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Exsqueeze Me?

With all due respect to Mike Meyers as Wayne Campbell, I saw something on the internet and the only possible response was Exsqueeze Me?. The quote started out OK, not great, but OK, then, right there at the end, it took a sharp left into crazy town.

Good teams can and will delete tests that have high false positive rates – or that never fail.

Here’s the thing. If you’ve got a test with a high false positive rate, that’s bad. Flaky tests are very bad. Bad in many dimensions. To list just a few of them,

• They waste your time: Every time a test fails, you’re expected to go analyze what failed and why. Then figure out how to prevent that specific issue. However, if you go analyze the failure and it turns out the really wasn’t a problem, you’ve wasted however long it took you to figure out there wasn’t a problem.

• Failing tests become business as usual: It’s the broken window effect. When no tests are failing then a sudden failing test is noteworthy. When you have a test that randomly fails then an additional failing test isn’t nearly as noticeable. If it wasn’t important enough to fix the only failing test, then each additional failing test is that much less important.

• Alert (or Alarm) Fatigue is real: When the siren sounds it’s supposed to be unusual and noteworthy. If your alarm is going off all the time then it’s not an alarm, it’s just life. Just like the boy who cried wolf, if the alarm keeps going off and there’s nothing you can, should, or must do, you start to ignore it.

• Flaky tests indicate a lack of understanding: It could be a lack of understanding of the domain, the environment, the test setup, or any combination of those three. If you don’t understand the system and situation in this specific case, what else aren’t you understanding? What are you missing that’s going to cause you problems later on?

That’s just some of the reasons flaky tests are bad. Deleting them isn’t the worst thing you could do, and it will fix the first three problems above, but it doesn’t do anything to fix the fourth. In fact, it just hides the problem. Ignoring a problem rarely makes it go away.

Therefore, most of the quote is almost correct. Instead of just removing a flaky test, a much better response is to fix the test so that it’s not flaky. It could be a bug in the code, a bug in the test, a problem with your test methodology, or a lack of understanding. Whichever it is, once you make the test pass consistently, you’re in much better shape. You don’t waste time. You’re incentivized to keep things clean. Alerts mean something. You understand your situation that much better. Which means you get to sleep that much better at night.

It’s the last part of the test that’s just plain WRONG. Some will say that a test that never fails serves no purpose and it’s wasting resources. Time. Bandwidth. Cognitive load. For no measurable benefit. They haven’t stopped a single bug from getting through.

That facts are real. All tests take time, bandwidth, and add some amount of cognitive load to the developers. But all of that, for all of your unit tests, should be minimal. If they’re not minimal then you have other problems (bad tests) you should fix¹.

Just because a test hasn’t caught a bug yet, you can’t know that it won’t ever catch a bug. Even if no-one is changing the code directly, those tests can still help keep you safe. They do things like:

• Protect against changes in dependencies: Dependencies outside of your control can change. Those changes can make your code break. If you don’t test, you don’t know.

• Protect against environmental changes: There are lots of things in the environment that can change. Networks come and go. Clock speed changes. Processors get replaced and new ones show up. There can be subtle differences in the environment. If you don’t test, you don’t know.

• Protect against bugs in tooling changes: Similarly, tools change. Runtime environments, compilers, and interpreters can change. Are you relying on undefined behavior? That can change without you knowing it. If you don’t test, you don’t know.

• Provide examples of how to use the code being tested: Tests are great examples. They can be documentation. They can be used as a learning environment. They can be a reference design.

• Acknowledge Hyrum’s Law: Given eough time and users, everything your code does, intended or not, is going to be relied upon by someone. You never want to change behavior on your users without knowing about it. That is not how you want to surprise your users.

• Prevent bugs in the code under test: Finally, and certainly not the least important, you never know when a test is going to show you that you’ve broken something. Past performance is a good indicator of future performance, but it is not a guarantee. If you don’t test, you don’t know.

And that’s why the only possible response to someone saying you should delete tests that never fail is Exsqueeze Me?

