Coté (00:00)
now Whitney, I know ⁓ that you are a very ⁓ kind, ⁓ joyful, pleasant person. But also because we're friends, I know every now and then you're kinda like, mm, fuck that guy. And

Whitney Lee (00:12)
Arguably

I'm like that more. I just know how to hide it.

Coté (00:18)
And and so

so so that's that's what I I in thinking about one of the things I wanted to ask and talk about with that with our guests this week. I was thinking, how does one manage being polite, ⁓ having having kind of like this persona of politefulness and joyfulness as like the a legitimate thing that they have and make sure you don't ruin it with your occasional real thinking? Like, do you is is there like a little person in your head that's that's like the gatekeeper?

Or do you train yourself? Like how do you get to the point where you're where you're generally a kind, loving person when you're interacting with people? But but you are also not naive inside.

Whitney Lee (00:59)
⁓ I mean the the real answer to that is probably ⁓ born in trauma and saying the wrong person to the wrong thing or to the person, one of the people who raised me and ⁓ getting in trouble for that. So yeah. Let's dig in. Strap in everyone. also I suppose you could say really mean and dark things as long as you laugh really big afterward and people seem to think get that confused with kindness too.

Coté (01:11)
Hm. Well this got serious really quick.

Mm. So there you go. See there you go. You've got a

you've got a tactic. And I rem I forgot about this, but I do remember the tactic of, you know, laughter is like a good it's a good de frosting on top of whatever you might be serving. So it kind of kind of level sets with people.

Whitney Lee (01:29)
Ha ha ha.

Mm-hmm.

And and if you make fun of yourself a bit, it helps you make fun of others or say darker things if you're if in a self deprecation cloak. Yep.

Coté (01:46)
That's true. That's true.

Yeah. Now now let me let me ask you this. We're our guest this week is John Willis, old old friend of mine, ⁓ all over the place. W I was gonna ask the Whitney of this, but if I can remember what the question is by the time the words escape my mouth, I think you, John, and and man, this sounds like terrible questions I'm asking, but you're not a very self effacing person.

⁓ as much as other people might be. Like you kind of don't show up and say like I don't really know what's going on here. This is wacky. Like but you also seem to balance like you don't show up and you're not like pedantic and like annoying like let me tell you how this works. And is that is that's is that something that you you you know or does it just happen?

John Willis (02:27)
Well yeah. Well it

No, I actually I have sort of I I am when when I need to be. ⁓ and so I might hope well, yeah, it is, it is and I've never that's a great way. Let me write that one down. That's good. I'll put that in my resume. But ⁓ the no, I mean what I do I mean I was you know, think about like ha some of the people that I've met that

Coté (02:39)
Mm, tactical effacement.

Whitney Lee (02:48)
Yeah.

John Willis (02:56)
Well, I'd like to learn more and and you know, I mean there's egos in this industry, right? Big time. I've been doing this for five decades, you know. I always think of the when do I have to do the Oliver, you know, please sir, may I have you know, ca have some more. You're like so you find somebody like, you know, I I want to like learn from this person. I want to create a relationship.

Coté (03:11)
Mm.

John Willis (03:20)
And so in those scenarios I come up and like, hey, I'm a bum duck, knuckin, I don't know nothing about nothing. But can I ask you about Capturum? You know, you know, and but don't don't confuse that I actually know anything about it. I just heard the word from somebody, you know. And you know, so yeah, I I you know, like and you know, and I get accused sometimes of being like, Yeah, hey, you're always kinda making yourself so I I do use it. You must you just don't see it from me because I mean

We have this you know, we've always had a mutual respect from almost day one, you know. So in that case, then I don't you know, why waste the oxygen?

Coté (03:59)
I I guess I guess there is a form as you're going over that's like signaling that like I I I wanna be a student here. Like I w I wanna learn something.

John Willis (04:05)
I like I used to do

a podcast with Michael and I d you know, he I'd say something, he'd say it way better. I'm like, That's great. I why didn't I say that in the first place? But yeah.

Whitney Lee (04:12)
Yeah.

Coté (04:17)
So so the the other the other thing, you've you've written several books and ⁓ I I was thinking, let's see, you you got yeah, let's you get the the famous IBM Red books, which for some reason we never talk about, but that's fine. And then you know, on and on into all sorts of DevOps things, a handbook, and then you got the dimming and the AI and and and the quantum. I think I and and I might even be leaving something out, but I was thinking ⁓

You know, in in in the the the book you have writing about kind of like the the life and the thinking of of ⁓ of dimming there, what is like what is something that like you don't know about what what what what he was doing that you wish you could kind of like go back and be a fly on the room and and learn about it? Like I heard someone asking a similar question today of an Egyptologist, and they were like, I I need to figure out ⁓ like

How Cleopatra like held on to control for so long. And it was like, whoa, I haven't heard that name in a long time. But like there must be scenes where people just like have no idea what was going on and you're super curious what was happening.

John Willis (05:20)
I think ⁓ there's a lot of people I wish, you know, I could you know, even people living today, right? You know. I you know, just since it's your show and we always go off the rails here, I wish I could sit down for fifteen minutes with Bill Clinton. You know, I I would just love to just sit in a room and discuss. But with Doctor Deming, I think I'm pretty sure how it would go. And again so y I'm blowing your whole theory about me that, you know

self effacing and all because I'm pretty sure he'd get real mad with me. And I'd have to go in and play the whole, you know, please, sir, can I have some more? I'm don't know anything. But ⁓ I I think that one of the things about Dr. Deming that was really, really fascinating is you know, we ta say certain people don't suffer fools, but like this guy didn't suffer fools. And his own grandson asked him a question one time and his answer was like one seventy two.

Coté (06:12)
Hmm.

John Willis (06:19)
And his son his grandson like shook his head and he was like all day he's trying to figure out what the heck, you know. And he doesn't like it's not like you get it. Well well what do you mean? You know, move on. You know, give you the answer. And he realized it was one seventy two in his page in his book, you know, I think it was out of the crisis, you know. ⁓ and he was that

Coté (06:30)
Uh-huh.

⁓ Now now that

like as as as a sub note, are you one of those authors who knows the pages that things are on? Okay. All

John Willis (06:42)
Absolutely not. No.

I I got this thing called Genav AI right now. It really helps a lot, you know.

Whitney Lee (06:49)
F for

those who are listening who are uninitiated and myself too, can you e talk about Deming and who this person is and and what he means to you?

John Willis (06:57)
Sure, sure. Yeah,

yeah, I mean the the I mean w my discovery from h for him was that you know, as we were learning about what DevOps was, we thought we were creating, you know, so I ⁓ you know, as sort of for people who don't know me or uninitiative anything about my career, which is you know, I'm considered pretty much one of just less than a handful of people who were the founders of the DevOps movement, right? I I was the only American over at the first DevOps days in Ghent.

And I me and my friend Damon Edwards bought it back actually. Michael, we started the Barcamp PSM stuff, which was pre DevOps. But ⁓ so we were sort of like rolling in our like, this is great and it's all new and everything. And by the time I started working with Gene Kim on the DevOps handbook, we started to learn more and more that what we were doing wasn't really new. ⁓ most of it was

coming a lot of it was coming from agile, but really more importantly, the sort of delivery and the way we did software d delivery and software supply chain and all that stuff was coming from really Toyota to Proxy Systems, right? Like it was it was we were we were mimicking manufacturing economies and knowledge economy. And there was this interesting debate that went on where people said you can't map the two. And and I think we did a really good job of disproving that with the DevOps handbook.

