Hi, everyone. Welcome to non-trivial. I'm your host, Sean mcclure. In this episode, I discuss deadlines and the critical difference between the artificial ones we create to manage our lives and the real ones dictated by nature. I discuss how our manufactured deadlines run counter to the natural rhythm humans use to create and how we need to somehow reconcile our modern use of manufactured deadlines with the realization that good solutions only exist at nature's deadlines.
I spend time describing nature's deadlines as high points in complexity. I put forward what I think is the best way to ensure we build what nature expects while still meeting the deadlines we create. Let's get started. So deadlines are a major part of our modern life, right? We all have deadlines, whether those are self imposed or you know, from a, a boss at a company that we're working for.
We're all very familiar with deadlines because, you know, life presents us with a host of challenges and we need to find solutions to those challenges. And really what that means is, we need to build things that produce, you know, a certain output that contributes to, let's say a larger project or whatever it is. And uh and the things that we build are expected to be done on time. Uh deadlines allow our life to be kind of visualized on a timeline. It gives us a sense of control and progress.
I mean, again, it allows us to communicate with other people more effectively. Here's when you can expect my piece to be done. So, deadlines are very much a part of modern life, but that's just it, they're a product of modern life. They're not really a phenomenon that occurs naturally, right?
I mean, the kind of natural rhythm or cadence that we have in everyday life, you know, we feel the contrast of that to the deadline anytime the deadline comes up and we find ourselves scrambling, which is common right? Here comes the deadline. We don't really have as much done as we thought we would or it kind of became something that we didn't think it was going to be. And now maybe the scope has changed whatever it is.
And uh and so we feel this kind of contrast between this artificial deadline that was created and the natural cadence or rhythm that we have of, of just the way that we want to work and giving up one's task according to time seems to go against our natural rhythm. You could say, you know, and people today, you always hear about discipline, you know, people get very enamored with this idea of discipline but the reality is, our emotions are not something to be fought.
Emotions operate kind of in that high dimensional space that our structured lives cannot. In other words, you know, emotions exist for evolutionary reasons, right? They've been evolving for a very long time. They're I would argue the primary way in which we actually do solve problems because emotions have a way of anchoring onto the most salient aspects of a high dimensional problem and helping guide our efforts.
And so as our emotions come and go as we do and don't have willpower that, that cadence, that rhythm that we have to, our natural lives is very effective. If you listen to it, it must be there for a reason and to try to, to try to map your emotions onto some artificially constructed timeline can actually be quite problematic. So we've got this kind of dichotomy, we've got these two things, these two truths that seem to, to operate in our modern lives.
One is that deadlines are needed, but also two that they seem to be unnatural. OK. So it, it, you know, we have to accept that things are required by a certain date, but also that there's something wholly unnatural about the deadlines that we create. Now, you know, how do we kind of reconcile these things? What do we do? Well, we can kind of redefine what done looks like, right? This is pretty common uh especially in industry.
So you kind of take a look at a project and you look at the requirements, then you say, OK, well, we can't boil the ocean, we can't have it all. And so maybe we just redefine what dun look done looks like this is common with like minimum viable products or NVPS, for example.
So you control the scope of the project, you kind of shortlist, the, the everything that you want or you, you, you kind of narrow down your total vision down to the things that are the must haves that will kind of tease out uh you know, customer interaction really early or just just make something viable and then we'll build from that point going on, you know, but the reality is there's no way to know what it's going to take to build something worthwhile.
You know, I sometimes I say there's no such thing as a small MVP, right? Whatever that minimum viable product is, it still has to have all the requisite pieces because it's, it's not for us to really design what the thing is going to look like. It's for nature to decide what doesn't, doesn't work things either work or they don't. You know, if, if, if Michelangelo is carving away the stone on his sculpture, right? The sculpture is, is already there inside the stone, right?
It, it's something that exists in nature pieces that work is it, it's not for us to decide what does and does not work things are either coherent with nature or they're not. So there, it's not, you know, the size of something is not really something you can control. You can't just say up front that. Well, this is in scope and this is out of scope and this is a must have, uh you can't really define done. Nature, defines done.
And it, it's, it's up to us to find that done as we chisel away if that makes sense. So there's really no way to get to, to know what's going, what it's going to take, to build something up front worthwhile. You know, we want to think that our creations follow, you know, a deterministic set of rules like cogs and pistons that produce very well defined outputs, but it's not really the reality, it's not true.
