Speaker 1: So what if I told you that, over the entire
Speaker 1: lifespan of a you know, multi million dollar app, the
Speaker 1: average software engineer only produces about ten lines of final
Speaker 1: code per day.
Speaker 2: Yeah, just ten lines.
Speaker 1: Ten lines. I mean, it sounds completely impossible, right, It's
Speaker 1: it's like someone building a house by laying literally a
Speaker 1: single brick every afternoon.
Speaker 2: It really does sound absurd when you frame it like that, right.
Speaker 1: But the digital infrastructure that you rely on every single minute,
Speaker 1: like your banking app or the computer's keeping your car
Speaker 1: on the road, you know, global financial networks, it's not
Speaker 1: built like a physical house or a suspension bridge.
Speaker 2: Exactly because we can't see the steel cables, we can't
Speaker 2: measure the concrete.
Speaker 1: Yeah, it operates by these invisible, almost bizarre rules.
Speaker 2: It is the ultimate invisible infrastructure. Yeah, and I think
Speaker 2: because we can't physically watch it go up piece by piece,
Speaker 2: we just assume it's manufactured on some kind of you know,
Speaker 2: digital assembly line. Right, But the rules governing how software
Speaker 2: actually comes into existence are totally alien to traditional manufacturing.
Speaker 1: Completely alien. So welcome to our deep dive today. We
Speaker 1: are exploring the fascinating and honestly just wildly misunderstood world
Speaker 1: of how software is actually built.
Speaker 2: It's such a great topic, it is, and.
Speaker 1: We're pulling our insights today from a really incredible source.
Speaker 1: It's called Write Great Code, Volume three, Engineering Software, and
Speaker 1: that's by Randall Hyde.
Speaker 2: Yeah. Hi does just an incredible job pulling back the
Speaker 2: curtain on the entire industry here.
Speaker 1: He really does.
Speaker 2: And what makes this exploration so valuable is that he
Speaker 2: looks at the psychological and human elements of coding just
Speaker 2: as much as the technical frameworks.
Speaker 1: Absolutely, so, our mission today is to decode the hidden processes,
Speaker 1: the crushing productivity traps, and those really radical management frameworks
Speaker 1: that software engineers use to construct the digital world around.
Speaker 2: You, right, giving you a shortcut to being a much
Speaker 2: more well informed consumer of this.
Speaker 1: Stuff exactly, or maybe even a creator of complex projects,
Speaker 1: because look, you interact with software all day, but tracing
Speaker 1: how it goes from just a vague idea in someone's
Speaker 1: head to a perfectly functioning tool on your screen, it
Speaker 1: reveals a staggering amount of human friction.
Speaker 2: Oh massive amounts of friction.
Speaker 1: So okay, let's unpack this.
Speaker 2: To start unpacking it, I mean, we first have to
Speaker 2: look at the fundamental, unique nature of software itself. Right.
Speaker 2: Unlike a physical bradge or a car engine, software doesn't wear.
Speaker 1: Out, right, there's no rest exactly.
Speaker 2: Friction doesn't degrade code over time, and it's also highly customed.
Speaker 2: We don't have the equivalent of a standard universal integrated
Speaker 2: circuit that you can just snap together to instantly build
Speaker 2: any app you want.
Speaker 1: Yeah, it's not legos, it's not.
Speaker 2: And crucially, once the design is finished, it costs literally
Speaker 2: pennies to duplicate it a million times. The actual manufacturing
Speaker 2: cost is practically zero.
Speaker 1: Right, So the real cost is entirely in the intellectual
Speaker 1: design and the implementation precisely, which means before we can
Speaker 1: understand how software is built, we really have to understand
Speaker 1: the peace people building it.
Speaker 2: Yeah, the human element is everything here, it is.
Speaker 1: And the source outlines four distinct historical metaphors for a programmer,
Speaker 1: and they perfectly track how the industry has had to
Speaker 1: evolve as the stakes got higher they do. So let's
Speaker 1: start with the first one, which is the programmer as artist.
