Are you running as fast as you can just to stay in the same place? In this Season 2 premiere of the Make An Impact Podcast, Makoto Kern and Brynley Evans dive into the Red Queen Effect—a phenomenon from evolutionary biology and Lewis Carroll’s Through the Looking Glass that perfectly explains the burnout, anxiety, and exhaustion of the 2026 AI landscape.
In this episode, we explore why "effort no longer guarantees progress" and how the baseline of competence is shifting so fast that standing still is actually falling behind. Whether you’re a senior leader, a junior developer, or a business owner, understanding this system is the key to surviving the next wave of disruption.
In this episode, you’ll learn:
The 1871 Origins: How a Victorian satire predicted the struggle of the modern knowledge worker.
Evolutionary "Fitness": Why your success is now comparative, not absolute.
The Death of the Entry-Level Role: How AI is cutting the bottom rungs off the career ladder.
Legacy vs. Adaptation: Why loyalty to your past success is a "career death sentence."
Augmentation vs. Abdication: Practical strategies to choose which races to run—and which to walk away from.
Stop running faster. Start running wiser.
Key Moments & Timestamps 00:00 – The Red Queen Paradox: Why effort no longer guarantees progress. 00:30 – Season 2 Premiere: YouTube awards and the reality of AI burnout. 01:25 – The Anxiety of the Gap: Trying to keep up with the weekly tech shifts. 02:15 – Through the Looking Glass: The 1871 origins of the Red Queen Effect. 04:10 – Cheetah vs. Gazelle: Why evolutionary fitness is comparative, not absolute. 05:45 – The Rising Baseline: You aren't failing; the environment is moving. 06:50 – Business Darwinism: Kodak vs. Fujifilm and the cost of loyalty to history. 08:15 – Innovation Half-Life: Why new features become "standard" in months. 10:45 – The 2-Year Rule: Why staying too long at one company is a career risk. 12:15 – Death of the Entry Level: How AI is cutting the bottom rungs of the ladder. 15:30 – The Business "Cheat Code": How IIIMPACT helps you bypass the learning curve. 18:20 – Skill Expiration: What happens when learning loses its payoff? 20:45 – Augment or Abdicate: Choosing your path in a high-velocity market. 25:00 – Managing "Brain Fry": Why rest is a strategic move for survival. 27:15 – Final Thoughts: Running wiser, not just faster, in 2026.
The "Cheat Code" for Your Software Challenges | iiimpact.ai In a Red Queen system, the steepest cost is the Learning Curve. Most organizations spend years making costly mistakes while trying to build specialized software expertise internally.
IIIMPACT.ai acts as your "Cheat Code." We provide instant access to over 20 years of specialized UX and AI product strategy. While the market forces you to run faster, we help you skip the "grind" and move directly to solutions that work.
Reduce Go-To-Market time by 50%. Transform complex tech into intuitive human experiences. Solve in weeks what takes others years.
Don't run the race alone. Schedule a free strategy session focused on your unique challenges at iiimpact.ai
Follow Makoto Kern on Twitter/X for more on Agentic Workflows and UX Strategy: @makeaniiimpactyt
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We emphasize strategic planning, intuitive UX design, and better collaboration between business, design to development. By integrating best practices with our clients, we not only speed up market entry but also enhance the overall quality of software products. We help our clients launch better products, faster.
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Speaker 1:
Everybody, welcome back to another episode of Make an Impact podcast. I'm your host, Makoto Kern. I'm joined here with my cohost, Brinley Evans.
Speaker 2:
Hey, everyone.
Speaker 1:
It's been a while since It has. We have filmed our last podcast.
Speaker 2:
It been a while. And a few YouTube awards as well. So thanks to everyone which for is great. I think, what is it? We're kind of unofficially season two now.
Speaker 1:
That's right. This is our new season two. I mean, we've been both so, I mean, just like anybody else, we've been so bogged down with every single week, there's some kind of new AI and just trying to keep up with the tech. And I think that really relates to our subject today.
