The pattern that gets missed in these discussions: every "no-code will replace developers" wave actually creates more developer jobs, not fewer.<p>COBOL was supposed to let managers write programs. VB let business users make apps. Squarespace killed the need for web developers. And now AI.<p>What actually happens: the tooling lowers the barrier to entry, way more people try to build things, and then those same people need actual developers when they hit the edges of what the tool can do. The total surface area of "stuff that needs building" keeps expanding.<p>The developers who get displaced are the ones doing purely mechanical work that was already well-specified. But the job of understanding what to build in the first place, or debugging why the automated thing isn't doing what you expected - that's still there. Usually there's more of it.
Classic Jevons Paradox - when something gets cheaper the market for it grows. The unit cost shrinks but the number of units bought grows more than this shrinkage.
Of course that is true. The nuance here is that software isn’t just getting cheaper but the activity to build it is changing. Instead of writing lines of code you are writing requirements. That shifts who can do the job. The customer might be able to do it themselves. This removes a market, not grows one. I am not saying the market will collapse just be careful applying a blunt theory to such a profound technological shift that isn’t just lowering cost but changing the entire process.
You say that like someone that has been coding for so long you have forgotten what it's like to not know how to code. The customer will have little idea what is even possible and will ask for a product that doesn't solve their actual problem. AI is amazing at producing answers you previously would have looked up on stack overflow, which is very useful. It often can type faster that than I can which is also useful. However, if we are going to see the exponential improvements towards AGI AI boosters talk about we would have already seen the start of it.<p>When LLMs first showed up publicly it was a huge leap forward, and people assumed it would continue improving at the rate they had seen but it hasn't.
>every "no-code will replace developers" wave actually creates more developer jobs, not fewer<p>you mean "created", past tense. You're basically arguing it's impossible for technical improvements to reduce the number of programmers in the world, ever. The idea that only humans will ever be able to debug code or interpret non-technical user needs seems questionable to me.
Machinery made farmers more efficient and now there are more farmers than ever.
This suggests that the latent demand was a lot but it still doesnt prove it is unbounded.<p>At some point the low hanging automation fruit gets tapped out. What can be put online that isnt there already? Which business processes are obviously going to be made an order magnitude more efficient?<p>Moreover, we've never had more developers and we've exited an anomalous period of extraordinarily low interest rates.<p>The party might be over.
Look at traditional manufacturing. Automation has made massive inroads. Not as much of the economy is directly supporting (eg, auto) manufacturers as it used to be (stats check needed). Nevertheless, there are plenty of mechanical engineering jobs. Not so many lower skill line worker jobs in the US any more, though. You have to ask yourself which category you are in (by analogy). Don’t be the SWE working on the assembly line.
I've watched this pattern play out in systems administration over two decades. The pitch is always the same: higher abstractions will democratise specialist work. SREs are "fundamentally different" from sysadmins, Kubernetes "abstracts away complexity."<p>In practice, I see expensive reinvention. Developers debug database corruption after pod restarts without understanding filesystem semantics. They recreate monitoring strategies and networking patterns on top of CNI because they never learned the fundamentals these abstractions are built on. They're not learning faster: they're relearning the same operational lessons at orders of magnitude higher cost, now mediated through layers of YAML.<p>Each wave of "democratisation" doesn't eliminate specialists. It creates new specialists who must learn both the abstraction <i>and</i> what it's abstracting. We've made expertise more expensive to acquire, not unnecessary.<p>Excel proves the rule. It's objectively terrible: 30% of genomics papers contain gene name errors from autocorrect, JP Morgan lost $6bn from formula errors, Public Health England lost 16,000 COVID cases hitting row limits. Yet it succeeded at democratisation by accepting catastrophic failures no proper system would tolerate.<p>The pattern repeats because we want Excel's accessibility with engineering reliability. You can't have both. Either accept disasters for democratisation, or accept that expertise remains required.
All abstractions are leaky abstractions. E.g. C is a leaky abstraction because what you type isn't actually what gets emitted (try the same code in two different compilers and one might vectorize your loop while the other doesn't).
