It's a more like a.
3:38
Yeah, yeah, big, flat, big flat, open grills at a slight incline.
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So the fat will roll down into a tray that collects the fat and
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it's got a big crank handle. You basically control
3:49
temperature by raising or you're lowering, you know, the grill
3:53
what's what's particular about Argentinian grilling if you're
3:57
using that kind of meat, which is really lean, right?
4:01
The equivalent would be sort of grass fed grass finished beef
4:04
here in the US is it's so lean you can't overcook it and you
4:07
can't use too high a heat. So typically low temperature,
4:13
you know, sear, sear fast and then raise the grill and cook
4:16
slow. Otherwise you're going to be
4:18
eating pretty tough meat if you're using that, you know,
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that kind of meat also, you know, also great for veg and
4:24
just I, we put everything, I'll do it, I'll do anything and
4:26
everything on the grill. Just get that smoke on it.
4:29
Love it. And you know.
4:31
It's cool. I, I moved recently, I moved a
4:33
little bit further down South into the sticks and I had to
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make a tough decision because the kitchen is kind of on the
4:38
second level and there's like a little balcony out there.
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So I was going to put like the, the whole rig down there, but I
4:45
kind of lazy, you know, so I don't want to necessarily like
4:47
be going up and down the stairs. So I was like, well, got this
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balcony. So I got an electric smoker, an
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electric griddle, which is kind of sacrilege, you know, to the
4:58
people of Argentina, like, please forgive me for that, but
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man, it actually puts out a pretty good.
5:03
Product. Yeah, No, I I had an electric
5:04
smoker for a while. For a bunch of years.
5:06
I loved it, I thought. It was great.
5:08
Great. Yeah.
5:08
You know the reverse here. It's no problem at all.
5:11
Yeah, yeah, totally great. Greg, let's kind of take a dive
5:16
and. Now let's talk about AI exactly.
5:19
I want to talk to you about AI BBQ smokers.
5:22
There you go. Yeah, I got, I've got, I got a
5:25
business plan. I'm looking for some seed
5:26
capital. Yeah, exactly.
5:29
Amazing. No, I'm excited to have a
5:31
conversation with you largely because you know, you're, you're
5:34
well versed with multiple XS to the tune of $250 million from
5:38
what I could see from multiple startups.
5:40
And it looks like now you're doing some consulting work,
5:43
particularly with, with enterprise companies.
5:46
And I, I think that the challenge right now with larger
5:49
organizations, more established organizations is we all saw 2025
5:53
exploding with all this opportunity and threats from AI.
5:57
But now it's starting to be like, OK, how do we, you know,
6:00
keep guard rails up? And how do we integrate some of
6:02
these tools into the workforce so our people can be more
6:05
productive and kind of create new things?
6:07
So that it's a, it's a perspective we rarely have on
6:10
this show of kind of like what is enterprise adoption looking
6:14
like? So maybe we could just start
6:15
there. Yeah, I mean, the, the, the, the
6:18
short answer is not good. So let let let's zoom out just
6:21
for a SEC, right? 2 billion, two billion consumers
6:24
use and love AI. So that's amazing.
6:27
They just don't want to pay for it.
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So, you know, enterprise has to be the, you know, the majority
6:33
of revenue for AI needs to come from enterprise.
6:35
If enterprise doesn't pay for AI, these AI companies won't
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make it. And all the investments that
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they're making in data centers and, you know, this build out
6:43
is, is not going to get returned if we can't convince enterprise
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to turn on AI in a significant way beyond the pilots and the
6:49
testing and the, you know, all that stuff that's going on.
6:53
So, yeah, this is a really, I think it's a Crucible moment.
6:56
I think about this, Ryan, as does the money run out, meaning
6:59
all the investor capital going into AI, does that run out
7:03
before or after, you know, enterprises are willing to pay
7:07
for it? Because again, the AIA companies
7:09
made a strategic decision, including Open AI, ChatGPT and
7:12
Tropic, all these guys. They made a really important
7:15
decision as relates to the consumer business, which was
7:17
they give away free AI. So once you give away something
7:20
that's great and free to consumers, it's pretty hard,
7:22
right to start charging for it. Or of course, you do what Open
7:24
AI is doing, which is you got to jam some ads.
7:27
So consumers get it for free, enterprise got to pay.
7:30
And here's the issue with enterprise.
7:32
We've been telling workers, the workforce for the last three to
7:35
four years that AI is coming for their jobs.
7:38
So like, it's not rocket science here that a lot of people aren't
7:42
exactly anxious to be using AI tools at work because they've
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been told and by by the way, their CEO is running around
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bragging to Wall Street and, you know, a cocktail parties and on
7:51
the golf course that they're going to be have all this AI and
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they're going to stop hiring and or they're going to shrink their
7:57
workforces, which I don't think is happening and probably won't
8:00
happen nearly to the extent that we think it will in terms of job
8:03
loss. But we've certainly freaked out
8:05
a lot of a lot of our workforce around, you know, do I really
8:09
want to outsource all this work to AI?
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And if I do like what's going to happen to me?
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So we're at a we're in a moment where there's a lot of anxiety.
8:16
There is low proficiency. So it's a kind of a, it's kind
8:19
of the perfect storm of workers are anxious and they're not very
8:22
good at understanding how to use AAI for work.
8:25
They're great at using AI at home, right?
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Travel planning, parenting advice, you know, health advice,
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you know, we, we, we're all using AI that way.
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We bring it into work, say go out, how, how do I use this?
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And you know, is it reliable? And you know, will I get fired?
8:40
So I just think we, we got to get through this moment and it's
8:42
all about, man, I forget CE OS, you know, CE OS, they're
8:45
delusional. They always are.
8:48
It's all about managers. Yeah, you know.
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People who run the teams, you know, and they, the change needs
8:54
to happen there totally, you know.
8:56
Let's hang on this point for a minute, too, because this was a
8:58
big theme of 2025. So much hysteria.
9:01
And when you see it in hindsight, you see like, it was
9:03
really kind of like dishonest, you know?
