Chapter 1: What is the main topic discussed in this episode?
What's going on, everybody? Welcome to another Saturday Conundrum. I'm Brian. I'm one of the co-hosts of The Daily Eye Show, and I appreciate you being here. This week, we're sort of talking about struggle and how it applies to a future with AI and whether struggle is necessary in order to have things like competency. Now, this particular episode this week is actually fairly long.
It's a good one, though.
Chapter 2: What is the messy middle conundrum related to AI and struggle?
I think you're really going to enjoy it. So I'm going to keep my intro short so that we can get right into it. We don't make this too long of an episode. But here's sort of the intro to it. We're going to call this the messy middle conundrum.
And it was inspired by a lot of news that came out this last week, even especially Anthropix interviewer that just came out where they were bringing in information and doing like reports on how people feel about AI. And anyway, here's the intro to it. See what you think. So for all of human history, competence required struggle. To become a writer, you had to write bad drafts.
To become a coder, you had to spend hours debugging. To become an architect, you had to draw by hand. The struggle is where the skill was built. It was the friction that forged resilience and deep understanding. AI removes a lot of that friction. It can write code. It can draft a contract. It can design a building instantly.
We're moving towards a world of outcome maximization, where the result is all that matters, and the process is automated. This creates a crisis of capability. If we no longer need the struggle to get the result, do we lose the capacity for deep thought? If an architect never draws a line, do they truly understand space?
If a writer never struggles with a sentence, do they understand the soul of a story? We face a future where we have perfect outputs, but the humans operating the machines are intellectually atrophied. So here's the conundrum.
Do we fully embrace the efficiency of AI to eliminate the drudgery of process work, freeing us to focus solely on ideas and results, or do we artificially manufacture struggle and force humans to do things, quote, the hard way, just to preserve the depth of human skill and resilience? So with that, let's jump into the actual conversation with our two AI co-hosts.
As always, I use a little bit of AI to help me come up with these ideas from, like I said, from different stories and news stories and whatever's rolling around in my brain this week. And then what I do is I have Perplexity do some deep research on it. And then finally, that's fed into Google Notebook LM, where we come out with this two AI co-hosts that are about to discuss it.
So with that, I hope you enjoy. Have a great Saturday.
Welcome back to the deep dive. So today we're getting into something that I mean, it feels like it's the question on everyone's mind right now. If you work in, well, any kind of knowledge job. It's this massive collision, isn't it? Yeah. A head on clash between how humans have always built skills and this new world of, you know, completely frictionless machine efficiency. It is.
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Chapter 3: How does AI remove the friction necessary for skill development?
You had to spend, what, hundreds, thousands of hours drawing lines? Over and over. Right. You were internalizing how spaces work, how materials behave under stress. The friction was the teacher. Or a coder. You didn't just write a perfect program on your first try. Oh, definitely not. You spent days debugging. staring at the screen, pulling your hair out over some tiny error.
But that's what forced you to build this like three-dimensional map of the system in your head. That wrestling match, that resistance, that's what gave you the deep understanding, the intuition. And now we've brought in the ultimate friction remover, AI. It just poof. It generates the code, the legal contract, the building layout in seconds.
And we are moving so fast toward this model of outcome maximization. The result is everything. The process, that painful, slow process, that's just overhead. It's just inefficient noise that we can and maybe should automate away. Which leads to what some of our sources are calling the crisis of capability. If AI hands us the perfect output without the process, do we do we lose something?
Does our, you know, our intellectual muscle just atrophy? Yeah. Does the architect who never actually draws a line, do they truly understand space or are they just a really good curator of what the machine produces? And that is the absolute core of the issue for you, the listener. It's what we're calling the struggle fetish conundrum. It's a stark choice when you boil it down. It really is.
Option one, do we just go all in, embrace AI, get rid of all the process work, and just focus on big ideas and the final product? Or option two, do we deliberately, artificially, you know, manufacture a struggle? Do we force people to do things the hard way? Even if it's slower.
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Chapter 4: What crisis of capability arises from AI's efficiency?
Even if it's less efficient. Just to make sure we preserve that deep human skill. So our mission today is to really get into the weeds on this. We're going to present the full evidence based case for each side. And this isn't about finding some easy middle ground. It's about understanding the strength of both arguments, because I mean, the choices we're making right now
They're going to define what work looks like, what expertise means for decades to come. Absolutely. OK, let's start with the argument that's probably easiest to make the enthusiastic, full speed ahead case for embracing radical efficiency. If you're running a business or a team, I mean, the argument for just adopting AI is just so compelling. It's better, it's faster, it's cheaper.
And the people who advocate for this view, they don't see this as some crazy, unprecedented disruption for them. It's just the next logical step in a very long story. Right, which is their first pillar, historical precedent. The idea that new tools have always separated the outcome from the ordeal. Always.
AI is just the latest and, well, obviously the most powerful version of this, this technological divorce. So give us an example. Go beyond the obvious ones. How have past tools really redefined competence? Okay, let's take the printing press, but really look at the shift. Before Gutenberg, Woe was an expert in knowledge transfer. A scribe. Someone with incredible handwriting. Exactly.
It was a physical skill. Painstaking hand copying. The struggle was literally manual dexterity. So that skill was completely tied to the spread of knowledge. Inseparable. And then the printing press comes along and just destroys the value of that skill. But what happens? Human competence doesn't disappear. It shifts up. It moves to a higher level.