If Kubernetes didn't in any way reduce labor, then the 95% of large corporations that adopted it must all be idiots? I find that kinda hard to believe. It seems more likely that Kubernetes has been adopted alongside increased scale, such that sysadmin jobs have just moved up to new levels of complexity.<p>It seems like in the early 2000s every tiny company needed a sysadmin, to manage the physical hardware, manage the DB, custom deployment scripts. That particular job is just gone now.
You’re absolutely right that sysadmin jobs moved up to new levels of complexity rather than disappeared. That’s exactly my point.<p>Kubernetes didn’t democratise operations, it created a new tier of specialists. But here’s what’s interesting: a lot of that adoption wasn’t driven by necessity. Studies show 60% of hiring managers admit technology trends influence their job postings, whilst 82% of developers believe using trending tech makes them more attractive to employers. This creates a vicious cycle: companies adopt Kubernetes partly because they’re afraid they won’t be able to hire without it, developers learn Kubernetes to stay employable, which reinforces the hiring pressure.<p>I’ve watched small companies with a few hundred users spin up full K8s clusters when they could run on a handful of VMs. Not because they needed the scale, but because “serious startups use Kubernetes.” Then they spend six months debugging networking instead of shipping features. The abstraction didn’t eliminate expertise, it forced them to learn both Kubernetes and the underlying systems when things inevitably break.<p>The early 2000s sysadmin managing physical hardware is gone. They’ve been replaced by SREs who need to understand networking, storage, scheduling, plus the Kubernetes control plane, YAML semantics, and operator patterns. We didn’t reduce the expertise required, we added layers on top of it. Which is fine for companies operating at genuine scale, but most of that 95% aren’t Netflix.
<i>> accept disasters for democratisation</i><p>Will insurance policy coverage and premiums change when using non-deterministic software?
It's not so much about replacing developers, but rather increasing the level of abstraction developers can work at, to allow them to work on more complex problems.<p>The first electronic computers were programmed by manually re-wiring their circuits. Going from that to being able to encode machine instructions on punchcards did not replace developers. Nor did going from raw machine instructions to assembly code. Nor did going from hand-written assembly to compiled low-level languages like C/FORTRAN. Nor did going from low-level languages to higher-level languages like Java, C++, or Python. Nor did relying on libraries/frameworks for implementing functionality that previously had to be written from scratch each time. Each of these steps freed developers from having to worry about lower-level problems and instead focus on higher-level problems. Mel's intellect is freed from having to optimize the position of the memory drum [0] to allow him to focus on optimizing the higher-level logic/algorithms of the problem he's solving. As a result, software has become both more complex but also much more capable, and thus much more common.<p>(The thing that distinguishes gen-AI from all the previous examples of increasing abstraction is that those examples are deterministic and often formally verifiable mappings from higher abstraction -> lower abstraction. Gen-AI is neither.)<p>[0] <a href="http://catb.org/jargon/html/story-of-mel.html" rel="nofollow">http://catb.org/jargon/html/story-of-mel.html</a>
> It's not so much about replacing developers, but rather increasing the level of abstraction developers can work at, to allow them to work on more complex problems.<p>People do and will talk about replacing developers though.
The goal of AI companies is to replace all intellectual labor. You can argue that they're going to fail, but it's very clear what the actual goal is.
I think one thing I've heard missing from discussions though is that each level of abstraction needs to be introspectable. LLMs get compared to compilers a lot, so I'd like to ask: what is the equivalent of dumping the tokens, AST, SSA, IR, optimization passes, and assembly?<p>That's where I find the analogy on thin ice, because somebody has to understand the layers and their transformations.
“Needs to be” is a strong claim. The skill of debugging complex problems by stepping through disassembly to find a compiler error is very specialized. Few can do it. Most applications don’t need that “introspection”. They need the “encapsulation” and faith that the lower layers work well 99.9+% of the time, and they need to know who to call when it fails.<p>I’m not saying generative AI meets this standard, but it’s different from what you’re saying.
Sorry, I should clarify: it's needs to be introspectable by somebody. Not every programmer needs to be able to introspect the lower layers, but that capability needs to exist.<p>Now I guess you can read the code an LLM generates, so maybe that layer does exist. But, that's why I don't like the idea of making a programming language for LLMs, by LLMs, that's inscrutable by humans. A lot of those intermediate layers in compilers are designed for humans, with only assembly generation being made for the CPU.