⁓ but there was some nagging things about that process that like, okay, where did Toyote get that stuff? And I just kept digging and long story short, ⁓ I found out about this guy called Edward Deming, Doctor Dem Edward Deming. And and he basically has this incredible history, but he's probably most known for after World War Two, when we're when ⁓ Americans had to r help rebuild Japan.

He got sort of inserted into that process. There was a my book has a longer version of this. And he focused on ⁓ it he's known as the quality king, but he really was more about epistemology and and it it's a way deeper ⁓ discovery, you know. ⁓ he was a physicist at the turn of the you know, the at right at the beginning of the twentieth century when everything about physics is changing, right? Like so

⁓ anyways, long story short, he's been credited with as the miracle maker of Japan, which really is sort of nonsense. There are a lot of contributions to that. But and then came back to America and ⁓ and literally at some point in the nineteen eighties. So he did this in the fifties. By you get to the nineteen eighties, America's trying to figure out why we're getting our knock socks knocked off by Japan in from TVs to cars to washing machines to everything.

And there's a documentary on NBC. It's called If Japan Can, Why Can't We? And we realized there's this octagenarian, eighty years living about fifteen miles from the White House, that basically taught the Japanese. And he creates the whole second revolution of quality in the US and and but but my interesting was that the deeper story than just the guy who invented quality or the the the guy who created the miracle in Japan.

which were just bylines, I went deeper to try to figure out why was where was this connection between what Deming was doing, you know, throughout his whole career to like how we're running modern data centers. And that's very

Whitney Lee (10:33)
So can you give me an

example of like a concrete idea that came from deming in Japan that found its way through Toyota through to DevOps that is relating to software today? Yeah.

John Willis (10:43)
Yeah, yeah. I

mean y y you know, there's a number of things like a Toyota supply chain ⁓ book, which is about their supply chain about ⁓ you know the sort of ⁓ well, I mean th th if I want to go deep, the the first part he has this thing called system profound knowledge. And it's made up of sort of the idea is to understand complexity and all systems are complex systems, right? So let's agree with that, right? And then

If to understand complexity, his idea was this there was this idea of profound knowledge to be able to understand it. It wasn't something he was venting. My book is about how he built that knowledge of what how he like within the last couple of years of his life wrote about it. ⁓ and the first sort of element of this lens to understand or dissect or try to understand complexity is called the theory of knowledge. And theory of knowledge goes back to scientific method.

It's epistemology. It's how do we actually know what we think we know? Always question or no. So ⁓ if you go back to Toyota, Toyota was l you know, you could read all the books, the surface level books about lean, the 14 steps to lean, the you know, all that nonsense. Or you could really understand what Toyota was doing. They were doing scientific method. You know, Steven Speer in his book, his award winning ⁓ Shingo Award book.

high velocity edge. he did originally a paper on Toyota, the DNA of it was it was one of the most downloaded horror business review review papers ever written ⁓ on the DNA of Toyota production systems or production Toyota systems DNA I forget which order it was in. But he said one of his quotes in there was Toyota was a community of scientists continually experimenting.

So that's the thing you don't really get on all the lean and all that. And so so that was and I've interviewed people who worked in the sixties in Toyota, and they will tell you Deming taught them how to understand data, how to under question data, how to knowledge. I mean, if you move further up the chain, things like variation, analytical statistics, that was a big part of his. ⁓ that was the second element. The third element was psychology. So all of our cognitive biases.

Whitney Lee (13:07)
Yeah.

John Willis (13:07)
I'm

not saying all that was in Toyota, but like by the time he gets to nineteen ninety and he's writing his final book, he dies in nineteen ninety-three, he's codifying all this as what he calls system of profound knowledge. And then the last is system thinking. So Deming influenced Toyota, you know, at at a sort of meta-level, heavily on theory of knowledge, ⁓ which actually goes back to to American philosophy called pragmatism. Again, don't get me started, but and then system.

Whitney Lee (13:35)
Too late.

John Willis (13:37)
Yeah, system thinking,

a lot of sort of junk in between. But but I mean, as you start evolving how if you talk to the people and again this is sort of a book I talked to Mary Poppendick, right? ⁓ you know, and she wrote Lean Software Development, which was a canonical book for our industry, right? Like it was it was the first person to take lean and map all those sort of waste ideas. I've interviewed her, you know, I did podcasts with her. She says when she was at three

they were heavily influenced by Dr. Deming. Right? Right. So the woman who writes the stake in the ground for lean software development, which literally drives a lot of lean IT, ⁓ heavily influences in role in our industry is, you know, monumental. ⁓ and and so you and then I I could go on and on, but like our whole idea of waste and software delivery and

feedback loops and you know and you know, like even when we get to the DevOps handbook, we talked about the three ways of DevOps. You know, and a lot of that came from Toyota and then a lot of it not all of it came from Deming. Now some people like the Demingites literally, you know, sort of ⁓ become sycophants of him and it that's just the wrong way to think about him. He's incredibly important person, but so are others. But in the in the DevOps handbook, one of the things we tried to do

Which was we thought about this flow of like there's the three ways of DevOps and there was sort of light left to right. How do you sort of accelerate a process? You know, and that becomes the things like Mary Poppendek always said, and we didn't quote her in the book, but which like, you know, how do you get one line of code through the system? Well, how do you make a one line co change? No, that that tells you how good you are, right? And then but then like everything about that acceleration process, which became CI C D, which became all this stuff.

Whitney Lee (15:19)
Mm-hmm.

John Willis (15:30)
And then the second way was the sort of right to left, which is I know people don't like the shift left, and I'm not sure why people don't like shift left, because I I can't find an argument of why it's a bad thing to say, because it's it's a cybernetic feedback loop. It's everything that is positive about learning. ⁓ and it there's some argument against it, but not today's sauce, as my younger son used to say.

Whitney Lee (15:31)
Dora.

John Willis (15:55)
You know, so that's the feedback process, you know, like the thing, you know, and and you know, so I'm a historian of this stuff. You know, Jez Humboldt, Dan North and Chris Reed in two thousand eight, before we even used the word DevOps at an agile conference wrote that famous screen of the green, green, green, you go green, green in the in the pipeline. You get red, you go back, you go green, green, green, you go red, you go back, you go green, green, green, green. It's a slide from their original presentation when they were at ThoughtWorks, right?

Whitney Lee (16:16)
Mm.

John Willis (16:25)
⁓ that is that sort of hitting the red, going back and then getting an extra sort of green and the bars of going left to right. That red represented the feedback loop. And then the last piece was the ⁓ the the systems of the you know, the the systems approach or anyway, so yeah, that that's so

Coté (16:44)
So

if if if ⁓ I I mean I'm I'm diminutivizing. I'm I'm ⁓ I'm I'm I'm ⁓ it it's it seems like a lot of a a a lot a lot of ⁓ theory of knowledge to put it my jokey way is to like have some ⁓ and and also kind of like and like pay attention to it and figure out a system of like gathering it and then also responding to it, right? Like right, right, right.

John Willis (16:48)
Don't ask me another question like that, Whitney. No people get mad at us.

Whitney Lee (16:51)
Ha.

John Willis (17:02)
Yes. ⁓

Whitney Lee (17:09)
And don't get it too attached to it. It seems like part

John Willis (17:10)
Yeah. Yeah, don't get too

sad. I mean that that's epistemology right there. I mean that is th I mean, all joking aside, that is the first question, which is how do we know what we think we know? Don't get attached. I mean, the critical thinkers of the world are sorry, the critical thinkers of the world are always the ones who just like always accepting that whatever you know now is just a placeholder.