Speaker 2:
Absolutely.
Speaker 1:
So, you know, our subject, just because I'm sure everybody else is feeling, some of the same type of like, burnout, you know, whatever. I mean, are you feeling, you know, like overwhelmed? It's basically we're trying to keep up with the pace of this technology now. And it's just, you know, if you don't feel that way, you feel like you're gonna be probably behind. I mean, just talking to people who are either keeping up or not, it's just like there's a huge It
Speaker 2:
is fascinating when you do, you know, speak to some people, they're like, yeah, I've to look at AI. It's like, no, no, we're so far ahead of that now. So far. Like, you gotta get on the boat. The ship is sailing very fast.
Speaker 1:
Oh, yeah. I mean, it's, you know, you see everything that says, oh, this changes everything. And there's always like this anxiety that I'm feeling. Because I'm always trying to learn like, Oh, is this working? Okay, next week.
Speaker 1:
Okay, no, don't do it this way. This is not the best way to do these things. And it just feels like you're not advancing. And so, you know, there's a feeling and the title of our podcast today, which is basically it's called the Red Queen effect. It basically explains why effort no longer guarantees progress, and why keeping up always feels exhausted and why, you know, your successes that you have just feels temporary.
Speaker 1:
And to explain where it comes from, I'm gonna give it to you, Brinley.
Speaker 2:
Yeah, enjoy a bit of history, so here we go. Let's let's dive deep because I found this like, researching this topic really fascinating as well. So the Red Queen effect comes from, I guess, literature first and, science second, and and that order really matters. So why? Because we dive into, you know, the first kind of literary origins.
Speaker 2:
And it didn't begin as as a kind of a formula, this idea, you know, or a model or a theory. It began as a a felt human, experience. And, you know, there was there was confusion. There was exhaustion, like, you know, we're feeling today as well, and that sort of effort feeling of effort without progress. It was really the science that came later to explain something that people had already felt.
Speaker 2:
So if we look back at the the literary origin, anyone know Lewis Carroll's Through the Looking Glass? And this was any believer, this was the the sequel to Alice's Alice in Wonderland, or it's actually called Alice's Adventures in Wonderland. Was written in 1871, which is pretty crazy, long time ago. And often, you know, mistaken, which maybe through my ignorance, was also thinking, well, it's sort of a a strange children's book, and, you know, it's often mistaken for that. But in reality, it was was satire of the time.
Speaker 2:
It was social commentary, and it was a book about logic, about time, and absurd systems. And kind of to give the context of the Red Queen scene and the sort of Red Queen system or effect that we're talking about. So Alice steps through this mirror into a reversed world, and it's a place where the rules have been inverted, logic doesn't really seek to behave normally, and progress doesn't work the way you expect. So she goes and meets the red queen, and they begin to run together. And what Alice is expecting is, well, the scenery is gonna change.
Speaker 2:
We're running. We're we're going somewhere. She expects to go somewhere. But what happens instead is they start running faster and faster, and everything stays exactly the same. And this is where the line comes in.
Speaker 2:
It says it takes all the running you could do to keep in the same place. If you wanna get somewhere else, you must run at least twice as fast as that. And what's interesting, the author Lewis Carroll was really pointing at was the bureaucracy of the time, the the social systems, the power structures that informed, and this feeling of constant effort with no visible progress. And now, as I mentioned, Through the Looking Glass was Britain in 1871, and this is during that sort of late industrial revolution period in Britain. So society was becoming faster.
Speaker 2:
It's obviously more mechanized. It was more bureaucratic, but it wasn't necessarily more humane. So people were starting to work longer hours. A lot much stricter systems they're working on under, yet they often felt no more secure or fulfilled from that. So productivity was rising, but this sort of feeling of an an actual personal progress and meaning, we're keeping pace with this.
