From Bottlenecks to Breakthroughs: How AI and Platform Engineering Drive Continuous Progress – Platform Engineering Show Ep 09

Hey everyone, it's Alan Shimo. Welcome to another edition of the Platform Engineering Show. I have to apologize.

We've been, we've been negligent the last couple weeks. I've been traveling. Luca's been traveling, doing our things, and, uh, we try to do these every other week, but sometimes just life gets in the way.

But, Luca, how are you? We're back, man. It's good to see you.

We're back. How's everything? Yes, I'm good.

I'm good. I've actually, you know, I've actually been traveling less than usual. Yeah, well you, yeah, you, you lead a, a vagabond life usually, man.

You're all over it. Exactly. Exactly.

But no, yeah, I'm in, uh, I'm in very, very hot Milan right now, um, for, for a few more days than Greece. Greece. Good for you.

Where in Greece are you going? Uh, s you know, it's like a little island in front of Italy. Oh, so you are in the Ionian Sea?

That is right. Yeah. Yeah.

Very hot. I haven't been, I haven't been on those islands yet. Yeah.

But sounds like it's going to be good. I heard it's really, really hot in Europe right now. It Is really hot, in fact, like, on that island.

Um, so my mom's there already and she sent me a picture yesterday of this like massive wildfire. Um, so it is very hot. Although, you know, like they keep saying these are, are like men started, uh, which really sucks.

I never underst understand people that start fires. Yeah. Well, I mean, some, sometimes they're not on purpose, right.

Obviously they're not on purpose. Purpose. No, this is saying It on purpose because it was like, it started like six in the morning.

It was like, oh, you know, and it's like, ow, how do you start fire? Yeah. Because they think they're just gonna do a little burn off here so they could plant or whatever, and then stuff gets outta control.

Yeah. Maybe, Maybe. Um, yeah, I mean, you know, we have problems out west with that all the time in California, in Colorado, and most of them are man started like carelessness and ridiculousness.

Yeah. But there's ways I, you know, depending who you believe there's ways of doing your forestry so that it's easier to contain these things. Absolutely.

Absolutely. Yeah. Yeah.

Right. Because, you know, fires are a natural part of the cycle, right. We, we can make it to platform engineering and technology.

There. You, you know, you burn down, you get new growth and, and so it's, it's not bad necessarily. It's bad if it's your land that's burning, but, you know.

Yeah. Then it's bad. You've Gotta be insured.

Yeah. It's part of the cycle of life, man. The circle of life, right?

Yeah. So that's where that is. Anyway.

Hey, um, well, That's what I wanna talk about today, right? The, the cycle of life and engineering burning. Exactly.

It was, was a good, it was a good segue. We didn't even plan that. I loved it.

So I loved, so it works, sometimes it just works. I was like, damn smooth. Mm-hmm.

org, and it was written by, I think it was like the field CTO of GitLab Brian. Yeah. Brian Ross.

You left Brian Ross. And, and it was about bottlenecks in, in, uh, platform engineering running in, running into bottlenecks. And I read it and, you know, it struck me, I, I get the point, and, and we all strive to remove bottlenecks.

Mm-hmm. But it, it reminded me of, of lessons learned. You know, I, um, I forgot who it was.

I think it was my friend Brad Feld, when we had started, one of our companies, mate, everyone on the executive team read this book, the Goal, and I always forget the author's name, uh, of the gold doctor. Someone, someone, but I'll, I'll to figure out the name. I, ilial Goldratt, right?

Gold. Gold, right. Goldratt.

And this, right, there was a time where this book was like mandatory reading at every MBA class MBA program in the world, and it's eliahu gold, gold rat. And it's really, it introduced the theory of constraints, which is kind of his thing, right? And, and though it was really written for a manufacturing kind of business, it has direct, uh, application to it.

As a matter of fact, you know, this is when I first met Gene Kim, and he showed me a, a manuscript of what became the Phoenix Project. He, he told me the Phoenix Project, which is of course, like the Bible for DevOps, is, is, is really an it version of the goal of the goal, right? It's the same thing, but the lesson of the theory of constraints and the goal, and, and, and the Phoenix project is sometimes we see a bottleneck and it looks like the world's biggest problem.