Speaker 2: Right. So the artist dates back to the very early
Speaker 2: days of computing, where coding was viewed as this mysterious,
Speaker 2: almost god given.
Speaker 1: Talent, you know, like magic.
Speaker 2: Yeah, like magic. The artist bends the rules, relies on
Speaker 2: pure natural aptitude, and often does their absolute best work
Speaker 2: when they are completely unconstrained by corporate oversight.
Speaker 1: Just left alone in abasement somewhere exactly.
Speaker 2: They're fantastic for solo, highly creative, intensely focused projects.
Speaker 1: See. I always picture the artist like a solo musician
Speaker 1: recording a track in their bedroom.
Speaker 2: Oh, that's a great way to look at it, right, Like, they.
Speaker 1: Play all the instruments, they mix it themselves, they don't
Speaker 1: care about standard time signatures. The final product is pure
Speaker 1: personal expression and it can be totally brilliant. But a
Speaker 1: solo artist in a bedroom can't build a global banking system.
Speaker 2: No, definitely not.
Speaker 1: The stakes are just too high. When software started impacting
Speaker 1: human safety and you know, managing billions of dollars, the
Speaker 1: industry had to evolve. Someone needed to see the big.
Speaker 2: Picture exactly, and that necessity birth the second metaphor, which
Speaker 2: is the programmer as.
Speaker 1: Architect the architecture.
Speaker 2: So if the artist is the bedroom musician, the architect
Speaker 2: is the composer writing complex sheet music for an entire
Speaker 2: symphony orchestra. Oh I like that right. The architect exercises
Speaker 2: large scale creative control. But they leave the actual building,
Speaker 2: the you know, coding of individual components to other people.
Speaker 1: Got it.
Speaker 2: They're focused entirely on the structural integrity of the big picture,
Speaker 2: because a badly designed building falls down and hurts people,
Speaker 2: and a badly designed software architecture can crash a hospital's
Speaker 2: life support.
Speaker 1: System, which is terrifying. But the industry couldn't stop at
Speaker 1: architects either, because the demand for software just exploited. It
Speaker 1: really did, which leads us to the third metaphor, the
Speaker 1: programmer as engineer, And this one has a very specific,
Speaker 1: almost panicked origin story from the late nineteen sixties.
Speaker 2: Right, Yes, the nineteen sixty eight NATO conference. Right at
Speaker 2: the time, the world was facing what they literally called
Speaker 2: the software crisis.
Speaker 1: The software crisis.
Speaker 2: Yeah, the demand for complex software was vastly outpacing the
Speaker 2: number of naturally talented artist programmers available. Yeah, I mean,
Speaker 2: budgets were spiraling, projects were failing left and right.
Speaker 1: So they needed a reliable system exactly.
Speaker 2: So NATO sponsored a conference and officially coined the term
Speaker 2: software engineering. The explicit goal was to take the predictable,
Speaker 2: standard building block approach of civil or mechanical engineering and
Speaker 2: just force us onto software development.
Speaker 1: Okay, so, keeping with our music analogy, then if the
Speaker 1: engineer steps in, they are like the studio executive trying
Speaker 1: to mass produce pop hit.
Speaker 2: Yes, exactly right.
Speaker 1: Like they use standardized coord progressions and heavily enforced production
Speaker 1: schedules to just guarantee a baseline of financial return regardless
Speaker 1: of who is actually in the recording booth.
Speaker 2: That is a perfect way to describe it.
Speaker 1: But wait, if we treat programmers entirely like engineers on
Speaker 1: an assembly line, strictly following predetermined procedures, don't we completely
Speaker 1: kill the creative greatness of the code.
Speaker 2: Well, what's fascinating here is that this is the exact
Speaker 2: historical tension highlights in the book. Oh really Yeah, software
Speaker 2: engineering was invented specifically to make less talented programmers reliable. Okay,
Speaker 2: it raises the floor of quality, ensuring a baseline of
Speaker 2: safety and function across a massive team. But by enforcing
Speaker 2: rigid bureaucratic rules, it inevitably lowers the ceiling.