Speaker 2:
And the the sort of bureaucratic systems expanded into more rules, more procedures, less visible reward for your effort. And this sort of there was almost like a social mobility that was promised, but for many, it it always remained out of reach. And that amplified that feeling of constant effort, constant motion, little or no forward movement. And Lewis Carroll used that sort of absurdism and fantasy to capture what it felt like to live inside the live and kind of work inside those systems. So the Red Queen's rule of run or fall behind really reflects systems that demand effort really just to maintain position and not to advance.
Speaker 2:
And the metaphor points a world where energy is consumed to preserve the equilibrium but, you know, not necessarily to create progress. So in other words, it's a sort of metaphor for systems that consume energy just to maintain balance. Now this is where it moves from sort of the the literary angle to the the science. So you almost jump a hundred years later. There was a biologist by the name of Lee Van Valen, and, he borrows this metaphor and he applies it to evolutionary biology, where I kinda feel it gets even more interesting.
Speaker 2:
So what he observed is that species don't evolve in isolation. So you have your predators, your prey, your parasites, your competitors. You apply the Red Queen principle to them, and it's that fitness is not absolute. It's more comparative. So if the environment improves, you have to improve or you fall behind.
Speaker 2:
So if we look at an example of predator and prey. So let's take a cheetah and a gazelle. So the the cheetahs evolve to run faster, but then the the gazelles need to evolve, for instance, faster acceleration, sharper turns, better stamina. And what's important is there's no finish line to this. So there's no, like, well, the fastest species, you know, is this one, and that's fixed and, you know, that's static.
Speaker 2:
If the cheetah gets faster, the gazelles have to get better at at adapting to that speed. If the gazelles improve, the cheetahs have to improve again. So, really, neither side is winning. You're just playing to not lose in this sort of evolutionary game. And its evolution isn't about progress.
Speaker 2:
Again, it's just not about losing ground. So I guess the key point that, you know, for anyone listening to this to remember is if it takes all the running you can do just to stay in the same place, then ask yourself, how much harder do you actually have to run to get ahead? And why this matters is because most people feel exhausted because their effort is real. Like, we know we're we're continually staying up to date with the tools. And, you know, you feel you've got there, but the baseline just keeps rising.
Speaker 2:
And you're not failing. You're just in a red queen system. And the trap is really confusing. Well, I'm I'm working hard at this with I'm progressing because you're not necessarily progressing just by working hard in these red queen environments if it does not directly translate to advancement. So I guess once you see this, you sort of start noticing it everywhere.
Speaker 2:
And I know we've been working on the script for a while, and you do just especially in business, you just see this more and more as, you know, how everyone is is having to behave. Kind of with that aspect, I'll kick it back to you, Makoto.
Speaker 1:
Yeah, definitely. I think, you know, with business, it's basically evolutionary biology with spreadsheets. So you've got, I mean, you've got e commerce, where you have physical retailers, they built online stores, they added delivery, they optimized their supply chains, not really to just win, but really to, just avoid irrelevant irrelevance. And so, you know, the adaptation, it becomes more defensive versus offensive. And so investment no longer really guarantees that advantage.
Speaker 1:
And one perfect case study of psychological resistance is really like Kodak versus Fujifilm. You know, Kodak invented the tech, but was just too loyal to its past success. And, you know, it really, you know, Fujifilm, they accepted the disruption and they really bet against its own legacy. So yeah, you know, the Red Queen lesson here is really, you know, past success creates that structural inertia and that psychological resistance. But really the market rewards adaptation, not really loyalty to history.
Speaker 1:
And, you know, tech is where this effect is really the most unforgiving.
Speaker 2:
Absolutely. I mean, that's tech competition just compresses time. I think, you know, that's what again, with AI, we're looking at that. But let's look at some other examples that are sort of relatable to this Red Queen system. So we look at the big players like, you know, Apple, Google, Samsung, and the smartphone business.
Speaker 2:
You really have like, feature parity is is mandatory there. Your differentiation windows are so short. I mean, if you look right back to, you know, when, when you started having your first, cell phones, you know, the the the features rolled out slower. Now they're you know, the the windows are just, you know, again, just getting shorter and shorter, and you're that innovation half life is shrinking. So, you you look at one manufacturer, introduces maybe a breakthrough feature, it rapidly becomes a baseline.