And we, and we put a lot of resources into that bottleneck, and then we, or we're removing the bottleneck, and then we remove the bottleneck only to find out that there's another bottleneck behind it. Mm-hmm. And we remove that bottleneck.

There's another bottleneck behind that. And, and so I, I think sometimes we suffer from, if we can only do this, that'll open the flood waters, right? That'll Yeah.

Break the dam. It don't work like that. Yeah.

It don't work like that. I, I think part of being a mature IT person is recognizing that your job is, and I don't wanna bum anyone out, but it's, it's to go from bottleneck to bottleneck to bottleneck. Yeah.

Yeah. You know, there's, You think, Go Ahead. Yeah, sorry.

So there's, there's, there's this, um, there's this other book that I really recommend, uh, to people. It's called The Beginning of Infinity by David Deutsch. Um, and there's a lot of interesting stuff from there, but one of the, of these concepts is this like inevitability of problems, right?

Where it's like, you know, by, by progressing as humanity, um, you're, you're constantly like creating new technologies, new things. And by doing so, you're creating new problems. Um, and then you basically need to then solve those problems.

And, and then it's interesting because it's like, it kind of reframes a lot of the, you know, the modern thinking around, you know, climate change. And like, some of these, you know, very real, like, current challenges as like, yeah, of course that's real, but it's not the last problem. It's just our problem.

You know? Um, and, and, and, and, and it's, it's, I think it's a half way of thinking about it. 'cause to your point, you really realize this, like, our lives just like a series of problems that you need to solve.

And that's okay. That's, that's the beauty of it. And he also says essentially, you know, so problems are inevitable.

That's kind of the first kind of cannet. And then the second one is, all problems are solvable as well. Um, you know, unless you need to violate the, the laws of physics somehow to physics.

Like, but even then, like you, so, you know what? It's funny you bring this up. I I, I was reading something else about like sci-fi traveling faster than the speed of light.

Mm-hmm. We may have to somehow violate the laws of physics, right? 'cause according Einstein, nothing goes faster than the speed of light.

Yeah. But yet that does to, to humans. And, and what you just described is really the essence of humanity, right?

Which is no matter how big the problem is, we keep chipping away at it. We keep chipping away at it and think that it's solvable because every problem is solvable, we believe. Yeah.

Right. Exactly. Which is, and, and not to get all philosophical, but that's a very different kind of human way of looking at the world versus sort of primitive man who said, oh, must be God.

Right? Yeah. God, we, why is it raining?

Why did it get dark? Yes. Why does it get light?

Because there's gods at play there, right? Yeah. The God made the sun come up and the God, you know.

But as we've, as we've progressed and learned science and learned all these things, we recognize, no, it, there's very totally, it's not supernatural reasons. It's Yeah. They're all problems.

And we Just, there's no other authority, right? Essentially. Right.

And, and that's, and that's actually the, the, the, the title of the book is the beginning of Infinity. The beginning is the scientific revolution, uh, according to David do, right? 'cause it's like that's, if you then apply this method of learning and progressing, that's the beginning of like an infinite, uh, progress essentially, and process of like constantly doing that.

Right? But, so bringing it back through platform engineer, oh, you're just gonna say, okay, Luca, we're way, way up here. Let, let's, yeah, yeah, yeah.

Let's bring it back. Let's, looking back, I think, you know, I, I read the article, but Brian, I thought it was very, very interesting. Um, you know, I think like, um, one of the, um, and I would love to hear your thoughts on this because like, one of the, the interesting things that he points out, of course is like, you know, we've always had this bottlenecks, right?

And now because of ai, you know, these bottlenecks are emphasized, right? Because if I had, I think he gives this example of like, I push at like five prs, maybe on average as a human team. If I, now I have a bunch of bots or coding and, you know, have all this like agent workflow, whatever, I can push out like a hundred times that, uh, in terms of pr, right?

But then you have certain CI and certain CD bottlenecks, and, and, and while maybe that was like a, a small bottleneck now becomes this jam bottleneck because it's just like completely blocking everything. Um, and so that's, that's obviously very interesting, which is why I keep saying, I really believe that while AI is really overshadowing and sort of like quote unquote killing a lot of other trends within it, enterprise IT and so on, I really believe that it's actually really powering, um, platform engineering because everybody's realizing, well, I need that standardization. I need that, you know, those golden paths that are really stable, right?