I think the thing that’s so weird to me is this idea that we have to all somehow internalize the concept of transistor switching as the foundational unchangeable root of computing and therefore anything that is too far abstract from that is not somehow real computing or something mess like that<p>Again ignoring completely that when you would program vacuum tube computers it was an entirely different type of abstraction than you do with Mosfets for example<p>I’m finding myself in the position where I can safely ignore any conversation about engineering with anybody who thinks that there is a “right” way to do it or that there’s any kind of ceremony or thinking pattern that needs to stay stable<p>Those are all artifacts of humans desiring very little variance and things that they’ve even encoded because it takes real energy to have to reconfigure your own internal state model to a new paradigm
> Which brings us to the question: why does this pattern repeat?<p>The pattern repeats because the market incentivizes it. AI has been pushed as an omnipotent, all-powerful job-killer by these companies because shareholder value depends on enough people believing in it, not whether the tooling is actually capable. It's telling that folks like Jensen Huang talk about people's negativity towards AI being one of the biggest barriers to advancement, as if they should be immune from scrutiny.<p>They'd rather try to discredit the naysayers than actually work towards making these products function the way they're being marketed, and once the market wakes up to this reality, it's gonna get really ugly.
Yes very much so, if they could make their product do the things they claim they would be focused on doing that, not telling people to stop being naysayers.
>The pattern repeats because the market incentivizes it.<p>Market is not universal gravity, it's just a storefront for social policy.<p>No political order, no market, no market incentives.
I'm reminded of this: <a href="https://www.astralcodexten.com/p/heuristics-that-almost-always-work" rel="nofollow">https://www.astralcodexten.com/p/heuristics-that-almost-alwa...</a>
I think that programming as a job has already changed. Because it is hard for most people to tell the difference between someone who actually has programming skills and experience versus someone who has some technical ingenuity but has only ever used AI to program for them.<p>Now the expectation from some executives or high level managers is that managers and employees will create custom software for their own departments with minimal software development costs. They can do this using AI tools, often with minimal or no help from software engineers.<p>Its not quite the equivalent of having software developed entirely by software engineers, but it can be a significant step up from what you typically get from Excel.<p>I have a pretty radical view that the leading edge of this stuff has been moving much faster than most people realize:<p>2024: AI-enhanced workflows automating specific tasks<p>2025: manually designed/instructed tool calling agents completing complex tasks<p>2026: the AI Employee emerges -- robust memory, voice interface, multiple tasks, computer and browser use. They manage their own instructions, tools and context<p>2027: Autonomous AI Companies become viable. AI CEO creates and manages objectives and AI employees<p>Note that we have had the AI Employee and AI Organization for awhile in different somewhat weak forms. But in the next 18 months or so as the model and tooling abilities continue to improve, they will probably be viable for a growing number of business roles and businesses.
As I have heard from mid level managers and C suite types across a few dev jobs. Staff are the largest expense and the technology department is the largest cost center. I disagree because Sales couldn't exist with a product but that's a lost point.<p>This is why those same mid level managers and C suite people are salivating over AI and mentioning it in every press release.<p>The reality is that costs are being reduced by replacing US teams with offshore teams. And the layoffs are being spun as a result of AI adoption.<p>AI tools for software development are here to stay and accelerate in the coming months and years and there will be advances. But cost reductions are largely realized via onshore/offshore replacement.<p>The remaining onshore teams must absorb much more slack and fixes and in a way end up being more productive.
> The reality is that costs are being reduced by replacing US teams with offshore teams.<p>Hailing from an outsourcing destination I need to ask: to where specifically? We've been laid off all the same. Me and my team spent the second half of 2025 working half time because that's the proposition we were given.<p>What is this fabled place with an apparent abundance of highly skilled developers? India? They don't make on average much less than we do here - the good ones make more.<p>My belief is that spending on staff just went down across the board because every company noticed that all the others were doing layoffs, so pressure to compete in the software space is lower. Also all the investor money was spent on datacentres so in a way AI is taking jobs.