9:06
It's like the first thing was like AI so powerful that only we
9:12
can have it and we have to try and create regulatory, you know,
9:15
bodies around open AI because if anybody else gets this
9:18
technology, the world's going to end.
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Now. Not to say that there's not some
9:22
some dangers, but really they're just looking for like, you know,
9:25
legislation from the government to increase their monopoly.
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Or to the second point, AI is going to take our jobs.
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It's going to take all of our jobs.
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Well, that was also kind of in hindsight kind of a play to get
9:38
the expense for some of these tools off of like the PNL for
9:42
like marketing or whatever technical tools and rather bring
9:45
it over to HR like, oh, it's not that you're spending 100,000,
9:49
it's that you're saving 400,000, you know, and so.
9:53
Yeah, I know. That's right.
9:54
I mean, I think, I think, yeah, we've had these narratives,
9:57
these the narratives that the AI industry have been using for
10:01
their own gain, right? Which is either fundraising,
10:03
like, like this idea of artificial general intelligence,
10:05
AGI, like who, who, what the hell is that anyway?
10:08
And we don't even know what it is.
10:09
And, and do we really care? Probably not yet and maybe we
10:13
will one day, right? But again, it's a fundraising
10:14
narrative or it's, you know, we're all going to lose our jobs
10:17
and we're going to be on the beach collecting Ubi.
10:19
Check. That's another narrative, right?
10:21
And so on and so on, right. And I think the reality is these
10:23
tools are pretty good. These technologies are are
10:25
getting better and yeah, they can help us do knowledge work.
10:29
And so we should probably figure out how to put them to work and
10:33
and hopefully gets a little bit of productivity out of it.
10:35
And then quite frankly, figure out what to do it with the extra
10:38
hours that we gained. Like if we can save a few hours
10:42
a week with AI doing grunt work for us, that's great.
10:45
Then, you know, like, what do you want to do with that time?
10:47
Maybe you want to go walk the dog or, you know, make dinner
10:49
for the kids. That's cool, you know.
10:52
I was hoping it was going to be that, but it it turns out it's
10:55
not. It just means I'm going to do 10
10:57
times more and now I have like. I'm going to do more work.
10:59
I'm just going to get more work done.
11:02
Yeah, like, exactly. I mean, that's how I as ACEO of
11:05
a, of an AI company where everybody uses AI all the time.
11:09
My point of view is if I do, you know, if you just save 10 hours
11:11
a week, Yeah, Work, work, you know, please 10 hours.
11:14
Yeah. Don't, don't, don't go walk the
11:16
dog or take a yoga class. Totally.
11:18
But you know, it's easier for startups, right?
11:20
Because there's always, if you're a startup or a small
11:22
business or a side hustler or an entrepreneur, your To Do List is
11:26
endless, endless. So you really don't want, you
11:29
don't hesitate trying to try trying out AI because if AI can
11:32
be helpful to you, you know that's great because you got
11:35
like an endless To Do List, right?
11:37
There's always too much to do. If you work in a big company,
11:39
it's different, right? You've only got so much to do
11:41
each week. And again, that's where the
11:43
anxiety comes from. You know, every marketing
11:45
department will likely be smaller in three to five years,
11:48
right? That is a reality.
11:49
I think of AI. I don't think it's going to be
11:51
wiped out, but I do think we'll see contraction in some of these
11:54
functions. Having said that, Ryan, I think
11:57
if AI makes you more capable, I think we'll see more hiring.
12:01
Like it's software engineering to me the perfect example.
12:04
You know, we now know AI makes software engineers way more
12:07
productive, right? I want more software engineers
12:10
then I don't want less. Like find ACEO, whoever says my
12:14
road map is on schedule and like everybody's hitting it's their
12:17
deadlines for developing new products and new features.
12:20
The answer is never. That's never happened, right?
12:22
So if AI makes engineers more productive, frankly, I want
12:24
more, I want more AI enabled engineers.
12:26
If AI makes salespeople more productive, give me more
12:29
salespeople, not less. Like salespeople are revenue
12:31
generated, so I want more revenue, right?
12:34
So I think again, I think this whole thing about job loss way
12:36
overinflated. And I, and I do think we'll see
12:39
more hiring in certain functions because AI makes those people,
12:43
you know, that much more effective, you know, more
12:45
productive. Agree, you know, and it gives
12:47
people agency across every single domain, which is just
12:51
another a new entry point for people to become engaged.
12:54
So if you look at like a couple years ago, the big tech layoffs,
12:57
you know, that just kind of it was more of a correction really.
13:00
I mean, I remember at during COVID, I saw this video and it
13:04
was 2 project managers and these two young gals and they're like
13:09
at the pool and they're like, yeah, we're project managers and
13:12
they're, you know. Yeah, yeah, yeah, I remember
13:13
that. You know, and, or there's like
13:15
that classic job or that classic book bullshit jobs where it was
13:19
just talking about the the glut that happens.
13:22
And like, you know, tech will kind of ebb and flow.
13:24
And when it's it's flowing, like they're just scooping up talent
13:28
and they're just kind of sequestering them somewhere.
13:29
They're like, we don't really. Have they're parking talent.
13:32
Yeah, yeah. We don't really have a job for
13:34
you. So just kind of do stuff I don't
13:36
know, show up or don't like, whatever.
13:39
Yeah, make, yeah. Make decks, yeah.
13:40
And so you have this whole portion of the workforce that is
13:44
underemployed, which is kind of the most soul sucking, demeaning
13:47
thing you can possibly have. Where where now if you have new
13:50
tools, it's like, oh, there's whole new sectors of kind of
13:53
work that can be done. Not only that, but you can work
13:55
at an incredible clip and you have like the because the pace
13:59
is so high, it's very satisfying, you know, so.
14:02
Yeah, no, I agree. I think it's great if you're
14:04
ambitious. I think these tools are great.
14:06
I mean, to your point, I called jumping capability boundaries.
14:09
I think AI allows people, people and teams and then even
14:13
companies, right, to jump what I call capability boundaries.