Not just kind of like shaky golden paths, but like really solid, uh, paved roads that, you know, I can, I can have like five people running on. I can have like a a thousand people running on really, really fast because they're really solid roads. Right?

And I think that's, that's really important. And, and I guess, like, I'd be curious to hear, um, how you think, um, you know, the, you know, that that applies vis, because, you know, you mentioned the Phoenix project, um, and, and how is bays and sig manufacturing principles, right? Um, and, but the interesting thing I would say is, for me, I really look at platform engineering as actually this kind of like industrialization moment for this like software factory, right?

This, this, this manufacturing line of, of, of, of workloads and applications and so on. But that is in reaction to DevOps, actually, right? And how DevOps was a lot more of a, um, I guess like almost like artisanal approach to, to, to, to, to software creation.

So like, like how do you square that, right? Because if they were building, I didn't know that they were building this, their philosophy, right? In a FedEx project on top of, um, essentially like a manufacturing, you know, industrialization book.

Yeah. So I think that's where DevOps never having like a manifesto or a, uh, uh, you know, it was, it was ill-defined on purpose. Patrick wanted it ill-defined he didn't, him and some of the other folks who started the movement didn't want to have a standard definition.

If you look at the Phoenix project as perhaps a manifesto or a, a, you know, a DevOps, how too, you begin to realize that it was built on manufacturing principles. That it was built on bringing predictability to the development process. Because, you know, before DevOps, what you really ha, you know, you developers were, it was chaos developers.

I, you know, I I I liken them to like 18th century, 19th century German craftsman, or Swiss Swiss cuckoo clock people, right? Yeah, yeah, yeah. They make beautiful cuckoo clocks, right?

You look at some of those old cuckoo clocks they made, and they're like, my God, what a piece of work and art and engineering that is repeatable, mass produced, can't do it. Yeah. Right?

Because each one is a, a unique piece of work. I, I think the, the, you know, the Phoenix project is no, we, we can't have that, and we can't have one person be the fountain head of all knowledge, right? And in the Phoenix project, it's the guy, Brent, everything has to flow through Brent and Brent becomes the bottleneck.

Right? Right. And, and so the idea behind DevOps and the Phoenix project anyway, was that we need to, um, we need to democratize that knowledge.

Everybody has to have that knowledge, that developing software into like, you know, what became what we call CICD pipelines, how to be more industrialized, how to be more normalized, how to be more, That's first like assembly line in the, in the software factory where I was the CICD Pipeline. That's right. See that Right before everything.

Well, remember Agile came first. Mm-hmm. Mm-hmm.

So if you go talk to developers and say, what was life like before Agile? Mm-hmm. You know, scrum and, and all of those things, they'll tell you it was worse then it was really bad then, right?

Because then it was like individuals working, not even the developer team. Yeah. Yeah.

I, I, I think, you know, if you look at a timeline, agile makes individual developers into a developer, a teams into a team. Yeah. And then DevOps makes teams between dev and Op on the assembly line.

Yeah. Right? So it's the next, it's the bottleneck paradigm.

Yes. It's the theory. Yes.

First you gotta get the developers together, then you get the developers together with the ops people. Now we need, okay, but wait a second, what about the sre? What about security?

What about, Hey, we need a platform for all this. Yeah, exactly. Right.

Exactly. And, and the heads platform engineering. Yeah.

Right? And I, I, I, I look at that as, as the factory floor essentially, Right? Absolutely.

OS for the factory. Exactly. Exactly.

And before we went, I think you to, to continue your analogy, right? Like we went, we went from this like, you know, very small factory, right? That had like one, one line, essentially very simple One line, they only made one model.

Yeah. You can get it and you can get that car black, or if you don't like it, you can get a black. Exactly.

Yeah. And, and, and you can Exactly. You don't have the flexibility.

And then you wanna start adding that complexity, right? Like, and, and the complexity is both in terms of things that go into the car, for example, and you know, what the outputs of different types of models of cars and different cars, different options, whatever. Mm-hmm.

And complexity. And, and complexity. And to handle that complexity, that's where you need like a centralized, um, uh, uh, you know, orchestration through all these assembly lines.