Whitney Lee (17:12)
of it. Uhhuh.

Coté (17:22)
Sure. There's cla classic ⁓ classic

Yeah, yeah.

And and and so so like like if you know with that with that ⁓ whatever gloss of it in so so there there's two things. One, do do people well but the here here here's the more interesting thing is so you know, you got the let's go back to that ⁓ if if Japan can do it, why can't we? Like did I think we're all Americans here, so I can say we. Did did we ever figure it out?

John Willis (17:37)
No.

Why does keep online?

Coté (18:02)
Or did we or like what happened? What what was the story of of went on there? Like, you know, cause it anyways, did we figure it out, John?

John Willis (18:06)
Yeah I ⁓

This is a very timely question.

It's very timely question because I I've been getting one of the things I've been doing a lot of you know, I used to just run my own podcast, right? Or I used to podcast with you and then I did it as a hobby. You know, like, you what, I find somebody interesting, let's get on a show, I'll ask a bunch of dumb questions, they'll teach me something. It was really for my right. ⁓ yeah. I never got I never did marketing or anything, you know. Damon and I did the DevOps Cafe, which is not dead, it just hasn't run in like four years or five years. ⁓

Whitney Lee (18:24)
I can relate. Mm-hmm.

Coté (18:35)
It's on

hiatus.

John Willis (18:36)
We we we are we we are trying to make the Guinness World record of the slowest podcast to get to a hundred episodes. So the ⁓ but I get a lot of questions now about ⁓ you know like so I'm trying to go out and I'm now I'm doing other people's podcasts because I want people because people aren't reading books anymore. They're not reading four hundred page books anymore, so I at least have to do a better job of trying to find people who might even try to write a hundred pages, four hundred page.

Whitney Lee (18:37)
Sleeping.

John Willis (19:06)
⁓ so but I'm getting these this question a lot about ⁓ you know, is AI failing? Is this the same thing over again? Is it going to fail? And all this stuff and and what's interesting is, you know, in reflection when I prepare for these, because people don't know who I am, I'm like, okay, let me start with I've been doing this. I've been through five decades of of technology.

Whitney Lee (19:30)
Mm-hmm.

John Willis (19:31)
like flat out five decades. And, you know, I've done mainframes, I did the first wave of distributing computing, and then, you know, I've done actually multiple versions of AI from expert systems in the eighties. You know, and then, you know, on and on and on. I did cloud, cloudarati, all that nonsense. ⁓ and the thing that happens with every at least technology, because that's how I don't know what it's like in any other transformation, but technology transformations I know pretty well.

is you start off with this sort of ⁓ fluorescence, this glow, and it starts blinding everything. And then you sort of set back and then you start seeing through like what's different, what's new. There are some things that are new, but it's not all new. And and I think ⁓ you know the the the problem that we run into is ⁓ you know we we over rotate on the new and we under rotate on the what's old again.

Whitney Lee (20:16)
Yeah.

John Willis (20:29)
And then as the things that we learned two times ago we get even worse at remembering. Like you know, one good example right now is I and again, I'm sitting here in my little perch at sixty seven saying, I should probably retire. This is getting too out of hand. ⁓ I mean, right now we are basically bragging about K Locks for AI metrics.

And then and these are not dumb people. These are smart people that are saying, you know, you got CIOs going, we went up 35, we went from 25% last year to 35% AI. ⁓ well, what we've actually increased our pull requests by 35%. ⁓ we've got token leaderboards. Like who cares? Where's the value? And like

Coté (21:21)
Well there there's there's there

there's there's another angle here.

John Willis (21:23)
Twenty years ago we

were measuring Kalox, because what was that? What was new? What was the fluorescent? What was it was computers. my goodness. These computers can produce they're distributed now. We can produce a lot of stuff. How do we measure ourselves? Okay, you wrote thirty thousand lines of code last month? my God.

Coté (21:37)
And do you do you think that's because people do you think that's

Do you think that's because people forgot or they maybe retired? Like what

John Willis (21:47)
There's some math

equation here, like every two transformations we forget the third one. I you know, I don't know. ⁓ but I mean so you asked me did did

Whitney Lee (21:57)
Well well well people aren't necessarily trying to say they're not trying to learn from their mistakes. They're trying to say, I didn't make a mistake. Look at this thing that shows whatever choice I made is a great choice and I'm gonna find the right metric to show that, even if it's a very silly one. Like I I don't see a lot of critical thinking happening around it. Especially from the people who invest in it, because they have no incentive to be like, actually this isn't so great. We could do better.

John Willis (21:58)
Yeah, yeah.

Right, right.

Coté (22:07)
Mm.

Mm.

John Willis (22:16)
No, I don't I don't either. In fact, you know, there's another the vestors are worse. Yeah, the vestors don't they don't care about critical thinking at all, right? They that's

the last thing they don't even get me started on VC. That's a whole nother. But ⁓ no, ⁓ no, I I think you know, you would ask ⁓ you know, critical thinking. I mean, that's the thing we you know, the the the thing I try to express on these podcasts to people, because a lot of times there are people that are sort of

I'm getting a lot of inbound requests for for people who do organizational discovery or sort of like are sort of people who are, you know, sort of metaheads, if you will. But you know, I mean that it with all respect, right? 'Cause a lot of them actually do help, you know, leadership understand. And I, you know, I I try to point out that there's a hundred years of incredible knowledge of people who have told us how to deal with these types of flashes and fluorescence.

And what we keep losing, you know, and I I can go on and on about deming, but like deming, Acoff, ⁓ you know, Senge, Drucker, you know, there's stuff out of Toyota, Ono, you know, Toycho Ono, ⁓ Teguchi. I mean, it's all there. There's a body of work. And every time we get into one of these new transformations, nobody thinks to sort of raise the and and here's another problem too, is

It it's the so that the new kids don't know anything about it and the old kids and this is why you know again I like if I sound like I'm full of myself, just f give me thirty seconds and then I'll shut it off. But I'm rather and my I Michael, you would agree, I'm kinda unique in that. I probably have as much new knowledge and old knowledge and v there's a very few people on this planet can talk about what it was like in the eighties in commerce

with mainframes and can explain to you what Jan Lacoon is trying to do that's different from a generative AI neural network model. Right? With his Jeppa. Right? I mean, and so the problem is there's a lot of people who have incredible knowledge about Taguchi and Deming and organizational design and hearing, but they don't know how to tell and explain it to the kids.

Coté (24:35)
Mm. And so it gets forgotten. It or n or not passed down.

John Willis (24:37)
Yeah, well d there's a a mismatch.

It's an impedance. Like it's a it's they can't really walk in and say, Okay, I see what you guys are doing here or you people are doing and I think you should do this, you should that 'cause they one they they don't really they can't talk to

Coté (24:50)
So so when when you do you ever encounter a I'm I'm gonna use the word system in a broad way, not like systems thinking, but like like a system or an industry or like a ⁓ I don't know, a work culture, ⁓ where where you're envious that they like have it figured out, where where it's sort of like, they're always improving, they don't regress things, they don't have the fluorescence problem, as you were saying. Like, you know, I don't know.

Do they have it figured out in shipbuilding and th they don't make the same mistakes over and over again? Or is it sort of just like across industries things are are is is endemic the word? It it it happens everywhere.

