> I've never seen anything like it for a technically optional tool

Cloud had a very similar vibe when it was really running advertising to CIO/CTOs hard. Everything had to be jammed into the cloud, even if it made absolutely no sense for it to be run there.

This seems to come pretty frequently from visionless tech execs. They need to justify their existence to their boss, and thus try to show how innovative and/or cost cutting they can be.

It's using a bad tool to try to aim at something reasonable-ish: Developers not taking advantage of the tools in places where it's very easy to get use out of them. I have coworkers like that: One spent 3 days researching a bug that Claude found in 10 minutes by pointing it at the logs in the time window and the codebase. And he didn't even find the bug, when Claude nailed it in one.

But is this something that is best done top to bottom, with a big report, counting tokens? Hell no. This is something that is better found, and tackled at the team level. But execs in many places like easy, visible metrics, whether they are actually helping or not. And that's how you find people playing JIRA games and such. My worse example was a VP has decided that looking at the burndown charts from each team under them, and using their shape as a reasonable metric is a good idea.

It's all natural signs of a total lack of trust, and thinking you can solve all of this from the top.

> t just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.

This is exactly what's happening. The top 5 or 6 companies in the s&p 500 are running a very sophisticated marketing/pressure campaign to convince every c-suite down stream that they need to force AI on their entire organization or die. It's working great. CEOs don't get fired for following the herd.

>I've never seen anything like it for a technically optional tool

If you broaden the comparison (only a little bit) it looks suspiciously like employees being forced to train their own replacement (be that other employees, or factory automation), a regular occurrence.

Tech directors, CEOs, managers, etc tend to be people with a certain personality and ( learned behaviors/thinking ) just like "technical people".

Yes, they tend to be incredible gullible to certain things, over-simplistic and over-confident but also very "agile" when it comes to sweep their failures under the rug and move on to keep their own neck in one piece. At this point in time even the median CEO knows AI has been way overhyped and they over invested to a point of absolute financial insanity.

The first line of defense about the pressure to deliver is to mandate their minions to use it as much as possible.

We spent a fortune on this over-rated Michelin star reservation, and now you kids are going to absolutely enjoy it, like it or not goddammit!

Yeah, I found this strange as well, if the tech is so amazing why do developers need to be forced to use it?

I'm just using Copilot CLI for mindless stuff and set it to the premium models to meet the quota, as long as they can't see the prompts I think I should be fine

It's really insane what is happening. My wife manages 70 software developers. Her boss mandated that managers replace 50% of the staff with AI within a year. And, she's scrambling trying to figure out if any of the tools actually work and annoying her team because she keeps pushing AI on them. Unsurprisingly it's only slowed things down and put her in a terrible position.

> I've never seen anything like it for a technically optional tool.

It has often been the case for technologies though, like “now we’re doing everything in $language and $technology”. If you see LLM coding as a technology in that vein, it’s not a completely new phenomenon, although it does affect developers differently.

This is largely due to the age old fact that corporations rarely make decisions based on actual data, introspection, and good judgment. Usually the decision is made first and then the justifications are invented afterwards.

In this case, every executive is terrified of being "left out" in the AI race. As we saw with the mass layoffs across companies, most of CEO decision making is just adhering to herd behavior. So it is literally better for execs to have shoveled a shit ton of money into 'strategic' AI initiatives and have them fail than potentially deal with the potentially remote chance of some other exec or company succeeding with 'AI enabled transformation'.

What makes it even more fun is that nobody really has a good understanding of how to measure the ROI of AI. Hence we have people burning a lot of money due to FOMO and no easy way of measuring the outcome, which is usually how the foundations for good Ponzi schemes are laid.

This is unlikely to end well. However, as usual, it's us, the common plebs, who will suffer regardless of outcome.

I am seeing this at my work right now. They are about to start using token consumption as _part_ of the performance review process. Obviously this is a coarse and problematic proxy for productivity.