The Messy Middle

I’ve talked about a messy middle before, but that’s not the only messy middle. That one was about the land of It Depends. Where you can’t know the answer until you have all of the context. But there are other messy middles.

Consider the space between products and platforms. The split between what customers bought, and what the company built internally so that they could then build the things that customers bought. In almost all cases people know about the products, but almost no-one outside the company knows about the platforms.

Not every company has this split. There has to be enough products, or at least enough engineers, for that to happen. At some point they become not just different things to work on, they become entirely different organizations within a single company.

It’s something you don’t see working at small, single product companies. You often don’t see it at companies with only a few products. Back when I was working for Microprose the company was working on multiple games at any given time, but each game development team was completely self-contained. There were a few people who worked across games, but the code for each one was essentially unique. Everything was a product.

The first place I really saw the split was at Microsoft, where there was a Windows team, that was the platform for everything else, a few other big teams, like Office, Developer Division, and SQL, and a bunch of smaller orgs, like Online, Home, Games, and Research. The odd thing there was that Windows was also a product itself.

At the time I was on the Product side, working on games, and didn’t pay too much attention to the platform side of the world. I only cared about what APIs it provided. Also, we were such a tiny part of the Microsoft world that Windows, our platform, might as well have been made by a different company. We could ask a few questions that outsiders couldn’t, but we didn’t have much influence. The Office and Developer Division teams had that.

Then I moved to Uber. When I started at Uber they had just gone through their split. By that time I was on the platform side, working deep in the engine room. At that time Uber was already big enough that we even saw differences between real-time and offline/batch processing.

You see, down in the engine room we spent a lot of time building a product agnostic processing engine. We didn’t care what folks did with our CPUs and persistent storage. We abstracted away the complexity of scheduling and placement engines and let our customers focus on data and control flow. And that’s where I ran head-first into another messy middle.

Besides making sure the system worked and was easy to use, I spent a lot of my time as an evangelist. Showing countless other teams how they could benefit from our platform. The more success I had, the more I saw that there was at least one missing layer. I saw multiple teams creating their own internal platforms, based on our platform.

Being closer to the product, those platforms were more opinionated than ours. Because they knew what their 2-5 teams were doing and didn’t care about the other 20 or 30 teams using the base platform, they were able to make assumptions and build decisions into their tools.

That’s where it started to get messy. Those little infrastructure teams didn’t really know about or talk to each other. They started to duplicate work. Or at least solve similar problems. Not just each other’s, but problems that we were solving. The line between the different teams and the platform ebbed and flowed as problems were identified and solved.

It was a bit of a surprise, but it wasn’t all bad. Yes, it was messy. Yes, there was some duplication. But there was also a lot of advancement. By not forcing groups of teams to wait for a shared solution those teams were able to move faster. By waiting for consensus on the solution to form and move into the platform we avoided churn. By consolidating known shared work we freed up resources for product specific work.

We turned what could have been a bottleneck into a competitive advantage. Through lots of communication. Of problems. Of intent. Of timelines. By keeping the cross coupling down. By encouraging teams with similar context to make those specialized solutions that kept domain specific work with the domain. By taking the common parts of those domain specific solutions and commoditizing them. By recognizing that one team’s platform is another team’s product.

Speed Vs. Quality

As I’ve noted before, while I’ve worked all over the computing stack, from simulation for games and requirements analysis to 3D rendering to fleet management to tools, I’ve spent the last 15 years or so working down in the engine room. Working on internal platforms, tools, and infrastructure. And time and time again I’ve seen teams and entire organizations micro-optimize for immediate velocity only to run into a wall and find themselves stuck. I’ve seen it happen around data storage, data shipment, data ingestion, and data validation. I’ve seen it happen custom processing engines and backwards compatibility.