The most valuable skills suddenly become editorial judgment. Translation. Critical organization of ideas. The physical struggle vanished. And society reframed competence around the intellectual struggle of what was worth printing.
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Chapter 5: How does the debate between efficiency and struggle impact our future?
Society basically said, great, we've solved that problem. Let's move on to harder ones. Precisely. Or think about the calculator. When I was a kid, you had to do long division by hand. That struggle was considered part of mathematical rigor. Right. Memorizing times tables, carrying the one. But when calculators became common, we didn't stop teaching math.
We just stopped wasting time on the rope mechanics. We moved the difficulty. So instead of spending hours on arithmetic, a student could start tackling, what, calculus? Statistics? Much more ambitious problems and much earlier. The calculator removed the drudgery, which actually accelerated their ability to get to the deep conceptual stuff.
And more recently, something like CAD, computer-aided design for engineers and architects. Perfect example. It completely removed the intense manual effort of draftsmanship. But human expertise didn't vanish. It just got reframed around things like parametric modeling, material science, complex simulation. Things you could never even dream of doing with a pencil and paper. Exactly.
So the lesson from this point of view is that when a tool comes along that delivers a better result with less pain, society always, always redefines what competence means. and holding on to the old way of doing things. Starts to look a lot like nostalgia. It's like arguing a real mathematician can't use a calculator or a real author has to use a quill pen.
It's defending inefficiency just because it's what you're used to. It's confusing the price you paid with the value you gained. The pro-efficiency argument is that the friction you endured wasn't necessarily the cause of your expertise. It might have just been the unfortunate byproduct of having bad tools.
OK, so this is where it gets really powerful, because this isn't just a historical argument anymore. It's backed up by hard data. Pillar number two is the empirical evidence. The data is really, really clear on this, especially with the latest large language models. The big one was that 2023 paper in Science. Right.
And they looked at common white collar tasks, writing emails, summarizing reports. Yes. And the results were, well, unambiguous. The group using AI finished their tasks about 25 percent faster. Which is already a huge deal. It is, but this is the crucial part. Independent evaluators who had no idea if AI was used rated the AI-assisted work as being of higher quality.
So wait, it's not just faster, it's actually better? It was rated as more coherent, more complete, more professional. The AI acts as a sort of quality floor. It catches the basic mistakes. It ensures a logical structure. So the human can focus on the bigger picture. Exactly. And you see this confirmed in other reports.
MIT Sloan found massive time savings and, importantly, more consistent quality. The Nielsen Norman Group saw the same thing for business tasks provided. And this is a key caveat. There's proper human oversight. You still need a human in the loop to check for facts and, you know, to make sure the AI hasn't gone off the rails conceptually. And this isn't just for like writing marketing copy.
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Chapter 6: What historical examples illustrate the evolution of competence?
Or it's the programmer spending all night debugging some horrible legacy code until they finally have that aha moment and understand the deep logic behind it. And if a beginner just gets the perfect answer from an AI right away. They bypass that entire developmental process. They learn how to operate the tool very well, very quickly. But they don't become a true expert in the craft itself.
They get a shallower kind of skill. A much shallower skill. They're less likely to experiment, less likely to explore weird alternatives. Because the AI always gives them the safe, standard answer. They don't know why that answer is the right one. But the efficiency side would say that AI is providing instant feedback, that curated struggle. They would.
But the counter is that AI feedback is usually about optimization within a known framework. It doesn't force you to grapple with total conceptual failure. Doesn't it force you to feel the resistance of reality itself? You can't develop an instinct for physics just by approving a computer's drawings. You have to at some point struggle with the materials yourself.
And this is where the stakes get really, really high. This is the safety argument, the manual reserves argument. This is the hard-edged practical fear. In fields like medicine or structural engineering or aviation, society depends on having humans who can step in when the automation fails. When the system goes down, when the data is bad, when something completely unexpected happens. Exactly.
The classic scenario. A whole generation of radiologists grows up relying on AI to read scans. What happens when the power goes out or a new bizarre disease shows up that fools the algorithm? You're left with a massive competence gap. A terrifying competence gap. The organization has been silently eroding its manual reserves of human expertise. They've become brittle without even realizing it.
The flight simulator analogy they use is so powerful. It's perfect. You would never argue against automation in a modern cockpit. It's essential. But how do you train pilots? With hours and hours of intentional artificial struggle. You force them into simulators and you make things fail. You turn off the autopilot. You disable the instruments.
You force them to fall back on their core manual flying skills. So we need to be doing that in knowledge work. Forcing trainees to do things the hard way in a controlled setting. It's essential preparation for failure. And this goes for critical thinking skills too. If all we ever do is check an AI summary of research.
We lose the ability to critically appraise the original source material ourselves. We stop being able to spot a weak methodology or a flawed argument or a biased conclusion. Our critical thinking muscles just atrophy. OK, so beyond the practical and safety concerns, this argument goes deeper. It gets philosophical.
It's about character and agency and what it even means to be a master of something. This really resonates. It's about the dignity of professional life. For so many people, a master carpenter, a veteran surgeon, a seasoned journalist, their identity is tied to their history of overcoming difficulty. Their work is a testament to their own effort and their own decisions. Right.
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