Speaker 1: Oh I see, it brings the top down exactly.
Speaker 2: It restricts the truly great programmers from doing their best work.
Speaker 2: It actively discourages divergent thinking because in an engineering pipeline,
Speaker 2: divergent thinking is view as a schedule risk.
Speaker 1: Which makes sense from a management perspective, but it's terrible
Speaker 1: for innovation, which brings us to the ultimate ideal. Right,
Speaker 1: the programmers.
Speaker 2: Craftsmen, Yes, the craftsman.
Speaker 1: This is someone who operates in that sweet spot between
Speaker 1: the artist and the engineer.
Speaker 2: Precisely, if we finish the musical analogy, the craftsman is
Speaker 2: the master luthier building a custom strata areas violin.
Speaker 1: Oh wow.
Speaker 2: They intimately understand the strict physics and engineering of the
Speaker 2: wood and the acoustics. But they use that rigid knowledge
Speaker 2: to create something of profound artistic beauty.
Speaker 1: That's a beautiful way to think about.
Speaker 2: It, and their lifelong learners. They start as an apprentice,
Speaker 2: move to a journeyman, and eventually become a master craftsman
Speaker 2: by creating a masterpiece that proves their skills.
Speaker 1: So they know all the rules basically exactly.
Speaker 2: They know all the rules of the engineer, but they
Speaker 2: have the earned wisdom to know exactly when to bend
Speaker 2: them like an artist.
Speaker 1: You know, think about your own workplace right now. Understanding
Speaker 1: these identities helps you recognize the different types of problem solvers,
Speaker 1: you're actually collaborate reading with Polly. If you are managing
Speaker 1: a team and you treat a highly creative artist like
Speaker 1: a factory line engineer, they are going to fail or
Speaker 1: they're just going to quit.
Speaker 2: Oh, they'll quit in a heartbeat.
Speaker 1: Right. Recognizing whether a specific task requires an artist's flare,
Speaker 1: an architect's vision, or an engineer's discipline is really the
Speaker 1: secret to unlocking any collaborative project, and.
Speaker 2: That actually leads directly into the most dangerous trap in
Speaker 2: the entire industry.
Speaker 1: Let's hear it.
Speaker 2: If we increasingly view coding through this engineering lens, where
Speaker 2: we need to manage large teams, management naturally wants to
Speaker 2: measure their productivity.
Speaker 1: Oh right, and this is where the industry's attempts to
Speaker 1: measure human output become almost comical, truly comical. First of all,
Speaker 1: the baseline skill gap is just mind boggling. The book
Speaker 1: points out there is a staggering ten to twenty times
Speaker 1: difference in productivity between average programmers and grand master programmers.
Speaker 2: Yeah, just think about the mechanics of that. In literally
Speaker 2: any other field, you don't usually find a plumber who
Speaker 2: can pipe a house twenty times faster than the plumber
Speaker 2: and the next nameborhood.
Speaker 1: Right, physical limits exist.
Speaker 2: Exactly, but software is pure intellectual leverage. A grand master
Speaker 2: can look at a problem, synthesize the logic, and solve
Speaker 2: it in one elegant line of code, while an average
Speaker 2: programmer might take three weeks and five hundred lines of
Speaker 2: code to clumsily force the computer to do the exact
Speaker 2: same thing.
Speaker 1: So how on earth do managers measure output when the
Speaker 1: gap is that wide?
Speaker 2: Well, it's right, right.
Speaker 1: For decades, the most popular metric has been lines of code,
Speaker 1: or LOC, and it is a wildly dangerously flawed metric.
Speaker 2: It is, and it became popular solely because it's.
Speaker 1: Easy to count, because it's just a number.
Speaker 2: Right. You literally just run a script that counts the
Speaker 2: lines of text in a file. But measuring productivity by
Speaker 2: lines of code assumes every single line requires the exact
Speaker 2: same amount of intellectual effort, which is fundamentally false.
