Unknown Speaker 0:00 Welcome to the deep dive. Today we're plunging into the the really high stakes, high speed world of entrepreneurship. It's a place where every second counts, doesn't it? It absolutely does no time to waste. And we're exploring this really interesting idea called perishable commitment. Think of it like this. You know, new ideas, quick action. They're like, two sides of the same point, a very important coin, exactly because opportunities and, well, the willingness of customers or partners to actually commit. They're fleeting, like, like fresh produce at a farmer's market. That's a great analogy. Snooze, you lose. It's gone, or someone else grabs it. You gotta seize them before they spoil. Basically, it's true, and that window, it can just snap shut incredibly fast. Entrepreneurs face this constant dilemma. Moving quickly means, you know, new opportunities are popping up all the time, and that just dramatically increases the number of decisions you have to make the variables right, more choices, but you have very few fixed rules constraints to actually guide you. It's kind of like trying to navigate a really busy city with a map that's just been printed, but half the street names keep changing on you. Okay, that paints a picture. So that's the core challenge. Then, how do you secure that commitment when it's so well, perishable, yeah, that's the million dollar question. Do you try to, like, predict everything stakeholders might do? You know, do tons of learning, gather all the data, analyze every angle you could try that, but then, like you said, you risk competitors just swooping in while you're stuck in analysis paralysis, exactly, or just go for it, make a confident bet. Say this is the quality, prescribe the solution and just push it out there, hoping people commit. That's the other extreme the betting approach. But the risk there sounds huge, too. Your whole venture could just completely fail from misalignment, if your assumptions about what people actually want are just wrong. Dead on Arrival, it happens. This really brings us to why the standard strategic tools. Well, they often fall short. Our research points to this idea that entrepreneurial stakeholder prioritization is fundamentally Unknown Speaker 2:09 degenerate, degenerate. That sounds bad. It sounds intense, yeah. But it just means you're trying to make decisions with a massive number of variables, all those possible features, prices, partners, markets, you name it, and a surprisingly tiny number of constraints or known boundaries, things you actually know are fixed, so tons of choices, very few rules. It's like trying to hit a moving target in the dark, maybe when you only have a vague idea where the target even is. That's a good way to put it. It's not just complex. It's often fundamentally chaotic, like you said. So our mission today is to really unpack this intricate challenge for you listening. We want to dive into why those traditional approaches just predict or just prescribe, why they don't really cut it in these fast moving situations, right? They're too brittle on their own. And then we'll get into this innovative push pull framework that emerges from the research. It sounds like a more dynamic solution. It is. It's designed for this exact kind of environment, and we'll even look at how it played out in the real world, like with Tesla's early roadster development. Yeah, that's a fantastic case study. They had to coordinate wildly different stakeholders, luxury customers, cutting edge battery partners against the clock. Okay, sounds good. Let's unpack this. So let's really dig into that core problem. First, what does it actually feel like for an entrepreneur when their situation is degenerate? Is it just constant overwhelm? Unknown Speaker 3:34 Yeah, overwhelm is a good word. It often feels like navigating through thick fog. It's that situation, huge number of variables, very few constraints, right? Imagine you're developing a new app. Your variables are almost endless, right? Features, Target, demographic, pricing, UI, design, marketing strategy, all things you could do exactly. Meanwhile, your constraints, the things holding you in place, they're few, maybe just your initial seep funding and like a rough guess about the market side. Everything else is up in the air. And if you're trying to move fast, that just makes it exponentially harder, doesn't it? It sounds like, as the research says, new opportunities increase the number of variables relative to constraints even more precisely, when an entrepreneur spots something genuinely new, a truly novel opportunity, it often lacks those established rules, or, you know, precedent, no playbook, no playbook at all, so you have this flood of unknowns, which vastly expands your choices, while the clear guiding constraints stay minimal. And yes, that faster market clock speed the sheer need to move quickly, that further reduces the stability of the few constraints you might have thought you had. Wow. So not only are there few rules, but the rules you do have might just evaporate before you even get going exactly right. The faster the market moves, the more fluid and unstable those boundaries become. It just intensifies the degeneracy. So it means entrepreneurs are forced to prioritize stakeholders deciding. Unknown Speaker 5:00 Which customers, partners, investors are most critical right now, but they're doing it in this environment full of significant randomness, where even the ground rules can shift under their feet. It's like trying to build a bridge while the river is changing course constantly. Okay, given that fundamental messiness, it does seem kind of intuitive that the pure strategies just trying to predict everything, or just trying to