14:16
Meaning, you know, we've got limitations right around our own
14:18
skills, our own experience. And I think AI just allows us to
14:22
flex and allows A-Team to flex and then a company to flex,
14:25
right? You can just put you can punch
14:27
above your weight class with AII think it's the best thing for
14:30
young people. This idea that AI is
14:32
disadvantaging young people and some of the data seems to be
14:36
indicating that in terms of hiring data, I'm not sure it's
14:38
all AI related, but listen that that could be the impact of AI
14:43
in in part. I actually think the opposite
14:45
thing. You know, I think it's great for
14:46
young people. Like young people don't have a
14:49
lot of experience and really only have, you know, a small a
14:52
less number of skills, right, less skills.
14:54
And I think AI kind of, you know, bulks you up, you know, I
14:56
think it's testosterone for the brain.
14:59
So, you know, I encourage young people like just just let it RIP
15:04
with AI and kind of round yourself out, you know, be a
15:07
storyteller that can do data analysis, you know, be be a
15:11
product manager that can now build prototypes and walk over
15:15
to engineers and say, I got a working prototype.
15:17
I mean, I got a working app already like, you know, let's
15:19
that that kind of thing, like really flex.
15:22
Totally. It's a major.
15:22
It's a, it's a really because there is so much apprehension
15:26
around AI. Obviously we're evangelists
15:29
right around here. We live in a bubble.
15:31
But like, I really try and push that to friends and family as
15:34
much as I can. Case in point, our #1 fan of the
15:37
show, my daughter who's studying finance at Mizzou.
15:41
Shout out. But she, she, she listens to the
15:42
show. What's up, Boo?
15:45
But, you know, years ago when Chachi BT came out and the
15:47
students immediately jumped on that, you know, it's going to,
15:51
it's going to either destroy education or just make education
15:54
change forever. And I was talking to her about
15:57
it. You know, I, I get her the pro
15:58
model. I'm like, OK, sweetheart, like
16:00
this is what it is now. Like no longer are you a writer,
16:03
now you're an editor, you know, so the the first thing you want
16:07
to do is be able to beat the test so that if they can tell
16:11
that you know, you're writing with ChatGPT and they and they
16:14
banned it, then obviously you're going to pay a penalty.
16:16
So there's a little bit of game theory here, but from standpoint
16:20
of her education, which I'm obviously very invested in, I
16:22
think the editing process actually is better and more
16:25
stimulating for the brain, at least for me.
16:28
If I'm sitting down to try and write something with a blank
16:30
page, I'm going to get stuck on the first paragraph first thing.
16:34
Rather, if I can begin with the 1000 words and begin editing and
16:37
being like arranging and adding anecdotes and things like that,
16:41
I'm much more engaged with with the material itself, you know,
16:45
so. Yeah, I, I, I, I agree, Although
16:47
I think, I think it's early to say meaning like there's a risk,
16:51
right, that we do cognitively offload too much.
16:54
And I, I think it's almost like, yeah, we're going to have to
16:58
experiment and learn into this. Like the whole, the whole all of
17:00
humanity will, right. I mean, this is a giant
17:03
experiment, right? On, on, on, on on the human race
17:07
is how I think about AI and we are going to have to be open
17:09
minded around what works and what doesn't work.
17:11
There are some days where I want to struggle with the blank page,
17:14
Ryan and force myself to get some thoughts down.
17:16
There are other days where like I don't want to and you're
17:18
right, I think it's faster and probably better to, to kind of
17:21
get the 1000 words and start editing.
17:23
It's probably a mix. I, I, I do think in, in five
17:26
years, just like we've done with social media and, and, and
17:28
devices and, you know, our phones, we're going to want, you
17:32
know, AI free days or AI free hours or AI free weekends.
17:36
I think these, these technologies are very addictive.
17:39
And, and listen, we know what Silicon Valley is all about.
17:42
It's, you know, the subscription business model, which is what
17:44
most of Silicon Valley now lives off and not just Silicon Valley,
17:48
of course, Netflix, Amazon Prime, but you know, it's
17:50
subscription is a, is a fancy word for addiction.
17:54
And so, you know, these AIS, the makers of these AIS are going to
17:58
make them very seductive. And you know, we're going to
18:01
love working with these AIS. We, we do already, right.
18:04
So we're going to have to watch that.
18:06
I don't want, I describe it, Ryan, as AI drivers and AI
18:09
passengers. And we're very clear when we do
18:12
our hiring and onboarding of new employees.
18:14
And then, you know, we, we have this conversation a lot
18:17
internally with the team around do not be an AI passenger.
18:20
If you're just cutting and pasting or lightly editing, you
18:23
know, work from AI, that's not going to end well for you or,
18:26
or, or be good for the company. So don't do that like you are be
18:29
an AI driver. AI drivers, you know, you own
18:32
the task, you own the output, you know which AIS to use, you
18:36
know how to use the AIS and all that, you know all that.
18:39
But it don't be cut and pasting AI and it's not if you're going
18:42
to work for me, you know, it's, it's a one strike.
18:46
We have a one strike. Cultures relates to that.
18:48
And when when someone says to me, this is what AI thinks, or
18:50
we're going to do this because it's what AI says, I'm like, OK,
18:53
I don't pay you to tell me what AI says.
18:54
I I can get that right for free. So, yeah, you know, we we got to
18:59
figure this out, which is, you know, what's that, what's that
19:02
blend right of, of man and machine.
19:07
Love it. That seems like a sticky area.
19:08
I want to do that but you said something too that kind of
19:10
brought something up. You said subscription is a fancy
19:14
word for saying addiction. You know I am subscribed on
19:18
Amazon to post oak chips, so you know, maybe that's my BBQ
19:23
addiction. You know what?
19:25
I yeah, you got, you got, you got BBQ addiction right there.
19:27
Dude, listen, we all, we all, we all have our addictions.
19:30
That's not a bad one considering the other options.
19:33
But yeah, listen, you know, subscription business models are
19:36
about, you know, you got you, you know, we want you to hang
19:38
around for seven plus years. And so, and if you don't have
19:42
this, that kind of business model, you know, obviously
19:45
Instagram and social media is not, it's an ad model, but it's
19:49
sort of the same. You know, you're looking for the
19:51
same outcome and the outcome is we want you coming back a lot.
