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I miss the good old days when technical debt
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I miss the good old days when technical debt
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and student debt were the only debts we had to worry about in the software world.
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Looking back at it, technical debt was actually quite fun.
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We would take shortcuts to move faster,
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ignore best practices to meet a deadline,
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or delay updating a library because we were in no mood to work through the side effects.
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The worst that could've happened was
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that you would end up paying for it later through increased complexity in systems that nobody wanted to touch.
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But ultimately, this increased complexity would increase your job security because
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if If you were the only person who understood a messy part of the system,
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you would suddenly become very hard to replace.
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This is why for the past 20 years,
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software engineering has been a pretty nice gig,
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with a lot of perks and more than decent paychecks.
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And I'm not making this video to tell you that all these are going to end,
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but I want to discuss a new emerging kind of debt which will actually make our lives much more difficult.
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Adios Money recently posted about a new concept called Comprehension Debt
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and in this Monday morning review will look at the importance of being able to think
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and solve problems in the generated code era.
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Comprehension debt is the growing gap between how much code exists in your system
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and how much of it anyone on the team can genuinely explain.
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At first, this doesn't look like a problem,
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since most AI-generated code looks really clean at first and it might look like all the best practices are being followed.
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But underneath that, something subtle starts to shift because the understanding of the system begins to slowly degrade.
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In a recent study, engineers were asked to learn a new library.
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Some of them used AI to generate solutions,
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others used it more as a guide to explore concepts and ask questions.
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Both groups completed the task in roughly the same time,
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so from a productivity perspective, everything looked identical.
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But when they were tested afterwards,
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the group that relied on generation performed significantly worse,
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especially when it came to debugging.
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And debugging is where things become very real very quickly.
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If you've been involved in software for a while,
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you probably know that reading and understanding code at a deep level is one of the most difficult parts of the job.
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I'm not talking here about going through some random syntax filled with for loops and if statements.
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I'm talking about complex implementations,
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which have interactions, implicit assumptions,
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and behavior that only makes sense once you follow the entire execution path end-to-end.
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This part is so difficult that you'll go through code you wrote yourself six months ago
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and you'd still be confused trying to reconstruct what you were thinking at the time.
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When everything works, speed feels like the most important metric.
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The moment something breaks, the only thing that matters is whether someone can explain what the system is doing and why.
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And, if I have issues understanding my own code from six months ago
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and I can proudly call myself an average developer with a weird accent and a copied YouTube channel,
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I expect
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that most of us will have a lot of issues understanding thousands of lines of code generated in a couple of minutes.
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For a long time, writing code and reviewing it acted as a natural bottleneck in development.
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It slowed teams down, but it also forced engineers to read code written by others,
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to question decisions, and to build a shared understanding of how the system behaved.
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The process was one of the main ways knowledge spread across a team.
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With AI-generated code, the volume changes the nature of that interaction.
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Code is produced faster than it can be meaningfully reviewed,
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and because it is usually clean and well-structured,
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it gives off all the signals that historically indicated quality.
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And the AI labs are telling you to ignore the problem.
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You spend your tokens generating code,
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and then they provide you the tools to review that generated code for the convenient price of $15-25 for each review.
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In other words, the token sellers just want you to buy more tokens,
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ideally hundreds of thousands of dollars worth of tokens.
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I keep trying to avoid highlighting the absurdity of some of these CEOs' comments,
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but they are making it very tough for me to ignore them.
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screams trying to inflate the bubble more than focusing on quantity over quality.
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Sure, you could easily spin up Todo apps in an endless loop to burn through $250,000 in tokens.
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That would be completely pointless and will consume a few more million gallons of water,
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but at least the billionaire Jensen Huang with his geeky leather
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jackets will be happy to see his Nvidia stock keep going through the roof.
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But I digress.
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Back to the topic of comprehension debt,
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there's also a more subtle failure mode that starts to show up in AI-heavy workflows.
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When an implementation changes and the associated tests are updated to match,
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the conversation quietly shifts.
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Instead of asking whether the code behaves correctly,
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you now have to ask whether the behavior itself is correct.
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The test passing no longer answered the most important question.
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As we discussed in last Monday's episode,
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another common reaction is to rely more on specifications.
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You define the behavior in detail,
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review the specification carefully, and let AI handle the implementation.
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On paper, this creates a clean separation between intent and execution.
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In practice, translating a specification into working software involves a large number of implicit decisions that rarely get captured in full.
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Data structures, edge cases, error handling,
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and performance trade-offs all shape the final behavior in ways that are difficult to fully describe up front.
