dasil003

Buy me a beer and I can tell you some very poignant stories. The best ones are where there is a legitimate abstraction that could be great, assuming A) everyone who had to interact with the abstraction had the expertise to use it, B) the details of the product requirements conformed to the high level technical vision, now and forever, and C) migrating from the current state to the new system could be done in a bounded amount of time.

My view is over-engineering comes from the innate desire of engineers to understand and master complexity. But all software is a liability, every decision a tradeoff that prunes future possibilities. So really you want to make things as simple as possible to solve the problem at hand as that will give you more optionality on how to evolve later.

When you’re out in the infinite empty of space many light years from any livable environment, you damn well better know how your warp drive works to be able to fix it, and that is what Star Trek portrayed.

This is such a middlebrow dismissal. Like yeah, people are speculating based personal experience and knowledge, so what? If you have a different viewpoint or something specific you'd like to see data on call it out and engage in the discussion. Don't just be the "data or GTFO" guy because that's a super bland and pointless take.

This article is not bad overall, but it does over-index on the cost of making software development costs and tradeoffs legible. Of course leadership does need to make decisions, and so the quest for better data and better cost modeling will continue, and rightly so, Goodhart's law notwithstanding.

I do like this bit though:

> A large codebase also carries maintenance costs that grow over time as the system becomes more complex, more interconnected, and more difficult to change safely. Every engineer added to maintain it increases coordination costs, introduces new dependencies, and adds to the organizational weight that slows decision-making. The asset and the liability exist simultaneously, and for most of the past twenty years, the financial environment masked the liability side of that equation.

And the insight that LLMs are exposing this reality is absolutely true. The funny thing is they are exposing it by accelerating both good and bad engineering practices. Teams with good engineering judgement will move faster than ever with fewer people, and teams with bad engineering judgment will bury themselves in technical debt so fast the wheels will come off.

For me, running an engineering org is primarily about talent acquisition and empowering those ICs with judgment to move quickly. How well systems and teams scale depends on the domain, product, and how it allows you to decouple things. With the right talent and empowerment there are often creative ways to make product and system tradeoffs and iterate quickly to change the shape of ROI. Any mapping to financial metrics is a hugely lossy operation that can't account for such changes. It might work in mature companies that are ossified and in the second half of their lifecycle, but in growing companies I think it's fundamentally misguided would amount to empowering the wrong people.

It seems really premature to talk about AI being the end of anything. What’s at an end stage is adoption of smart phones and monetizing human attention. That’s been the fuel that powered the last quarter century of tech gains, and while still huge in absolute terms it has been running out of steam as a growth engine and facing cultural pushback (eg. Social media lawsuits) for a while.

AI so far has really only shown massive utility for programming. It has broad potential across almost all knowledge work, but it’s unclear how much of that can be fulfilled in practice. There are huge technical, UX and social hurdles. Integrating middle brow chatbots everywhere is not the end game.

Totally agree with this, the beauty of software is the right abstractions have untold impact, spanning many orders of magnitude. I'm talking about the major innovations, things like operating systems, RDBMS, cloud orchestration. But the majority of code in the world is not like that, it's just simple business logic that represents ideas and processes run by humans for human purposes which resist abstraction.

That doesn't people from trying though, platform creation is rife within big tech companies as a technical form of empire building and career-driven development. My rule of thumb in tech reviews is you can't have a platform til you have three proven use cases and shown that coupling them together is not a net negative due to the autonomy constraint a shared system imposes.

That's fair. Yes they are all individuals with their own unique perspective and approaches, and we should definitely hold them accountable for their impacts. I'm not saying the systemic incentives absolve them of responsibility, I'm just saying that we can not depend on CEOs of corporations to do the right thing. This is the role of government, but even moreso, elected representatives are people too, so actually it depends on a more fundamental movement of the people en masse to make it known to our representatives that this is way bigger than partisan politics.

I guess the people in Wall-E didn't really seem unhappy so perhaps you're right. My gut instinct though, is that there is a qualitative difference in the level of abundance and concentration of wealth, power, and influence we have today that needs to be taken seriously on its merits and not hand-waved away with tenuous historical analogies.