Speaker 2:
It's it's not even a long term advantage anymore, and so that innovation quickly shifts from differentiation just to survival. So we look at an example with with smartphones like your camera technology. So all those big players, they push the multiple lenses, the night mode, the computational photography. Within a year, you you're looking at every flagship phone has to have that because just having a good camera is no longer a selling point. It's it's expected now.
Speaker 2:
And you've got things like biometric security as well. So, know, Apple introduces that touch ID and then the face ID. And then you've got you know, competitors immediately come out with maybe a fingerprint under the glass or a face unlock or iris scanning. And the security features escalate just to maintain that trust. And the same's seen with screen technology and battery and and charging methods and and your AI features.
Speaker 2:
The features move from really just sort of something that's wow to, well, why why wouldn't it do this? You know, the short advantage windows is, you know, you have a feature that differentiates for mere months, and then it becomes standard with a product cycle. And then it just disappears into the background. And, you know, you could really never have something without that again. And I think the sort of how this applies again to that Red Queen effect is, you know, smartphone companies aren't racing to win.
Speaker 2:
They're just racing to avoid looking obsolete. Because if we looked at them standing still, what is it gonna mean? It's gonna mean slower devices, worse reviews. They're gonna lose market share. If they're running faster, just keeps them in the game.
Speaker 2:
And that's the red queen effect. You know, constant innovation just to stay competitive, not even to get ahead. You know, really, in tech, standing still, you'd expect it to be neutral, but it isn't. It's basically just falling behind in real time, which, you know, then also looks you know, this this sort of thing spills into into careers and the job market.
Speaker 1:
Yeah. And I think before I jump into that, I think just what you're saying, you know, in 2024, when kind of AI was more like a tool, now in 2026, it's really the environment. You don't just use the environment, basically inhabit it. And if you aren't adapted, you are basically extinct. So it's a
Speaker 2:
Yeah, well said. Yeah, absolutely.
Speaker 1:
Yeah. And you know, with the job market now, I mean, people that are finishing their four year college degree, I mean, basically, you know, there's a 50% rule. 50% of what you learned is already obsolete by the time you graduate. And we're moving to from like a just in case education to more like a just in time survival. And so it's, you know, the Red Queen effect really explains why careers feel unstable, even when you're doing everything right.
Speaker 1:
And so, you know, kind of what's changed is really roles no longer age slowly. And your skill relevance decays much faster. And experience alone is really insufficient right now. And so, you know, this, this psychological shift, you know, really used to come from your identity, used to come from mastery, being, you know, your seniority of something and or at a company. And now it really comes from more being adaptable, your adaptability and then, your learning velocity too.
Speaker 1:
It's really not, you know, the question isn't really what do you know, it's how fast you can learn again. And this is kind of where I've been just where you're really trying to keep up with everything that's out there and just having this kind of this death of this entry level kind of role. And it's, you know, if AI defines like the baseline competence, you know, how does a junior ever become a senior? You know, we're pretty much effectively cutting the kind of the bottom rungs of that career ladder, and creating kind of like a permanent underclass of workers who can gain the foundational experience needed to lead.
Speaker 2:
Yeah. Yeah. Or how do they? I mean, you know, what is it? Do they do they work for free?
Speaker 2:
And then do you want to if you're more senior, do you do you wanna spend time with those? Well, I can do it a lot quicker this way. Yeah. You know, do I have to sort of invest knowledge? It's really tricky.
Speaker 2:
And I think that kind of also looks at what does that mean? Like, does it mean stagnation for, you know, the people that, you know, are working at the moment that can't adapt? Does it mean reskilling? And, just researching this, I found an interesting fact. So, you know, back in early sort of twenty twenties, it was the World Economic Forum warned that by 2025, you know, we're in 2026 now.