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This year's DORA report also highlights this new emerging comprehension crisis.
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In teams where engineering practices are already solid,
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where workflows are clear, and where people have a strong mental model of the system they are working on,
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AI tends to make everything move faster in a way that actually compounds quality.
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Developers spend less time on repetitive tasks,
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decisions are made with more context,
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and the system evolves in a relatively controlled way.
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But in teams where things are already messy,
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where architecture has grown organically without much discipline,
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and where understanding is fragmented across individuals,
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AI scales all of those issues at the same time.
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The same acceleration that helps good systems becomes a multiplier for bad ones,
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and the difference between the two becomes much more visible.
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What's interesting is that the report states
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that around 90% of developers are already using AI in their work and more than 80% believe it improves their productivity.
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But at the same time,
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a significant portion of them openly admit that they do not fully trust the code that AI produces.
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So you end up in a situation where developers rely on a tool daily,
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move faster because of it,
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and still carry a constant layer of uncertainty about what is actually being generated.
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The DORA data also highlights something that reinforces this dynamic even further.
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AI adoption correlates with higher throughput,
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meaning teams are shipping more changes and moving faster through their backlog.
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At the same time, delivery instability increases,
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which means those changes introduce more issues into the system.
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So basically, you move faster,
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create more issues, you fix those issues faster,
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and create even more issues.
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Your KPIs go through the roof,
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management has nice graphics to look at,
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clients are happy to see results,
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and token sellers are making a lot of money.
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Everybody is happy and Jensen Huang gets richer.
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If I'm being honest, I spent the past couple of months worrying about the future of software engineering.
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AI code generation is far from perfect,
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it has improved a lot in the past couple of months.
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However, something feels off.
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The AI labs are pushing these coding agents hard and we have to pay very little to use their models.
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However, their computing costs are huge and they are very far from running a profitable business.
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This is clearly the customer acquisition phase,
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where their pricing is so low you can't ignore them
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and their agents are slowly becoming an integral part of the software workflow.
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And I believe that this comprehension debt is exactly what they need to make sure you can't really walk away later.
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This is not going to happen overnight,
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but imagine a world 10 years from now where most of the software engineers have lost most of their critical thinking,
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problem solving and debugging skills because they are used to delegating all of it.
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At that point, Anthropic could increase the cost of their agents to thousands of dollars per month
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and there would be little we could do because most of the skills are lost,
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we have no idea what our code actually does
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and the jobs pipeline was destroyed when we decided AI can do a better job than juniors.
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이번 레슨에 대하여
이번 레슨에서는 소프트웨어 개발 분야에서의 새로운 개념인 "이해 부채(comprehension debt)"에 대해 배웁니다. 이 개념은 생성된 코드의 양과 팀 내에서 이를 실제로 설명할 수 있는 사람의 수 사이의 격차를 의미합니다. 우리는 코드를 읽고 이해하는 것의 중요성에 대해 논의하고, 코드를 이해하는 데 필요한 기술을 향상시키기 위한 연습을 하게 됩니다. 이 과정을 통해 여러분은 유튜브 영어 공부를 통해 더욱 효과적으로 영어 회화 연습을 할 수 있습니다.
주요 어휘 & 표현
- technical debt - 기술 부채
- comprehension debt - 이해 부채
- debugging - 디버깅
- implementation - 구현
- execution path - 실행 경로
- code review - 코드 리뷰
- AI-generated code - AI 생성 코드
연습 팁
이번 영상의 속도와 톤에 맞춰 영어 쉐도잉을 연습하는 것이 중요합니다. 코드를 읽고 이해하는 데 시간이 필요한 만큼, 영어 발음 교정을 위한 충분한 시간을 마련하세요. 처음에는 천천히 따라하며 각 문장의 흐름을 파악하고, 점차 속도를 높여보세요. 특히 디버깅 과정에서의 중요성을 강조하는 부분에서의 발음을 집중적으로 연습하면, 해당 용어들을 더 자연스럽게 사용할 수 있을 것입니다.
또한, 코드를 읽을 때 그 의미를 깊이 있게 이해하려고 노력하세요. 자신이 쓴 코드나 다른 사람의 코드를 읽으면서 영어 회화 연습을 하며, 필요한 경우 메모를 하는 것도 좋습니다. 이러한 방식으로 shadowspeaks의 기술을 활용해보세요. 코드의 동작 방식을 완벽히 이해하는 것은 소프트웨어 개발자에게 특히 중요합니다.
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