Uh You know, nature needs a commensurate level uh or requisite level of pieces and interactions. And, you know, when those are going to come together and pre precipitate out is not really for us to know until after the fact, right? All we can do is, is, is, you know, take action, react to signals, move accordingly, knowing that eventually the right solution will emerge. OK. So we have to accept that nature chooses our deadlines. OK?
So let's think about the underlying mechanism here about why nature, why and how nature, you know, chooses the deadline. If we can understand that, then maybe there's something in that understanding that we can use going forward to kind of reconcile the fact that look, we live in the modern world with artificial deadlines. But ultimately, it's nature that chooses the deadlines. How do we get those to work together?
Well, we can think of solutions that work well uh as kind of resolutions to optimization problems. So an optimization theory um you'll, you'll we typically think of problem solving in terms of a ball kind of rolling down a hill. It's kind of like hills and valleys and the lower that ball sits, the better the solution. And that's because the hills and valleys are really the amount of error.
So the error would be the difference between what you have now as a solution and what kind of that ultimate solution would look like. So the difference between those would be the amount of error. So a lot of error would be like a ball sitting high on a hill and a low amount of error would be the ball kind of sitting in the valley. OK. So in the valleys of our error surface, you could say uh are the configurations that work OK.
It's, it's think about this physically now it's the physical arrangement of pieces and their interactions that solve the problem of interest, right? But you know, optimization doesn't tell us anything about what those physical configurations are supposed to look like. Like what are those pieces and what are their interactions? What is this thing supposed to look like and that's all the more true when we build things that are truly complex, which these days is really becoming power.
Of course, right, we have so many pieces and interactions to the things that we create that there's a lot of opacity there, there's a lot of uh inabilities to understand how these things actually produce their outputs. And so trying to figure out the right physical configuration, what pieces and how they need to interact to produce the solutions we need is extremely challenging.
We don't, we, we, we can't just kind of reverse engineer things or peel back and see how they deterministically add up to produce the output because these are not deterministic things, right? So we, you know, this is why embracing randomness and naivete is so critical to creating innovative things because we have to explore a great deal of uncertain terrain in order to exploit the things that actually work. OK?
But that all takes time, it takes time to go exploring and to, and to try this and to try that and to set up experiments and, and and kind of whittle our way down to something that's that's good. So when it comes to our, our, you know, manufactured deadlines by which we live by in our modern lives, you know, we can't just wait for solutions to converge right.
Again, nature sets the deadline and we don't know when that deadline really is, it chooses when there's going to be enough pieces with the right interactions that come together to actually precipitate out a solution that works. But when that's going to be, we don't know and we can't just keep kind of playing in the sandbox until something eventually pops. Oh OK. But we can say something about the physical structure of good solutions.
Even ones that are novel any solution to a hard problem must have a structure that is neither too simple nor too random. OK. So what do I mean by this? Well, we should expect any solution that we build any good solution that we build to exist inside of a kind of sweet spot between simple structure and randomness where complexity is high. So you know, simple structure is not going to solve anything worthwhile because it's too trivial, right?
It's uh it's it doesn't have that many pieces and we know that it's complex things that solve hard problems. I mean, all you have to do is look at nature and look at the solutions that she comes up with. These are not simple things, these are highly intricate, highly sophisticated things, they have many pieces, many interactions. So many that we can't count them all that we have to get very statistical and probabilistic with our approaches to try to understand them.
It it's very much a a universe of aggregate level phenomenon at least when we look at things like life, right? Uh the the things that are solving hard problems, right? Genuinely hard problems. So simple things don't solve hard problems. We already know that you just have to look at nature to, to realize this. And, and so if you, so let's say we have something simple and then we start to increase the pieces, increase the pieces, it gets really, really, really complex things.
But then if you keep going, you kind of eventually just get randomness, right? Like just so many pieces and so many things going on that it's it's it's kind of like the white snow on the television, right? It's just a random mess of kind of nothing because there's no pattern, there's no pattern to what we're looking at. So what you can say about solutions that solve hard problems is that they, they seem to exist at a kind of maximum of complexity, right?