Speaker 1: It's totally absurd.
Speaker 2: A programmer might spend three agonizing days conceptualizing a really
Speaker 2: complex cryptography algorithm, but the final result is just two
Speaker 2: lines of code. Meanwhile, someone else could write four hundred
Speaker 2: lines of simple, repetitive formatting text in a single hour.
Speaker 1: So using lines of code to measure a programmer is
Speaker 1: like judging the quality of a book strictly by its
Speaker 1: page count. Imagine a brilliant editor spending an entire day
Speaker 1: meticulously cutting a ten page rambling draft down to one single,
Speaker 1: devastatingly perfect paragraph. If you judge them by line count,
Speaker 1: that editor had a heavily negative productivity day.
Speaker 2: It completely punishes elegance. I mean, in software, often the
Speaker 2: most productive, valuable thing a programmer can do is actually delete.
Speaker 1: Code, making the system lighter and faster.
Speaker 2: Precisely so, to get around the sheer absurdity of loc
Speaker 2: computer scientists develop more sophisticated metrics like cyclomatic complexity.
Speaker 1: Okay, explain the mechanics of that, because this dives right
Speaker 1: into the psychology of coding.
Speaker 2: Yeah, so, cyclomatic complexity abandons bulk size entirely. Instead, it
Speaker 2: measures the number of distinct logical paths through the code. Okay,
Speaker 2: it counts the decisions. You know that if this happens,
Speaker 2: then do that otherwise?
Speaker 1: Do this branches right the actual logic?
Speaker 2: Yes, And the reason this works is that it measures
Speaker 2: cognitive load. Yeah, the human brain can only hold so
Speaker 2: many competing threads in its working memory before it drops
Speaker 2: one and makes a mistake. Makes sense. So cyclomatic complexity
Speaker 2: measures how hard the human brain had to work to
Speaker 2: map out that specific piece of logic.
Speaker 1: That makes so much more sense than just counting lines.
Speaker 1: But even utilizing better metrics, we have to circle back
Speaker 1: to that shocking reality check we started with.
Speaker 2: The ten lines a day.
Speaker 1: Yes, over the lifetime of a major software project, if
Speaker 1: you average out the total output, a typical programmer produces
Speaker 1: only ten lines of debugged, documented code per day.
Speaker 2: It's wild.
Speaker 1: I really want you to walk me through the math
Speaker 1: on that, because when I first read it, my brain
Speaker 1: just rejected it. How is ten lines a day mathematically possible?
Speaker 2: Well, it sounds absurd until you map out the actual
Speaker 2: life cycle of a multi year project. On day one,
Speaker 2: a programmer might sit down and hammer out one thousand
Speaker 2: lines of code. It feels incredibly productive, right, they're in
Speaker 2: the zone. But that code doesn't exist in a vacuum.
Speaker 2: On day two, they have to write tests for it.
Speaker 2: On day three, they find bugs and have to untangle
Speaker 2: their own logic.
Speaker 1: Oh okay.
Speaker 2: On day four, they sit in six hours of meetings
Speaker 2: to discuss how their code interfaces with a database. Someone
Speaker 2: else is building always the meetings, and then on day
Speaker 2: thirty the client changes the requirements, so they have to
Speaker 2: throw out half of what they wrote and start over.
Speaker 1: Right, the code is constantly rotting and needing maintenance.
Speaker 2: Exactly, and eventually they have to rigorously document every function
Speaker 2: so the next person hired can actually understand it.
Speaker 1: Wow.
Speaker 2: So by the time that product is officially retired, maybe
Speaker 2: five or ten years later, the sheer crushing overhead of coordination, meetings, rewriting,
Speaker 2: and fixing mistakes dilutes that initial thousand line burst down
Speaker 2: to an average of about ten finalized lines a day.
Speaker 1: That is just incredible. So the realization here is that
Speaker 1: the real way to speed up a project isn't typing faster.