Every time, what it came down to was every case becoming a special case, so that nothing was special. Instead of developers having to understand a few simple systems that did one thing and one thing well and could be composed as needed, developers had to understand lots of highly specialized systems that were used for one thing only. Worse, those specialized systems weren’t isolated. They had unique and subtle interactions that developers had to keep in mind at all times. The cognitive load became too large, and most changes did most of what they were supposed to, but also caused downstream issues that now needed to be fixed.

Not only that, but because deadlines were always approaching, decisions were made based on those deadlines. Each one adding more coupling and cognitive load. The rate of change stayed high, but actual progress got slower and slower.

Inevitably, the cost of progress would get to be so high that someone decided to do something. Somebody would say “I’m declaring a Code Red”. People would focus on the problem, get back to fundamentals, and re-work things to both solve the problem, at the moment, but also eliminate complexity and coupling. The result was not just the solving of a specific problem, but also a step change in real progress.

I’ve experienced it multiple times. Sometimes I’ve been able to help people avoid the problem by pointing out the end result of the path they were on. But what I’ve never done is a systematic study of the problem. I’ve been too busy doing the work.

Luckily, someone else has. In March of 2022 Tornhill and Borg published Code Red: The Business Impact of Code Quality– AQuantitative Study of 39 Proprietary Production Codebases.

It’s not perfect. It’s based on Jira and Git histories for 39 projects, so it’s pretty good at determining correlation. It’s based on projects of various sizes, but none of them are all that large. It’s a cross section of languages. There’s some selection bias because all of the code bases were ones that had already chosen to at least purchase a specific code quality too. Finally, because there are no actual experiments, it can’t prove anything about causality.

There’s lots of interesting conclusions in there, but to me, the most interesting is not about tickets per file or line of code, or how long ticket resolution took on average, but the variability of resolution time. In the code bases that were identified as the least healthy, the variability in resolution time for a ticket was 9 times higher.

In other words, not only did it take longer to resolve issues in low quality code, but the issues were of wildly different sizes. We can’t be sure, but it’s likely that developers very often didn’t know how much work it would take to resolve the issue.

And that points at higher cognitive load. The more complex the problem, the harder it is to reason about, and the harder it is to solve. Particularly without making something else worse. And that means that you really have no idea how much work it’s going to be until you’ve actually done the work. That lack of predictability makes planning harder. And pushes you to the quick fix. Which just makes it worse.

But now, we’ve got proof that, at least in these cases, maintaining high quality and clean boundaries, avoiding coupling, and reducing cognitive load, is highly correlated with both faster execution and reduced variability.

While correlation is not the same as causation, it often is. And my personal experience lines up very well with their conclusions. So when someone asks you want the business value of quality code is, now you can show them the academic proof.

Culture: Not Just For Yogurt

Culture isn’t just for yogurt and sourdough. It’s also for teams. Or really, any organization, formal or informal. There’s a lot that goes into culture. What your goals are. How you incentivize. How you teach. How you do things, in good times, and in bad. And perhaps most importantly, how you handle uncertainty.

Overhead on the internet:

Your “Company Culture” is more about what happens when somebody on a team says “I don’t know” than what team building events you plan.

That right there. That’s what culture really is. It’s not the perks you have or the benefits you provide. It’s not your mission or your goal. It’s not even what you do when things get hard. It’s what you do when you’re faced with uncertainty. It’s what happens when someone on your team is asked a hard question and they say “I don’t know”, or worse, make a mistake.

The first thing to realize is that if people feel confident enough to say “I don’t know” you’re at least on the right path. You’re getting honest answers, which is a pre-requisite for having the kind of culture that helps people do their best. It’s not the best place to be, but it could be a lot worse.

Worse would be where, instead of saying “I don’t know”, the answer is defensive, makes excuses, or even worse, redirects blame. Worse is no accountability and everyone putting themselves first. At the expense of their co-workers, their teams, and of whatever it is they’re supposed to be doing. Worse can be paraphrased as “I don’t have to be good, I just have to make sure someone else is worse.”

