Show Notes
In this episode, Mon-Chaio and Andy dive into the intriguing concept of ‘Alien Chess’ and its relation to leadership and organizational diagnosis. They reflect on its connection with the four spheres of knowledge discussed in the previous episode and critically analyze its validity as a system within organizations. They draw parallels from the Cynefin framework, extreme programming, and real-world applications, debating whether alien chess is a useful model or leads to high-functioning, learned helplessness. Join the discussion on the balance between strategic planning and adaptable responses in leadership and technology sectors.
References
Transcript
Mon-Chaio: Good morning, Andy, and welcome to all our listeners. In today’s episode, we’re gonna be discussing the concept of alien chess. Now, I know that doesn’t sound like a leadership concept, and you might think, what are these folks up to? But I think, uh, we wanted to discuss the concept of alien chess in conjunction with what we discussed in the last episode, the four spheres of knowledge. So let’s give a summary of what alien chess is. What the four spheres of knowledge are so that we can get a little bit oriented.
And maybe we should start with the four spheres of knowledge. ’cause this is something that people probably heard if they listened to the last episode, um, but maybe forgot because I mean, you know, that was last time
Andy: That was last time. Or maybe you’re just picking up this one episode and you don’t listen to everything that we talk about. How dare you. In which case, we will tell you what it’s.
Mon-Chaio: All right, so maybe we’ll start with the four spheres of knowledge. Um, and the four spheres of knowledge is a way of thinking about organizational diagnosis. And what the authors talk about is when you look at organizations and you try and diagnose issues with them, You need to have a model to think about.
So Andy, do you want to take us through a quick summary of the four spheres of knowledge?
Andy: Yeah. Yeah. So let’s go through this. So the four spheres, let’s just name them first and then go into a bit of what they’re for and how we might use them. So the four spheres of knowledge are you have standards, you have symptoms. You have solutions and you have systems. I was thinking models. No, they’re all s it’s systems and, uh, the four spheres of knowledge were what?
This, this paper that we had read in our last episode, what they posited was necessary for having effective evidence-based diagnosis. And what they were saying was that there doesn’t seem to be any rigorous diagnosis regime for organizations, which means that there’s no rigorous diagnosis regime for leadership in organizations and in inspecting how diagnosis happens in medicine and how it happens ever so slightly.
They looked at this, how it happens in engineering. They posited that without these four spheres of knowledge. Diagnosis just won’t work well or won’t work at all. And to go through them very quickly, what they are is they give you, as a practitioner an understanding of these different areas.
And so standards is to answer the question, what does healthy look like? What is kind of like a target state to say in a medical field to say, this person is healthy. Uh, if things are away from that, then you have some indication that they’re not healthy and you might take some action.
Then you have, uh, symptoms. And symptoms are ways in which that different kinds of ill health will show. So it might be that, uh, a test of one form or another tells you that. Engagement is low. If we’re gonna go back to organizations, or it might tell you that, um, uh, turnover is high and those are symptoms of something.
Um, then if you have symptoms and if you have, uh, what your standard is, then you need solutions. These are actions that you can take, things that you can prescribe and say, do this. It has a chance of impacting that ill health and turning it closer to health and then your full understanding of how this all fits together.
That’s the system, that’s the model of how do things fit together, what does interacting with one piece, if you change that, what kinds of, uh, further impacts could that have throughout the whole system so that that gives you an explanation of how. That symptom might be connected to that, uh, that reading of ill health and how a particular solution might then actually cause the change that you’re looking for.
It’s not just a random shot in the dark. It’s not just to say, oh, this, it’s because there is a reasoned theory behind why this particular intervention will make this difference to this measurement.
Mon-Chaio: Good, good explanation. Right? And the system is what connects them, right? It’s the, the reason that a standard is a standard, say in the medical field, again, somebody’s, uh, healthy body temperature is 98.6 degrees or whatever, is because of an understanding of the system. The system says, uh, your metabolism does this, and you’re a warm-blooded creature.
That, and if you had a different system. Say a, a more archaic understanding of health, perhaps in the Middle Ages or, or older, then your standards would be different. The way you measure symptoms would be different. Um, although symptom measurements sometimes I don’t think is as system dependent, but the standards certainly are.