Speaker 1: Typing speed is completely irrelevant, totally irrelevant. The bottleneck is
Speaker 1: the mistakes in the communication overhead. But when a project
Speaker 1: starts running late, management usually panics. Right always, they fall
Speaker 1: into the trap of confusing man hours with real time,
Speaker 1: which leads to something called Brooks's law.
Speaker 2: Yes, Brooks's law is a fatal flaw in project management
Speaker 2: across the board.
Speaker 1: What is it?
Speaker 2: The assumption is physical? You know, if one person can
Speaker 2: dig a hole in sixty minutes. Two people can dig
Speaker 2: it in thirty minutes. Right, But software is intellectual, highly
Speaker 2: intertwined work. If you have a project that is running late,
Speaker 2: doubling the staff on that project does not have the
Speaker 2: time it takes to finish.
Speaker 1: It doesn't.
Speaker 2: No, it almost always makes it take longer.
Speaker 1: Because every new person adds an exponential number of communication nodes. Exactly,
Speaker 1: the original programmers, who are already behind schedule have to
Speaker 1: literally stop working to explain this complex, invisible architecture to
Speaker 1: the new folks. The communication overhead absolutely skyrockets, dragging everything
Speaker 1: to a complete halt.
Speaker 2: And when throwing bodies at the problem fails, managers predictably
Speaker 2: trigger what we call crisis mode. Oh no, they demand
Speaker 2: sixty to seventy hour work weeks. They order everyone to
Speaker 2: work through the weekend.
Speaker 1: Which sounds productive on paper, right, But cognitive data proves
Speaker 1: this is mathematically counterproductive.
Speaker 2: You aren't just getting diminishing returns, you are creating negative work.
Speaker 1: Because of the cognitive load we talked about earlier.
Speaker 2: Precisely, a tired brain cannot hold complex logic paths in
Speaker 2: its working memory. No way, a programmer operating on four
Speaker 2: hours of sleep on a Friday night at eleven pm
Speaker 2: is going to write a bug guaranteed, And because the
Speaker 2: logic is so intersigned, that single bug might cascade, taking
Speaker 2: three senior engineers four entire days to track down and
Speaker 2: fix the following week.
Speaker 1: So they actually went backwards.
Speaker 2: Yes, that friday night coding session literally destroyed a week
Speaker 2: future productivity.
Speaker 1: This is such a critical takeaway for you listening right now.
Speaker 1: No matter what industry you operate in, if you are
Speaker 1: doing collaborative knowledge work, throwing more bodies at a late
Speaker 1: project just makes it later every time, and grinding your
Speaker 1: team for seventy hours a week destroys their net output.
Speaker 1: It is a complete illusion of progress. So if individual
Speaker 1: metrics are this flawed and adding people backfires, how do
Speaker 1: massive organizations ever manage to organize armies of developers to
Speaker 1: ship global products without the whole thing collapsing?
Speaker 2: Well, they survive by relying on structural models. They map
Speaker 2: out what's called the Software development life cycle THELC SDLC. Right,
Speaker 2: this is a framework broken down into phases conceptualization, gathering, requirements, design, coding, testing, deployment, maintenance,
Speaker 2: and eventually retirement.
Speaker 1: That's a lot of phases, it is, and.
Speaker 2: How a company forces its team to move through those
Speaker 2: phases dictates their entire working culture.
Speaker 1: Which brings us to the models. The most primitive approach
Speaker 1: is the informal model, which is basically just hacking.
Speaker 2: Yeah, just winging it.
Speaker 1: You get an idea, you sit down and you just
Speaker 1: start banging out code. You skip the design phase completely. Now,
Speaker 1: it is incredibly fun and it's perfect for tiny, throwaway prototypes,
Speaker 1: but if you try to build a commercial banking app
Speaker 1: this way, it is an unmintainable disaster that will just
Speaker 1: collapse under its own weight.