They're not super simple things and they're not just a mess of things that are together, they have pattern, they have useful information to them. They sit at some kind of high level of complexity and this is the point where the properties of complex things becomes apparent. OK?
You have things like uh non linearity and you know, self organization and the formation of of hierarchy, the things that uh you know, engender an object with the ability or with the properties needed to solve hard problems. OK? So that you, you need those complex properties to emerge so that the hard problems can be solved OK. So we can say a good solution must exist somewhere kind of in that sweet spot, right? You bring it, you, you bring a bunch of stuff together initially.
It's too simple, it doesn't do anything. Eventually it gets really, really complex and it does something amazing. And if you keep adding a bunch of stuff together, it just becomes kind of crap, right? Like there's, there's, it's just a mess, it's just randomness. So it's, it's kind of a sweet spot somewhere in the middle. Now, we can visualize that sweet spot of complexity using uh kind of a quote unquote simpler system.
But one that is still a complex that we're all familiar with, which is like ink dropping in water. OK? This, this would be a system that evolves dynamically uh with time. OK. So think about dropping some black ink, let's say into uh a cup of water.
And you're visualizing this, you're looking at it now, at some point in time, uh you know, that evolving swirl of complexity must be a configuration of matter that is arguably the most complex relative to any other configuration the system reaches, right? So in other words, when you first put the ink in, you know, it's not if you just looked at that at that kind of first moment at time, you said, do you think that's complex?
Well, not really, it just kind of looks like this, this, this this ball of uh you know, of matter and it's, it's got a little bit of structure to it, but not a lot. Now, a few seconds go by and this thing starts to expand. Oh, you can do the same thing with cream and coffee or something like that right now. It takes on these kind of intricate folds. It's got like a depth. It's got interesting ness to it, you would say. And I say no, is that complex?
You say, yeah, that, that you know, that looks like a complex thing, right? There's a lot going on there. It it would be impossible to describe that object in all of its details. I would have to create a much higher level summary in order to even talk about what I'm looking at. And then if you wait even longer, the ink kind of pervades the entire body of water and it uniformly colors, you know, the the the entire environment that it's sitting in, in this this case, a cup of water.
And then, you know, if I said, well, is that complex? Well, no, it just kind of all looks like one thing, right? So the beginning was simple, the end, you know, it was kind of simple or random. But in the middle, we had this highly complex structure that was kind of hard to describe, but it was very high dimensional, had intricate folds, intricate patterns. It was very much nontrivial right now.
We would say that the system is actually most capable in that sweet spot in that middle point where it has the highest amount of complexity. Now, I don't know what problem, you know, ink and water is necessarily going to solve or could solve. But the point is is that it must be most capable at that sweet spot. Now, the reason is because hard problems are themselves high dimensional. They have a lot of things that need to be figured out.
They have many aspects to them that must be accounted for, right? Think about the problem of, you know, facial recognition or speech recognition, right? If you just try to deterministically program all the rules into the computer to try to solve that problem, you would fail because you'll never know what all those rules are.
There's just too many, what you need in your solution is in is an extremely intricate complex object or thing or physical configuration that has so much complexity to it that it can account for all that high dimensionality. In the problem. In other words, there was a matching between the solution and the problem, right, a very hard problem if we think of it as kind of a complex thing that needs to be figured out requires a complex solution.
So in that sweet spot, our solutions are the most capable and that's what we're aiming for. That's where nature's deadline should be, right? Because again, nature's deadline is where you have the right pieces enough of them and enough of the interactions between them to produce the output that solves the hard problem. And we should expect that to be where complexity is reaching a kind of maximum.
Now, of course, there's little reason to believe that we, you know, could predict where that sweet spot is going to be, you know, for a given project or thing that we happen to be working on in life, right? Um you know, where would that maximum, you know, complexity exist? You know, we couldn't really predict that with any decent level of accuracy, I mean, complexity is already post chaos, right?
So like chaos, if you think about chaos theory, you know, that's a determining a fully deterministic system which itself can't really be predicted for reasons of error propagation and all this kind of stuff, you know, and complexity is, is much more intricate and involved than what you would see in a chaotic system. So it's really, really unpredictable.
So there's no reason to believe that we're going to know where that sweet spot is in terms of prediction, which is another way of saying, you know, we don't know when nature's deadline is going to be, you know, you take a given project, you take, you know, these materials and these people and these goals that we want to achieve. You're not going to know when that deadline is when, when is it going to, when is the best possible configuration going to precipitate out.