> I disagree because Sales couldn't exist with a product<p>There are a lot of counterexamples throughout history.
The reverse is developer's recurring dream of replacing non-IT people, usually with a 100% online automated self promoting SaaS. AI is also the latest incarnation of that.
<i>Science is hated because its mastery requires too much hard work, and, by the same token, its practitioners, the scientists, are hated because of their power they derive from it.</i> - Dijkstra '1989<p><a href="https://www.cs.utexas.edu/~EWD/transcriptions/EWD10xx/EWD1041.html" rel="nofollow">https://www.cs.utexas.edu/~EWD/transcriptions/EWD10xx/EWD104...</a>
Don't take it personal. All business want to reduce costs. As long as people cost money, they'll want to reduce people.
Which is why quiet quitting is the logical thing.<p>Managers and business owners shouldn't take it personally that I do as little as possible and minimize the amount of labor I provide for the money I receive.<p>Hey, it's just business.
And you do this honestly, by negotiating reduced hours for the same pay or by negotiating piecework rather than time-based pay. Right?
Like any shrewd businessman, I negotiate to receive the highest price possible for the minimum cost on my end. This is how business is done.
What does the term mean? I think the answer to your question is obvious.
What a nihilistic perspective and empty life.<p>If the deck is stacked against labor and in favor of the owner, become the owner. Start a business. Create things that are better. Enrich the world. Put food on the table for a few people in the process.<p>Be something instead of intentionally being nothing. Win.
> What a nihilistic perspective and empty life.<p>Equally nihilistic are owners, managers, and leaders who think they will replace developers with LLMs.<p>Why care about, support, defend, or help such people? Why would I do that?
Let's say the average firm has 10 workers. 90% of people are nihilists and empty lifers?<p>Do I want to lead a business filled with losers?
The irony being that software, and developers, have often been the tool for reducing head count.
> Don't take it personal. All business want to reduce costs. As long as people cost money, they'll want to reduce people.<p>"Don't take it personal" does not feed the starving and does not house the unhoused. An economic system that over-indexes on profit at the expense of the vast majority of its people will eventually fail. If capitalism can't evolve to better provide opportunities for people to live while the capital-owning class continues to capture a disproportionate share of created economic value, the system will eventually break.
Some businesses want to reduce costs. Some want to tackle the challenge of using resources available in the most profitable manner, including making their employees grow to better contribute in tackling tomorrow's challenges.<p>A business leader board that only consider people as costs are looking at the world through sociopath lenses.
The link redirects back to the blog index if your browser is configured in Spanish, because it forces to change the language to spanish and the article is not available in spanish.<p>Here's an archived link:
<a href="https://archive.is/y9SyQ" rel="nofollow">https://archive.is/y9SyQ</a>
Kind of off topic but this has got to be one of my least favorite CSS rules that I’ve seen in recent memory:<p><pre><code> .blog-entry p:first-letter {
font-size: 1.2em;
}</code></pre>
<i>> Understanding this doesn’t mean rejecting new tools. It means using them with clear expectations about what they can provide and what will always require human judgment.</i><p>Speaking of tools, that style of writing rings a bell.. Ben Affleck made a similar point about the evolving use of computers and AI in filmmaking, wielded with creativity by humans with lived experiences, <a href="https://www.youtube.com/watch?v=O-2OsvVJC0s" rel="nofollow">https://www.youtube.com/watch?v=O-2OsvVJC0s</a>. Faster visual effects production enables more creative options.
It might just be companies I have worked for in past 25 years, but engineers were virtually always the ones to make sense of whatever vague idea product and UX were trying to make. It's not just code monkey follow the mockup stuff. AI code tools don't really solve that.
Can semi-technical people replace developers if those semi-technical people accept that the price of avoiding developers is a commitment to minimizing total system complexity?<p>Of course semi-technical people can troubleshoot, it's part of nearly every job. (Some are better at it than others.)<p>But how many semi-technical people can design a system that facilitates troubleshooting? Even among my engineering acquaintances, there are plenty who cannot.