19:54
This is why GPT is so seductive when it says, hey, that was, you
19:58
know, a Greg, you're so smart, you know, that was real.
20:01
You know, you're really top of your class.
20:03
And B, do you want me to make the PDF?
20:05
Do you want me to create the presentation?
20:06
Do you want me to do the, the next thing?
20:08
Right? It's like, yeah, sure, go ahead
20:10
and do that. Or, you know, the AIS are
20:12
getting very good at just keeping us engaged and saying,
20:16
well, I'll keep working with you on this and give you more value,
20:19
which means you'll spend more time and, you know, you're just
20:21
developing A cognitive dependency.
20:25
Again, that's a fancy word if you went to college for
20:28
addiction. And so that's, you know, that's
20:31
what's going on here. And we got, we just got to be,
20:33
we got to be careful about it. Having said that, that it's not
20:36
a reason not to use the technologies, right.
20:38
We can't we, we got to find the middle ground.
20:40
We and too much of the AI conversation is it's a disaster.
20:43
It's terrible. It's going to be addictive and
20:45
or like stay away from it, you know, don't touch it.
20:48
It's you know, that's not, neither are accurate and the
20:51
right extreme. We need to find this middle
20:53
path, being pragmatic, being optimistic, you know, being
20:58
focused on getting something good out of it and managing some
21:01
of the downside, right? It seemed like we kind of
21:03
touched a nerve there though as far as the cognitive offloading,
21:06
particularly when you said like, hey, we got a one strike policy.
21:08
I don't want to know what AI said.
21:10
So that seems pretty raw, you know, So what is it about
21:14
cognitive offloading or just like getting a summary from AI
21:19
where you're like, this is this is not passable.
21:21
What, what are you looking? What if the AI is better than
21:24
what their idea was? Yeah, that's a, that's a fair,
21:27
that's a great question. And and we can imagine where AIS
21:30
do get that good, right. And this this of course, is what
21:33
the big tech is telling us, meaning or Silicon Valley, like
21:37
AGI is, is this moment where the AIS are actually smarter than
21:40
us. So it would be irresponsible, in
21:42
fact, to listen to the human. You should be listening to the
21:45
AI because the AI is so smart. Listen.
21:46
Yeah, but we're not there yet. And my, my perspective is AISAIS
21:50
don't have the most recent data. They don't have context.
21:54
They don't know what's most important to us in the moment.
21:56
And so, yeah, you can get AI pretty close, I think to and
22:00
will certainly be helpful. But when it comes to making a
22:03
decision and, and it might be the decision that AI concurs
22:07
with or even suggested to you, but I want you to own it.
22:10
So it's, to me it's all more about ownership as it is what is
22:14
the decision or the idea? Because I don't want it to be
22:16
like, I, I kind of sort of believe this and it's a is idea.
22:19
So this is the one I'm submitting.
22:20
Like, no, that's, I mean, I'm paying you to make the decision
22:24
and to have the conviction. Because by the way, most
22:26
especially in a small company or startup, most of this shit
22:28
doesn't work out. So as soon as you bump into
22:30
some, you know, at turbulence or, you know, or, or, or some
22:34
bumps in the road, I need you to have conviction that you're
22:36
doing the right thing. Not that you know AI told you to
22:39
do this because you're going to have to work it out and figure
22:41
it out because you know, it's never, never a smooth Rd.
22:44
It's, you know, AI slop. It's like, it's like that
22:46
classic definition of pornography.
22:48
It's like, I know when I see it, you know what I mean?
22:51
It's something's like artistic and then something's pornography
22:55
and like AI slop is very similar and I, I don't know exactly what
22:59
the attributes are, but I can tell if someone's passing AI as
23:02
instead of using it has this weird domestication to it.
23:06
You know, like, you know, like a Bunny, a wild Bunny gets
23:09
domesticated. It's like ears fall and it's
23:11
eyes dull. Like you get that and you're
23:13
like, no, you know, this is not it.
23:16
You know what I mean? Yeah, that's, that's, that's a
23:19
great way to think about. I think that's right.
23:20
And so like don't, don't pass that work off, you know, or do
23:25
so at your own risk. You know, quick, quick story for
23:28
you too, that with the cognitive offload, that was something I
23:31
was holding really close to the chest too, is like, I want to
23:33
make sure I'm retaining my ideas and you're precious to me as
23:37
well. They should be.
23:38
And I started to loosen up a little bit.
23:41
Largely I'll tell you a story. So we're we're flying to San
23:44
Diego through with the layover in Vegas for some reason.
23:47
And I was talking to my son. He's like, oh, dad, you're going
23:49
to gamble. We're going to be there for two
23:50
hours. I'm like, no son gambling.
23:53
It's not a good look. It's not a good look, you know,
23:55
it's the I'm not into it, blah, blah, blah.
23:56
I'm lying a little bit, you know, and I'm like, you're being
24:00
a parent. Yeah, yeah, you're being a good
24:01
parent, Yeah. I was like, watch, I'll show you
24:04
how fast you can lose 20 bucks. OK, I'm like, I'll go over
24:08
there, just follow me over there.
24:09
I'll go to the the slot machine or whatever.
24:11
I'll put 20 bucks in and watch how fast it goes.
24:13
I, I put it in Ding, 2000 bucks. I was like, Oh no, this is the
24:18
worst lesson I ever showed you. Like this, This never happens.
24:22
What you don't want to do is when the first time like this is
24:24
bad for you. But you know, the where with the
24:27
kind of analogy is, it's like once I kind of loosened up a
24:29
little bit and I was like, no, actually I'm just going to go in
24:32
with a set of parameters and have a conversation with Claude
24:35
or, or open AI or whatever and just be like, Hey, help me think
24:38
through these things. So like kind of dispersing some
24:41
of that cognitive load. And what I found is like gets
24:44
every once in a while you hit that jackpot and you're like,
24:46
oh, actually I didn't think about that.
24:49
Now let me take that and put it in this direction.
24:51
So you become kind of an arbiter, you know, like there
24:55
was like. A.