Speaker 2:
They said half of all workers would need new skills just to keep up with technological change. At the time, it sounded like a a big sort of, you know, over the top number, but, you know, that was fast forward five years or or sort of six years to where we are now, and it feels understated. You look at the AI tools rewriting what job expectations and and skills are and sort of pushing those to evolve each year. And I I guess what they're really pointing at is a world where learning never stops, where, you know, adaptability is the skill that that matters most. And and what this really means is that, you know, people don't retrain once, but there's this sort of continuous skill churn.
Speaker 2:
And, you know, the stagnation signals in yourself to to look out for are, you know, are you relying on on your legacy expertise or your institutional memory? Are you avoiding things like new tools, new mental models? Because, remember, stagnation isn't stopping. It's just assuming that, as you're saying, like you put it, the AI environment is going to wait for you. And it's not.
Speaker 2:
And that's that's really you know, after that point, AI arrived and, you know, the acceleration really started.
Speaker 1:
Yeah. I think, you know, your point on, stagnation, pretty much the market rewards that adaptation and not loyalty. So, you know, in the Red Queen era, staying at a company for more than two years could be a career death sentence. If you're learning, you know, basically your learning velocity probably plateaus versus the open market. I think everybody at IMPACT, we've always tried to push to be at the cutting edge, so constantly like pushing.
Speaker 1:
And I think we all kind of like all our seniors and myself and everybody, we try to lead by example by just learning and adapting to new tech. I love learning new tech. I mean, my background is engineering. So I always love to learn how to figure things out. So I've always pushed to try to learn new things.
Speaker 1:
But you know, this is I mean, I'm, know, know, as well as I do, just putting in the hours every day, just trying to keep up is just daunting in itself. But there is definitely a hunger to learn and keep up with that. Because otherwise, it's like, if I don't look at it for a few days, I'm like, what did I miss? What's, you know, Exactly. What's
Speaker 2:
And also just figuring out, I think one of the things that I find takes so much sort of concentration and and attention is there's so much emerging that you you sort of figure out, well, what business need does this fulfill? You know, is this valid, or is there is there hype involved? Because there's obviously a massive hype cycle, you know, for for a lot of things, but then some offer amazing advantages. So sort of evaluating without burning ridiculous amount of time on on each tool to sort of figure out where do you actually want to concentrate the effort on where there's real value.
Speaker 1:
I'm curious. In video games, cheat codes let you skip months of grinding to unlock special abilities instantly. Have you ever wished for something similar for your software challenges? What if there's a way to instantly access twenty plus years of specialized expertise instead of developing it all internally? What if you could solve in weeks what might otherwise take months or years?
Speaker 1:
Would you agree that most organizations faced a steep learning curve when implementing new software solutions? At my company, IMPACT, we serve as that cheat code for companies looking to transform complex software into intuitive experiences that users love and that drive real business results. Would it be valuable to explore and how might this work for your specific situation? Visit impact.io for a free strategy session focused on your unique challenges. Yeah, for sure.
Speaker 1:
And I think, you know, on subject of kind of the AI acceleration, I think, you know, AI really didn't introduce change, it changed the rate of change. And so, you know, with Chad GPT, that was, I mean, it was had the fastest consumer adoption ever. And like LLMs, it was immediately useful, immediately disruptive. And I guess what's different this time is there's no really barrier for specialists. There's no long onboarding.
Speaker 1:
You see immediate productivity gains. And basically, don't have tools that just don't assist. They actually redefine baseline competence. And that pretty much creates a very dangerous curve.
Speaker 2:
And that's the curve that we've talked about before, that sort of exponential curve that that really I I think that that humans are just bad at at intuitively understanding exponentials. You know, we we consider it an early phase where you feel, this is manageable. Mhmm. I'll catch up later. If you're maybe thinking, oh, next year, I'll do this or next quarter, I'll look at it.
Speaker 2:
No. You can't Because we're at that point in the curve where it's just getting steeper and steeper, and we're gonna reach that inflection point where everyone's adopted, where the standards are just jumping so quickly. And if you're a late adopter, you're seriously going to struggle. The the key risk really, the the cost of delay is just increasing nonlinearly. So your catch up time is just gonna start exceeding your available time.