Speaker 2: Oh totally. And to avoid that disaster, the industry created
Speaker 2: a granddaddy of all formal structures, the waterfall model.
Speaker 1: The waterfall model.
Speaker 2: Yes, it is rigid, it is highly sequential, and it
Speaker 2: is heavily documented. First you spend months figuring out exactly
Speaker 2: what the requirements are. Okay, Then you spend months designing
Speaker 2: the architecture. Then you hand it to the coders, then
Speaker 2: you test.
Speaker 1: It so step by step exactly, you.
Speaker 2: Flow down the steps like a waterfall, and gravity dictates
Speaker 2: you never ever go backward.
Speaker 1: It's exactly like building a skyscraper. You dig the foundation,
Speaker 1: you pour the concrete, you weld the steel, frame. You
Speaker 1: absolutely cannot get to the fifteenth floor and suddenly say,
Speaker 1: you know what, but we should really move the elevator
Speaker 1: shaft to the outside of the building.
Speaker 2: Right, it's physically too late.
Speaker 1: But software isn't concrete, it's totally malleable. Applying physical engineering
Speaker 1: constraints to a digital medium seems I don't know, fundamentally arrogant.
Speaker 1: It as soons you can perfectly know what you are
Speaker 1: building before you pick up a tool.
Speaker 2: And if we connect this to the bigger picture, that
Speaker 2: arrogance is Waterfall's fatal flaw.
Speaker 1: Right.
Speaker 2: It assumes you know exactly what the end user wants
Speaker 2: before they have ever seen, touched, or interacted with the.
Speaker 1: Product, which never happens never.
Speaker 2: So to solve this, the industry invented the iterative and
Speaker 2: spiral models. Instead of pouring concrete, it's more like sketching
Speaker 2: with a pencil before applying ink.
Speaker 1: Okay, I like that.
Speaker 2: You build a minimum viable product and MVP, you give
Speaker 2: it to the users, immediately, watch how they actually use it,
Speaker 2: and then loop back to redesign based on their real
Speaker 2: world feedback.
Speaker 1: Now I understand the logic, but I have to push
Speaker 1: back here, isn't it terrifying to start building something and
Speaker 1: when you don't even know what the final product will
Speaker 1: look like. Doesn't that just invite chaos into the budget
Speaker 1: and the timeline.
Speaker 2: Well, it feels terrifying to traditional management because it requires
Speaker 2: a radical mental shift. How so you have to accept
Speaker 2: that you aren't building a finished, static product, You're cultivating
Speaker 2: an evolving system. You're managing uncertainty rather than trying to
Speaker 2: pretend uncertainty doesn't exist. I see, iterative models are entirely
Speaker 2: about risk mitigation. You don't spend two years and ten
Speaker 2: million dollars building the wrong thing. You spend two months
Speaker 2: building a piece of it, check if it's actually solving
Speaker 2: the problem, and a just course.
Speaker 1: Okay, that makes sense. And this intense desire to manage
Speaker 1: uncertainty to finally ditch those rigid waterfall factories sparked a
Speaker 1: huge revolution in the industry, the Agile revolution.
Speaker 2: The famous agile. Right.
Speaker 1: We hear agile used as a corporate buzzword all the
Speaker 1: time now, But at its core, agile is about straight
Speaker 1: sprints of work. It values functional working software over sick
Speaker 1: binders of theoretical documentation, and it demands constant face to
Speaker 1: face communication. But Inside the Agile framework, there's a fascinating,
Speaker 1: almost extreme offshoot. It's literally called extreme programming or XP.
Speaker 2: Extreme programming takes the best, most effective practices of software
Speaker 2: development and dials them up to eleven.
Speaker 1: It really does.
Speaker 2: It relies on a set of strict rules that actively
Speaker 2: challenge almost everything traditional management stands for.
Speaker 1: Okay, let's run through some of the wildest ones, starting
Speaker 1: with the concept of JBGE just barely good enough.
Speaker 2: Oh, this is a great one.