24:56
There was like a saying that like, art in the future won't be
24:59
actually producing anything. It's just acknowledging when art
25:02
exists, you know, right? Like, yeah, judging what is art
25:06
and what is not. Yeah.
25:07
I think, listen, I think this idea of AI as a thought partner,
25:10
I think that to me, it's one of the most valuable use cases for
25:13
AI and in all parts of our lives.
25:15
And I think you're right. Listen, AI, we all have blind
25:18
spots, right? We all have bias.
25:19
We all have a set of experiences.
25:20
We all make decisions a certain way based on who we are in our
25:23
lived experience. And I think AI doesn't have any
25:26
of that in a way, right? It doesn't have the blind spots
25:29
that we do or that it's got bias for sure.
25:31
But sort of like it can approach any decision, I think from any
25:33
angle, especially if you prompt it the right way.
25:36
And absolutely, you know, I think, I think almost every
25:39
decisions can be, can be optimized a little bit, probably
25:43
with AI and or just increase your own confidence in your own
25:47
decision. Sometimes AI is useful for that,
25:49
like you've got the right idea and AI can kind of help you
25:52
confirm that before you go pitch it maybe.
25:55
Yeah, AI is thought partner, great use case and I think
25:58
undervalued in business actually.
25:59
Yeah, 100%. Now I want to talk about section
26:02
a little bit and you guys see Greg's work at sectionai.com,
26:07
which is really kind of like this enterprise level AI
26:10
integration. So when you're brought on
26:12
initially, what are kind of the pain points that like managers
26:15
are coming to you to say is there, is it, are there hands in
26:17
the air? They're like, what do we do or
26:19
like? Yeah.
26:20
I mean it's, it's, yeah, our prospects, so to speak.
26:22
You know, our two best, our customers come from 1:00 and
26:24
2:00 two places. One is they've just signed or
26:27
about to sign an enterprise wide or team wide agreement with
26:31
ChatGPT Enterprise Oregon, Microsoft Copilot or you know,
26:33
Google demo. We don't care, right?
26:35
And so they're about to make that big investment for 12/24
26:37
months. In some cases, they've signed a
26:39
multi year agreement and you know, and these LLMS, these AIS
26:42
are 30 bucks a month, you know, per employee basically.
26:45
So it adds up. Or they did that a year ago.
26:49
And what happened in the in the subsequent year is not much, you
26:53
know, And because what, and here's what happens, every
26:55
organization's got 10%, even the federal government of, you know,
27:00
growth mindset, ambitious, typically younger, but not
27:04
always, you know, employees that want to figure this out there
27:07
that we have that level of self motivation.
27:09
So they get there pretty quickly.
27:10
It might be 5%, it might be 15 in a startup, it's probably 70%,
27:15
right? I mean, like you go faster or
27:17
slower based on, you know, what kind of organization you are and
27:20
what your culture is. But basically anybody can get,
27:22
if an organization can't get to 10% of AI adoption, they've got
27:25
a much bigger problem, right? But assume they can get there,
27:28
but they get stuck there. So almost all of our prospects,
27:31
our clients are people that are, are just not getting the value
27:34
yet that they expected. And they got ACEO again, running
27:37
around saying, you know, we're AI, we're AI first, we're AI
27:40
enabled. We're going to slow hiring,
27:42
we're going to grow with less headcount, all that stuff, you
27:45
know, and, but the reality is no, after that 10% to get to 30%
27:50
or 40 or 50%, you know, active monthly, weekly, daily usage,
27:54
it's a lot harder. And because what the, for the
27:56
reasons we talked about anxiety and, and kind of resistance and
27:59
not knowing how to use AI at work.
28:01
So that's, that's when we get called and we go in, we
28:03
basically work with clients and we have a software and services
28:07
model. So I think this is the right
28:08
model for enterprise AI. I think any startup now just
28:12
selling software only to the enterprise, it's a tougher sell
28:16
because he's, he's, these companies don't know how to
28:18
deploy AI, don't know how to buy it, don't know how to get it
28:20
working. So I do think the right model if
28:22
you're an entrepreneur is some sort of, you know, software or
28:25
even just straight service, like, you know, be be someone
28:27
that helps companies use AI and get value from it.
28:30
Those are great businesses right now in terms of unlocking kind
28:34
of dollars, enterprise dollars. But yeah, so we go in and help
28:37
companies, small would be a small client for us would be 100
28:40
employees and a big one will be, you know, thousands or 10s of
28:42
thousands because they're, they need to get going.
28:46
You know, if you're spending 30 bucks a month per seat, you
28:49
know, per employee for ChatGPT, for Enterprise, you know, you
28:54
want to get value from that, you know?
28:56
Is the the the big push kind of like technical capabilities,
29:01
like saying, hey, here's use cases specific for you, here's
29:04
use cases specifically for you? Or is it more of like a change
29:07
management, which is kind of easing the humanness of it,
29:10
where people do have anxieties and you know, they're they're.
29:13
I think it yeah, great question. It's both.
29:15
It's both. It is like learn how to use the
29:18
most powerful parts of the AI. So basically the reasoning
29:22
models and the problem solving. So not just the superficial sort
29:26
of summarize my e-mail. So like teach people proficiency
29:29
in sort of advanced capabilities and at the same time show them
29:34
how to apply that to their job. So we use software for that.
29:37
So our, our, the way we teach AI and upscale the workforce is we
29:41
use, we use AI and we built a platform called Prof AI, which
29:45
is powered by AI. So what's amazing about that?
29:48
And by the way, once you've had an educational experience that's
29:50
powered by AII, don't think it matters which one.
29:53
But once you've had one of those experiences where the, the, the
29:56
educational, the learning is powered by AI, it's immediately
30:00
personalized. And as soon as you have one of
30:02
those experiences, you're not going to go back and do it the
30:04
other way. You're not going to, you know,
30:06
watch courses on Udemy or Coursera or whatever like that
30:09
whole, you know, those days are gone.
30:11
And once you've have interacted with a coach or a tutor, an AI
30:15
coach or tutor and realize they can on the fly customize the
30:20
content, the curriculum, the exercises that you know, for you
30:23
based on who you are and where you live and the language and
30:26
the job, whatever. It's kind of amazing.