Speaker 2:
And at some point, no matter how much effort you put in, you can't close the gap.
Speaker 1:
Yeah. I think, you know, with the you have to basically ask, is this sustainable?
Speaker 2:
Absolutely.
Speaker 1:
You know, there is definitely cognitive overload that you'll see. You know, I'm definitely sometimes I feel pulled Yeah. Mean, what is it that you get the
Speaker 2:
brain fry or what is it? Yeah, brain fry.
Speaker 1:
That's it. I'm already fried trying to think about that. It's, you know, you think about, you know, this very shallow, superficial learning replaces actual mastery of things. The systemic risks that you see, like innovation arm races, you get diminishing returns. And basically the humans, all of us have our as kind of the collateral damage of all this.
Speaker 1:
Really when it comes to capitalism and evolution, it really doesn't care about comfort, but you know, us as humans do.
Speaker 2:
Good to meet you. Yeah, exactly.
Speaker 1:
Yeah. There's definitely, it leads to a fork in this road.
Speaker 2:
And I guess that fork is probably, it's probably around, I guess, augmenting or just stepping away, abdicating. You know, I think those are the two responses that you can have to acceleration. And, you know, if we look at probably under the augmentation side, we can look at a few different scenarios of, you know, well, you know, you can pair up with AI. So, you know, you look at developers, for instance, who they they pair pair up with an AI coding assistant, you know, that's able to, you know, assist them with with coding, explain unfamiliar code bases, and, you know, run multiple agents that that really extend their their ability. Then there are things like cognitive scaffolding, where someone is learning a new domain and they leverage AI to do that.
Speaker 2:
It can translate the jargon. It can explain concepts in plain language in sort of the context that you understand, provide analogies, and overall probably reduce cognitive load from doing that. That. And then you've got another scenario where, you know, you can sort of use tool mediated intelligence. So if you look at someone like a knowledge worker, they can query company documents, data dashboards.
Speaker 2:
They can get AI to do summaries for them. And they can they can leverage AI to be able to process and comprehend so much more data than what they would be able to do and, you know, that any sort of single source could could provide, which, you know, can can benefit. And then you have stepping away or or abdicating. You know, do you just step off the treadmill? So, you know, for instance, a a senior professional, maybe they choose to just stop chasing constant upskilling.
Speaker 2:
Maybe they wanna stay in a more stable, well understood role and just accept slower career progression, you know, where maybe they wouldn't prioritize predictable hours, reduce exposure to rapid technological change, and and focus on mastery of a a narrow domain where, you know, it may be more fulfilling as well. And the trade off is is less stress and and more control, but probably fewer future options if that role disappears. So, you know, looking at stepping off the treadmill doesn't doesn't mean giving up. It it just means consciously choosing which race you're willing to run. Then you look at another another way of abdicating is is trade competitiveness for or should I say trading competitive competitiveness for stability.
Speaker 2:
So maybe you're a contractor freelancer, and you move from, you know, what would be a high pay, fast changing tech job to maybe a salaried role in in a slower moving industry. So maybe something like education or public sector, utilities, and where you accept a lower income ceiling, slower innovation cycles, and, you know, in in exchange or reward for that, you you get job security, clear expectations, and, I guess, reduce pressure to constantly reskill. So the trade off is is stability today with the risk of being harder to reenter what is a a fast moving market later. And I think the the tension is that, you know, augmentation raises the bar yet again, whereas abdication may not be economically viable. So opting out might itself become a disadvantage.
Speaker 1:
Yeah. I mean, I'm sure, like, throughout time or just in recent times, just any kind of disruption that we've seen is obviously like the .com boom and bust. You have a website, just quickly just spin up a website, spin up an app, you're gonna make millions, you're gonna, you know, this is what everybody needs or wants. But then when it started to slow down, you know, of course, like printing newspapers, magazines, you know, they're still magazines, newspapers obviously have been disrupted. A lot of companies went out of business.