It's, it's, it's a completely kind of stochastic arrival time if you will a completely random or at least unknown uh arrival time for all intents and purposes. But what would be useful is, is knowing if the sweet spot is something smeared out over a long period of time or something that lives for a fleeting moment. Right? Because if that sweet spot of kind of in between complexity is something that, you know, is kind of smeared out over a long period of time, it lasts for a while.
Then maybe that means that the things that we build, you know, even if we kind of undershoot nature's deadline that we can still build something pretty good, or if we overshoot Nature's deadline, then uh maybe it's still pretty good. In other words, the, the the part of maximum complexity isn't really a super sharp peak, maybe it's kind of like uh a plateau that goes for a while and then kind of drops off. And so maybe there's a lot of wiggle room there.
But in the latter scenario where it is really a sharp peak, then that means there's not a lot of wiggle room and that means we, if you undershoot nature's deadline or overshoot nature's deadline, even by a little bit, then whatever you produce is going to be AAA very subpar thing, it's gonna be very non uh solution. It's not really going to be that good at all.
In other words, you really do need to somehow match nature's deadline to get something that is viable that is actually worth producing, that's gonna, it's gonna be turning out to be good in the long run. And so if you think about the kind of artificial manufactured deadlines that we use as part of modern life, because there's no way to know where nature's deadline is. Those, those manufactured deadlines that we come up with are highly, highly unlikely to match up with nature's deadline, right?
It's probably going to undershoot or overshoot the peak of the complexity and, and all the more so if, if that sweet spot is a very sharp well-defined peak. OK. So, so, so how could we know this, right? How could we know if nature's deadline is kind of this sharp al define peak or if it's kind of smeared out and we have a lot more wiggle room so that maybe our manufacture deadlines could overlap with nature's deadline to some to some extent.
And it's not all dire well to get some insight into that direction, we can attempt to quantify complexity so that we could, you know, calculate it for systems that evolve in time.
In other words, if you come up with a way to look at a system and and quantify or calculate the complexity level and then you kind of watch that system evolve over time and see the shape of, of the curve that would be drawn out for the amount of complexity over time, then you could take a look and say, OK, did it kind of go up really quickly and, and, and create this well-defined peak or was it kind of smeared out over time?
It would give us a sense of, of of kind of how broad that sweet spot is or narrow, right? So there's a number of ways that we might go about doing this. There's all kinds of ways you can try to quantify complexity. You know, the literature is, is loaded with a number of different possibilities. Uh Probably one of the most common or popular now would be something called algorithmic complexity. And that's also called of complexity. And uh you know, it's got some good aspects to it.
It basically thinks about the amount of complexity as uh you know, the size of the program needed to give a description of the object you're looking at. So for example, if you had like a bunch of characters, a babababab that just kept repeating, and then you compare that to another string of characters which is like CJ two Y, whatever that looks a lot more random.
Well, you would say that first string of characters is uh is is of lower complexity because you could describe it by saying something like repeat a B 16 times. In other words, I could produce a kind of short sentence or to describe the, the kind of the, the structure that I'm looking at, whereas in the second string, which seems kind of jumbled and, and random, or maybe it is random, there's no way to summarize that simply. Right.
In other words, the quote unquote program size required to summarize that object would still be quite large. Right. There wouldn't be a nice compressed way to do it. So that's kind of a way to uh quantify the amount of complexity of something that we're looking at the problem with that with respect to what I'm talking about in this episode is that it, it doesn't um that that or algorithmic complexity kind of assumes that complexity will increase the more random you get right?
Because if you take a look at a completely random string that has no pattern to it, no useful information to it. It would say that that thing is, is of high complexity because it can't be compressed, the program size needed to summarize it would still be quite large. But that's not what we're talking about here because we're talking about complexity being a maximum at some middle point.
That sweet spot in the middle, if you keep adding randomness to that sweet spot, that's not going to be more complex, that's going to be less complex. So this this kind of algorithmic complexity, it's useful in terms of thinking about programs kind of computing outputs. I mean that part is useful, but it's not useful in terms of trying to define that sweet spot right.