30:29
It's like, OK, this is the way learning should be, whether
30:31
you're in high school or certainly for enterprise, for
30:33
like, you know, worker upskilling.
30:35
It's like so much better. So yeah, we we help people find
30:40
the right use cases for their own job based on where they work
30:44
and what what role they're in. Incredible.
30:47
I would love to hear if there's examples of people who are
30:50
really killing it. You know, I've seen
30:52
organizations doing N 8 N automations that have like 80
30:56
steps and it's just like, my goodness, you know, and they
30:59
output like some really incredible stuff.
31:01
Or, or I've seen organizations that were doing a go to market
31:05
push by creating a product with multiple VPNs to, to go and do
31:11
commenting on YouTube videos. Like just the, the capability is
31:16
really kind of just like limited by your imagination of like the
31:20
different kind of experiments and the different kinds of
31:22
things you can do. Is there any organizations that
31:25
are kind of in that advanced phase where they're, they maybe
31:28
they've integrated into the LLMS and their workflows, but now
31:31
they're doing like big automations and actually
31:33
creating custom products in order to kind of like try new
31:37
experiments? Yeah, the, the, there are a
31:40
bunch and no one's talking about, you know what my take on
31:44
one of the challenges we have right now with enterprise AI is
31:46
no one's talking about it basically meaning if it's not
31:49
working, you're not really bragging about that, right,
31:51
Because it's not working. And that's a lot of, you see a
31:53
lot of the research around enterprise pilots that are
31:56
stalling out or not making it a production.
31:58
And if it's really working, like I've talked to clients of ours
32:01
who said you're under NDA, we'll tell you about this agent that
32:04
we built, but you know, you can't, we're not talking about
32:08
it. You're not talking about it.
32:09
Our CE OS not bragging about it because it is so transformative
32:13
to the economics of the business or to the you know what,
32:16
whatever it might be. So I think we are in a tough
32:18
moment. I the rally is most leaders,
32:20
even startups, you know, we, we tend to prefer to fast follow.
32:25
If you think about sort of from a business strategy standpoint,
32:28
you could make the argument that it's usually better to fast
32:30
follow rather than be first. You know, being first is, is
32:33
pretty risky. Sure, there's outsized returns
32:35
if you, if you're 1st and win, but you know, there's more risk.
32:39
So we, we tend to like to fast follow as leaders in, in
32:41
business at least. And that means we're waiting for
32:44
proof points, we're waiting for data, we're waiting for case
32:46
studies. And there aren't that many yet.
32:49
That's the reality. Because I think when it's
32:50
working, people are keeping it pretty quiet.
32:52
I'd say, listen, what's going on right now is, as you know, every
32:55
startup is starting today on the last couple years.
32:58
They're an AI first organization.
33:00
I mean that section where 3132 people, we're probably doing the
33:04
work of 45, you know, we, we, we feel it like not just me, but
33:09
you know, and because I'm the delusional CEO, but I'd say, you
33:12
know, my management team would, would say, listen, we're just
33:16
way more productive, you know, pretty much in every function,
33:19
sales, marketing, you know, software engineering for sure,
33:21
right? Because, you know, so, yeah, I,
33:23
I think what's going on right now is the, the success of AI
33:27
in, in the startup world in terms of it's, it's, it's
33:29
enabling founders to build companies and build products and
33:32
services faster with less, less headcount, less capital.
33:35
That's great. That's not yet applying to
33:37
bigger teams and bigger organizations.
33:39
That's the jump we have to make. Otherwise, you know, again, I, I
33:43
don't think these AI companies will get the revenue they need.
33:45
They need, but they need the Boeing's and the Walmarts and
33:48
the, you know, the big organizations paying for a lot
33:50
of AI. So it's, it's got to jump from
33:53
startups to to bigger teams and organizations.
33:56
It will. I think it's, you know, it's
33:57
going to take another couple three years, but it will well.
34:00
There's like just kind of change management of it.
34:02
There's a thought shift. You know, even as inundated I am
34:06
with this stuff, you know, I've got 4 terminal instances open.
34:09
I have Claude, Gemini and GBT are all going just ripping
34:14
through all this stuff. I talk about AI constantly, but
34:17
still like it. It's like being AI native it.
34:21
It really is a cognitive shift. I, I with my daughter, we put a
34:27
recurring meeting every two weeks where it's like, like
34:30
literally every problem you have.
34:32
I want you to think, how do I solve this with AI?
34:34
Just to like get into the habit of like there has to be
34:37
something better. For example, you know, we're
34:39
recording this on the script, which is an amazing, amazing
34:43
tool and editing tool. And I, I ran into this jam where
34:47
things weren't syncing up. And so I was like, fine, I'll
34:50
hit the chat bot. And of course it's, it's AI
34:52
driven. So you're, you're accustomed to
34:54
thinking the chat bot, you'd be like, I can't sync the blah,
34:57
blah, blah. And they're like, we'll get back
34:58
to you and you're chatting. But no, it's like, oh, I see the
35:01
problem. It's this, this and this.
35:02
You want me to fix it? And I'm like, oh hell yeah,
35:05
yeah. Fix it.
35:07
And it just fixes it. And you're like, Oh my God, you
35:09
know it. So, you know, there's.
35:11
Yeah, I think we're in this again.
35:12
We're in this really. I mean, I feel the same way as
35:15
you, meaning I feel like I'm always behind on my AI knowledge
35:17
and I'm in this all the time. And I run an AI company, right?
35:20
And, and there's always new tools and new plug insurance and
35:23
I got to get that working or I got, you know, I got to try
35:25
that. I think the, the pace of this
35:27
stuff is kind of overwhelming. I think most of us have day jobs
35:30
that aren't AI, right? And then we got to do things
35:33
like, you know, go walk the dog and make dinner for the kids.
35:36
And yeah, I just, I think we're in a moment where we're starting
35:40
to see its power in all these unusual places like that, that
35:44
your example, right? Or we use it for, you know,
35:46
medical advice or whatever it is.
35:49
And we like, we have these, oh, shit, this stuff is so good.