John Willis (25:24)
I mean, yeah. Well

I I mean the so what you y you have a constant evolution, right? Like so th your question earlier about like the in the nineteen eighties, the if Japan can't want a community created a revolution. ⁓ this guy named Donald Peterson brought Deming into Ford. Ford hadn't been, you know, a leader in automotives ⁓ since like the thirties. Deming said if you follow what I'm about to tell you, you'll be a leader again within five years. In less than five years, Ford overtook GM.

Right. and and from the eighties to the like early nineties, there was this incredible craze of t quality. And so there was amazing improvement. You know, the if we think about what we've been through with DevOps, you know, like you can talk about DevOps being dead and ⁓ this replaced DevOps and you know It this is why I don't mean to use the fluorescence as a negative metaphor. It's a metaphor in that the energy is always there.

And it just becomes bright and blazing, and then it sort of dies down. So, like this argument that deming does you know, that DevOps doesn't matter anymore and all that, it matters mainly because you can't run open AI or anthropic cloud models without all of us contributed to the DevOps movement over the last fifteen years. You I mean the the miracle, so the Whitney, I hold on to the one second.

Because this I said this the other day. The miracle of Chat GPT or Claude Code is not, I mean, like again, I wrote a whole book about like the hundred years of, you know, from the history of a neural network in the 1943 to all the way through all the amazing stuff that happened. That's not the miracle. The miracle that that hundreds of millions of people can put up their phone and invoice, say, hey, tell me, you know, what

with the ten principles of, you know, whatever, blah, blah, blah, and it answers you within ten seconds, that miracle is that they're running Kubernetes. And where do Kubernetes come from? They're running, you know, Chef Puppet or or Terraf probably Terraform. I mean, they're running Datadog. They're running they're literally running all this stuff that happened over the last fifteen years. So when somebody says DevOps is dead, I'm like, yeah, like and and and like and what the real miracle is

To be able to get a model that can on your phone and I could ask right now through their whisper implementation who is Michael Cote, and it's probably gonna give me a pretty accurate answer in less than ten seconds at DevOps. So so in other words, the fluorescence of what happened in the eighties, the energy never left. The fluorescence, the glow.

Whitney Lee (28:23)
So we're talking about systems knowledge building on itself and new things coming along, but but really the old things all still exist and a lot of old patterns now can be applied to AI and it's not as different as everyone's treating it. And I agree with all of that completely. But what I'm wondering, what feels different to me is the kind of business environment we're in now with this like VC magic money that gets thrown at something. I don't believe, and I could be wrong, I'm happy to be wrong.

But I don't believe in other times in history you could there was ⁓ this this type of business where you don't have to prove value, where you could lose lots of money before you make any money whatsoever. And how d how does that affect?

John Willis (29:05)
Yeah, no, I I you know, I mean I again, the the fluorescence is brighter now. But but I mean like ⁓ you know, I I remember having a a startup. I I've done a couple of startups, right? And and I've been through the a lot of the sort of the wars of dealing with V Cs. But I remember I had a pretty good idea of a of an open source project that I wanted to find. And I remember

Whitney Lee (29:11)
Yeah.

John Willis (29:32)
Well, it was my socket plane. Socket plane was an S DN for Docker basically, right? And we sold that. We wound up yeah, we sold it to Docker, I guess if we sold it too. ⁓ the ⁓ I remember calling ⁓ it was Sierra Ventures or something like that. And I get, you know, the the rule of thumb is the only thing I'll mention about S V C advice is if you have any sort of credibility, don't only talk to partners. I mean, you talk not talking to partners is just a waste of

everything that is hero terrible in the universe. the so anyway but I'm like I made the mistake. ⁓ so non-partner. And he starts going through all this list of stuff that I don't have as a startup. And what I have is one slide. Because when I went to the next VC, we got a us ⁓ a term sheet that afternoon on this one slide. Because they happen to be the ones that invested in Nasera, right? so they knew exactly what we do.

But he starts listening to me all the stuff I gotta hoover, my business plan and all this stuff. And I'm like, Did you know that you guys invested in Chef? He goes, Yeah, I knew that. I said, Did you know that Chef had literally gotten the first investment was like two million? And then now these are the days when two million was twenty million, but and we didn't even have ⁓ we we only had we didn't even have a beta customer.

And you gave us two million dollars. And then we didn't have a revenue base for four or five years. Right? And like, so like, so the the the point was they were spraying money in the early days of cloud all over the place with no metrics. It was, you know, founders would tell me that you your seed money, and people were getting seed five, six, seven million dollar seeds just based on who they are.

Whitney Lee (31:16)
Okay.

John Willis (31:29)
And so I think I I you know I think

Whitney Lee (31:30)
That isn't that also

when the dot com bubble burst and that sort of thing, when that type of behavior was happening?

John Willis (31:34)
No, but no, that was even that was even

a whole nother revolution before, right? No, this is I'm talking about from like two thousand ten to two thousand and twenty ish. There was a window there where all you if you had some credibility, if you had any you know, if you were perceived by a venture capitalist as a winner, and I was told this, you know, you don't get seed you get the the true V C is they point you know, I I just saw a documentary the other day.

Whitney Lee (31:54)
Mm-hmm.

John Willis (32:04)
And and so this has been going on for a while, not just now. This is before AI. They actually pay seniors at Stanford, pay them to spot freshmen at Stanford that they can poach out of the university to give them money to do. And that this has been going on. This is this isn't just started with AI. You know, so this idea of like there's this idea that you invest in ten geniuses.

And if you get two of ⁓ so if you put you know, ten million and ten, you got a hundred million, but if you do a billion dollar exit on at least one or two of those, then the math works. So they've been doing this for quite a while. And I I

Coté (32:46)
So w what what if if let's let's

say let's say, you know, maybe you and like the ghost of dimming takes over. How how how do they how do they change the how do they change the VC thing? How do they change creating a company over? Or is it the same or like what how d how does it operate?

John Willis (32:52)
But let me let me say one more thing though.

Well, I would say right now

I think where the VCs are shifting right now. I I've I've gotten into a couple of Bouts with Sunil Dawali and you he's Amplify and he he was our advisor at chef and he's a great. He's one of the good ones. I mean there are very few that I would say are the good ones and he is one of the good ones. And I was saying that I you know, I would hate to be a V C right now because n the very few things have a moat. I mean, like we all know that, right? I mean that you know I mean the moat then becomes

Whitney Lee (33:24)
Mm-hmm.

John Willis (33:29)
your business itself. Right? but the but if you're like five year You know, I I talked to a friend of mine who is a prominent, you know, was a fellow at one of the largest banks and now he's a lead architect at another, you know, a a sixteen trillion asset holding financial institution. And he flat out said to me, If you have a software startup that's less than five years old, you're toast. I mean, 'cause internally, organizations

Coté (33:33)
Right, right. Just like the mass of it and difficulty of doing it.

John Willis (33:58)
That one of the most interesting conversations in large organizations right now is buy versus build. And that's never been an interesting conversation in a large organization. I had talked to somebody the other day, they're thinking of replacing JIRA. That's something you would never hear three years ago. But what the reason they're going to do it, one is they don't believe anybody in the organization actually uses the UX for JIRA. Two, about 50% of the ruse of JIRA is their own scaffolding.

Whitney Lee (34:20)
Ha ha ha.

Coté (34:26)
It's just all customization.