OTOH, it’s an attempt to address a real problem. There are people who are in fact falling behind (I’m talking literally editing code in notepad), and we can either let them get PIPped eventually, or try to bring them along. There is a real “activation energy” required to learning new tools, and some people need an excuse/permission. Not saying that token count is a GOOD signal, but I haven’t heard many better ideas

We're doing that in my office, forced Cursor use. A good chunk of the "edited by AI" lines in my history were just auto-completing about the same as a traditional intellisense-alike would do (and actually Cursor doesn't seem to supply that, which is frequently annoying and wastes my time, in particular when I need to make sure it hasn't hallucinated a method or property on an object it should be able to "see" the definition of, which it does constantly; IDK maybe there's a setting somewhere, but I don't have to fiddle with settings in vanilla VSCode to get that...)

It's actually kinda useful in some cases, but the UI is terrible and it needs to integrate much better with existing tools that are superior to it for specific purposes, before I'll be happy using it. I'd say the productivity gains are a wash, for me, so far. Plus it's entirely too memory-hungry, I'd just come to accept that a text editor takes a couple GB now (SIGH), and here it comes taking way more than that.

A certain YC company fired a few employees for not using AI, the CEO bragged about it on X and incidentally it was a crypto company.

Yes it's very weird - why is my CEO being so nosy about my text editor all of a sudden? Stay in your lane, buddy.

I don't doubt you, but I'm out of the loop.

Who does this?

As an employee of a big tech company doing this, it's all fear mongering. We are being told that if everyone doesn't use these tools, our competitors will wipe the floor with us because they are using them and will ship features 10x faster. But many engineers are suspicious as well.

>> It just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.

Exactly this: "Jensen Huang says he would be 'deeply alarmed' if his $500,000 engineer did not consume at least $250,000 of tokens" : https://www.businessinsider.com/jensen-huang-500k-engineers-...

The only thing I've mandated for engineers is that folks give it a try occasionally, as models, best practices, and tooling improves.

I'm currently tracking exactly two numeric metrics: total MAUs (to track the aforementioned), and total DAUs (to gauge adoption and rightsize seat-licensed contracts.)

I was thinking about this but sometimes its hard to distinguish between what the marketing of OpenAI, Anthropic and other companies say and what actually all the companies are doing throughout the world. Any thoughts on that?

For example even the layoffs, nowadays seem to be because of AI or so they said but just a year or two ago there were quite some layoffs and people said "its because of the high demand in COVID and now its over, or Ukraine or inflation" but then that ignores that exactly during that time earlier were there many layofs but it was super easy "Oh COVID and supply chain!" and earlier maybe something else.

Surely there are also economic booms but when did the whole world jsut suddenly started seriously listening to public statements of companies (and that jsut a few with no real income, just money from VCs) and nobody shows us the real data of whats actually happening? E.g. the companies saying they fired 10K due to AI, how much did they actually now direct their budget to AI? How many products are actually being build? Is the productivity the same? Are the customers thinking that support is suddenly amazing or actually it has seriously dropepd in quality? Or no change at all? Is it a company like KFC, your local hardwore chain store, financial isntitutions, truck manufactures or anothet AI company with funding using another AIs company with other funding using now one more AIs company products up to the power suppliers?

For me it seems that its definitely impacting things and a cool technology to be more productive (for example it helps me a lot daily but its not like my life really changed) but the other things I haven't seen yet.

Another point each actual AI generated app is either something akin to a toilet game or not really working (like the C compiler). So where are the amazing enterprise complicated apps fully built via agents? In banks, in government, in apps that respect GDPR and actually are secure but proudly build only or mostly with agents? The only ones, not even secure, are other AI apps to do AI stuff but its whole value it says is to be more productive in the "real" economy but it still hasn't done it yet anywhere. People still struggle with Word or AWS infra or debugging why some specific user cant log in with their custom auth provider at some esoteric region with their laws and audits and GDPR variant.