35:51
And it's just going to take a while for it to really
35:54
consistently appear in our lives at a high level.
35:58
And we do need the hallucinations to kind of get
36:01
fixed. I don't know how they get fixed,
36:02
you know, kind of thing like, you know, but it does feel like
36:05
to me the error rate or the failure rate still just a little
36:07
bit too high. It's certainly for mission
36:09
critical work. You know, it's fine for
36:11
brainstorming. You know, it's, it's, it's a
36:12
feature, not a bug if you're brainstorming.
36:14
But you know, a lot of the time I'm not brainstorming right.
36:16
I need the right answer or I need the right, you know, I need
36:19
the right data or the right, right output.
36:21
So, you know, I think the technology's got to get better
36:23
too. And yeah, it's, I mean, again,
36:27
it's just an amazing time. I, you know, I should be
36:30
retired. And I just felt like this was a
36:33
wave that I could not sit out. And and I think we do all need
36:38
to lead in this moment. So I appreciate you and your
36:41
audience because you know, this is a scary moment and A and a
36:46
lot going on it kind of not just in the world, but in the world
36:50
of work and, and the and the impact of these technologies.
36:53
And so we need people that will figure this out and then
36:56
frankly, help their neighbor, help their Co worker, help their
36:59
friend, right, help their kids. You know, I tell a lot of
37:02
parents, you cannot, you cannot outsource this to the school.
37:05
If you are a parent of a middle school or high school or a
37:07
college kid and you're kind of not having a conversation with
37:10
your kids about this, I think you're being irresponsible and
37:12
you're and you're not doing your job because, you know, you did
37:16
not. You shouldn't have outsourced
37:17
the conversation about sex, drugs, social media, you know,
37:21
or alcohol to someone else. And you shouldn't outsource the
37:24
conversation about AI to someone else, especially not the school
37:26
because they're overwhelmed. I mean, these poor teachers and
37:29
and administrators, you know, are like deers in a headlight.
37:32
They don't know what to do. So I think it is our
37:35
responsibility as parents to help our kids figure this out
37:38
and at least try to instill some habits in them that that mean
37:42
they don't just cut and paste from AI.
37:45
Unfortunately, Listen, the reality is Sam Altman made the
37:48
great the greatest homework helper in the history of
37:51
mankind. And that's just that's just the
37:55
reality of it. And so, yeah, education, when I
37:57
think about where AI is causing the most chaos, Ryan, like if I
38:01
had to pick like Ground Zero for the chaos on the ground of AI,
38:05
it's schools. It's really schools.
38:07
It is the homework helper, you know, that that we never could
38:12
have imagined. And it's just really tough, I'm
38:15
sure on on teachers and and administrators and so on.
38:18
So I have empathy for them. They won't move fast enough.
38:21
The unions will get in the way. Like they will create some of
38:25
the some of the chaos themselves by not really wrapping their
38:28
heads around this, especially at, you know, tenured teachers
38:31
and professors. But anyway, you know kids will.
38:36
Kids will find the path of least resistance because they want to
38:38
go party and have fun, not not write history essays. 100% All
38:42
right, We're it's time for the quantum round.
38:45
Are you ready? Yeah, I'm ready.
38:47
I don't know what it is. It's scary, but I'm ready.
38:49
It's terrifying, but at first I I do have to comment.
38:52
I I like that it was the the list is sex, drugs, alcohol and
38:57
AI that you have to have conversations.
38:59
It's in the fun rips, you know. It's in.
39:01
It's in Good. Exactly.
39:03
It's in good company, yeah. Exactly.
39:05
OK, cool. You mentioned retirement and one
39:07
thing I was looking at your resume, I was just like, God
39:10
Dang man 7. Times old 7.
39:13
Times Founders CEOs $250 million in exits, leadership roles at
39:18
Apple, AOL, Son Microsystems I mean, I already know the answer
39:23
to this, but what brought you back to the game?
39:26
I love the game, you know, I love the game.
39:29
I like building teams. I like building products and
39:31
services that don't exist. Yeah, I like, I like the
39:35
financial outcome potential at least it doesn't always happen,
39:38
but I like that. Yeah.
39:40
Listen, I'm, I'm, I'm too young to retire.
39:43
At least that's what I think. And I suck at golf and, you
39:46
know, and I can't BBQ all day long, right?
39:49
So yeah, you know, for the foreseeable future, you know,
39:54
I'll stay in the game, you know? Well, you got you slow cook.
39:57
You know you slow cook. Yeah, that's true.
39:59
Exactly. Cool, did I?
40:03
Did I read correctly? You have 130,000 newsletter
40:05
subscribers. I do, yeah.
40:08
Wow, how did you grow with that big?
40:11
I got lucky just, you know, this, this AI moment, you know,
40:15
just generate a lot of a lot of interest.
40:18
Yeah, so and, and we got some and we do a lot of events and
40:20
marketing. So we pick up we pick up
40:22
subscribers that way. I I think by the way, just
40:26
because I know you have a lot of founders in your audience.
40:28
I think events, both virtual and in person are really
40:33
underutilized. Enterprise go to market
40:36
strategy. You know, they are, they are
40:38
hard to do and they take time and effort.
40:40
And so they're not easy. I know that.
40:42
But I just think I just think in this moment of chaos and
40:46
uncertainty, like getting people together either face to face or
40:49
on Zoom and offering value when you do that.
40:52
I don't they don't think it has to be like really expensive
40:55
venues or anything. I can it can be casual, but high
40:57
quality. I just think that, you know,
41:00
your customers or prospects will value it and, and there's not
41:04
enough of it. So we're like we make a big
41:05
investment in virtual events and now in person events, small in
41:09
person events, you know, 5/10/15 people kind of like, you know,
41:12
like a little mini meetups. I just think they're great.
41:15
They're great for marketing for marketing and sales for
41:18
enterprise. Yeah, I selfishly I'm doing an
41:21
event for the first time, a little bit nervous about it in
41:24
April. Any tactical advice on selling
41:27
tickets? Yeah, just basically it's just,
41:33
it's what you'd expect underpriced over deliver, you
41:37
know, like just just or, or, or or, you know, price, right.