prescribe your path, would struggle, but let's break down why they fail. Okay, let's take pure prediction first. This is all about learning, gathering intelligence, right? Entrepreneurs might use analytical tools, maybe things like random utility models to try and forecast stakeholder commitment, okay? What's a random utility model? Roughly, think of it like trying to predict which ice cream flavor someone will pick from a huge selection based on their past preferences, maybe for sweet things or fruity things or chocolatey things. These models help forecast choices when there's lots of variety and different factors influencing the decision. Got it so you learn you understand the market better. That's the strength, definitely. You gain a deeper understanding. But then this is the critical part. The failure mode in these fast entrepreneurial contexts, is that prediction without timely action is just too slow. Right? By the time you've gathered all the data run the models made your perfect forecast, a competitor has probably launched something maybe not perfect, but good enough. And that window for perishable commitment, it's slam shot. So learning is crucial, but if it makes you miss the boat, it's useless. Unknown Speaker 6:31 Okay? What about the other side? Pure prescription, just making the confident bet. So pure prescription is where you basically make assumptions about commitments, and then you prepare based on those assumptions, like, if we build it, they will come kind of, yeah, a classic model. Here is the news vendor model. Think about a street vendor selling newspapers. Okay, how many papers should they order each day? If they order too many, they waste money on unsold papers. Too few, they lose potential sales. It's about betting on demand and preparing the right amount of supply makes sense. Decisive its strength is decisiveness, absolutely. You act quickly, you move you get something out there. But the crucial failure mode is that prescription without prediction is just too brittle, too fragile. How so? Well, if your initial assumptions about those stakeholder commitments are just plain wrong. If the customers don't actually commit like you thought, or the key partners don't materialize, your venture can just die from fundamental misalignment. You've built something perfectly, perhaps, but for a world or a market that simply doesn't exist or doesn't want it. So one's too slow, the other's too fragile. If you guess wrong, it really sounds like if both of these pure strategies have such killer flaws, you absolutely need something else. This must be the big aha moment in the research finding that third way. That's precisely it. These failure modes demonstrate really clearly the critical need for something more robust, something that can handle both the learning and the action kind of in tandem. You need to be both agile and decisive somehow. Which brings us then to the integrated solution you mentioned, where prediction meets prescription. How does that actually work? How do you unify them? Well, the core idea is to frame stakeholder prioritization not as two separate steps, but as a single, unified prediction, prescription model. Okay? So instead of first trying to predict commitment, then optimizing your quality or product, you integrate them. You're doing both simultaneously in a dynamic process. You're not just trying to figure out what people will do. You're constantly optimizing what you should do given those potential actions and how they might respond. And the research shows this integration is more robust and efficient than keeping them separate. That seems key. Absolutely, that's the payoff. This integrated approach fundamentally overcomes those limitations we just talked about, being too slow or too brittle. It allows for a much more dynamic, adaptive response to that fast, changing, messy entrepreneurial environment, right? And, you know, while we won't get bogged down in the complex math, it is built on some pretty strong mathematical foundations, good to know. It's rigorous, yeah, for instance, the models account for commitments being quality, non linear, meaning stakeholder commitment isn't just a simple straight line. More quality equals more commitment. Sometimes a small improvement can cause a huge jump in interest or maybe even adding more features beyond a certain point doesn't help or even hurts. Okay, complex responses Exactly. And it also considers asymmetric stakeholder sensitivities, different groups, maybe early adopters versus mainstream customers or different types of partners. They respond very differently to the same changes you make. The model handles that. This sounds like where the push pull framework really comes into play. This is the core contribution the practical application of this integrated model. It is that's the heart of it. The push pull framework is designed specifically to jointly optimize your investment in quality. Unknown Speaker 10:00 How responsive you are to stakeholders and your coordination speed. It's a dynamic capability built to tackle that degenerate problem head on. Okay, let's connect back earlier we established that moving fast increases variables relative to constraints leading to this need to control clock speed. How does push pull help an entrepreneur actually do that, right? Controlling clock speed is crucial. The push pull framework gives you two simultaneous reinforcing strategies to manage it. Okay. What are they? The first is learn a thin bet. We sometimes call this the pull, or maybe an agile approach, learn Beta. Beta is how much they respond exactly. You're actively trying to infer how intensely stakeholders respond to different product qualities or features? That's beta. You're pulling insights directly