Speaker 1:
Everything went digital. But there has after that disruption and destruction, there was a huge boom in like really looking at better user experience, better interfaces, things like that versus just starting just an ugly web app or website up there. And so with this technology, it does feel similar in a sense, but it's so much faster and more disruptive. So it's probably going to do a lot of destruction. But if you jump on that, you know, in a certain way, I think it could be very useful, for sure.
Speaker 1:
But I mean, you know, what what is our end game, and what happens if this continues in this way?
Speaker 2:
I mean, you look at that sort of singularity I think we've touched on before, the singularity concern, where skills just expire faster than you could acquire them. And, you know, eventually, learning just loses any payoff. You know, mastery becomes fleeting, and that's you know, that directly impacts on us. We lose our loss of meaning. We just have reduced satisfaction.
Speaker 2:
And I I guess it's just stuck in a loop of permanent beginner syndrome. So, you know, when that effort stops translating into progress, it's obvious that the bottom drops out from motivation.
Speaker 1:
Mhmm. I mean, so do we always have to run faster? You know, if you stop running today, how long would it take for the world to move past you? Three months, three weeks, or has it already happened?
Speaker 2:
Yeah. And and also, you know, if you're going to you're going to keep racing, how do you cope with without burning out? And as we say, we both felt that where, you know, there there is that real feeling where it's just often too much to keep up with. And, you know, survival isn't about speed itself. It's it's about sustainability.
Speaker 2:
So, you know, what what can we do? What are some practical suggestions, I guess, we could offer listeners? And it's maybe maybe to take an approach of of lifelong learning, small but continuous. You know, avoid these sort of boom bust learning cycles where you feel like, okay. Have to I I have to just go without sleep for a few days and just catch up with everything.
Speaker 2:
You know, just normalize that that what is going to require what is going to be required from you now is allocating a portion every day and saying that I'm gonna learn for an hour each day. I'm going to look and and evaluate certain things and continually, learn. And then also learn leverage. So focus on meta skills, on on transferable frameworks. Don't don't necessarily look at every tool.
Speaker 2:
There's also then things like adaptability over optimization. You've always gotta remember that flexibility beats efficiency in these sort of volatile systems. Then probably something else to do is is leverage collective intelligence. So what what are communities you can tap into? What are, you know, shared learning, communities, platforms, knowledge networks?
Speaker 2:
Be able to tap into those to stay up to date, to have, you know, just a slow feed of of what's happening, where things are changing, and how you should adapt. And lastly, probably energy management because burnout destroys your adaptability. If you're burned out, you're just saying no more. Not interested in this. I'm going to going to, you know, go farming or do something else.
Speaker 2:
I'm gonna start building a a doomsday bunker. You know? Don't go there. Just just keep it you know, keep regulating your energy. You know, rest is strategic.
Speaker 2:
It's not necessarily indulgent. Just remember that your ultimate goal, you know, isn't winning the race. It's just staying capable of running in this Red Queen system.
Speaker 1:
Yeah. And I think just consciously choosing which races not to run. I've been I I must have 50 different or a 100 different tabs open. Okay. This is Claude with Obsidian and Claude with this, Gemini with that, anti gravity with this.
Speaker 1:
And it's just like, you get to a point where, okay, let me just go down one path. Let me just focus on this and then everything else, let the rest of the noise go because you can just get ADHD just jumping from one thing to another to another. That just it's quicker burnout. So I think this is a good place to close. I think just to tell our audience, the Red Queen effect isn't pessimistic.
Speaker 1:
It's honest. Change isn't slowing. Standing still isn't safe, but burnout isn't success. So we want to hear from you. Are you running?
Speaker 1:
Are you adapting? Or are you stepping back? Share your thoughts in our comments. We're glad to be back for this new season. Excited to see where 2026 takes us.
Speaker 1:
And just for everybody, just remember, sometimes the smartest move isn't running faster, it's running wiser. So with that, like to say, thanks again for joining us and till next time.