Yes, two hundred years ago, many people thought reading was a dangerous distraction for young people, just as film, radio, TV and the internet became later. But there is a qualitative difference to having social media in your pocket with vibrating notifications. Pretending its just more of the same honestly feels like slightly willful blindness at this point.

I like Tristan Harris' take on the situation, which is both more nuanced and more actionable. The idea being that the system and incentives are set up to select amoral technologists who will make money for shareholders, so inevitably the ones that come into power will be the ones don't see a problem with replacing all of human labor (because that's the only outcome that can justify the investment made). Reading Cory Doctorow's article from yesterday (https://pluralistic.net/2026/04/11/obvious-terrible-ideas/) was a poignant example of how the incentives are stacked against anyone with a conscience. The only solution, is political action, because the interests of the 99.9% are aligned here. And I say this as someone who loves technology and sees lots of value in AI, but it needs governance, and while in the past I was wary of government regulation in technology, in this case it's way broader and more existential to our civilization than one category of labor being disrupted.

> technology rarely yields a net positive

This is a super bold statement that I guest most people would disagree with, and I suspect if you somehow brought people forward from the past, even fewer would agree with.

I do agree with you the subtle cost is rarely discussed. I also would say that the unintended consequences of technology are sometimes very very bad in unforeseeable ways, but that's very different from the "net negative" framing which I think is too reductive to be useful. Technology is not a zero sum game, effects are multifaceted, so any quantitative comparison relies on extremely subjective value judgements.

I think it's more fair to say that with every technology there are tradeoffs. Consider the wheel, before the wheel people probably were more physically fit, but they couldn't move as large of loads. Well, except in the Andes where they figured out how to move gigantic stones well beyond the weight that any wooden wheel would have been able to carry anyway and cut and place them into configurations that were earthquake resistant.

Technology and civilization is path dependent, and I think it's silly to make blanket statements about the merit of technological progress overall. Everything choice (including the choice to do nothing) has unintended consequences. I would never condemn anyone for inventing a new technological solution to a problem, but once the systematic effects are understood then we do need the collective ability to course correct (eg. social media, AI, etc).

What do you mean? A kid is anyone younger than the speaker. My step dad used to refer to Bill Clinton as a kid because he was the first president younger than him.

What was the Bjarne Stroustrup quote about two types of languages again? I certainly have gotten more mileage out of Markdown at this point in my long career as a programmer and web developer than I have out of hand-written HTML, XML, or other markup languages. The latter are good for automation and unambiguous representation needed for system building, but for the type of informal writing that is my bread and butter outside of coding, Markdown kills, and Obsidian is the killer app.

Define interesting. In my experience most business logic is not innovative or difficult, but there are ways to do it well or ways to do it terribly. At the senior levels I feel 90% of the job is deciding the shape of what to build and what NOT to build. I find AI very useful in exploring and trying more things but it doesn’t really change the judgment part of the job.

I agree that the way that today's generation of seasoned programmers learned their craft is going away, and that we don't know how the next generation will learn. I disagree very much with the conclusion:

> They didn’t trade speed for learning. They traded learning for nothing. There was no trade-off. There was just loss.

I believe this conclusion is due to a methodological problem, a form of begging the question if you will. One thing I am certain of is that humans who set their mind to something learn something, and good programmers are among the most tenacious in setting their mind to something. With agentic coding, they definitely learn different things, and so I would expect syntax knowledge to be weaker, but debugging and review skill will increase overall. Why? Because there will be more code, and more breakage, and I still haven't seen any tooling that allows a non-technical person to be effective at this.

Programming knowledge has always had a half-life. The way I see it, this is a big sea change that will fundamentally change the job of software engineers, and some non-trivial percentage will either change careers or find a sheltered slow-moving place to finish out their working years. But for those who were not attached to hand-crafted code, AI provides power tools that empower technically minded people more than anyone else. I have full faith that younger generation still has the same distribution of technical potential, and they will still find ways to develop their craft just as previous generations of hackers have always done.