On the other hand, high performing teams, the ones with the best cultures, don’t just say “I don’t know.” They continue from there. They finish the sentence with something along the lines of “and we’re going to do X, Y, and Z” to find out.” That’s not only being accountable for where they are, that’s taking ownership for fixing the issue and moving forward. To the benefit of their co-workers, their teams, and whatever it is they’re supposed to be doing. The best can be paraphrased as “A rising tide lifts all boats.”

Assuming that’s what you’re aiming for, you might need to make some changes. Changing culture is hard. It’s been said that

In the battle between change and culture, culture almost always wins.

One of the biggest parts of building that modeling honesty and trust. One easy way to do that is when someone is honest enough to say “I don’t know”, don’t just jump in and say what you want or need, or start being directive. Instead. trust them to finish the sentence with both an explanation and next steps. Offer help. Help via guidance. Help with resources. Help with the time and space to take those next steps.

While doing it yourself might get you those answers sooner, it comes with a very high cost. You don’t get done what you were supposed to be doing. The next time this sort of situation comes up (and it will) the same thing is going to happen. The only thing people have learned is that you don’t trust them.

If instead, you wait for them to finish the sentence and then make sure they’ve got what they need to find the answer you still get to do your job. And next time, instead of them saying “I don’t know.”, they might say something like “It took a bit more work than I planned, but here’s the answer to your question.” And even if they don’t get it that time. They might get it the time after that. Or the one after that. What they will do is keep getting closer.

That’s a rising tide lifting all boats. That’s a better culture. And, you’ll have more fun at your team-building events. Because the team is trying to lift each other up, not keep each other down.

Docs First And The Definition Of Done

I’ve talked about doc culture before. About the importance of writing things down. About how you can use a narrative doc, that everyone reads before talking to make meetings more efficient. Most folks think of this as the Amazon approach.

Amazon does run on docs. Many meetings start with a document, not a slide deck, that people read in the meeting. They comment on it live. The authors provide direct feedback. Then, when everyone is in roughly the same place, the discussion starts. The meeting is then about the questions raised and not answered by the document. Hopefully from people bringing different contexts and perspectives, but sometimes just because the doc isn’t ready. In the latter case, the meeting gets rescheduled until the doc is more finished. Either way, it saves a lot of time.

There’s another way that documents can help. And again, Amazon is pretty well known for it. It’s working backwards. For a new product, you start with the press release and the matching frequently asked questions. You get buy in on the end goal, the user problem you’re solving, and what the business case for solving the problem is. When you’ve gotten that approved, you have your north star. Your mission.

What it doesn’t do is say how you’re going to get there. You might have references to things that explain why it’s important and how the user (and business) will benefit, but it’s not about the technology. It’s not about the mechanism of the solution. It’s about solving the problem

That’s what makes the documentation first approach so powerful. It puts the user front and center. It emphasizes their why. It’s your new north star. Whenever you have to make a decision, that North Star provides the context to make the decision.

It’s constraining and freeing at the same time. The press release that you’ve written has locked in the user benefit and why people are going to care. Sure, you can make small changes in the details, but if you did a good press release, you don’t need to. You can’t go solve some other problem because it seems like a good idea. You can’t make some visual change and claim that you’ve solved the problem. You know what you’re going to deliver.

At the same time, the press release and accompanying FAQ also give a tremendous amount of freedom. You know what you’re doing, but the how isn’t defined. Whether you’re doing something new, adding a new feature to an existing product, or just making your current users happier while they do what they’ve always been doing, you get to define how to do it yourself. Are there interface changes? Possibly. Internal data structure changes? Probably. New/better algorithms? Maybe. Whatever you need to do. It’s up to you, as the software engineer, to engineer the solution.

The nice thing is, you don’t need to be done before you share what you’ve done. You can share it internally and with your users. You can get feedback on how you’re doing as you approach your goal. You take small steps in a direction. You see if you’re getting closer to the north star. You take some more small steps. You check again. You keep working towards that goal. With the knowledge that when you get there, you’ve done something your users will consider worthwhile.