So another approach you could use uh and some authors have done this across a few journals is uh is is something called self dissimilarity, self dissimilarity. And the idea is that something that is truly complex, let's say in that sweet spot would have uh kind of the greatest degree of self dissimilarity between or within its structure. So what do I mean by that? Well, uh imagine there's a tiger.
OK. And you could look at that tiger at two different scales, one would be at the level of the blood cells, right? So you literally go look at the tiger's blood cells and then you step back and then you view it at a different scale where you view the whole tiger. Well, those are two very different things, aren't they?
Those two scales that we look at, we're looking at the same thing, we're looking at the tiger, but one is the scale of blood cells, the other is stepping back and looking at the entire tiger. So we're looking at a tiger at two different scales. Those two different scales are very, very different from each other, right? The whole tiger gives, gives you a lot more, more or at least different information than uh than looking at a tiger at the blood cell level.
So we could say that complex things have a lot of differences between those scales. OK? Compare that to something like ink and water, which still has complexity. But it's a lot simpler than a tiger, right? And, and why is that? Well, if you look at ink and water up close, it's got swirls and patterns and then you look at it from far away. It still kind of has swirls. There's not a lot of difference between the two scales, you could argue.
So how self dissimilar things are across scales is a way to measure the amount of complexity in the thing. OK. So why am I talking about this? Well, it overlaps with what we're trying to do. Uh what I'm trying to do in this, in this uh in this episode or this essay, which is to get at a sense of complexity that, that, that is structural in nature. OK?
Because of self dissimilarity between scales, it's, it's a way of measuring complexity that takes into account out the the the kind of intricacy of the structure that we're looking at because you can imagine the more different scales something has and the more those different scales are different from each other, that must mean that object kind of has intricate folds and patterns to it, it has a pattern to it.
And that's the kind of uh you know, complexity that we would perceive if we look at something. And that's what we're after we're trying to get that structural aspect again, going back to framing problem solving in terms of optimization. We're saying that the low points, those, those we got the hills and the valleys, the low points in the valley is where the good solutions exist, they have the least amount of error.
And we, we, we expect that to be um the point where uh the, the, the configuration, the physical configuration of the object is computing the right outputs that we need to solve the problem. And we're also saying that that configuration should exist in a kind of sweet spot where complexity is very high. And so we want a way to uh to measure complexity of systems that looks for the peak in that complexity, right, whether it's narrow or whether it's broad, but does.
So in a way that understands complexity in terms of the sweet spot in terms of the structure. OK. So hopefully that all made sense. So that's why we're going after this kind of self dissimilarity measure of complexity. It's one that works with the kind of, you know, narrative that I'm using in this in this particular episode. Now, one way you can do that is through uh you know, a process of renormalization. I'm not going to get into all the details.
You know, I write more technical versions of these pieces on medium and substack. So you can go check out those if you want some more of the details. But basically what you're doing is you say, OK, let's take one scale like the blood cells of the tiger and let's take another scale, you know, maybe like the like a picture of the entire tiger. And there's a way to compare those two scales and look at the additional information that was was present between them.
And you can do that for all the different scales, all the all the different kind of levels at which you could look at a system and kind of add them all up and then get this complexity measure at the end. So anyway, let's say we do that. OK, we've got this measure of complexity. It takes into account the physical structure, which is what we want because we're thinking we're, you know, we're trying to understand the physical structure of that sweet spot where complexity is maximum.
And we can take a look at, let's say a video of ink um dispersing in water and we can take snapshots of the images of of that ink dispersing in water and do this kind of renormalization process, which takes a look at those images at different scales and understands the structural complexity over time.
OK. So step back from all that, even if they got a little jargony for you, all we're doing is we're going to measure the complexity of something we're interested in and we're going to see that as that complexity moves or or increases and decreases over time. Whether or not we get that broad peak or whether or not we get that narrow peak again, a narrow peak means nature's deadline is a very specific point in time.
And every manufactured, any manufactured deadline that we come up with is unlikely to match up with it. A a broad kind of peak or plateau of structural complexity for that sweet spot would mean that our manufacture deadlines have a good chance of overlapping with it. So maybe we can still make something good. OK. So it turns out that if you do this, you do actually see the sharp peak, OK.
If you measure the structural complexity, that's what we'll call it just, you know, structural complexity, right? Uh of that system. So ink and water, let's say you'll see. Uh at first, obviously, the complexity is very low, just like we said, it's kind of like this tight ball of matter.
