dasil003

It's odd to me how quickly the author devalues their own experience just because AI can do certain things well. There's a huge chasm between what AI can do when prompted by an expert software engineer vs a non-technical person. Sure the models and the tooling will get better, but it still needs to be driven by someone with an intuition for how software works and able to dig in when necessary to unpack and correct the hallucinations, misplaced assumptions, or straight up borked code that will come from the gap between what a human wants and what they can express in words.

I have no idea how things will play out, but so far I am not worried because the amount of software continues to increase, and AI only accelerates that trend. This will require the same mental modeling, first principles thinking, and relentless curiosity that already formed the foundation of the software engineer skillset.

I think this is right, but it can be stated more simply as companies hire to invest in growth, and they conduct layoffs when growth slows (not because of AI or "improved productivity"). Everything else is storytelling and emergent phenomena.

Incentives in companies are such that there is never a shortage of people pitching projects that require more headcount. Growth justifies the decision to hire more headcount, but the connection from increased headcount to growth is tenuous and usually difficult to impossible to demonstrate with any real confidence. It wasn't so difficult pre-industrialization, but mechanization, automation, computerization and now AI have progressively made it harder and harder to really understand the economics of labor. You do need to hire people to pursue new areas, but also every incremental person adds to communication overhead. The effects of this depend on the org structure and the operating environment over time, so what may have been a good idea at the time can flip to net negative due to outside forces beyond the control or foresight of any decision maker. This explains why companies do layoffs while still hiring at the same time.

You're misunderstanding the point, a CEO's control of the company is contingent on the boards approval. Yes, of course you can hold the standard for yourself, but you serve at the pleasure of the board and investors. The system selects for amorality because the incentive is profit. I agree with your statement in general, but even if 95% of people live by that rule, there still is systematic pressure to select CEOs out of the other 5%, because then 401k go up (including yours and mine).

As someone who has been CTO at a small company, senior leadership at a scaleup, and now middle management at a large co, I can tell you that what you are imagining is not structurally possible in our current system.

That's not say leaders here and there aren't thinking about what they're building and the macro effects, but you have to understand that unless you're bootstrapped and self-funded, even the most morally minded CEO is still beholden to investors who primarily care about money. You can only be as ethical as your board allows, and that primarily comes from profitability and financial success. In the good times its easy to talk a big game (eg. Google's "Don't be Evil"), but eventually competition comes for us all, and if your morality is hurting the bottom line you will be replaced. The backstop would be customer sentiment, but most buyers (whether B2B or B2C) are also not morally motivated. That's why free + ads is the dominant model, why micro payments failed despite years of techie hand-wringing, and paid consumer apps outside of streaming are vanishingly rare.

It's not all hopeless though. If things reach a high enough threshold of public sentiment then we can put legal and cultural pressure that will actually change things top-down. I think this is where AI is probably going as it's the most universally feared and hated impact across party lines that I have seen during my life, and the leaders in charge seem incredibly tone deaf about how it's being perceived, so I do expect regulation and softer forms of social enforcement to affect that trajectory. But if you're hoping for individual CEOs and leaders to fix our systemic problems, don't hold you're breath, they are just as replaceable as the workers under them.

In practice, Ruby is much more opinionated than Perl, mostly because of Rails being the catalyst to drive mass adoption of Ruby. So you basically have Rails, and then the RubyGems/Bundler package management was pretty linear. There's no equivalent of python2->python3 schism and the package management churn, and more stylistic continuity since ruby adoption is lower and more concentrated in the 2005-2015 time frame compared to python which had greater diversity of adoption and use cases both before and after the Web 2.0 era. I do feel pythons explicit imports and cultural aversion to meta programming win it some points. Overall though it kind of feels like a wash and I still choose the language based on the some criteria I would have used before agentic coding--claude does fine with either.

I'm all for fewer (and smaller) meetings, but there is a time and place for synchronous communication. Especially quick informal communication can be incredibly high leverage. Optimizing for heads down time is great if everyone knows exactly what they should be heads down on and thinks the same way, but that is not always the case.

If you call yourself a manager--which is a questionable role at startups--then you need to be optimizing for the entire output of the team. Rigidly declaring everything must be async text is no better than scrum by numbers.