And then I think the solutions are definitely based on the understanding of the system.
Andy: For instance, uh, uh, like on the medical thing to, to bring it back to, uh, old understanding versus new understanding. We still use bleeding. We still use leeches in very specific circumstances, but the, the underlying model and the standard of health that those are all based on is very different.
Mon-Chaio: That’s really good example. Alright, so that’s the four spheres of knowledge around organizational diagnosis. Today we do wanna focus on the systems part and we wanna focus on that with regard to this article that came through our feeds called The Resilience of Alien Chess, which you can find linked in the show notes or with a quick search online.
So what is Alien Chess in our context? In the context of this episode? It is based on an article that was originally published internally back when Meta was known as Facebook. So I believe the original published date was something like 2020 maybe, and so it’s written by, an employee of Facebook at that time, I would imagine, I don’t know, a fairly senior engineer, um, slash engineering leader I believe these days. Still very, very senior and, so in 2020 he mentions that he’d been at Facebook for 13 years. So this is a person that grew up essentially through Facebook’s evolution, um, their start and all the way through, uh, to where they’re now. And what he talks about is he talks about his understanding of how to be successful at meta and. In a way, I would say that he also posits that this is what he believes is required for success at the fastest moving largest, best technology companies in the Silicon Valley space. Um, he doesn’t, I don’t think he says that outright, but I feel like the, in my reading, um, it felt like that. Did it feel like that to you, Andy?
Andy: Yeah. In fact, I, he, he didn’t quite say that. He did say that, um, he was publishing this, uh, externally in the hopes that it would resonate for people because he believes that these things are, are out there elsewhere.
Mon-Chaio: Mm-hmm.
Andy: I.
Mon-Chaio: So what he says is, when you first joined Facebook. He thought the way to become successful as an engineer and a leader was to play chess. You sit down at a chess board, you look, and then you try to win the game of chess. You try to create from that board position a better position, um, in order to win. And he says what he would do is. Do what everybody does if they’re a reasonable chess player. Look, one move in the future, two moves in the future. What might my opponent do? How would I respond to that? And he realized that that wasn’t working for him, that he was.
Kind of getting crushed. The rules would change a little bit and he would be like, well, I couldn’t see that far into the future. I thought they would do this, but they actually did that instead. Um, I didn’t think about that. So he said he dug more into it and said, well, I just have to look more moves into the future.
Uh, something computer chess calls ply. Right? Like how, how deep do you go in the, in your move tree, in your analysis tree?
Andy: he said he tried to be more strategic. Think months ahead. Think, think as far ahead as he could and understand how all of this was gonna play out.
Mon-Chaio: Right, and at some point he decided that’s the wrong model to think about. The right model to think about is this concept of alien chess. So what’s actually happening, he says it’s like an alien comes in regularly and takes the chess board that you are playing the game on, takes it away and puts a completely new board in front of you. So your strategizing makes less and less sense because you never know when that board might change. And so you can look 20 moves deep, but then in the next second the board will be completely different, and that work was all wasted. And so he posits this opinion that instead of strategizing, playing chess in the normal way, what you should be learning how to do. The strongest thing you could you should do is get good at alien chest.
The adaptability, the ability to constantly change what you’re doing, take a new situation, and analyze it quickly, and to make short term changes in order to get to the best state possible, step by step in an iterative fashion. Uh, kind of like development because you never know when that thing’s gonna get pulled.
You wanna get value quickly, right? So you get as little value as you can, as quickly as you can, and then that board might get pulled and then you’re dealt with a new situation. So that’s what he pauses. Don’t play chess, play alien
Andy: yeah, he, he says, um, stuff has changed. Sometimes you become the opposite color. Sometimes it’s all the same, but your single most key piece is missing. Sometimes it’s just a totally new board, so you hunker down and you play one for a while. Then the alien whisks it away and gives you a new board again. So
Mon-Chaio: Okay.