As Uncle Ben said, “with great power comes great responsibility”. You’ve got the power to do anything you want. But you’ve also got the responsibility to solve the user’s problem. How you use the power and how you handle the responsibility is up tp you.

System Goals

As CEO and Co-Founder of Honeycomb, Charity Majors has a lot to say about observability. She also has a lot to say about software development in general. And software leadership. Over the years I’ve learned a lot from her stuff.

Recently she was on the How AI Is Built channel. It’s a pretty good overview of how to do observability and why it’s important. On top of that, the title of the episode really spoke to me. It’s one of those non-functional requirements, even if it’s not strictly worded as an -ility.

Build systems that can be debugged at 4am by tired humans with no context

That right there. That’s the goal. Make sure the maintainer has life as easy as possible.

Of course, we want to write perfect code. Code that always works. That handles upstream and downstream issues. That scales seamlessly. That never fails and doesn’t need maintenance. But the reality is that code can fail. You will need to maintain it. Especially when the environment it’s running in changes. Different inputs. Different scale. Different latency requirements.

And of course, when it does fail, it won’t be at 10:00 in the morning while the original author is standing there expecting it. Instead, it will be early on a weekend morning, and you, as the on-call engineer, need to notice there’s a problem, figure out what’s happening at that moment, come up with a remediation, and implement it. While you’re barely awake working on code you’ve only seen before in passing.

That’s where runbooks help. They tell you where to start looking and how to deal with similar issues. That’s when observability shines. It tells you what’s really happening. Not what you expect to be happening or what you think_ is happening, but the actual ground truth. (Current) Comments and design docs are critical as well. They tell you want the original (and hopefully current) intent and constraints were. That’s when you need accurate logs and databases to show you how you got to where you are.

At 4 AM you have no context. You have no understanding. Before you can do anything, you need to build the context to understand the situation. Then, and only then, can you make a considered change to remediate. Sure, you can just start changing things and seeing what happens, but more likely to make it worse than to fix it when you do that. Remember, Slow is smooth and smooth is fast.

And that’s Coding for the Maintainer

Superpowers And Optionality

A long time ago I talked about distributed systems superpowers. Those are great things to have. When you’re building a distributed system, you really want your core functionality to have those superpowers. They make building the features your users want much easier. That’s your superpower

A quick recap. Here’s the 5 superpowers I listed.

• Idempotency: The system should do the right thing if asked to do the same thing twice.
• Availability: The system should always work.
• Scalability: The system scale (up/out) automatically when it gets slow.
• Durability: The system to remember what I told it.
• Consistency: The system should give the same (correct) answer to everyone, always.

As usual, there’s some tension there. Especially when you consider some of the common programmer’s fallacies. Those pesky laws of physics don’t change, so you can’t be everything to everyone all at once. You have to make trade-offs.

Those superpower are not, however, something you only need if you have a distributed system. There’s the simplest question of what are you building that isn’t a distributed system? Pretty much every integrated circuit you interact with, from simple logic gates to FPGAs to multi-core CPUs or custom built GPUs is itself a distributed system. A system made up of discrete parts that talk to each other and may, or may not, have some of all of those superpowers.

If everything is a distributed system, then those aren’t distributed system superpowers, they’re just plain old superpowers. And that’s true. But there’s another way to think about distributed.

Regardless of the spatial distribution of a system, almost all non-trivial systems have distributed ownership. When you think about distribution that way those superpowers become even more important. Because the stronger they are, the more they constrain how much you need to keep in your head. How much cognitive load you need to carry around just to understand the environment. How much work you have to put in before you go to actually do something.

What those superpowers do is help you define, build, and maintain your bounded contexts. They help you isolate things into small components that embody the Unix way. They do one thing and do it well. And that opens up so many possibilities. It gives you optionality.

Just like with Unix, you don’t know what you or your users are going to want tomorrow. But having components that are idempotent, available, durable, stable, and consistent gives you superpowers. It lets you safely combine them in new ways without having to worry about having unintended consequences on other parts of the system. And do that at the same time as other people are doing other new things.