John Willis (34:27)
So why pay like some ridiculous

recycle re you know, re up when you also have just condensed your ability of your developers that you don't want to let go. You know, and so right now, I mean you better have a really strong moat in the software industry. And I don't believe anybody unless you figure it out and so the s Snill Dwali is they've just invested and they've got a good round of funding on a company that's doing

That's taking all the AI stuff to come up with pattern matches for something, you know, on how you can s check categories of potential cancers. And so I think those VCs you're gonna see less investing in people like Chef and Puppet and software companies like that, and looking for the the new new net goal. But I it what we've been doing for

Coté (35:06)
Mm.

See things like

closer to real life in the in instead of just tools and configuring things.

John Willis (35:21)
Because that's the thing, we're getting the

technology's getting to the place where those kind now you can hand biology you know, biomed engineers, PMEs, Claude, and say, Use your genius here and try to figure out how to solve this thing, right? Where I don't know,

Coté (35:28)
You can be closer to the user.

So so you you've you've written many books. I mean I you you like the style of y

John Willis (35:42)
Is this is this podcast

going way south or what? Are we

Coté (35:46)
I think I think it's going well, you know? That's right. The the the thing's always spinning. But you know, like like like a l w when you know, you you have you have a style in your books of like tracing ⁓ through time, like how how we got to where we are. Like whether it's, you know, the the the history of dimming stuff and quality control and all that or

Whitney Lee (35:48)
We have no north, so we're we're good. Yeah. Uhhuh.

John Willis (35:49)
Okay, that's okay. No, that's all good, okay, good.

I was a really smile and so I'm feeling okay, so

Coté (36:08)
In the in the AI book, like how that goes. ⁓ and you know, the in in the comical way, like the you lots of people use this format in a good way. And the comical one is like, you know, it always starts with Socrates, right? And then eventually you go from there, and then all of a sudden here we are having a nice cold Coca-Cola or or or whatever. But but when you when you go through that and the and kind of the amount of research that you do in comparison to

John Willis (36:23)
Yeah, yeah, probably.

Coté (36:32)
I don't know. A lot of the measuring and controlling and having knowledge and r scientific method, like when you read through that, like my sense is that a lot of the reason we have ⁓ I don't know, a lot of the reason that there were many steps in for example, how we have AI, where like, I don't know, some guys were just hanging out in Michigan for a weekend and and like something happened. Or like there's there's these weird little like by chance things that happen ⁓ that lead

to to to the next thing and the next thing. And so it makes me wonder like where the limits of like engineering innovation are in the sense of like I don't I I you know this much better than I do, but I don't know. If you go back to like nineteen thirty where they like one day what would our goal is that you will be able to type in on a small handheld device, can you explain to me Schrdinger's cat? And it'll give you a pretty good explanation.

John Willis (37:13)
Yeah.

Yeah,

yeah. Not to go too freaky but Star Trek, right? Like you know, like the fact that you could hold up this device and ask these crazy questions, right? that didn't seem like reality when I was growing up. ⁓

Coté (37:40)
That

no that's that's that's that's a good point. Is there probably are. There there's well, maybe rolling back to like nineteen fifteen. Like was there was there a body of like science fiction that would motivate ⁓ innovators and scientists to to do that. Well, y I n I know you read extensive newspapers. ⁓

John Willis (37:52)
Yeah. I I was born in like nineteen seventeen, Michael, so I I kind of didn't know it. ⁓

I I like your so I don't know, nineteen fifteen. I and I think the the as you were asking the question, I think the it was interesting 'cause I think there's sort of two things. One is are we are are you know, are you know, you said this earlier, you it's critical thinking you, right? Like are we and and that to me is epistemology one on one, like this idea of like

Like we think we know something. We don't really know anything. And like I can get into quantum consciousness and all that stuff where it literally starts blowing the doors off everything we think we know. But like even if we're staying away from that subject, we think we know. We don't really know, but like this is a good enough know at this time. And but then the real scientists or the real achievers on anything realize that's just a placeholder. And that's why scientific method is not like I do it once

I mean Demi called it plan do study act. And that was a feedback loop. You plan something, all right, I got an idea. So I it's in fact the the real the real beauty is that it's always a hypothesis. It's never like a fact. Everything is a hypothesis, right? So the so I'm gonna plan, I'm gonna do this the experiment, I'm gonna study the output, and then I'm gonna act. And part of the study might be I was wrong. And then I

Whitney Lee (39:19)
W when's the

last time you've you've done an experiment? How conscious is that for you?

John Willis (39:25)
I mean I try to live my life through it, right? I've I've like I've taught my I you know, I try to raise my you know, I hears one of the now they get it's sort of deep is ⁓ you know, I I tried to I wanted to instill in both of my young boys ⁓ critical thinking. And one of my and I I'm not like a devout religious person, but I you know, I I'm faithful at times, you know. but the ⁓

My oldest son is an atheist. You know, so like, oops, you know, that one backfired. But it didn't because I couldn't raise my children to be any other way. So in other words, I try to live by the sword that is critical thinking. And and what that sort of goes into is how you treat other people, empathy, how you know, what like even the words you use, you know, I'll mistakenly say stuff like, us guys, and I'll like you'll catch me real quick, and that's forty years.

of white male, fat guy institutionalized, you know. But but I'll catch myself as fast as I can. I think I've already done it here. ⁓ you know, because I'm again I'm thinking about, not just you, but I'm always trying for so I, you know, and I and I to the most part, when I'm doing research, I'll I'll, you know, I'll create, you know, I'll I'll sort of create a rubric. I'll there's a lot of stuff that I do do ⁓ where like I'm literally trying to flow

this this flow of the and I I and and I by default I I think for me the best sort of methodology for this is deming system of profound knowledge. So I do try to live through, you know, his like, okay, how do I know what I think I know? How am I going to experiment? Now I don't always break out like variation in control charts, but sometimes I do. And then I look for the the the sort of cognitive biases, the intrinsic or extrinsic

things that could sort of cloud way I'm thinking or the way somebody else is thinking or how I have to explain it to somebody else. And then I put the sort of the the loop on it which is the system thinking. Like, okay, so now I think I have this, now let me take take it one step outer and outer and outer. So I again I do try to manifest that in most of the things I do.

Coté (41:46)
So i is is is there like would it be too trivial to think I was I was talking with my mother, not not to think that, but but earlier. And and you know, I I I saw some tofu in the store ⁓ recently and I bought it and you know, ⁓ I cooked it and it was fine. I I don't wanna re reveal too much about my process. But would it would it could you use all of the stuff we're talking about to basically say, ⁓ normally tofu doesn't taste so good. I wanna figure out how to make it taste good.

Whitney Lee (41:52)
Yeah.

John Willis (41:54)
Yeah.

Coté (42:15)
till I enjoy it. Like do you do you kind of throw that out there and you you s you start, you're like, you invite your friend Socrates and and he's like, how do you know anything? And then somehow you end up with tasty tofu. Like what is the what's the process that you go through for that?

John Willis (42:22)
Well I

Whitney Lee (42:28)
Yeah.

John Willis (42:30)
Well, I mean I d I s you know, I think to me, one of the more helpful books I've ever read on in and I I think it does you know, system thinking is sort of core to everything. You know, I think that you know, I I talk about epistemology and thing, but system thinking is the can opener. And and so I think Danella Meadows Thinking and Systems is a book for good or bad is gonna change your life.

Like you'll never be able to stand in a grocery line again and not over analyze the cues. Right? Every you know, like and she does an incredible job of like bringing it down to sort of just so everybody can understand what the what systems thinking science is. And so I don't sit down and like write out a rubric for creating tofu, which I don't I don't dislike tofu, but probably not gonna ever cook it. ⁓ the

Whitney Lee (43:03)
Yeah.