And then as it starts to spread out the structural complexity, as we're measuring it through renormalization goes up to a very high point and then it precipitously drops down again and, and goes back to a low point. So, so it's indicative of that sweet spot that we're talking about where we expect complexity to be high for only a moment in time. But it also shows us that moment in time really is just a moment, it really is quite a sharp peak. OK?
And so the so, so again, the consequence of this is that, you know, we go through life and we come up with all these kind of manufactured deadlines and, you know, presumably we're putting some thought into these deadlines, you know, as if we know what we're doing. Right. But at the end of the day, we really have no idea when something will precipitate out as a good solution.
And so it's just statistically highly unlikely that a manufacture deadline that we come up with will overlap nicely with nature's deadline. And because nature's deadline is, is a fairly sharp peak, there's, there's good reason to believe that whatever we do come up with is actually going to be quite so par unless, unless we have a way of somehow knowing uh you know what that really good configuration at Nature's deadline should be right?
So imagine that peak going up high and then you're drawing kind of a line down that peak and saying this is where you know the right pieces with the right interactions come together to part to, to precipitate out what's needed to solve the problem. If we could somehow know, even though we can't know where Nature's deadline is, if we could somehow know what constitutes those pieces.
And if we could somehow choose the right pieces and, and choose the right interactions, then it's kind of like entering the peak earlier. OK. So in other words, that peak exists at some point in the future as Nature's deadline, we don't know where it is. We could easily undershoot it by making artificial deadlines that are just too early, we could easily overshoot it.
Think of scenarios where we second guess ourselves or we have too many cooks in the kitchen and we're just bringing too many details into the thing and we overshoot nature's deadline and the optimal solution would have been way, way back. We could have cut that off way earlier. Right. The both the undershoot and the overshoot are, are going to be quite common scenarios unless we know something about the right pieces to choose and, and the way that they're supposed to interact, right?
And that would be a way of we wouldn't be able to shift the peak, right? We can't bring in nature's uh deadline closer to our manufacture deadline, but maybe we could enter the peak earlier, right? Even though we're not there in time yet. In other words, we could, we could do things that are commensurate with the, the structure that actually needs to be there.
And that way whenever our manufactured deadline happens to be, it will definitely contain, contain pieces that are viable that are tractable. We, we we'll have parts of nature's deadline configuration into the stuff that we're building, even if we're too early or we're later than we're supposed to be. Even if we under or overshoot the deadline, we'll have pieces that are good that are tractable. So how would something like this be possible?
What we're calling my last episode where I was talking about how we create categories in order to make sense of our world, right? We we operate in environments that are extremely detailed. There's way too much information, way too much data for our minds to be able to, you know, process all of it and see how it all adds up.
Uh We have to create these higher level of abstractions in the mind, these categories, these labels that we use, and we use those labels to navigate the world to make decisions. That's very much how the human brain works. And the human brain is really our best example of a solution to the hardest problem of all, which is general intelligence.
And it also happens to be the most complex phenomenon that we know of, right, the human brain is structurally uh you know, the most complex thing as far as we know in existence, right? It's got the most pieces and the most interactions. So we've got that extreme high level of structural complexity, solving the most difficult challenges of all. So what does that mean for this deadline stuff?
Well, the way we can make definite correct moves at the onset of any project, even though we don't know what that structure is supposed to look like, but we could still enter the peak, we could still choose pieces and interactions that must be correct is by focusing on the most abstract aspects of the challenge and ensuring that details are introduced only when absolutely needed. So I don't understand what I mean. Imagine.
Um you're looking at an artist who's really good and they are sketching someone's face, they're doing a pencil sketch and they're going to do a very kind of realistic version of that person's face as a pencil sketch. We notice the artist's movement and if you go to my article online, you'll actually see a, you know, a video of this, right?
Well, rather than working away at the details, the artist is actually just lightly touching the canvas, they lay down only the most abstract outline of the desired result, right? They kind of just just barely start stretching, sketching the outline of eyes and nose and mouth and and the head and kind of where the eyebrows are going to go. They're doing the most high level, most invariant parts of a person's face invariants are the absolute must haves or the parts that don't change.