That’s optionality. That’s flexibility. That’s having the ability to add value later, as you discover it. And that’s the ultimate superpower.

Coding Genies and Code Review

Speaking of writing for the maintainer, what about coding assistants? They write code too.

First, an axiom. Just like auto-complete in your IDE is your code, code that you ask an AI assistant to write and then share as your own is your code. There’s nothing wrong with that per-se, but it’s not an excuse to not care about the implementation details.

Second, a position. Kent Beck has taken to calling coding assistants Genies. Because, just like the traditional genie, (and kind of like a compiler, when you think about it), they do their best to do exactly what you tell them. Any flexibility you give them they’ll take in the way that fits them the best. Without anthropomorphizing them, they do it in the way that’s easiest for them and provides them with the most amusement.

When the genie writes code, they’re writing code that, in theory, does what you asked it to do. If you want it to be readable, that’s another request. If you want it to be in the same style as the rest of the code, that’s a third request. And if you want it to play well with other code and services in your system, that’s an additional set of requests. And requires you’ve given it enough context to work with.

Assuming your genie has produced code that works, and that you’ve enticed it to write unit tests as well, you’re done, right? Wrong.

Before you inflict someone else’s code on your team, with your name on it, you need to do another step. You need to review your own code. You’re already doing that, right? It’s even more important when working with a genie.

It comes back to that software engineering thing. It’s as much about being prepared for what you don’t know as it is handling what you do know. Because, It Depends.

You want to be SOLID. You want to be DRY. You want to build exactly what you need now. You want to build for the future. You want abstraction. You want clear domain boundaries. You want things done now.

You don’t want to be clever. You don’t want extra abstraction. You don’t want to eliminate future options. You don’t want to have to change things later.

You want all of those things, but they’re in tension. Some are direct opposites, while others in one category impact things in the other in non-obvious ways.

You, as the software engineer, have to find the right balance. No genie today can do that for you. They don’t have the context needed. The don’t have the experience in your specific situation.

You need to be very clear when working with the genie. You need to make sure it’s properly constrained because it doesn’t know (or care) about you. Or the maintainer. Or truthfully, even what your real goal is. It’s just doing a best-fit match to what you said. And often, that works, or at least gets you close enough to see where you’re going.

That’s when reviewing your own code really becomes important. Not in the individual lines of code, but the overall change. The gestalt if you will. Before you ask anyone else to look at the code, you need to look at it. Does it do what you want in a way that fits with what you’ve already done? In a way you and your team are prepared to live with in the future.

That’s your responsibility. To provide the right balance of solving the problem at hand without creating more problems for the future. Solving a problem, adding specificity, generally reduces optionality. How much specificity you add and how much you reduce optionality is where not something you can let your genie choose. It has no past and no future. Just the eternal now. And it optimizes¹ for the now.

So whenever you’re sharing Because you are the one that’s going to have to live with it. The genie doesn’t.

Who Are You Writing To Anyway?

I’ve written about this before. When you’re writing code, who are you talking to? The simplest answer is that you’re writing to the computer. To the assembler, compiler, or interpreter, depending on the language you’re using. And at some level, that that’s true.

However, there’s a lot more to it than that. As the great philosopher and programmer, Anonymous, once said

A programmer does not primarily write code; rather, he primarily writes to another programmer about his problem solution. The understanding of this fact is the final step in his maturation as technician.

In fact, we generally, have at least three audiences. Of course, the assembler/compiler/interpreter is one of them. The second audience is the folks that review the code after¹ it’s written or pair with you while it’s being written. The third, and most often forgotten, is the maintainer.

The thing is, your compiler (or assembler or interpreter) doesn’t understand what you wrote. Instead, it follows a very strict set of rules to translate what you wrote into a series of 1’s and 0’s that the computer can execute. (NB: The computer doesn’t understand it either. Ij just knows how to execute it). It’s completely on you, the developer, to ensure that what you write does what you want. The compiler doesn’t care. As long as you follow it’s grammar rules it will come up with something for the computer to do. If it does what you want and expect it to do, great.