John Willis (43:27)
But what I will do is I will, you know, I I I think I think I've forced you back to your question, Whitney, what do you do? I have I I now the sort of the norm for me is to think like that. It is to I think my default ⁓ speed limit now is critical thinking and and it is systems thinking.

⁓ and it it includes like understanding the sort of some level of psychology in the form of like motivation and bias. you know, another I and and I anchor things on like those so I don't know if you guys ever heard of Dav David Foster Wallace. there is one of the most amazing it's ⁓ if you get a chance, watch. It's called ⁓ it's called it's ⁓ This Is Water. It's a commencement speech on the value of liberal arts education. And he wanna

Whitney Lee (44:10)
Infinite just.

John Willis (44:23)
You want to pearl it surprise for his infinite jest. ⁓ and don't that book is way too hard to read. ⁓ but his What is Water, it it'll just it'll show you how you always have to question and I try to watch it once a year. Because you know, the you know, like it the the the whole moral of that story ⁓ is that he tells is what you think you know could be wrong. And he uses real life examples like he says

Whitney Lee (44:30)
Mm-hmm.

John Willis (44:53)
a story where he tells there's some woman in a you know a hum hummer whizzing in and out down the highway and you're just convinced you know she's a gas guzzling, just terrible human. And what he says, why don't you just force yourself to take an alternative question to say maybe she's heading to the hospital 'cause her kid is sick. Right? And and and there's a guy named Thomas Argoras who calls the ladder of inference, right? And he he has a very sort of

⁓ analytical version of this, which is very deming like, where like you should constantly make sure you're breaking out of a reflective loop, right? Because what what happens is we're constantly falling into a reflective and I know this is like getting very far off of the how do you cook tofu discussion, but the point is

Coté (45:44)
I mean, I think I think

people who cook and enjoy tofu would have this kind of thinking as well. You gotta break out of the reflective loop.

John Willis (45:48)
Well, I mean th the the the point of

that is that

Whitney Lee (45:50)
What what is

the reflective loop?

John Willis (45:53)
So he the ladder of inference is this idea that like what you know, it's sort of a selfish view of humans, but humans are selfish, so maybe, you know, he is probably more likely right than wrong. But like if you say something to me, I'm really thinking about like I've got a whole and he calls it a ladder of inference. So I'm taking what you just said. Like you asked me a question earlier. So the first thing I'm doing is I'm sort of working on like, okay, what it was the interpretation of what what

Whitney just asked me. Okay, then I'm gonna sort of look for in my mem my neural network really. You know, I do explain the neural network in very much the way the brain works. I'm looking for these aggregate senses of what might be knowledge and I'm thinking and then what I'm l then what happens then as you get to I forget what lat lat level as you're going up the ladder, you wind up this is where the biases fall. And and biases, by the way, are good things because when we drive to the coffee shop in the morning

We don't wanna like have to analyze put key in hand, move key four inches into the ignition or button and now turn this, do this, now put gas at thirty percent down to go to get out of the you know, like like that's all built into our you know, frontal lobe or whatever, right? Like so that's that's a good way, you know, and it's all based on energy of your brain, right? Well that stuff becomes

are dangerous thing too because it's how we start thinking things like, you know, this group is not as smart as that group, or right, and and so the reflective loop is this what just happens because your way your brain's wired.

Coté (47:36)
And and it's it se it seems like connected to the the the profoundness and and stuff before, what you're missing out on there is of a ⁓ r gathering data to evaluate what's actually happening. You you you just you just assume you you know what's happening there.

John Willis (47:50)
Well or at least questioning. It c

it was with questioning. Look so, you know, John Osbar, right, like considered one of the the sort of Vanguard's I would say one of the founders of DevOps, certainly certainly his influence of bringing ⁓ things like learning from incidents and all that stuff, right? ⁓ you know, he he is one who introduced us to ⁓ Richard Cook, you know, why complex systems fail, all that stuff, right? He used to talk about at Etsy, they would literally ⁓

Do things like if there was like if they were in sort of a ⁓ a planning meeting or something, everybody agreed, that was a red flag. Right? Like the fact that we all agreed was a red flag. That's that's the re that's recognizing a reflective loop. So do to answer your question. they even talked about one time in in a postmortem where ⁓ some young person came into the postmortem and said, Hey, we don't have to talk about this. I know I screwed up and they're like, Yeah, that's not how we do it here. He's like, No, no, trust me, I

I I don't you know, I I know what I did wrong. And they're like, No, no, we're gonna work it through. And when he worked it through, turns out he didn't do anything wrong. You know what I mean? Like so there that, you know, I other people have different names for this. I think Argus does a great job of sort of explaining and I think the what is water, ⁓ from ⁓ f David Foster Wallace is a great, you know, consumable version without having to do all the research of the but the

Whitney Lee (48:58)
Mm.

John Willis (49:18)
But it is this idea of like questioning what you think you know. Like there's a point of like you have to understand how your brain is wired and like are you falling into traps. It's even as a leader, right? Like is the one of the things I try to understand is people who have worked for me in the past or in startups I've done is I try to figure out what motivates people. You know, and and it it's it starts with simple versus people who are intrinsically or extrinsically motivated.

Whitney Lee (49:22)
Mm-hmm.

John Willis (49:45)
And then there are like nine levels of intrinsic motivation that most people don't. That mostly think it's a binary d decision. There there's actually lots of layers of intrinsic motivation. That's where the most fascinating people are, of course. So yeah, so that's the reflective loop and then the idea that you and it all ties back to that original how do we think how do we know what we think we know? Are we questioning what we think we know?

Whitney Lee (50:07)
So here's I have a question that is gonna not come as a surprise. But you said you've you know the past five did you say five decades worth of tech? Yep.

John Willis (50:15)
I've been doing five five decades, yeah. So it's the

end of one and the and I don't know, this might be the end of my last one, but I don't know, we'll see.

Whitney Lee (50:21)
you

know the past really well and you're unique in that you you don't just rely on all that past knowledge you're really current on on what's current what what's going on in the tech industry. And so you're really poised to answer this question that you already said that people keep asking you. And that question is what is the future of AI? And wait, I wanna know both from a tech perspective and a business perspective. Like how is it being overused? Like once it settles

How do you think it will actually provide value in technical systems once it all settles? And do you think that will be worth the big investment that ⁓ we've all put into, or VCs especially, but I'd say us as a society has has put into the technology?

John Willis (51:10)
Yeah, so I mean, ⁓ I think the answer is anybody who says they know is lying. I well, I mean my first sort of like ⁓ exit ramp to that question is what are we doing wrong? And what are we don't what are the things we're we're not understanding like

Whitney Lee (51:19)
Yeah. But your guess is better than other guesses.

huh. ⁓

John Willis (51:36)
I mean, it flat out the difference. And I I'll try to get to an answer where I I think I can come up with something, but the 'cause I think history has shown you know, one of the things about the fluorescence is it's hard to get out of that glow. And even with me with five decades, I know we're in that glow. But the fact that I can do these amazing things right now and the people I'm talking to, and we're all talking to are doing amazing things. But the ⁓

Whitney Lee (51:49)
Mm-hmm.

Mm-hmm.

John Willis (52:04)
You know, the thing I I I'm concerned right now is do we understand two two fundamental things that have changed or different? One is we have unlimited knowledge at machine speed. We've never had that before. ⁓ and then the second thing is we're moving into a probabilistic or, you know, sort of non deterministic

Whitney Lee (52:20)
Mm.