Speaker 1: In XP, heavyweight documentation is explicitly banned. You only write
Speaker 1: exactly enough documentation so the next person can pick up
Speaker 1: the code and understand it. No more five hundred page
Speaker 1: specification binders. But wait, if Agile bans heavy documentation, how
Speaker 1: on earth does a new employee know what they're supposed
Speaker 1: to be doing. Doesn't institutional knowledge just vanish?
Speaker 2: Well, it forces the code itself to be the documentation.
Speaker 2: What do you mean if you have to write a
Speaker 2: ten page manual to explain what a single piece of
Speaker 2: code does? That code is too complex. JBGE forces programmers
Speaker 2: to write simple, elegant, readable logic.
Speaker 1: Oh, so the code is so clean you don't need
Speaker 1: a manual exactly. That makes total sense, which brings us
Speaker 1: to TDD. Test driven development. Now, this is the one
Speaker 1: that completely broke my brain.
Speaker 2: It's so counterintuitive.
Speaker 1: It is. In traditional coding, you write the software and
Speaker 1: then you write a test to make sure the software works.
Speaker 1: In TDD, you literally write the test first.
Speaker 2: Yes, it completely flips the psychology of coding. You write
Speaker 2: a strict test for a feature that doesn't exist.
Speaker 1: Yet, which means the test fails.
Speaker 2: Naturally the test fails, then your only job is to
Speaker 2: write the exact minimum amount of code required to make
Speaker 2: that specific test pass. Wow, not a single line. More.
Speaker 2: It ensures that every single piece of logic in your
Speaker 2: system is constantly automatically verifiable.
Speaker 1: That is brilliant, and that leads right into the rule
Speaker 1: of simple design, which has just the greatest acronym. Ever,
Speaker 1: ya aga, you aren't gonna need it. The rule is
Speaker 1: you never ever write code for a feature you anticipate
Speaker 1: needing in the future. You only solve today's immediate problem,
Speaker 1: right like, don't build a complex data portal just because
Speaker 1: the marketing team might want to track analytics next year.
Speaker 1: Yag and I.
Speaker 2: It aggressively stop speculative coding, which is honestly one of
Speaker 2: the biggest time wasterers in the industry, I bet. But
Speaker 2: the most controversial rule in extreme programming, without a doubt
Speaker 2: is pair programming.
Speaker 1: Okay, I have to stop us dead right here. Pair programming.
Speaker 1: The rule states, you have two programmers sitting at one
Speaker 1: single keyboard.
Speaker 2: One keyboard.
Speaker 1: Yeah. One is the driver who physically types, and the
Speaker 1: other is the navigator, who reviews every single line in
Speaker 1: real time as it appears on the screen.
Speaker 2: That's the rule.
Speaker 1: Wait, hold on, you are paying two highly expensive six
Speaker 1: figure engineers to sit at one computer and do one job.
Speaker 1: How on earth does a manager justify that labor cost
Speaker 1: to the finance department.
Speaker 2: Well, on a traditional spreadsheet, it looks absolutely insane.
Speaker 1: It really does.
Speaker 2: But the math is incredibly counterintuitive, and it goes right
Speaker 2: back to our discussion on the crushing cognitive load of debugging. Okay,
Speaker 2: finding and fixing a bug is exponentially more expensive than
Speaker 2: writing the code in the first place. When you use
Speaker 2: pair programming, it takes about fifteen percent more total man
Speaker 2: hours to write a feature, but it produces fifteen percent
Speaker 2: fewer defects.
Speaker 1: So it's essentially buying an insurance policy against crippling bugs.
Speaker 2: Exactly the navigator catches typos before the file is even saved.
Speaker 2: They suggest better, cleaner design patterns on.
Speaker 1: The fly because they aren't bogged down on the typing.
Speaker 2: Right, a solar programmer might get stuck for two days
Speaker 2: chasing a cascading logic error. In pair programming, that never
Speaker 2: happens because you have a second set of eyes constantly
Speaker 2: auditing the logic.