So one side says its basically a tool from God and they never have created more stuff but on the other hand the other group analyzing blood work, delivering food, writing reports, etc uses it a bit or not at all but all the 95% of problems they had are there with some new ones. Also I'm afraid most of them just write now their email better or with more volume, but no real work is getting done.

So yeah maybe my confusion simply lies in that fact that I have a real job and nobody can keep up with all the slop and shit generated online anymore. I'm open to feedback or learn.

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I've also never seen an optional tool become a step change like this

Even moving from assembly language to compiled languages was not as much of a step change.

> (prompt engineering, harnesses, mixture of experts set ups)

Prompt engineering as a specific skill got blown out of proportion on LinkedIn and podcasts. The core idea that you need to write decent prompts if you want decent output is true, but the idea that it was an expert-level skill that only some people could master was always a lie. Most of it is common sense about having to put your content into the prompt and not expecting the LLM to read your mind.

Harnesses isn’t really a skill you learn. It’s how you get th LLM to interact with something. It’s also not as hard as the LinkedIn posts imply.

Mixture of Experts isn’t a skill you learn at all. It’s a model architecture, not something you do. At most it’s worth understanding if you’re picking models to run on your own hardware but for everything else you don’t even need to think about this phrase.

I think all of this influencer and podcast hype is giving the wrong impression about how hard and complicated LLMs are. The people doing the best with them aren’t studying all of these “skills”, they’re just using the tools and learning what they’re capable of.

In my experience (and this may be confirmation bias on my part), casting a wide net and trying out new tech, while you maintain depth in the area relevant at the time, makes you ready for what's coming, even when you don't know what that may be.

Past the sea change: half the reason those prompt and harness solutions seem to work are LLM-lies, the testing is gassing you about how it works and the efficacy, defaulting to ‘yes’.

If you test specific features of those solutions over time you see very inconsistent results, lots of lies, and seemingly stable solutions that one-shot well but suddenly experience behaviour changes due to tweaks on the backend. Tuesdays awesome agent stack that finally works is loading totally different on Thursday, and debugging is “oh, sorry, it’s better now” even when it isn’t. Compression, lies, and external hosting are a bad combo.

Sometimes I imagine a world where computers executed programs the same way each time. You could write some code once and run it a whole calendar month later with a predictable outcome. What a dream, we can hope I guess.

All of these occulted skills, that we literally can't explain why they work are akin to gamblers superstitions. If I write something this way, it works. Its like a gambler who think they order in which the push the buttons on the slot machine makes a difference.

Kind of weird tools also incorporate addictive gambling game's UX design. They're literally allowing you to multiply your output: 3x, 4x, 5x (run it 5 times for a better shot at a working prompt). You're being played by billionaires who are selling you a slot machine as a thinking machine.

Even two or three years ago I had ideas for projects but I could see the models were not ergonomic for my uses. I decided to wait for better models and sure enough the agentic models showed up which are much easier to use.

Next thing I'm waiting on is building a new server for a powerful locally hosted LLM in 5 years. No need to go through the headaches and cost of doing it now with models that may not be powerful enough.

Agreed! Investing lightly at this stage seems smart if your time/attention budget is tight.

> There really isn't anything special to using AI anyways it's not rocket science

Could have fooled me, the way some people manage to confuse themselves with it

This isn't a good example - people were completing 6-month bootcamps and getting $100k offers to do web development not too long ago, decades after the web and HTML took off. After a few years they were making as much as anyone who learned HTML and Web 1.0 back in the 90s.

Are the bootcampers better developers? Probably not. But they still were employable and paid relatively the same.

I think this applies a bit less to the AI sphere, which has the purported goal of making things easier and more automated over time. 90% of the time if you have an AI question you can just... ask the LLM itself.

Remember all the hoopla over how people needed be a "prompt engineer" a couple years back? A lot of that alchemy is basically totally obsolete.