Yeah this article is in a real uncanny valley for me where it has some insight, but it also throws out some wild ideas that don't pass the sniff test for me.

To me what AI is doing is changing the economics of human thought, but the change is happening way faster than individuals, let along organizations can absorb the implications. What I've seen is that AI magnifies the judgment of individuals who know how to use it, and so far it's mostly software engineers who have learned to use it most effectively because they are the ones able to develop an intuition about its limitations.

The idea of removing the human from the loop is nonsense. The question is more what loops matter, and how can AI speed them up. For instance, building more prototypes and one-off hacky tools is a great use of vibe coding, changing the core architecture of your critical business apps is not. AI has simultaneously increased my ability to call bullshit, while amplifying the amount of bullshit I have to sift through.

When the dust settles I don't really see that the value or importance of reading code has changed much. The whole reason agentic coding is successful is because code provides a precise specification that is both human and machine readable. The idea that we'll move from code to some new magical form of specification is just recycling the promise of COBOL, visual programming, Microsoft Access, ColdFusion, no-code tools, etc, to simplify programming. But actually the innovations that have moved the state of the art of professional programming forward, are the same ones that make agentic coding successful.

Man, as a young programmer coming up I really looked up to Paul Graham, but now as a seasoned vet in the industry, it's remarkable (and disappointing) to see him publish an article based on such a false equivalency. I mean this level of missing the forest-for-the-trees is the type of thing that routinely prevents senior engineers from getting promoted to staff because they're pedantically fixated on the wrong details. And that's on top of failing to read the room as to why people are even calling for a wealth tax in the first place.

The more obvious reason to not tax wealth is because it's hard to measure, and if you try to do it you will incentivize hiding it. Meanwhile, there are obvious obvious loopholes that the ultra-wealthy enjoy which could be reasonably closed. Namely, close the buy-borrow-die loophole, don't allow step-up basis for inherited wealth, and tax capital gains at least as much as income. Now people with a lot of money can afford to fund a lot of premium think tanks to come up with fancy economic reasoning why those ideas are Really Bad™, but at this point it's clear that's bullshit propaganda and the unintended consequences are exceedingly unlikely to be worse than the current unchecked consolidation of wealth and power enabled by the current loopholes.

I think this depends a lot on the task, the existing codebase, and the taste of the operator.

In general I tend to agree with you if you're talking a codebase you are deeply familiar with, the value-add from have agents write the code probably ranges from very small to negative in most cases.

On the other hand if you're trying to make changes in systems you are not familiar with, LLMs are a huge speed boost to folks with enough experience to sniff out what would be a bad path essentially via socratic method to the agent.

Obviously there are no silver bullets and no substitute for judgment. I will say though, I'll tradeoff ugly local code for good data models and interfaces any day of the week, and there is definitely an archetype of engineer that is very precious about code without good judgment on where it matters and where it doesn't.

In the age of AI you can't "undo the claims" for randos on the internet. I mean it was hard enough before, but at this point it's now a direct money -> speech pipeline. Reputation will matter more than ever before.

I vividly remember the moment this clicked for me. I had spent the better part of a decade being interested in programming and essentially learning recipes. It wasn't until I was a couple years into a CS degree and starting to work professionally as a web developer, that I finally had an epiphany of what software actually was, and the degrees of freedom that it actually has. It's very hard to put into words because it was an internal phenomenon, but I can describe at a more visceral understanding of what is meant by "the map is not the territory", and "all models are wrong but some are useful". It's like, you can build anything in software, it's up to you to decide how to do it and make it relevant for a real world use case.

Of course I was still super junior and had so much to learn, but from that point I could at least interrogate any pattern or best practice to understand why it existed and where it should or should not be applied.

This is why you need sufficiently senior engineering leadership (both IC leadership and management). If you have engineers who meekly do whatever a non-technical stakeholder asks then you have a vacuum of responsibility, and sooner or later things will blow up catastrophically and whoever was least adept at CYA will get blamed.

On the other hand, almost any business problem can be solved in a reasonable way that doesn't send your system through any terrible one-way doors if you zoom out enough and ask enough whys. Of course not every place allows engineering to do that, but the ones that don't aren't able to retain senior folks because they will just go somewhere where their judgment is valued. Sometimes technical debt is the right thing for the business, but sufficiently senior engineers can set things up so there is always a way out. But what you can't do is uphold the purity of the system above the business problem. The systems are paid for by the business, so if you lose sight of that then you've lost the plot and the basis for your influence.