Speaker 1: That's huge.
Speaker 2: Plus, the institutional knowledge is instantly shared. If one of
Speaker 2: those programmers leaves the company, the other knows exactly how
Speaker 2: that entire section of code works.
Speaker 1: So no single point of failure exactly. But XP isn't perfect, right,
Speaker 1: It has a fatal Achilles heel.
Speaker 2: It does. XP requires an on site customer, oh right,
Speaker 2: Because you aren't writing five hundred page requirement documents. Someone
Speaker 2: officially representing the end user has to be sitting in
Speaker 2: the room with the development team available at all times
Speaker 2: to answer questions and steer the ship.
Speaker 1: And that's the problem.
Speaker 2: Yeah, in reality, clients have their own day jobs. Getting
Speaker 2: a customer to ship with an engineering team forty hours
Speaker 2: a week almost never happens.
Speaker 1: Yeah, that seems like a massive roadblock. But you know,
Speaker 1: even if you never write a single line of code.
Speaker 1: Taking these concepts out of the software world for a
Speaker 1: second provide some really brilliant life acts.
Speaker 2: Oh absolutely, think.
Speaker 1: About how you operate in your own daily tasks. Yah,
Speaker 1: G and I you aren't going to need it. Stop
Speaker 1: over complicating the future and just solve the problem right
Speaker 1: in front of you. Yes, and jbge just barely good enough.
Speaker 1: How often do you overplan, overdocument, and overthink a project
Speaker 1: instead of just doing the work. You can use these
Speaker 1: exact frameworks right now to start procrastinating and start executing.
Speaker 2: It really is a methodology entirely built around confronting reality
Speaker 2: exactly as it is, rather than how you planned it
Speaker 2: to be on a whiteboard.
Speaker 1: Well said, We've covered a tremendous amount of ground today,
Speaker 1: from the solo artist hacking code in their bedroom to
Speaker 1: the rigid, factory like waterfall models, all the way to
Speaker 1: the nimble, paired up, test driven world of extreme programming.
Speaker 2: It's been a journey.
Speaker 1: It reveals the staggering human effort, the philosophical battles, and
Speaker 1: the psychological friction behind the digital tools we blindly take
Speaker 1: for granted every time we swipe the screen.
Speaker 2: It serves as a great reminder that software isn't just
Speaker 2: cold math and logic. It is deeply human. It is organized, messy,
Speaker 2: brilliant human collaboration.
Speaker 1: It really is, which leads me with one final kind
Speaker 1: of provocative thought for you to chew on.
Speaker 2: Let's hear it.
Speaker 1: We've talked extensively about how measuring a programmer's worth by
Speaker 1: lines of code is a terrible, outdated metric. Well, right now,
Speaker 1: artificial intelligence is getting in incredibly proficient at churning out
Speaker 1: bulk lines of boilerplate code.
Speaker 2: It really is.
Speaker 1: So if AI eventually takes over the manual labor of
Speaker 1: actually typing out all those lines, what happens to the
Speaker 1: human software engineer? Do we revert all the way back
Speaker 1: to the beginning of our metaphor list?
Speaker 2: Oh?
Speaker 1: Interesting, well, human programmers go back to being pure architects
Speaker 1: or artists, where we just dream up the concepts and
Speaker 1: structural safety and the machine acts as our master craftsman
Speaker 1: doing the building.
Speaker 2: That is the exact frontier we are standing on right now.
Speaker 2: The tools are shifting rapidly, but the need for human vision,
Speaker 2: risk mitigation, and system level thinking is only getting more critical.
Speaker 1: It's gonna be fascinating to watch. Thank you so much
Speaker 1: for joining us on this deep dive. Next time you
Speaker 1: look at the apps on your phone or check your
Speaker 1: bank balance online, we hope you have a whole new
Speaker 1: level of respect for the invisible suspension bridges holding it
Speaker 1: all together. Keep questioning the structures around you, and we'll
Speaker 1: see you next time.