from the market, maybe through small tests or early versions, like a minimum viable product could be. Yeah. And then you rapidly adjust your bets, your next steps, your resource allocation, based on that fresh information. You might launch a very bare bones prototype to just a small group. Your goal isn't perfection, yet. It's intense observation, pulling in the reactions to quickly adjust. Okay, so pull in learning. Then make your move learning first. What's the other side? That's bet Q then learn up. This is the push, or maybe effectuation style approach, that Q, Q for quality, Q for quality, right here, you make a more significant initial investment in a certain level of quality, you push it out there with some confidence, and then you carefully study how customers and partners respond to that specific quality level to refine your strategy and learn their Beta. So push out quality, then learn how they react to it precisely. It's like, think back to Tesla again, launching the Roadster with really cutting edge tech, a big bet on quality Q then learning from those initial sales, the feedback from those early luxury customers about what they truly valued and how much they were willing to commit that informed their next steps. So it's not strictly one of the other. It's like a constant dance, a dynamic loop. You push out an idea or product, then you pull in feedback to adjust, then you maybe push again, but differently you got it. It's a continuous back and forth. And the research really demonstrates its supremacy, because this integrated push pull approach converges to the same optimal solution more robustly and efficiently than trying to just predict or just prescribe alone more robustly and efficiently. That sounds like exactly what you need in that chaotic environment. It gives you that ability to be both nimble, adapting to new information, and decisive, making timely bets, constantly adapting. Okay, let's really ground this with that Tesla roads, for example. Again, you said, it exemplified this challenge, new opportunities, short time frame variables skyrocketing compared to constraints, absolutely. Tesla really needed serious clock speed control to even get the Roadster off the ground. They were juggling these incredibly diverse stakeholders right, luxury buyers, luxury car customers, on one hand, expecting amazing performance, cutting edge design from a totally new, unproven company huge expectations, and on the other hand, they had battery partners who were also innovating like crazy in a very young EV industry. The tech itself was still evolving rapidly, so both the market and the core technology were moving targets exactly this demanded that constant push pull. Tesla pushed out early designs concepts. Even took pre orders based on renderings for a car that didn't fully exist yet. That was a bet a prescription, right? That push let them learn customer preferences, Gage real interest, and, importantly, secure initial commitments, actual cash deposits. Okay? But simultaneously, they were pulling in information constantly from battery tech advancements, making bets on which technologies would mature, adapting their designs based both on that customer feedback and the technological breakthroughs or limitations. Wow. So the constant interplay making a move, then learning while also learning, then making the next move, that really highlights the practical necessity of this integrated push pull approach, especially for something so groundbreaking, it really does. And you know, just as a final technical note, this dynamic capability, this ability to integrate prediction and prescription effectively, it's formally grounded in something called flexible Bayesian modeling. Okay, Bayesian that sounds like probabilities, updating beliefs. That's the essence of it. It's the rigorous mathematical engine that makes the push pull framework adaptable. It allows the model to learn continuously as new information flows in, constantly updating its predictions and its optimal course of action, a bit like how a detective keeps refining their theory with every new clue uncovered, right? It adjusts based on evidence. Exactly. That's what gives the framework its power, its adaptability and robustness in these really uncertain, degenerate environments. Okay, so let's recap our deep dive today for entrepreneurs trying to navigate this world of perishable commitment, where opportunities and stakeholder willingness fade fast, just trying to predict everything that might happen, or just trying. Unknown Speaker 15:00 Prescribe a fixed path forward. Neither is enough on its own right. Both pure strategies have those critical failure modes we discussed, too slow or too fragile. The real power the breakthrough highlighted by this research lies in an integrated push pull approach, one that dynamically balances that learning, the pulling with the betting, the pushing. It's about being both agile and decisive, like we said, constantly adapting your quality investment, understanding how stakeholders are responding, and crucially controlling your speed, your clock speed. It's maybe the closest thing you can get to a strategic GPS when the roads themselves are still being paved. That's a great way to think about it. So here's a final thought for you listening, given this inherent degeneracy of entrepreneurial opportunities, that crazy ratio of huge variables to tiny constraints. How might a deeper understanding of perishable commitment and this push pull framework change the way you approach strategic decisions, not just in business maybe, but in any fast moving, high stakes situation where you need both learning and action? So what does this all mean for you? 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