So in other words, if I'm sketching someone's face, there are many, many different ways I could add details to the eyes, right? We could do different shading different lines, different details of the iris and how the you know the the eye interacts with that iris.
And then you've got the white parts of the eye and then you've got the eyelashes and you there's so many different details and if you lay those details out at the beginning, you're gonna get in trouble because you have no chance to step back and think about the essence of the thing and how those details, details should be brought to bear on the challenge.
But if you only do the most abstract invariant pieces, in other words, the eye, no matter what the details are going to be, still has to look like an eye. So we can still draw the most general outline of an eye and we won't be wrong because the the the eye will no matter what details you you you bring to bear on the eye later, it will definitely still have that most abstract piece. It will definitely still have the outline of an eye. OK?
If we are writing a story and, and we want to tell a story to someone or a script for a play or, or you know a movie, a show, we could change characters at any given time, right? The details of the characters or the surroundings, those are all in flux, those could always change. But you still have to have a narrative structure. You still have to have a certain structure that will always be there, take anything that you build, anything that you create, right?
Whether it's a pencil sketch of someone's face, you know, script for a movie, you know a painting, uh you know a sculpture, a program that we're putting together a piece of software that we're writing, there are always going to be high level invariant parts, parts that don't change no matter what. And then there's going to be a whole mess of details that could change at any given time.
If you want to enter the peak early, if you want to make sure that you're getting aspects of the structural complexity that must be there, then it behooves you to only lay down the most abstract and variant pieces of what you're creating at any given time and kind of leave the addition of details to the last possible moment when you're absolutely or, or at least fairly certain that they should be added. OK? And, and you know, there's again going back to the human brain, right?
There's a reason why we use categories, why we use high level labels to to navigate our complex world because those things are true, the invariant pieces must be true out out of all the details that could change. Those are the sticking points, those invariant, you know, I like I like to say invariance is truth, right? Invariance is truth.
So if you want to enter the peak early, if you want to make sure that you're, you're getting parts of the structure that must be there regardless of where nature's deadline is and regardless of where you've decided to, to manufacture a deadline, right? If, if that cut-off date has to have parts that work, then you, then you then you, you need to make sure that you're always choosing the most abstract and variant parts of what we do. OK?
If I'm writing a story, let's think structurally about the story, let's think about the fact that we've got, you know, characters that get introduced and that maybe some tension starts to rise and that there's going to be a climax, that's what's important. Eventually, you'll start to add the details of the characters and the details of the surroundings and those could change at any given time.
But they won't change to the detriment of the project because the invariant pieces will always be there. You'll always be able to deliver the outputs that are needed. OK? Now again, the more in line your manufacture deadline is with nature's deadline, the better it's going to be. But regardless of where your manufacture deadline is, if you have those invariant pieces there, you will definitely deliver value if I'm going to sketch someone's face. And maybe I've got a really tight early deadline.
I might not be able to give hand someone off the most realistic pencil sketch, but it will be a good pencil sketch. It will still look like the person because I started by capturing the invariant parts of the project. OK? If we're on a big software project for a big company, multimillion dollar, whatever it is, we're gonna have to come up with milestones, we're gonna have to come up with, you know, manufactured deadlines because we've got to communicate with people all that kind of stuff.
I said at the beginning and the odds of that manufacture deadline matching with nature's deadline, right? Which is the point in time when the actual right pieces and connections really precipitate out, you know, to, to, to what's needed. It's unlikely that we're going to produce something at the best possible nature's deadline. But if we're always capturing the most invariant parts, the parts that need to be there, if we're thinking structurally, right?
Just like with the story, if we're thinking about the higher level abstract structures to the problems that we're solving, to the software that we're building, to the teams, to the programs that we're putting together, then no matter when that artificial deadline hits, it will be good, it might not be the best possible thing ever, but it will be good. It will be valuable because it has invariants, it has parts that are abstract and the details were only added when we absolutely had to.
OK. And it also serves as a much as a, as a much better kind of construct that can be built off of going forward because what you built were the invariant pieces, which means details can be changed and added. You know, it's much more modular and all that kind of stuff. And finally, what about scenarios where maybe you don't really care about manufactured deadlines at all? Maybe you are, you know, you've got a job but your, your real passion is kind of a side project.