John Willis (52:34)
structure of reliability for commerce. Now we've had pockets of it, but there were always sort of recommendations or, you know, market texture stuff. But right now we're moving from like chatbots of conswent consequence, and and that's not trivial. Like because if you know the Air Canada was an example of, you know, what happened there. But like the the you know sort of a a high consequence answer that is probabilistic.

Whitney Lee (52:38)
Mm-hmm.

John Willis (53:02)
And then we move into agents where we're giving ⁓ sort of changing the operational authority plane. Right? So now we've got this unlimited knowledge at speed execution, and we're trying to use old authority methods. We're thinking risk and people are writing whole manifestos about risk and agents, and they're just repeating what risk looked like for the last 30 years. Right? And risk is completely different because

Whitney Lee (53:27)
Mm-hmm.

John Willis (53:31)
There is no go no go.

It's go and it's it's either will I even start the thing and what is the percentage of risk I'm willing to take from what I'm gonna get? There was sort of an idea, and it was never really true, but it at least was within the realm of managing that we could write software, we could run it through a pipeline, we could run all these sort of SAS, Stas, all these things, and we could go no go it.

And we felt pretty safe that there was a deterministic, scalable, and it in sense it was, right? American Express, banks, they all work. What we're now not acknowledging is that what we're doing now is we're using probabilistic inference. We're using inference to get answers or get solutions. And what the problem with inference is it is a probability structure. And so therefore

Whitney Lee (54:09)
Mm-hmm.

Mm.

John Willis (54:32)
We can ha we can't think about it will never do that. I can guarantee boss it will never do that because I tested it. Like so that we have to rethink everything that we thought about ⁓ risk. And we have to first accept that if you are of the ⁓ idea that that like this could never, ever, ever happen, then you can't use this technology. So that's the first question people are not acknowledging.

Whitney Lee (54:57)
Mm-hmm.

John Willis (55:00)
They're sort of like, you know, ducking the heads on that one. But then once you accept that, then the question is, what level of risk are you willing to say? Ooh, well, that's squishy. I I can't no, no, no. That I you just answered the first question. The first question is you're willing to accept the level of risk, or otherwise you can't use this. So now you gotta get scientific about what level of risk you're willing to take. And then there's a lot of tools and mechanisms that exist. Evaluation software.

Whitney Lee (55:17)
Mm.

John Willis (55:29)
and then with agents, you gotta get into like what operational authority are you doing? Like it isn't just agent yes, agent no, it's will the agents be able to read? Will the agents be able to write, mutate production systems, will the agents execute across systems? And so I worry about that we're so far behind in the speed of everybody doing all this stuff that we're not e we're not asking those questions. Leaders are not asking the organizational, is my talent

You know, ⁓ is my t is there a talent gap related to my ⁓ so my innovation. Is there an innovation talent gap, right? Like do I even look at my people? All right, so back so that I spend way more time on that thinking about like if and I do get hired by organizations periodically to come in and people who wanna hear th this what I'm talking about. Most people don't wanna hear it. They wanna just have people come in and tell how to go faster. ⁓

But I you know, I'll tell stuff that they don't want to hear and it's gonna be slow down a little bit and ⁓ and you know, and it and this goes back to the all the things we've talked about for the last forty minutes or so, which is, you know, ethereal knowledge, epistemology, the way we think, or the organizational the the knowledge that we have from Deming, Shange, Senge, Ono, all those trucker. ⁓ all right, but the other question is

We've seen the pattern before. I mean there was, you know, I didn't I lived through expert systems. It seems different now than it was then. But there was a credible amount of hype in expert systems in the eighties. I mean these things seemed like they were incredible.

Whitney Lee (56:56)
Mm-hmm.

Coté (57:04)
Do you do think do you think a similar thing is like ⁓ to to to use another dated reference, let's call it the the the where's the beef problem of like you have you have the glow of ⁓ of these things, lots of claims we're gonna lay off all these people because it gets replaced with AI, or we're gonna have an expert system in your pocket and you can walk around and it's gonna help you out and suggest things in the context that you're existing in. And to the point of ⁓ measuring and knowing.

y there really wasn't a thorough enough or continuous accounting of like, does it actually do this thing?

John Willis (57:39)
Yeah, I mean i the first part of the question is the you know that maybe we've been getting mulligans for our dysfunction with all prior technologies. Right? I mean right, right, because I don't know. I mean maybe at this infinite speed or this machine speed and infinite knowledge, our dysfunctions are going to be incredibly dangerous and maybe put some really large organizations out of business. Right?

Whitney Lee (57:51)
Yeah.

John Willis (58:08)
But the the sort of the matter of all these, whether you go back to expert systems, you go to cloud, you go to all these different things, this is why I like the fluorescence. and it was actually Dr. Woods who gave me this idea. ⁓ the fluorescence is that it's not a winter and spring. In other words, it just doesn't sort of happen, then go away, then happen. The energy survives. So LLMs today, there'll be a lot of cost correction.

How what they look like in five years ago, there's some people that I'm not smart enough to know whether they're right or wrong, but they're smarter than me that kind of predict that LMs won't be around for some period of time after some period of time. That's why what you know what Fay Fe Lee's working on and and Pew you know, Vision and then what what Jan Lacoon's working on with Jeppa. I mean, I don't those are things that are very interesting. But the point is all that stuff is basically

in a sort of a chain of things. And so we're gonna have a washout. There's gonna be some tremendous failures of AI. There's some scale problems right now. People are ⁓ paying too much attention to the speed, not enough attention to the organizational dynamics. I've just got an article coming out about ⁓ Eric Trist in the the history of social technical systems.

Whitney Lee (59:24)
Agreed.

John Willis (59:32)
in nineteen fifties they were coal miners and they introduced all this new technology and these guys were sociologists and they went in and they studied that they realized you can't just dump technology on people. You've got to figure out how to make both work. We'll figure that out. Some will figure it out, some won't. The some of the technology will survive, some of the technology will s change. Things will change dramatically, ⁓ you know, ⁓ like

worse than like what happened in the textile industries. So jobs are will change. I I you know, there's there's all these people talk about Jevons Paradox. So, you know, I don't know if you heard of Jevons Paradoxes ⁓ I forget his first name, but Jevins was also sort of a philosopher, ⁓ and you know, e economist actually probably and then he his basic theory is that when you create abstractions, you create more abundance.

Right. So there's hit the examples of that all over the place. And that's been the case through almost every technology change that's happened, where we always think, once that happens, then all this is gonna go away and we wind up my five sort of decades have always proven that Jevons Paris is correct. I'm not sure it's right this time. but but but you know, I mean there is

Whitney Lee (1:00:50)
Hm.

Coté (1:00:52)
So we are gonna

have like a three day work week then, right? 'Cause Je Jevon's paradox would suggest that the more productive you get, the more demand there is for the output that that you have.

John Willis (1:00:59)
Well, actually, you

know, I you know, I I was I was thinking about writing ⁓ sort of a very Luddite ish article of why I don't really do ⁓ you know, all these bot stuff and what you're doing and clerks like 'cause I have a feeling that w you know, like I don't I look at other people's they tell me they got eighty billion emails a day, you know, I get real emails, I get four or five a day. Right? I don't like I don't know that I need a bot to like tell me to do a bunch more work.

Than I'm that I'm not doing already. I have a feel I'm watching all these threads that I'm on where people are literally automating all this stuff, and I'm like, did you do all that stuff before you had the bot? ⁓ and I and so to your point, like one of the problems with Jevon's Paradox is you wind up doing more. It's not that you're sort of being more creative, and and it is there's an efficiency, like now I can do the I can get rid of a lot of junk work and I can do the real work more efficiently.