> We’re still in that same fundamental situation. We have better tools—vastly better tools—but the thinking remains essential.<p>But less thinking is essential, or at least that’s what it’s like using the tools.<p>I’ve been vibing code almost 100% of the time since Claude 4.5 Opus came out. I use it to review itself multiple times, and my team does the same, then we use AI to review each others’ code.<p>Previously, we whiteboarded and had discussions more than we do now. We definitely coded and reviewed more ourselves than we do now.<p>I don’t believe that AI is incapable of making mistakes, nor do I think that multiple AI reviews are enough to
understand and solve problems, yet. Some incredibly huge problems are probably on the horizon. But for now, the general “AI will not replace developers” is false; our roles have changed- we are managers now, and for how long?
I recently did a higher education contract for one semester in a highly coding focused course. I have a few years of teaching experience pre-LLMs so I could evaluate the impact internally, my conclusion is that academic education as we know it is basically broken forever.<p>If educators use AI to write/update the lectures and the assignments, students use AI to do the assignments, then AI evaluates the student's submissions, what is the point?<p>I'm worried about some major software engineering fields experiencing the same problem. If design and requirements are written by AI, code is mostly written by AI, and users are mostly AI agents. What is the point?
Mythical Man Month -> Mythical AI Agent Swarm
Spreadsheets replaced developers for that kind of work, while simultaneously enabling multiple magnitudes more work of that type to be performed.
I do agree, that’s like my go to thought.<p>Citizen developers were already there doing Excel. I have seen basically full fledged applications in Excel since I was in high school which was 25 years ago already.
If anything, there were a bunch of low barrier to entry software development options like HyperCard, MS Access, Visual Basic, Delphi, 4GLs etc. around in the 90s, that went away.<p>It feels like programming then got a lot harder with internet stuff that brought client-server challenges, web frontends, cross platform UI and build challenges, mobile apps, tablets, etc... all bringing in elaborate frameworks and build systems and dependency hell to manage and move complexity around.<p>With that context, it seems like the AI experience / productivity boost people are having is almost like a regression back to the mean and just cutting through some of the layers of complexity that had built up over the years.
And I would argue speadsheets still created more developers. Analytics teams need developers to put that data somewhere, to transform it for certain formats, to load that data from a source so they can create spreadsheets from it.<p>So now instead of one developer lost and one analyst created, you've actually just created an analyst and kept a developer.
A few observations from the current tech + services market:<p>Service-led companies are doing relatively better right now. Lower costs, smaller teams, and a lot of “good enough” duct-tape solutions are shipping fast.<p>Fewer developers are needed to deliver the same output. Mature frameworks, cloud, and AI have quietly changed the baseline productivity.<p>And yet, these companies still struggle to hire and retain people. Not because talent doesn’t exist, but because they want people who are immediately useful, adaptable, and can operate in messy environments.<p>Retention is hard when work is rushed, ownership is limited, and growth paths are unclear. People leave as soon as they find slightly better clarity or stability.<p>On the economy: it doesn’t feel like a crash, more like a slow grind. Capital is cautious. Hiring is defensive. Every role needs justification.<p>In this environment, it’s a good time for “hackers” — not security hackers, but people who can glue systems together, work with constraints, ship fast, and move without perfect information.<p>Comfort-driven careers are struggling. Leverage-driven careers are compounding.<p>Curious to see how others are experiencing this shift.
Let’s not forget that we are just now recovering from the market corrections of the pandemic. Pandemic level tech industry hiring was insane and many of those companies who later held layoffs were just sending the growth line back to where it should be.<p>I think pressure to ship is always there. I don’t know if that’s intensifying or not. I can understand where managers and executives think AI = magical work faster juice, but I imagine those expectations will hit their correction point at some time.
> Service-led companies are doing relatively better right now<p>who
Business quacks being forever bamboozled because turns out implementation is the only thing that matters and hacker culture outlived every single promise to eradicate hacker culture.