Think about the hoops you had to jump through with early GenAI diffusion models: tons of positive prompt suffixes (“4K, OCTANE RENDER, HYPERREALISTIC TURBO HD FINAL CHALLENGERS SPECIAL EDITION”) bordering on magical incantations, samplers (Euler vs. DPM), latent upscalers, CFG scales, denoising strengths for img2img, masking workflows, etc.

And now? The vast majority of people can mostly just describe desired image in natural language, and any decent SOTA model can handle the vast majority of use cases (gpt-image-1.5, Seedream 4, Nano-banana).

Even when you’re running things locally, it’s still significantly easier than it used to be a few years ago, with options like Flux and Qwen which can handle natural language along with a nice intuitive frontend such as InvokeAI instead of the heavily node-based ComfyUI. (which I still love but understand it's not for everybody).

"It will grow more complex" is never a good reason to get into things early. It's just your mind playing FOMO tricks on you.

Many developers who picked up the web in the early years struggle with (front-end) web development today. It doesn't matter if they fetched jQuery or MooTools from some CDN as it was done in the mid 00s. Once the tooling became too complicated and ever changing they couldn't keep up as front-end dilettante. It required to commit as professionals.

If you started today, you'd simply learn the hard way, as it's always been done: get a few books or register for a course. Carve some time every day for theory and practice. All the while prioritizing what matters the most to get stuff done quickly right now, with little fluff. You will not learn Grunt, Bower, and a large array of historic tech. You'll go straight for what's relevant today. That applies to abstractions, frameworks, and tooling, but also to the fundamentals. You'll probably learn ES6+ and TS, not JS WAT. A lot of the early stuff seems like an utter waste of time in retrospect.

This is true for all tech. If you knew nothing about LLMs by the end of this year, you could find a course that teaches you all the latest relevant tricks in 5 to 10 hours for 10 bucks.

And yet, at some point most web developers will have picked it up after the "raw html" era -- that point has probably come, even.

The web/html is a great analogy. I too am in no rush to be hyper effective with LLMs. In fact i want to deliberately slow down because ai-native coding is so exhausting.

That said, your point about the leverage of learning html and web in the early days compared to now rings true. pre-compiled isomorphic typescript apps are completely unrecognizable from the early days of index.html

The counterpoint is that you will learn jank.

If you started early webdev, you learned lots of tricks, that dont benefit a modern webdev. E.g soap, long polling, the JsonP workaround... and so on

Many of the Llm frameworks will be seen simular. Mcp is already kinda heading in the obsolete direction imo, as skills took over

I agree with this point. There is absolutely a 'left behind' gap that is under-explored.

My last job was a cable technician - making house calls to fix wifi, satellite tv, phone issues. Mostly elderly residents. The majority of them all were computer and phone illiterate. They were slow adopters to the fast-moving technology and many of them did not know how to operate their devices after we (UI/UX/hardware/software engineer 'we') removed them.

I wonder if this also has contributed to the elderly lonliness problem - sure its probably mostly related to physical companionship, acceptance of aging, etc, but the world that they knew (in general and the technological world they grew up in) is no longer recognizable.

I've been playing with it on weekends for the last few months. 9 out of 10 projects, it's failed.

Projects as simple as "set up a tmux/vim binding so I can write prompts in one pane and run claude in the other". Fails.

I've been coding for over 20 years.

If there is no learning curve, why doesn't it work for me? You can't say I'm not using it right, because if that was true, then all I need to do is climb the learning curve to fix that, the curve that you say doesn't exist.

> There is no learning curve*, that's the nature of AI.

There isn't? Then why is it that whenever devs have tried it and not achieved useful results, they're told that they just haven't learned how to use it right?

I gave Claude Code with Sonnet 4.6* a try a few weeks ago. I pointed it at a hobby project with less than 1kloc of C (about 26,500 characters) across ~10 modules and asked it to summarize what the project does. It used about $0.50 worth of tokens and gave a summary that was part spot on and part hallucinated. I then asked it how to solve a simple bug with an easy solution. It identified the right place to make the fix but its entire suggested solution was a one-liner invoking a hallucinated library method.