So I think we both read this article and we kind of set it on the shelf for a while. This was a while back. And then after we thought about the four spheres of knowledge, I think we thought, look, is this article worth discussing in context of the four spheres of knowledge? And I think we came to this idea of, well, what about if we think about this like a system? Remember a system. Standards, symptoms, solutions, right? What if we think about this as a system? Is this a system insofar as is this a model which helps us create standards, helps us diagnose things with symptoms and provide solutions, and that even if it isn’t, that doesn’t mean it’s not useful, but we thought this would be a useful way to think about this.
Andy: Yeah. So here’s my immediate take on this.
I don’t think it is a model. I don’t think it is a, a, a system now. I think it’s interesting. I think it’s a, a, a nice thought process, but my criteria and kind of like scientific criteria for what makes something a system or a model is that it makes predictions and to be a really good one, it should actually make a lot of predictions.
Because if it makes no predictions, if it makes nothing that I could test out to say this is a way that it works, or this is a way that it works, um, uh, there’s no way that I could ever say that it’s wrong.
Mon-Chaio: I like that. Okay.
Andy: And, and in my reading of this, I, I don’t see where I could ever say that his model is wrong. His model is essentially to say things will change around you. They will change around you.
Mon-Chaio: Right. I think I agree that it’s not it. It may be a system, but I don’t think it is a useful system a system is supposed to describe the state of the world in which you live, and it helps point you to what might be. Uh, like we were saying earlier, if you do something, if I pull on this arm, I know it will hurt.
Why do I know why it will hurt? It’s because the system tells me. There’s a nerve connection here and there’s a certain tolerance there, and that’s how I can do, to your point, predictions.
Andy: Mm-hmm.
Mon-Chaio: Alien Chest to me is a system that says there is no known interaction between anything in the system, and therefore anything can possibly happen. I.
Andy: And in fact, he even says it at one point. So he says that there is this three step process that is the system, which is that you need to be able, well, it’s not even three steps, it’s these three parts. One is you need to accept that the alien is gonna keep changing the board.
Um, you need to be able to detect. Because he, he says, you sometimes won’t even notice when the alien changes the board,
Mon-Chaio: Mm-hmm.
Andy: and you need to be able to adapt. So once you see that it’s different, you need to do things differently.
Mon-Chaio: And in the detect part, he also mentions being able to, not just not knowing when it changes the board, but also being able to predict when a board change might be coming in order to prepare for it.
Andy: How does that, how does that fit his model of an alien who sweep swoops in and you have no idea why or when or how.
Mon-Chaio: Um, I think he talks about, uh, making connections with peers to, uh, you know, get the sense that, uh, aliens might be coming on, uh, July 4th to take over your cities or whatever. Uh, you don’t know they’re coming, but you’re trying to like, think about, oh, it looks like the situation might be a board change. Uh, but I agree with you.
It’s, uh, eh, it’s a little bit shaky. Anyway. Um, yeah, go
Andy: so if I step back and I think about why, why was he doing this? I think the reason he was doing this was probably because he was talking about how quite often I’ve done this in the past, engineers get all tied up in saying like, well, if he would just stop changing things, I could get this to work. What this is, is more of a philosophy of the world will always change and sometimes you won’t know how and you just need to roll with it. And I think that can be useful, but it doesn’t give me any way of predicting what will happen.
Mon-Chaio: Mm-hmm.
Andy: In fact, it tells me I can’t predict what will happen.
Mon-Chaio: Exactly. I think that’s why it makes it a poor system. Like we were talking about it doesn’t describe interactions of the system, right? It doesn’t describe interactions from the various aspects of the organization and, uh, how might, might, I predict when an alien is coming and if an alien comes, what are the things that.
Are likely to change? What are the things that are likely to not change? And are there things I can do on particular types of boards that make it less likely that an alien coming will interact with my board, maybe nail it down or, uh, move it into a room or something. Right? Like there’s nothing like that.
Andy: Yeah, now as, as I read it, a thing came to my mind thinking. I think it is, it’s a part of a system though. It it on its own. It isn’t. I don’t think it’s, but I think it is part of one and what came to my mind was, I think what he’s describing here is Facebook, or at least portions of it at a time, individual employees or or teams or divisions getting pushed on at an irregular basis.
At an unknown time into the conne chaotic domain. And that one I would say is, is a system. It’s not a complete system, but it does give us a framework for a system. Because what it says is that there are different ways in which things will interact. And I think what he’s saying is Facebook will at times just, or the world will push Facebook into the chaotic domain.