This is the best explanation of (my take on) this I've seen so far.<p>On top of the article's excellent breakdown of what is happening, I think it's important to note a couple of driving factors about why (I posit) it is happening:<p>First, and this is touched upon in the OP but I think could be made more explicit, a lot of people who bemoan the existence of software development as a discipline see it as a morass of incidental complexity. This is significantly an instance of Chesterton's Fence. Yes, there certainly is incidental complexity in software development, or at least complexity that is incidental at the level of abstraction that most corporate software lives at. But as a discipline, we're pretty good at eliminating it when we find it, though it sometimes takes a while — but the speed with which we iterate means we eliminate it a lot faster than most other disciplines. A lot of the complexity that remains is actually irreducible, or at least we don't yet know how to reduce it. A case in point: programming language syntax. To the outsider, the syntax of modern programming languages, where the commas go, whether whitespace means anything, how angle brackets are parsed, looks to the uninitiated like a jumble of arcane nonsense that must be memorized in order to start really solving problems, and indeed it's a real barrier to entry that non-developers, budding developers, and sometimes seasoned developers have to contend with. But it's also (a selection of competing frontiers of) the best language we have, after many generations of rationalistic and empirical refinement, for humans to unambiguously specify what they mean at the semantic level of software development as it stands! For a long time now we haven't been constrained in the domain of programming language syntax by the complexity or performance of parser implementations. Instead, modern programming languages tend toward simpler formal grammars because they make it easier for _humans_ to understand what's going on when reading the code. AI tools promise to (amongst other things; don't come at me AI enthusiasts!) replace programming language syntax with natural language. But actually natural language is a terrible syntax for clearly and unambiguously conveying intent! If you want a more venerable example, just look at mathematical syntax, a language that has never been constrained by computer implementation but was developed by humans for humans to read and write their meaning in subtle domains efficiently and effectively. Mathematicians started with natural language and, through a long process of iteration, came to modern-day mathematical syntax. There's no push to replace mathematical syntax with natural language because, even though that would definitely make some parts of the mathematical process easier, we've discovered through hard experience that it makes the process as a whole much harder.<p>Second, humans (as a gestalt, not necessarily as individuals) always operate at the maximum feasible level of complexity, because there are benefits to be extracted from the higher complexity levels and if we are operating below our maximum complexity budget we're leaving those benefits on the table. From time to time we really do manage to hop up the ladder of abstraction, at least as far as mainstream development goes. But the complexity budget we save by no longer needing to worry about the details we've abstracted over immediately gets reallocated to the upper abstraction levels, providing things like development velocity, correctness guarantees, or UX sophistication. This implies that the sum total of complexity involved in software development will always remain roughly constant. This is of course a win, as we can produce more/better software (assuming we really have abstracted over those low-level details and they're not waiting for the right time to leak through into our nice clean abstraction layer and bite us…), but as a process it will never reduce the total amount of ‘software development’ work to be done, whatever kinds of complexity that may come to comprise. In fact, anecdotally it seems to be subject to some kind of Braess' paradox: the more software we build, the more our society runs on software, the higher the demand for software becomes. If you think about it, this is actually quite a natural consequence of the ‘constant complexity budget’ idea. As we know, software is made of decisions (<a href="https://siderea.dreamwidth.org/1219758.html" rel="nofollow">https://siderea.dreamwidth.org/1219758.html</a>), and the more ‘manual’ labour we free up at the bottom of the stack the more we free up complexity budget to be spent on the high-level decisions at the top. But there's no cap on decision-making! If you ever find yourself with spare complexity budget left over after making all your decisions you can always use it to make decisions about how you make decisions, ad infinitum, and yesterday's high-level decisions become today's menial labour. The only way out of that cycle is to develop intelligences (software, hardware, wetware…) that can not only reason better at a particular level of abstraction than humans but also climb the ladder faster than humanity as a whole — singularity, to use a slightly out-of-vogue term. If we as a species fall off the bottom of the complexity window then there will no longer be a productivity-driven incentive to ideate, though I rather look forward to a luxury-goods market of all-organic artisanal ideas :)
I don't even think that "singularity-level coding agents" get us there. A big part of engineering is working with PMs, working with management, working across teams, working with users, to help distill their disparate wants and needs down into a coherent and usable system.<p>Knowing when to push back, when to trim down a requirement, when to replace a requirement with something slightly different, when to expand a requirement because you're aware of multiple distinct use cases to which it could apply, or even a new requirement that's interesting enough that it might warrant updating your "vision" for the product itself: that's the real engineering work that even a "singularity-level coding agent" alone could not replace.<p>An AI agent almost universally says "yes" to everything. They have to! If OpenAI starts selling tools that refuse to do what you tell them, who would ever buy them? And maybe that's the fundamental distinction. Something that says "yes" to everything isn't a partner, it's a tool, and a tool can't replace a partner by itself.