This is very naive and reductive thinking. Experiments have a cost, you really have to think carefully about what you are trying to learn. Even when code is cheap, traffic and time are still huge constraints, and you better make sure your hypothesis actually makes sense for your goals, because AI is more than happy to fill in the blanks with a plausible but completely wrong proposal.

More broadly, it's well understood that experiments are not a replacement for design and UX. Google is famously great at the former and terrible at the latter. Sure the AI maxxers will say the machines are coming for all creative endeavours as well, but I'm going to need more evidence. So far, everything good I've seen come from AI still had a human at the wheel, and I don't see that changing any time soon.

I agree with you, so far what I see is that AI amplifies an individuals output in many domains, but the value of that is 100% contingent on their judgment. It changes the economics of many tasks, but fundamentally it can't really help you if you don't actually know what you want—which is sort of a shocking number of people in the corporate world where most people are there for a paycheck, and perhaps to pursue some social marker of "success".

I'm under no illusions about the goals of AI company execs to justify their valuations (and expenses!) by capturing a huge chunk of global employment value, and the CEOs of many big companies whose financials are getting squeezed for all sorts of reasons and are all too happy to jump on the efficiency narrative of AI to justify layoffs that would have been necessary anyway. Also, AI will keep getting better and it could certainly will move up the food chain—it's already replaced a lot of what I did and I assume capabilities will continue improving for a while even after model capabilities plateau as we improve harnessing, tooling and practice.

So yeah, it can replace a lot of what we do, but I'm not running scared because every step of the way I've seen software people are the ones who actually get the most out of LLMs. Sure it can write all the code so the job changes, but even our workflows completely change, it's giving us more of an edge (if we're open to it) than it does to anyone non-technical. At this stage it still feels empowering on an individual level.

Now I do worry about the consolidation of power and wealth in a tech oligarchy, but that's an issue we need to deal with at a societal and government policy level. Essentially, I can see AI as having radically different outcome potential based on how it's governed. In one way it can be very empowering to small teams, and reduce coordination costs, and increase competition by allowing smaller groups of people to make more scalable companies. But it could also lead to unprecedented concentration of wealth and power if a small set of AI companies are allowed to capture all the economic gains. I don't think there are any easy answers, but I do feel hopeful that we can figure something out as a society—it certainly seems to be creating some unified sentiment across political lines that have been so polarized and divisive over the last decade.

I get that working in the corporate world is often alienating, and also that one might have to accept a bullshit job in order to put food on the table, so on some level I say do what you gotta do.

But beyond that, man this is such a depressing way to live. I've worked a lot of jobs I don't like and put in varying degrees of effort based on how I feel about the people and the situation. But generally one value I live by is that if I'm paid to do something I'm going to try to do a good job at it. That doesn't mean burning myself out or going above and beyond for a boss or company that doesn't deserve it. But for my own integrity and self-worth I have to at least put in a baseline professional effort. If I can't stomach even doing that then it's a clear sign I need to be planning my exit, anything less is disrespectful to my basic self.

Kudos on the analysis, the conclusion is right. I would go further and say even if the metric was completely fair and unbiased it would still not tell you anything useful, and any manager or executive that used it as part of any kind of headcount decision (firings, layoffs, which team to grow, etc) is a moron who probably should be facing tough questions from their own management chain.

AI is a tool, everything it does is attributable to the person who prompted it. Anything else is no different from the long-understood fallacy of counting lines of code.

I agree with you, the "replacing people" narrative is not only wrong, it's inflammatory and brand suicide for these AI companies who don't seem to realize (or just don't care) the kind of buzz saw of public opinion they're walking straight towards.

That said, looking at the way things work in big companies, AI has definitely made it so one senior engineer with decent opinions can outperform a mediocre PM plus four engineers who just do what they're told.

This seems like dramatically overstating the mistake. Yeah it was expensive, and yes this could easily been foreseen, but that’s really small potatoes compared to mistakes I’ve seen. I mean I’ve seen promos off shit that never even fully worked beyond pilot scale and had to be rolled back because it was fundamentally flawed on purely technical level.

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.