VIN’S framework. And what do you do in the chaotic domain? Uh, you act since you respond. You don’t know what’s going on. So you just do something and you see what’s happened and then you respond. So what he’s saying is that you’re gonna get pushed into that and you won’t even know it at times.
So you’ll get pushed into that and you will know that it’s
Mon-Chaio: Mm-hmm.
Andy: and. What he’s saying is that there’s a, in some ways he’s saying that there’s this step before Ken’s act, which is accept,
Mon-Chaio: Mm-hmm. Mm-hmm.
Andy: accept that it’s happening, and now act start getting some sense of what’s going on. Because what the expectation is in the Conne framework is they say as knowledge increases, there’s a clockwise drift.
From chaotic through complex and complicated to clear. So you’re gonna constantly get pushed into this chaotic domain, and you’re gonna start moving your way back towards something that you can, uh, control a bit more. The thing is, is his system doesn’t say that. His system is just like, Hey, this stuff just keeps happening and there’s no order, no, no sense you can make of it.
Uh, I, I actually find his message, uh, disheartening. Um, and if I, if I was told this by my manager of like, Hey, all this stuff happens, and we can never explain why. I’d be like, what is wrong with this place?
Mon-Chaio: Right. I like that you connected it to Conne. Look, in my mind, I think what he’s, what he’s proposed here is kind of a tactic for us about how to respond when you’re in that chaotic I. Domain, the chaotic realm of Conne, and I think it’s a useful tactic. I think it’s a tactic we like a lot.
Um, earlier on when I was talking about it, I kind of drew a small parallel to engineering where we say, look, take short steps, make sure your code is always deployable. Um, and a lot of why we do that is because we think like we don’t know what the future might hold. We might be in the chaotic realm. And so if tomorrow product or your product owner comes and says, look, we’re working on something different, we’ve shipped value.
It’s running in production versus we’ve done nothing because we’ve done all this like detailed strategic planning building platforms upon platforms. We like this. But to your point around it being disheartening, his model, I won’t call it a system, but his model has nothing but chaotic,
Andy: Yes.
Mon-Chaio: right? Gne says, look, the goal is to move from chaotic to complex.
That is the goal. But he says the goal is not to move from chaotic to complex. It’s to sit in chaotic because it will always, you never know when the next chaotic time will come, which I don’t know that I can agree with that.
Andy: Yeah, he does elaborate a little bit on it, which is that in these points where all of these unknowns suddenly show up, that’s where you get the huge difference between someone who’s very highly skilled and someone who’s not. If, if everything is going really well, if everything is clear and you know what you’re doing, there’s not a huge difference between the unskilled and the skilled. It’s, it’s this point when. Everything starts falling apart and it becomes very uncertain that the difference really shows up. If I was gonna say that there’s a system here, I, I think it would be more that, now that I think through this a bit more, I think it would be that, that he’s saying.
In those moments of chaos, that’s when having those really skilled people around is important, and that’s where you as an individual can show off what it is you’re capable of. And that’s how you get ahead you notice when the alien has changed things and you react to that and you possibly do that better and sooner than anyone else around you.
Mon-Chaio: I agree that that’s what he says, but I don’t agree that that is true. I think. It places too high an emphasis, again, to come back to the Conn framework since you mentioned it, and I like that as a way of thinking about it. It puts too high an emphasis on the skill sets that can navigate
the chaotic realm of Conn are more valuable and more difficult to become good at, and. Higher paid, more rare, whatever you want to say. Then the skill sets it takes to navigate well in the known domain of bin,
Andy: Yeah.
Mon-Chaio: which sounds romantically great. Especially in the Silicon Valley world where you think, oh, we’re trying to build unicorns and we’re trying to take moonshots and we’re living in the chaotic domain.
And we talked about this in the, uh, episode, I think it was season one, actually way back when the undeserved malign of bureaucracy. There’s this big thing of, oh, if you are a bureaucrat and you have bureaucratic skills and you know how to jockey your spreadsheets and keep things in order, um, bureaucracy is really great in the known domains. And so we put down bureaucratic skills as, oh, these are skills that are useful years ago, but they’re not really difficult to action. And if you just read your 450 page project manual. You’ll know exactly how to do everything. How is that Even talent. Talent must be existing in the chaotic domain, and that doesn’t sit well with me.