And that's really what the backend usually of the platform is, right. Is the thing that is actually, and, and the thing that to go back to, to the, to your initial point on, on bottlenecks, right? Is the thing that really should be designed to minimize this, you know, the, the, the impact of this bottlenecks, not necessarily to eliminate the bottlenecks, because we said like, it's not really possible to just eliminate them entirely.

Right. But really to minimize the impact that they have on each other, right? And, and, and, and this is why, for example, you know, we, we've spoken a lot before in the pod about, um, you know, the importance of, you know, starting from the backend of your platform and really, you know, getting clear, clear business logic, um, for, for all this like components in this golden paths.

Because otherwise, the, that we've seen a lot of plans and that we've discussed here before, is this idea of like, starting from the front end, which is really basically starting with like, um, you know, it, it's as if I, I, I was saying like, okay, well, you know, I only care about like, you know, the, the, the different colors of the cars, but I don't care like how I get there. Like what are the problems to, to output the scholars, right? And, and that's where, um, and that's where like, you know, I think like a very helpful exercise for platform teams in general, but in, you know, organizations, any, any type of organization is really to like, map out what the current state of these paths is looking like, right?

Mm-hmm. And then essentially have like a repository of like, okay, um, you know, you know, these are paths that, that kind of work, this we need to be fixed. This, we're like acknowledging and ignore and whatever.

And then within those paths, like really understanding, uh, what is the most painful thing, you know? And, and, and, and then, and it's, it's interesting, but like a lot of times, you know, apart from teams make this mistake of, you know, looking at a path, for example, chronologically, which is normally how you look at it, right? Like, this needs to come before that and so on.

Um, and then it's like, okay, let's fix the first thing, even if like, the first thing is not necessarily like the, the most important thing, right? Yeah. And so it's then the question's like, well, how do you, how do you find the most important thing?

How do you find the bottleneck? And, and it's actually, you know, it's, it's by, it's by talking to people mostly, right? And, and figuring out, well, look, you know, person X is spending like y amount of hours doing this step, and person Z is spending a amount of hours, you know, on the same step.

Then you multiply that, right? And you can do this for like, every step of every path. And essentially that gives you like a, a metrics, uh, you know, view basically of like where the, you know, you can even have like a heat map, for example, right?

And it, it will tell you like, well, there's a, there's a big concentration of hours on this step, right? Which A tells you, okay, well that's if, you know, if we, if we alleviate that, we already save a lot of time. Um, but then b it's like, well, and, and this goes back to Brian's article, right?

Like, if we then like 10 x the throughput of this thing that goes from like being red to being like, you know, purple, right? And it's like, okay, then, then it becomes an even an even worse button. com, what you just described is the reason for ai.

Because instead of looking at this in a linear fashion, a chronological fashion, step one to step two, which is, you know, that's, that's theory of constraints, bottlenecks. Yeah. Right?

We gotta get through this bottleneck before I could get to the next bottleneck. 'cause I may not see that bottleneck until this bottleneck is is cleared. Yes.

I think ai AI looks at it differently. AI looks different at it instead of linear, almost in parallel. Yeah.

And, and tries to handle that, or at least the promise of AI when it works. Right, right. Tries to handle that holistically, if you will.

And, and, and takes care of multiple bottlenecks. Right. Well, he finds it finds different patterns that our brains Right.

Don't, right. That We don't recognize, we Don't recognize. 'cause it's looking at it more holistically like that.

Exactly. Exactly. And, and that, you know, um, but, but here's the other thing too, is, you know, I was listening to you talk, I, I just wrote another article.

I think it just got published today's, um, Wednesday, right? The, the 12th or something? The 13th, I forget.

Yeah. 13th. Yeah.

I I just published it. It's based on a study I read from my friends at JumpCloud about the ever-growing complexity of it in general. And that the only way for us to deal with it is AI and to try to simplify it.

So what you just described is the, the complexity of the factory, the complexity that these platforms to do it to work at scale Yeah. Need to have. Yes.

And we're constantly, it's our, it's the human in us that's constantly trying to say, this is so complex. I gotta, I gotta simplify it, I gotta make it manageable. I gotta make it, you know, step 2, 3, 4, 5.

And, and I could do that. Well, it's the same problem in all of it, whether we're talking about security or identity, you know, in, in, in that piece of it or, or cloud and everything else. Well, in All of enterprise really, like Yeah.