Maybe it's a business that you're building, but you're willing to, to build it slowly. You're going to let this, you know, Germanin kind of and gestate naturally and when it's ready it's ready. Um, maybe you're an artist who paints not so much to get things out in front of people, but just as a learning process, you, you see painting almost as a kind of meditation and you just want to paint and you know, that eventually things converge and then you release it to people and that, and that's ok.
Uh whatever it is, any kind of situation like this where the manufacture deadlines are not really that important. Well, this is a much better way to be in general, no matter what corporate setting or whatever you might be in, no matter what the expectations are, we should try to approach that kind of situation where we really don't care about the manufactured deadline.
They might still be there because, you know, that's just part of kind of the middle language we use to communicate with people and to structure the live structure our lives or, you know, structure our communication with our teammates, whatever, but we don't really care about them. We want to just work at the natural cadence rhythm of our emotions of our willpower. Sometimes we're in the mood, sometimes we're not and, and nature will choose the deadline.
I mean, our best possible work will be the work that overlaps as close as possible to the peak of nature's deadline when the the the right amount of pieces and their interactions have really come together when it, when it's really precipitated out as the best possible thing. I mean, that's what we want to create. Well, if that's the most natural way to be and that's something to work towards, is there a way to kind of just operate as though we don't really care about the deadline.
Well, the manufacture deadline. Well, one way to do that I would say is by staggering your work. I, I I'd say it's, it's a good thing to have a lot of um projects going in parallel. So, you know, let's take this podcast for an example as an example, you know, it, it's easy to kind of think. Well, my next episode is going to be this particular topic.
Well, maybe, but maybe not, it makes more sense to have 234, even five kind of ideas in play and to just be working and thinking structurally about those only laying down those most kind of invariant pieces of those episodes and let nature choose what the next episode is going to be because out of those four or five that I might be kind of working on in parallel.
Um you know, based on my mood, based on my emotions, based on just kind of laying down that high level structure when I feel like it, one of those is going to pop out as being ready when it's ready, right? It's going to have the right requisite amount of pieces and interactions that it needs to be. Hopefully a pretty good solution. And that's not for me to decide. So, have a lot of things in play allow the cross pollination of ideas between things to happen. Naturally.
It's a much more, it's, it's probably more how our ancestors built back in the day before the invention of the mechanical clock when they, you know, just allowed their feelings and their emotions to rise up as needed. And then that's when they kind of pounced on the project. You know, they weren't measuring their progress in, in juxtaposition to the time of day or to the schedule that was laid out, right? It's a much more natural way to be. So, have different things in place.
Sometimes you pick it up. Sometimes you don't, when something inspires you, you lay it down and by lay it down, you do it structurally, you do it by the abstract invariant piece that's supposed to be there and then let nature choose the deadline. So I don't know what my next episode is going to be.
Maybe you don't know what your next essay is going to be or your blog post or your next sermon that you're writing or your next piece of software or whatever it is that you do, you, maybe it's not for you to decide what the next one is, have things in play stagger your work and then even if it's a deadline that comes up, ok, now it's time. Maybe you take the one that has germinated the most, right?
The one that has kind of coalesced naturally to, to, to the most final product that it can be at that manufacture deadline if that makes sense. So stagger the work don't assume that you need to do things in order, right? Allow things to come in and out, have that natural cross pollination, take a few days off and then look at it again with that fresh perspective.
It's a much more natural way to let your work breathe, to allow it to kind of gestate, to allow it to settle, precipitate, crystallize to gel as it needs to and let Nature choose the deadline. OK?
Let Nature choose what the next episode project, whatever it's going to be and even on a, a corporate project, right, you could, you could split that project into a lot of pieces, you know, a lot of times we put road maps together and we assume, well, first you got to build this as the foundation and then you gotta forget all that, you know, if you can convince others or at least for yourself, you know, work on a number of things in parallel. Let nature choose what's going to be ready.
When knowing that even with the manufactured ultimate deadline, you will have a lot of viable pieces there because you were building structurally, you're building foreign variants, you're allowing things to kind of go with your emotion with your willpower and, and you're going to overlap much more effectively with nature's proper structure at nature's deadline. That's it for this episode.
If you'd like to take a deeper dive on this topic, I write more technical versions of this material on both medium and substack. You can find them on medium dot com slash nontrivial and sean mcclure dot substack dot com. So, go ahead and check those out as always. Thanks for listening until next time.