Whitney Lee (1:01:49)
Yeah.

John Willis (1:01:58)
actually doing more of the efficient work, so you're actually doing more. So the chances are we're not going to the three day work week. We're gonna go to six day work week. AI is gonna make us do more. And and so but but but the anyway, the point being that I do think one of the things people are going to have to learn really fast like you university students. I I'm I'm an advisor for Auburn University and I was at a conference two weeks ago where

at a Tennessee university and I was talking to students. Like, you know, and these students, ⁓ they're coming out CS degrees and the first time they're talking with Git, Jira, all the tools, is their senior project. And there was some freshmen and stuff, like, what should we do, John? I'm like, if your professor's not teaching it, you gotta learn it on your own. Just by the time you're ready to get a job, if you're just no coding and you want to go to work for a bank,

Whitney Lee (1:02:49)
Mm-hmm.

John Willis (1:02:57)
The hiring manager is gonna want to see your repo. And if your repo is created within the last six months, and the next person their repo is created in their freshman year, right? ⁓ you know, and and by the way, you need to understand how to deliver so how to manage deliver and all the things about software in a bank, other than just being a coder, right? ⁓ so that so that's the first fundamental change. Anyone who thinks just being a coder.

Whitney Lee (1:03:17)
Mm-hmm.

John Willis (1:03:27)
is gonna like get them a great job today or keep a great job is on a dangerous path. People who basically are sort of the polyglots or the people, you know, at least in the sense of what does it take deliver software in a large bank? What does that look like? That's what I need to be teaching kids at their freshman year.

Whitney Lee (1:03:32)
It's not enough. Mm-hmm.

It's more more h

holistic and more about all the surrounding tools and more about interacting with other humans than necessarily writing the code itself.

John Willis (1:03:58)
Yeah, yeah, well then yeah, of course all the

all the other stuff we've talked about for the last year. I mean, understanding how to you know, how to deal with people, how to you know, how to network. I mean it's like I see you know, I I tell these ⁓ professors and advisors at universities like, you know, these kids, you know, you sit in a room, they can't they they're in the senior year of a of a c credited college diploma, very you know, and and they can't even have a conversation with you. Like you gotta f they

Whitney Lee (1:04:26)
They c they could if they

were online holding virtual guns at each other.

John Willis (1:04:28)
Yeah, they they need to have good communication

and networking as their freshman first one on one class, right? Like Yeah, you know, like we know this, right? But what most people how do most of us learn it? It's after we got out of school, right? Like and it's gonna be harder and harder and harder and harder to you know, to get over that like automation is going to hurt a lot of people.

Whitney Lee (1:04:32)
Absolutely.

I can't believe it, but an hour has passed already. It's it's gone by in the blink of an eye. I have one last question for you. And that is, why Bill Clinton? You said you want to be a fly on the wall for Bill Clinton. Why that guy?

John Willis (1:04:53)
there you go.

Yeah.

It's funny.

Well, you know, it it's it's funny. I I we have this thing in our family, ⁓ the Willises don't lie. And it's it's we've lived our life on it. And so I have this like real problem with him in that I I I despise what he's done but I do like the way he thought, right? And and ⁓ you know, I saw him speak at a Dell conference.

Whitney Lee (1:05:25)
Mm.

John Willis (1:05:35)
He was the orn he was a keynote and and I you know, I d I d you know, I and like there I could go back to so I've read some of his works when he was young and I do believe he you know, he he has a despicable side of him. There's no question about like there's things like so. ⁓ but the thing I do admire is I do think he is a humanist. I think he cares about the human condition. And unless it's all fake, well maybe that's what I find out, right? Is

Whitney Lee (1:06:04)
Yeah,

yeah, that's the secret you'll discover.

John Willis (1:06:05)
Yeah, but

but he I saw a presentation on and he he you know, this is like who who goes to a Dell keynote speak and he tells the audience, like it was three, four thousand I don't know how many people at the Dell conference in the keynote, Michael, probably three thousand people or something like that, right? And and he says, You let's talk about water. He says, You know, any of you right now listen to me and you're just tired, don't wanna hear me, you could go right out out that hallway, go get some water, go to use the bathroom.

And I forget what percentages like sixty or seventy percent of his planet can't do that.

Like y you know, what you take for granted, like a water fountain and a a flushing toilet and you know, like you know, and he he just built on that and like who talks like that at a Dell conference, right? Like there's some boldness and there's I don't know. I just I do I I probably would wanna figure out where what is the core if there is a humanistic you know, ⁓ value system him, like where is it then? So yeah, I I I do my best I can

could to go and say, Hey, I'm just a dumb old nugget knucklehead who don't know anything about anything and I wonder if you could tell me about, you know, world economics and, you know, whatever.

Coté (1:07:18)
Water.

Well, I think I think we've found some humanist stuff in your core in this long discussion that that we've had. It's been great. Thanks for being on as always. It's it's always it's always fun fun to discuss. Now now we've mentioned several things that you have, but if if you were to tell people one URL on the world wide web to go to to get more John or more of your stuff, maybe they don't want to get you. They just want to get the product.

John Willis (1:07:29)
Yeah, it was fun. It was good, yeah.

I you know, I would say LinkedIn.

They're LinkedIn. I'm John Wills Atlanta, you know, so

Coté (1:07:48)
I think

I think maybe to correct me if I'm wrong, Whitney. what episode is this? ⁓ I think maybe we're like you forty eight for fifty for people saying like my my entire online life is in LinkedIn now. Go there.

John Willis (1:07:59)
I mean it's it's

right off the bat you'd find a link to my authors portal portal 'cause I would I was gonna say authors portal, but if like if you you know, maybe you're like, Okay, I've got a little bit of taste of this game, I'm not ready to buy his book, let me read some of the stuff he's writing and then then it's like, ⁓ I think I should stay away from him or Yeah, maybe I will buy his book.

Coté (1:08:13)
Mm.

Well, I if someone's listened this far and they know to look in your LinkedIn profile, they'll know if they like your material or not.

John Willis (1:08:24)
I I would probably imagine. So then just go to my LinkedIn to find my author portal and then you'll find my Rebels of Reason and ⁓ and you we need to catch up, Michael. You know, I realized I don't know if I told you I was told you I was writing the history of quantum computing, but I think I'm gonna do it as a science fiction book.

Coté (1:08:40)
Mm, that would be fun. You can you can try a new format.

John Willis (1:08:41)
Yeah, 'cause I think

Whitney Lee (1:08:42)
Exciting.

John Willis (1:08:43)
I think I I I I'm I'm ready to try it. Yeah, yeah. So I can still tell the history but I can tell it in a really cool science fiction format, so

Coté (1:08:53)
Maybe with some flashbacks or or or recountings of things. Maybe maybe you can do you can figure out the novelization of the found footage format where people find fold old find old cameras. That would be a good challenge. Really crack that nut wide open. Be a regular modern day ballzac or something. Anyways, speaking of modern day ballzacks, this has been software defined interviews. If you want to get a link to that ⁓ LinkedIn profile or a little description, some pointers, you can go to software defined interviews.com slash one two three.

Which I guess is literally the number, not a joke. ⁓ so with that, we'll see everyone next time. Bye bye.

John Willis (1:09:29)
Hey, thank you. Thank you all. Thank both. Yeah.

Whitney Lee (1:09:30)
Bye.