Andy: Okay, so we have a proposed model that we think is not a system.
Mon-Chaio: Mm-hmm.
Andy: It has. I think some interesting philosophical things to tell us, and I think it’s something that we would probably want to see in a system,
Mon-Chaio: Mm-hmm.
Andy: which is that when things change, you detect them and you react to that, you’re able to deal with that.
But as a whole, it’s, it’s not a system. It doesn’t tell us how the other parts will interact with this. In fact, it says that other. Parts will constantly be changing and never interacting in any predictable way is the only thing I can take from it. Um,
I guess the question is what do we do with this d Does this then give us anything useful for ourselves or for anyone listening beyond just this like. Philosophical approach that, uh, a, a good system would be one that can handle the change that this is talking about.
Mon-Chaio: I think we already mentioned that there are some good tactics from it that I don’t think are new.
Andy: Yeah.
Mon-Chaio: Because, um, if we get back to Conne, conne talks about a lot of those tactics in a different way, maybe less digestible. Um, that certainly doesn’t have acute alien coming down with a chessboard graphic. So, um, it gave us that.
I hadn’t seen that before.
Andy: Yeah, I think it, I, to me it gives, it gives anecdotal support for some of the principles of extreme programming. Which maybe we should examine that at some point and say, is that actually a system? By this definition of what we would need for diagnosis? And the reason I say that is because, like, well as the, the, the subtitle on the primary book about it says, embrace change.
Its core philosophy is the world is gonna keep changing around you, so you need to work in a way that can handle that. That can handle that. It’s changed and now you need to do something different.
Mon-Chaio: Yeah, and I think extreme programming and the way that agile development has run can give us a little bit of insight into some of the challenges. When you take something like alien chest and you leave it without examining it further. Something I think about is where architecture fits into extreme programming and where some engineering groups have come to the point where they don’t document anything and they don’t do any architecture because, well, why think when you can do, the world will just change and we’ll just react.
Mm-hmm.
Andy: Yeah. Yeah. And I think XP does end up sometimes with that as the failure mode where they’ve over anticipated change. And so started applying it to places where it was never happening and would not likely ever happen.
Mon-Chaio: Right, exactly. And I think you can correct me if I’m wrong, Andy, but my, my feeling of the mature XP practitioners these days, uh, which I think includes most, if not all of the original XP authors. Um, would say that not doing any architecture and not doing any documentation is wrong. It’s a failure mode of XP versus it’s a feature of xp.
Andy: Yeah, no, it’s, it’s a way that it can go wrong. Um, and that they would say, know you should be doing something like architecture decision records or some sort of writing things down of how it should be working. Then they would say, uh, your tests will provide a lot of that, but I don’t think many would argue that it provides everything.
Mon-Chaio: Mm-hmm. The other thing I think about is episode one of this season when we started talking about failure modes for organizations. One of the things we brought up was there was a, uh, a paper written by, uh, I think it was a Harvard Business professor, um, about his research on why organizations fail, and then he came up with five top reasons of why they fail. And one of those reasons was this idea that iteration and hypothesis has become misunderstood. The startup world. And his point was, look, you actually do need to do a little bit of research and have a directionality before you just write software and release it to the world and say, well, you know, is anybody going to use it?
Who’s using it? Where? Because you can spin there, right? Like user behavior isn’t the only thing that drives understanding. Sitting down and thinking and doing research and looking at market dynamics is a really important piece. And he says that that is often missing. And when he looks at startups to fail, they will more often than not, have not done enough upfront research. So we kind of at the extremes here, because on one side it’s the, I sit down and I play chess, and I think 30 moves ahead. And then on the other side, it’s. I sit down and um, I really don’t need to think that far ahead because it’s useless and a useless use of my brain power because an alien will come in and sweep the board away, but the author of this article doesn’t bridge the two. He only contrasts it, and I think that’s problematic,
Andy: Yeah.