Beyond it like marketing teams of the same problems, right? Like Absolutely. Yeah.

So to, you know, bringing a full circle back to the book. Yeah. What is it about infinity, the o the day, the age of the Beginning.

Beginning of infinity. Yeah. Beginning Of infinity.

Yeah. Yeah. Maybe that, that is the, the world.

We, you know, like one of the physical laws is that things keep getting more complex and we're constantly striving To make them more Yeah. Entropy, right? Yeah.

The entropy Factor. But I, but I totally, I totally agree with you because like, I was just listening to a podcast there like two hours ago. You know, the guy, the, the Google Deep Mind guy?

Yes. De Des that I, I remember. Mm-hmm.

I don't remember how he pronounce his name. Um, but you know, he recently won this like Nobel Prize, right? For like alpha fold, like protein folding problem.

Yes. And this type of stuff. And, and it's interesting because, you know, he was, he was basically talking about, um, for example, how with, you know, there nowadays with ai, you can start modeling like really hard problems that before were thought to be untractable from, um, um, kinda like traditional computing, right?

Uh, so like determination computing, which is what we, what we, what we, what we currently use, right? And it's like, well, you, well you really need like a quantum com computer essentially to solve like complex fluid dynamics and all these kind of things. And instead what they're, what they're showing actually is like, well, yes, if you try to brute force things, which is again, is a little bit the, the, the, the, you know, what we do as humans, I to like break it down and encrypt the root force.

But like, actually, like you already see again deterministic neur networks, not quantum neur networks that are already, um, you know, like finding their own pattern. So for example, he, he was getting example like VO three, which is this new like, uh, video generation thing by Google, right? Where like if you look at like, the way they, you know, it handles like fluid, uh, and fluid dynamics, right?

Well, actually it's already like very, very close to reality. And it's not that they built in any sort of like, um, physics, uh, you know, uh, kind of like formulas into it, it just figured out by watching YouTube videos basically. Yeah.

Crazy. And, and so it recognizes some sort of other pattern matching, right? That then it can use to then extrapolate things that actually look very, very real.

Right? So I think it's, what you're saying is, it's very interesting because obviously, you know, as much as corporate cultures and, you know, all these dynamics between people are complex systems. I don't believe them to be as complex as like, you know, uh, modeling, uh, fluids and stuff.

So it, you know, I definitely, we could be not too far from, from actually having kind of like, again, AI solvings AI problems, right? Going back to the, to the same thing is like, we're advancing, we're generating this new bottlenecks, this new problems, and then the, the, you know, the solution can actually be recursive in a sense. So I was at blackout last week.

I have, we spoke offline about it. I met with a company, a quantum company out there. Oh wow.

Luca, it's coming a lot sooner than you think, you know, 'cause they're already doing stuff. And he explained, I don't know how much you understand about how cubits like pure cubits, real cubits work. Mm-hmm.

Mm-hmm. And how much processing we could do in a cubit. Yeah.

Yeah. Right. Basically in one cubit it's, it's two to the 64th power.

Crazy. And so you generate in one cubit in the millisecond more than we generate in a year right now, right? Mm-hmm.

On regular computers. So yeah, it's brute forcing, but it's gonna change like fluid dynamics, protein folding, all of this stuff. Yeah.

I really blown Away. I I, I actually wrote an article on text strong AI about that, about, hey, forget AI for a second. Look in your rear view mirror.

That's quantum and it's coming fast. Yeah. But, uh, yeah, it was, it was mind expanding.

You know, this was some guy from Stanford and you know, they Q Secure was the company QU secure, but very, very interesting stuff. Anyway, hey man, we're about outta time. I felt like fun.

I've been on a trip with you today, man. It was, it was a lot of fun. Luca, we, we'll make sure we keep doing these every other week though.

Yes, sir. Just a quick dead article by Brian Ross is on, uh, platform engineering dot org's, uh, blog. Yeah.

com. I'll go all these other articles. Yeah, well I thought it was, yeah, I was just responding to it.

But hey, enjoy your trip to the, uh, ion and isles there. Stay away from fires. Thank you.

And I'll be in touch. Thank you. I'll do Alright, Tyler.

Okay everybody. Bye-Bye Luca. Hope you've enjoyed it.

This is the platform engineering show on a shovel. We're out.