If not, someone else is going to need to read the code. And that person is going to need to understand it. That person might be you 10 minutes after you wrote it, when you _probably remember what you meant. In that case it’s relatively easy to figure out where what you wrote and what you meant aren’t the same. Another very common case is you, 6 months or 6 years after you stopped thinking about that bit of code.

In that case, you look at in confusion, wonder what idiot came up with it, check git blame, and silently yell at your former self. Then you piece together what you meant, dust of the old constraints you mostly forgot about, and then move forward.

Worst case is that when you check git blame you find that the most recent changes where 5 years ago, and all of the people who ever touched the code no longer work there. You’re on your own. You need to figure out what the code is actually doing, why it does it that way, and what odd constraint, that you don’t know about yet, has changed.

And that’s the person you’re writing the code to. The maintainer. The person who has to live with the code long after the thrill is gone. As John Woods said,

Always code as if the person who ends up maintaining your code is a violent psychopath who knows where you live.

I usually maintain my own code, so the as-if is true.

Or maybe you prefer Martin Fowler’s way of saying it.

Any fool can write code that a computer can understand. Good programmers write code that humans can understand.

So write your code in a simple, straightforward manner. Make it understandable. Reduce cognitive load. Don’t require that the reader carry a lot of context. Don’t be clever. Don’t be a cowboy.

Because, if we’re doing it correctly, we ALL read way more code than we write.

AI Isn’t The Answer

If AI isn’t the answer, what is? Before you can say what the answer is, you need to be clear what the question is. If the question is “How can I get more code written faster?”, then AI might be the right answer. On the other hand, if the question is “How can I provide more value to the user faster?”, then AI probably isn’t the answer. At least not in any meaningful way. And not with what we call AI¹ today.

Let’s be clear. One thing today’s AI can do, and do quickly, is churn out code. It will probably compile. And might even do something close to what you want. That’s probably OK as a proof of concept. It’s fast, and not completely wrong. If that’s your goal, then go for it.

On the other hand, if you’re trying to do something that hasn’t been done, deal with your specific constraints, or work with a complex, deeply interconnected system (think legacy code), typing speed is not what’s holding you back.

Consider this

Learning to program has no more to do with designing interactive software than learning to touch type has to do with writing poetry. – Ted Nelson

Yes, programming is a part of what we do as software engineers. We do eventually need to write things down in a way the computer can understand them (after translation by the compiler). But just as important as the code we do write is the way the code we write is organized. In functions, files, libraries, executables, and services (to name a few). It needs to be written is a way to make it easy to modify. As we learn more about what the code is supposed to do and what we need from it, we need the code, and it’s related tests) to be easy to update.

Also, just as important is how we organize our data. Both at rest (in memory, on disk, or in a database) and in motion (method arguments, cache layout, network messages, and more). It needs to support our use cases. It needs to be hard to get the data into a bad state. How your data is arranged can impact what your program can do in the future even more than the code itself.

Even more important than the code that gets written is the code you don’t write. Some abstraction, but not so much that it hides your meaning. Your code needs the ability to handle incorrect inputs, but you don’t want to go to far. You need to expect things to change. If you’re writing a tool to sort things, you probably want to be able to sort on multiple parts of your data structure, or at least know how that fits into your system. But you shouldn’t also build it to handle language translation. If that becomes necessary, so be it, but don’t build that in at first.

Because what you’re doing is software engineering. Making the engineering tradeoffs that fit your exact situation. Not someone else’s exact situation. Of course, It Depends, but figuring out what it depends on takes judgement. It takes awareness. It takes larger contexts. It takes intelligence. Which is exactly what AI doesn’t have. Which is why AI isn’t the answer. There are lots of ways AI can help², but it’s not ready to help with the thinking parts yet.