> don't come at me AI enthusiasts!<p>no need to worry; none of them know how to read well enough to make it this far into your comment
It's like developers are only now awakening to the reality that despite being paid well, they never were the capitalists.
The link doesn't works for me, just get thrown on the main page after a second.
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The dumb part of this is: so who prompts the AI?<p>Well probably we'd want a person who really gets the AI, as they'll have a talent for prompting it well.<p>Meaning: knows how to talk to computers better than other people.<p>So a programmer then...<p>I think it's not that people are stupid. I think there's actually a glee behind the claims AI will put devs out of work - like they feel good about the idea of hurting them, rather than being driven by dispassionate logic.<p>Maybe it's the ancient jocks vs nerds thing.
Outside of SV the thought of More Tech being the answer to ever greater things is met with great skepticism these days. It's not that people hate engineers, and most people are content to hold their nose while the mag7 make 401k go up, but people are sick of Big Tech. Like it or not, the Musks, Karps, Thiels, Bezos's have a lot to do with that.
Devs are where projects meet the constraints of reality and people always want to kill the messenger.
No high paid manager wants to learn that their visionary thinking was just the last iteration of the underpants gnome meme.
Some things sound good at first but unfortunately are not that easy to actually do
Devs are where the project meets reality in general, and this is what I always try to explain to people. And it's the same with construction, by the way. Pictures and blueprints are nice but sooner or later you're going to need someone digging around in the dirt.
Some people just see it as a cost, one "tech" startup I worked at I got this lengthy pitch from a sales exec that they shouldn't have a software team at all, that we'd never be able to build anything useful without spending millions and that money would be better-spent on the sales team, although they'd have nothing to sell lmfao. And the real laugh was the dev team was heavily subsidized by R&D grants anyway.
Even that is the wrong question. The whole promise of the stock market, of AI is that you can "run companies" by just owning shares and knowing nothing at all. I think that is what "leaders" hope to achieve. It's a slightly more dressed get-rich-quick scheme.<p>Invest $1000 into AI, have a $1000000 company in a month. That's the dream they're selling, at least until they have enough investment.<p>It of course becomes "oh, sorry, we happen to have taken the only huge business for ourselves. Is your kidney now for sale?"
> Invest $1000 into AI, have a $1000000 company in a month. That's the dream they're selling, at least until they have enough investment.<p>But you need to buy my AI engineer course for that first.
Who fixes the unmaintainable mess that the AI created in which the vibe coder prompted?<p>The Vibe Coder? The AI?<p>Take a guess who fixes it.
The real question is, do you even need to fix it? Does it matter?<p>The reason those things matter in a traditional project is because a person needs to be able to read and understand the code.<p>If you're vibe coding, that's no longer true. So maybe it doesn't matter. Maybe the things we used to consider maintenance headaches are irrelevant.
For now, training these things on code and logic is the first step of building a technological singularity.
They don't need to put all developers out of work to have a financial impact on the career.
The day you successfully implemented your solution with a prompt, you solution is valued at the cost of a prompt.
There is no value to anything easily achieved by generative tools anymore.
Now it is in either:<p>a. generative technology but requiring substantial amount of coordination, curation, compute power.
b. substantial amount of data.
c. scarce intelectual human work.<p>And scarce but non intellectually demanding human work was dropped from the list of valuable things.
> who prompts the AI<p>LLMs are a box where the input has to be generated by someone/something, but also the output has to be verified somehow (because, like humans, it isn't always correct). So you either need a human at "both ends", or some very clever AI filling those roles.<p>But I think the human doing those things probably needs slightly different skills and experience than the average legacy developer.
How about another AI? And who prompts <i>that</i> AI? You're right - another AI!