Mon-Chaio: and I will say that. I think this is a reasonable thing to give a junior engineer coming into a company like Facebook.
Look, you came from school, they taught you all these things, but, uh, the real world doesn’t work like that. have to be able to adapt, you know, change your code, change your X, change your Y, right? I think this
Andy: not that we’ve done something wrong, that, uh, we’re changing what we’re doing. It’s that things change and we change what we do.
Mon-Chaio: And we change with it. Right. I think this is a very dangerous thing to be teaching your senior leaders.
Andy: Yeah. I, I’d be interested in how this doesn’t end up in learned helplessness.
Mon-Chaio: Mm-hmm. You can have, I guess what I would call high functioning, learned helplessness. And maybe that’s a great way to describe some of these companies as they operate. So sometimes when I think about learned helplessness, I think, oh, there’s nothing I can do. I’m in a morass, right? Like, why? Why even do anything? But as I’ve worked at some of these places, it feels like they use that basis as a way to. Again, amplify the behaviors of working in the chaotic realm and rewarding those behaviors above all else. And so examples are like executives will stop thinking about strategy. They’ll stop thinking about how to organize their organization, right?
Like why have an organizational like hierarchy? When the problem in the world can change at any time, and we don’t know when, so let’s just get a group of smart people together and they’ll just do what needs to be done as long as they also understand they’re playing alien chess. So my question then becomes like, as an executive, what is the value that you bring in a world where you think you’re playing alien chess?
Andy: This is probably why there’s a lot of people that I see say actually the first people you wanna replace with AI is the CEOs. Because if, if they’re not spending their time on thinking, what is the system? Well then what value are they bringing? If, if it is just that, then let, let them AI hallucinate the next maneuver and let have people just do that.
Mon-Chaio: I like it because why? Like why is that more valid than the other? Right? Why is a hallucination more valid than something that I spend a little bit of time researching? If your system. If your model is that, those are both equally likely to happen, I’m not spending time predicting which will happen.
Andy: Yeah. And so I, I think with that, let, I think that touches on what would be a criteria of a useful model. And I
Mon-Chaio: A useful system, a
Andy: I sorry,
Mon-Chaio: a useful model.
Andy: Useful, a useful system model. I’m using those interchangeably, which we might disagree about, but uh, uh, and you just touched on it right there, which is, well, if they both end up predicting the same thing, which is essentially nothing, what’s the difference?
And the answer is nothing. There is no difference between them, which makes them the same in the end. And, and this, this touches on actually a different alien chess that I found. So this was, uh, another article. I have no idea who this person is. I just found it online, uh, on their blog.
Uh, and what it is, is a description of, of basically what makes one scientific theory better than another scientific theory. And they use a very extreme example here, and in their case. The alien chess is not aliens coming down and changing your chessboard. The alien chess is aliens trying to watch humans playing chess, but the aliens are so far away and their optical systems are different, and so they just can’t make out what we’re doing. But what they can figure out is that there’s, uh, black pieces and they can tell that there’s white pieces and that this is about all that they can figure out.
And from that, they, they, they’re able to create a model that says 50% of the time, white wins, 50% of the time black wins. And that’s it. That, that’s, that is the extent of the model. It’s a, it’s a coin flip.
Mon-Chaio: Mm.
Andy: Who’s going to win? And I, I know Mcha, you’re probably thinking immediately, but that’s not true. That’s not the way it’s gonna work. But the thing is, is from the information they have available to them, that’s all that they can see, and that’s what they can predict. And in this iterated method, they can say that they’re absolutely right. The thing is, is that model is in many ways, absolutely useless because it can’t tell you anything about the next game.
So they’re, they’re, if they’re watching one particular game, this model of 50%, they can’t do anything to predict what’s going to happen. But if they watch a hundred of them, they can say that the probability is 50 of them white is going to win. And that’s all that, that’s, that’s the extent. Now another person, another alien then proposes, ano a different model and they say, Hey, look.
There’s got to be something going on here. We know that there’s something called white, and we know that there’s something called black. There’s probably pieces on this board. We don’t know what those pieces can do, but the outcome of the game has to have something to do with those pieces. And now they can’t say anymore about what’s going on, but now they actually have more.