41:41
But over delivered just basically and and, and and the
41:44
over is one of two ways. Some of the little details
41:47
matter, so avoid some of the, you know, basic mistakes.
41:51
And then the second is have one piece of content or one guest or
41:55
one speaker, whatever it is, that just delivers a lot of
41:58
value. The whole thing doesn't have to
41:59
deliver a lot of value, but you know, if it's an hour, you know,
42:03
I have 15 minutes of high value, or if it's two hours, have half
42:06
an hour of high value. Some of our events are 6 hours
42:08
long and that's crazy. And a lot of our events are like
42:11
an hour. And of course, in person,
42:12
they're typically a couple hours, but just just share
42:16
something of value that they wouldn't normally get, you know,
42:19
and people will be grateful. And I think you know it, it
42:24
pays, you know they'll pay it forward.
42:26
I love that, you know, the pricing part is kind of
42:28
challenging because, you know, a plate of brisket and a beer is
42:30
80 bucks now. So where do we price stuff at?
42:36
I know it's tough. It's tough.
42:38
That's why we do virtual events. You got a much, you got to buy
42:41
your own beer and brisket. We'll just do the content.
42:43
You know it's. So disgusting someone shows up
42:45
for a virtual event just like stuffing their face and like oh
42:48
man. No, we we've seen it all.
42:51
Believe me, we are. So our events, we, we hosted
42:54
100,000 people online last year section did 100,000.
42:58
So we've seen it all. What do your online events look
43:00
like? They're, they're basically big
43:02
Zoom events and they're, as I said, if they're the short ones
43:05
are an hour or around an hour and they're, you know, workshop
43:08
lecture, a high value keynote kind of presentation.
43:11
And the long ones are several hours and it's a lot of guest
43:14
speakers. So basically 20-30 minutes.
43:17
And the whole, the whole thing is don't pitch.
43:18
So you don't pitch your product or service and when you have a
43:22
guest, don't let them pitch their Proctor service, right?
43:25
Really add value, you know, and if you do that people, if
43:29
they're interested, they'll find your website and you know,
43:31
they'll, they'll buy from you. When you're promoting those
43:33
events, is that mostly through your newsletter?
43:35
Where? How are you getting the
43:36
conversion? Yeah, newsletter, we, we do run
43:38
ads for the big ones to see. You know, sometimes it works,
43:41
sometimes it's too expensive in terms of cost, cost per
43:44
registration, right? Or you know, cost per attendee.
43:47
But mostly newsletter, some ads, and then obviously a little bit
43:50
of word of mouth. Love it.
43:52
Do you have a favorite or most catastrophic failure you can
43:56
share? Oh wow, so many.
44:07
You know, listen, in most cases, I've been able to pivot out of
44:11
my failures. And here's my lesson.
44:16
Don't always do that. Take the failure.
44:20
I think there's been moments, Ryan, I've been to failure
44:22
adverse, right, too concerned about reputation, too concerned
44:26
about having, you know, a loss, you know, on the record.
44:30
And so I've pivoted and basically launched a new
44:33
business out of that, what would have been a failure into the
44:37
kind of a pivot. And they've worked out actually
44:39
my pivots have been some of my better successes, but they're
44:42
hard and they're expensive in terms of time.
44:46
And, you know, because pivoting, you know, taking a team and,
44:48
and, you know, closing down a business and essentially taking
44:51
that team and building a new business, which is what I did
44:53
with section. It's pretty much a, it's, it's
44:55
100, you know, 100% pivot or, you know, it's a complete pivot.
44:59
Section was a different business 3, three years ago, completely
45:02
different business. So, yeah, you know, my lesson on
45:06
that for other entrepreneurs is sometimes just take the loss and
45:09
and again, your timing wasn't right.
45:11
Maybe you didn't execute well, maybe executed great and it was
45:14
just, you know, you couldn't get product market fit, whatever the
45:16
reason, just call it a day. And and you know, take a break,
45:20
you know, for a week, a month, whatever, or longer, depending
45:22
on, you know, how much of a break you need and then
45:24
regenerate, restart with a new idea.
45:27
So yeah, I've avoided some of my biggest failures by pivoting,
45:31
but I'm not sure I should have. You know, brilliant.
45:34
I love it. I just looked up and we're just
45:35
about at time. Greg, this has been awesome.
45:38
Before I let you out of here, I want to give a shout out to
45:41
stcode.org. Now, I know this is an
45:44
organization near and dear to your heart.
45:46
Will you just kind of give us a breakdown of what they do?
45:49
Yeah, they're just, they're just helping kids that don't get
45:51
access to technology and technology skills.
45:55
Bad access, they take technologies that are donated or
45:59
they, or, or they, they are able to acquire and they help kids
46:02
learn how to code and design and, you know, get jobs in the,
46:05
in the, in the, in the technology economy, right.
46:08
So I just think we need more diversity in Silicon Valley and
46:12
in general, we need more people into these, you know, higher
46:16
value, skilled careers. And so I love what I love what
46:20
Street Code's doing, and I love the leadership of the
46:22
organization. Amazing.
46:23
You know, and one thing that AI is definitely going to do for us
46:26
is it's democratizing technology.
46:29
You know, there was a guy who programmed an app on Lovable
46:32
while he's running a marathon the other day on his phone, you
46:36
know, so now everybody has a phone.
46:38
We're talking about 7 billion people, you know, so you can
46:42
start to build and and do things if with, with these kind of
46:45
opportunities. So yeah, shout out to
46:46
stcode.org. Greg, where can people find you
46:51
and connect with you personally online?
46:54
Yeah, Greg shove.com or they can find me on LinkedIn.
46:56
I'm Greg Shove on LinkedIn. You know, just reach out.
47:00
We'd love to hear from people. Greg, thanks so much for the
47:02
conversation. I'd love to have you on again.
47:04
I feel like we barely scratched the surface.
47:06
I think that's right. I'm happy to come back.
47:08
Ryan, great to meet you. Appreciate it.
47:11
Hey founders, let me ask you this, are you building with AI
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