It's wild to me to hear this being spun as vanity, like it's some influencer clickbait or linkedin slop. You could argue anyone posting anything online is driven by vanity, but in this case we're talking about someone who took agency in his own medical outcome, and essentially experimented on himself. Sure it was selfish in the sense that he didn't want to die and he bent all his effort and resources to it, so what? I don't see exercising ones will-to-live in this way being a huge moral gray area. Other commenters are saying why don't we fund more research? Well sure we should do that too, but it's important to recognize that the type of approach he took here only works because it was one individual willing to combine a significant amount of personal effort with his own moral authority to try out risky things on himself. Even with orders of magnitude more funding, you can't ethically do this kind of thing without the consent of the patients, and there's not enough data on these types of approaches to adequately describe the risks to patients if they aren't specifically motivated to lean into the details like this guy did.

Agreed. One thing I’ve found striking is how far LLMs can get with pure language and the recognition that humans often operate with a similar kind of abstract conceptual reasoning that is purely language based and pretty far removed from facts and tenuously connected to objective reality. It takes a certain kind of mind to be curious and unpack the concepts that most of us take for granted most of the time. At best people don’t usually have time or patience to engage in that level of thinking, at worst it can actively lead to cognitive dissonance and anger. So of course a consumer chatbot is not going to be tuned to bring novel insight, it must default to some level of affirmation or it will fail as a product. One who is aware of this can work around it to some degree, but fundamentally the incentives will always push a consumer chatbot to essentially be junk food for the brain.

This is a weird hill to die on. As much as I resonate with many of the concerns, I don't see refusing to use AI as something that will actually help any of those things. Forking a stable version of vim is something I guess, but I don't really see the sky falling with mainline vim or neovim.

Personally the leverage I have as a bit of a cranky graybeard myself is that I understand how software works and I can distinguish between good and bad uses of AI and think critically about how to influence things towards better software. Just declaring AI as unequivocally bad and evil will do nothing more than make me irrelevant. At some point being right is useless without some measure of also being effective.

I love your take, but I had a hard time getting through the article because “science of entrepreneurship” already feels like a contradiction in terms to me. Each startup is a product of its unique time and context. There are huge swings in fortune based on seemingly subtle factors that are not necessarily under the control of the founder but need to recognized and can force the whole vision, approach, or even the problem statement itself to shift wildly overnight. This happens over and over again, and so creating a successful startup is more akin to bull riding than any formulaic process.

Because entrepreneurship and markets overall are at the center of so many disparate human contexts, I just don’t think the scientific method is particularly applicable. I also think the minute you try to generalize between startups the fidelity of understanding the factors of success fall off exponentially. The most common failure mode—almost by definition—is failing to recognize that some seemingly good idea or pattern that worked in many other businesses just does not work in this particular context for whatever reason. This is why entrepreneurs that are too focused on theory and not enough on the details of their particular space tend to fail.

To me, The Lean Startup is useful food for thought, and can be useful in surprising ways (even in bigger companies), but the generalized ideas and statements are of very little value without a keen sense of applicability in context. Any “science” of entrepreneurship would basically be combining the systemic chaos of macroeconomics less the precision of standard financial metrics plus all the human factors of psychology. Fascinating to think about, but I doubt the best pundits and theorists would themselves make good entrepreneurs.

Agree the jury is still out, but "More code in less time" is a shallow strawman. The better question is what is it good at and what is it not good at, and what are the ways to best leverage those capabilities. I've seen enough use cases from enough engineers now that I firmly believe anyone saying "nope never useful" is sticking their head in the sand.

These rules aged well overall. The only change I would make these days is to invert the order.

Number 5 is timeless and relevant at all scales, especially as code iterations have gotten faster and faster, data is all the more relevant. Numbers 4 and 3 have shifted a bit since data sizes and performance have ballooned, algorithm overhead isn't quite as big a concern, but the simplicity argument is relevant as ever. Numbers 2 and 1 while still true (Amdahl's law is a mathematical truth after all), are also clearly a product of their time and the hard constraints programmers had to deal with at the time as well as the shallowness of the stack. Still good wisdom, though I think on the whole the majority of programmers are less concerned about performance than they should be, especially compared to 50 years ago.

Don't shoot the messenger, I'm just telling you how some hiring managers might think, not endorsing the opinion and it's definitely not something I consider in my hiring.