That’s falsifiable.
and the thing is, is that makes it a better theory because you can now do more with it. You can, you can actually say, well, if, if we could figure this out, well, it’s making this prediction that there’s gonna be something here. They might be able to see, they can’t see the pieces, but they may, maybe they can see our hands.
And they can say, oh, oh, there is support for pieces being on the boards. ’cause we see as during play, we see their hands moving out and, and doing stuff. Okay. Maybe from where their hands go, we can start predicting what they’re doing. They, and so it lets them start coming up with this one thing after another, after another. And the thing is, is that it’s a better theory because it’s puts itself more out there to be wrong. Whereas the other theory, the one about, it’s just a coin flip. It, it doesn’t put itself out there to be wrong. They basically just measured what happened and said that’s what
Mon-Chaio: Mm-hmm.
Andy: And so that’s, that’s the thing that makes a better system or a better theory in terms of this organizational diagnosis, is it says more things that can be wrong.
Mon-Chaio: Yeah, I mean, I like that. I think it makes a lot of sense. I think, look, in the 50% example, the question might even be why are you playing the game at all? You can save time by flipping a coin.
Andy: Yeah.
Mon-Chaio: And I think to get back to our original alien chest, that’s kind of the problem with it because I, I think part of maybe what the author is saying is why spend time doing all of these other things when you can essentially flip a coin? And so instead of doing them don’t do them.
Andy: Yeah,
Mon-Chaio: Just hunker down and get shit done, I guess.
I don’t know.
Andy: just do the thing that’s right in front of you any connection to why it’s in front of you. What it’s connected to, what might be better to do than the thing that’s in front of you.
Mon-Chaio: Right. That’s a good point, Andy, which is like, what if the alien put a chess board down in front of you? You’re like, look, this isn’t the right board. I mean, there’s kind of
Andy: point in playing this board.
Mon-Chaio: right, right. We should work as hard as possible to get the alien to come back as soon as possible to change the board. But there’s no discussion of that, right?
It’s a, it’s fate that the alien comes with a board and then you must play whatever’s in front of you. Um, which, you know, might be why I think that, you know, for a junior developer that often happens, I think given their context and given their, uh, ability for impact, they kind of have to play the board in front of them.
I think when they start to think about many moves ahead and other things, like they do have to learn those skills, but, um, it’s lesson. Impactful because they don’t have the context. Um, but then to say like from an executive perspective, like, um, that that’s all you do. Yeah. It starts to get into why do you exist?
Andy: Mm-hmm. All right. With that very existential question, should we wrap this up?
Mon-Chaio: Well. Um, I think this was a good discussion. As many of our discussions are, um, it wasn’t meant to be an attack on the concept or the author of the concept. I think it was more meant to be an exploration about what do we think when we try to apply this to leadership, to leading technical domains and. I think the only way that we can, um, really explore this further and concepts like this is to have conversation, right?
Andy: Mm-hmm.
Mon-Chaio: Um, we came in here. The author is not on this podcast. It would’ve been great if he was on this podcast, or maybe he’ll reply in the comments or whatever, but I think that just continues to add to the discussion, and that’s what we’re about.
Getting more information, getting more brains, getting more voices to add to the discussion. To really think things through in a scientific way instead of just, oh, it’s a 50% coin flip because, um, I’m sitting way out here observing small sample sizes. We would love to know if any of you listeners have thoughts on alien chess.
This is kind of something we don’t discuss a lot ’cause it’s not a scientific paper. Um, but we thought we’d bring it back to science a little bit. Do you find that your organization plays alien chess? Do you find that you should actually play more alien chess? That it’s way too structured and rigid and can’t adapt to the world. Do you find that it should play less alien chess? Perhaps you feel the churn and turmoil. Do you have questions around how you balance between the, I’m playing chess, looking 30 moves ahead, versus I’m playing alien chess where I should exist in between. I don’t know that we know the answers to that, but we spend a lot of time thinking about it, and we spend a lot of time helping people solve problems like that.
So if you’re interested in discussion or diagnosis, or helping to get to a solution, let us know. You can reach us at hosts@thettlpodcast.com. That’s hosts with an S. Alright, until next time, be kind and stay curious.
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