I use LLMs pretty regularly, so I'm familiar with the kinds of tasks they work well on and where they fall flat. I'm sure I could get at least some utility from Claude Code if I had an unlimited budget, but the voracious appetite for tokens even on a trivially small project -- combined with a worse answer than a curated-context chatbot prompt -- makes its value proposition very dubious. For now, at least.

* I considered trying Opus, but the fundamental issue of it eating through tokens meant, for me, that even if it worked much better, the cost would dramatically outweigh the benefit.

I think working with the technology gives you powerful intuitions that improve your skill and lead to better outcomes, but you don't really notice that that's what's happening. Personally speaking - and I suspect this is true of most people in general - I have very poor recollections of what it was like to be really bad/new at things that I am now very skilled at.

If you have try teaching someone something from the absolute ground up, you will quickly realize that a huge number of things you now believe are "standard assumptions" or "obvious" or "intuitive" are actually the result of a lot of learning you forgot you did.

I tried it. Either I don't know how to use it, or it just doesn't work.

It’s only “easy to try” if you’re okay with using proprietary software and having to rely on an evil megacorporation that engages in cyber-warfare.

I think it comes down to your own personality, appetite, and also how external factors like hype might impact you (resent, annoyance, curiosity, excitement).

Then what is the point? If what I'm doing can be done by Claude, as operated by someone who "doesn't need to get up to speed", then I really need to look at another career.

There's no learning curve if you don't care about token spend.

> It's use AI or be replaced by AI

I think they want to sell that perception, but the biggest thing the tech execs want in their SWEs is fear.

Fear that makes us stay in our jobs, even when raises and bonuses stagnate, because the market is scary with all the "AI layoffs" (which have largely been regular downsizing with the AI label slapped on).

Fear that makes us use the LLMs and then put in extra hours when we don't see the 10x productivity gains that they expect.

Fear that makes us erode our own skills and become dependent on these gatekeepers to maintain even a base level of productivity.

So much of the advertising and discussion around AI is based around fear. It's inevitable. It will take your job. Render you useless. It will render humanity useless. You better get in now, or you'll both lose your job and could end up in a virtual hell (https://en.wikipedia.org/wiki/Roko%27s_basilisk).

Sure, but this is mostly irrelevant because software dev is one of the youngest fields. Something like 70% of professional devs are under 35 depending on which survey you look at. And the number of CS graduates exploded over the last decade.

The only reason the numbers aren’t even more tilted is because people stopped hiring juniors 2 years ago. But they’re out there, and if there’s a new technology around that makes them vastly more productive than the seniors today, there’s nothing stopping companies from hiring them.

I'm definitely having a blast, but I agree with the author. You're not going to get left behind, the "getting left behind" rhetoric was just cryptocurrency pump-and-dumpers. It's fine to wait and not engage if you don't want to.

But they have engaged with it, and made an assessment about it's current utility.

We have no reason to believe that they won't keep an eye on this.

Little to nothing about AI tools so far suggests that that one can't just as easily pick the skills later. Tools that will get "exponentially better" will almost certainly be unrecognizable to someone desperately engaging with them now, for not other reason the sake of "having 1-2 years of experience"

Someone might reasonably choose to to bet on the upside. That doesn't imply that everyone else ought to fearfully hedge.

I don't think there's such a thing as a "fast take-off" where human experience with 2026-era LLM coding remains economically relevant.

Won't AI getting better also mean AI will be getting easier to learn and use?

Feels to me like there are at least one or two more paradigm shifts coming in how AI gets used which will make current tools obsolete.

As one example, I think we will eventually get GUI dashboards to manage AI agents which will be easier to use than current CLI tools.

Eh, I'm not super worried. After all, Every six months or so, the latest model changes everything and the former model was complete garbage. It's not just a new model—it's a new paradigm shifting the landscape of agentic development.

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