I will say it's a little weird to frame it as "every fad" though. Do you really not see any net new or lasting utility for software engineering in AI tools? If not then more power to you, but software engineering being a fast-moving field where there are (fair or unfair) expectations to keep up is nothing new.

Having taken a startup from 2 to 100 a lot of the comments here resonate, but now that I'm in a company of 10,000 and trying to get things down across wide areas, I would say efficiency and communication overhead are all relative and make sure you're solving the problems in front of you and not "playing house" on problems you anticipate having down the line.

You will need more structure than you had before. For instance at 15 the idea of managers is silly, everyone still needs to be contributing individually, the only thing is that you do need to minimally subdivide work so that everyone isn't doing everything. Be wary of process for processes sake, you will start to need some but you really want to stay focused on concrete progress; is everyone doing the most important thing possible at every given moment? How fast are you shipping changes, closing sales, etc? Also make sure you have the right people, don't get starstruck by big tech vets. They have many skills that will be useful—if you are phenomenally successful—but if they don't have startup experience they likely will overengineer things by default, and a significant percentage of them can not wipe their own ass without the best-in-class tooling and infra support teams that allowed them to focus purely on one domain at BigCo. Basically you need pragmatic hustlers, veterans are good so long as they aren't cathedral or empire builders. Also watch out for weird team dynamics and nip any toxic interactions in the bud, one bad apple really can spoil the barrel; that's probably the most important thing to watch out for in the 15-100 range.

I suspect it’s not about agentic coding being a special skill, and more about why a competent programmer wouldn’t have tried it by this point, and whether that is a sign of ideological objections that could cause friction with the team. Not saying I agree with that thinking, but I definitely see why a hiring manager could think that way.

Looking for 5 seconds at the github profile I see a bunch of music-related stuff, and also a bunch of contributions to private repos that we have no idea what they are. I get the productivity guru anti-pattern, but I honestly don't know what you're looking at that merits this kind of reflexive personal attack.

If this was done well in a way that was productive for corporate work, I suspect the AI would engage in Machievelian maneuvering and deception that would make typical sociopathic CEOs look like Mister Rogers in comparison. And I'm not sure our legal and social structures have the capacity to absorb that without very very bad things happening.

I've long thought of the analogy as useful for human teams, and it even shows up in the corporate jargon (eg. "I'm blocked", "We need to align", etc). It's surprisingly common for whole branches of a large org to be doing net negative work due to conflicting goals or not realizing some implication that cuts across teams and local contexts. Sometimes these issues are technical but just as often they are pure product or business decisions with no explicit dependency until a lightbulb goes off somewhere.

With hand-written code, things generally move slow enough, and there's enough common sense sprinkled across the org chart that things can get uncovered organically. With agent teams, speed increased by several orders of magnitude and common sense is out the window, so I suspect the ceiling on productive use of agents will be far more limited in number than traditional engineering teams, and it will heavily depend on who and how humans are plugged into the right places.

One thing I suspect professional researchers underestimate is how much positive output can be produced by a human team with vague or hand-wavy direction, and surprisingly little deep thinking, let alone a robust specification or structure to keep them on track. The reality is any large team regresses to the mean, and it's usually a few savvy people that actually drive outcomes. These people don't necessarily have official authority, just a nose for the right thing. This won't spontaneously emerge from agents (at least until they become a lot more human like in terms of big picture common sense, and dial down the sycophancy to a more "skeptical engineer" level).

In other words they haven't really pushed the vision forward since the Jaws 19 hologram in Back To The Future 2.

This is spot on. I'm always amused when people talk about how much staffing a given product "needs". That's not how headcount works or even system design works.

What actually happens is that company headcount grows based on how much money they make (or how much they raise), this justifies hiring more people. Then, structure is added, both to systems (eg. microservices), and to operations (eg. jira, workday, salesforce, etc) so that communication overhead doesn't grind everything to a halt. Then, once the business stops growing, headcount must shrink. This may take a long time, and it will be couched in all kinds of corporate bullshit to try to preserve morale and investor sentiment, but that is all window-dressing.

Have you looked at the actual expenses and where you think they should cut rather than people? Or are you saying with all those people they should be able to turn their growth story around?