You’re describing task reallocation, but the bigger second-order effect is where the firm can now source the remaining human judgment.

AI reduces the penalty for weak domain context. Once the work is packaged like that, the “thinking part” becomes far easier to offshore because:

- Training time drops as you’re not teaching the whole craft, you’re teaching exception-handling around an AI-driven pipeline.

- Quality becomes more auditable because outputs can be checked with automated review layers.

- Communication overhead shrinks with fewer back-and-forth cycles when AI pre-fills and structures the work.

- Labor arbitrage expands and the limiting factor stops being “can we find someone locally who knows our messy process” and becomes “who is cheapest who can supervise and resolve exceptions.”

So yeah, the jobs mostly remain and some people become more valuable. But the clearing price for that labor moves toward the global minimum faster than it used to.

The impact won’t show up as “no jobs,” it is already showing up as stagnant or declining Western salaries, thinner career ladders, and more of the value captured by the firms that own the workflows rather than the people doing the work.

> automation tools ... eliminates the boring part of the job, and then the job description shifts.

But the job had better take fewer people, or the automation is not justified.

There's also a tradeoff between automation flexibility and cost. If you need an LLM for each transaction, your costs will be much higher than if some simple CRUD server does it.

Here's a nice example from a more physical business - sandwich making.

Start with the Nala Sandwich Bot.[1] This is a single robot arm emulating a human making sandwiches. Humans have to do all the prep, and all the cleaning. It's slow, maybe one sandwich per minute. If they have any commercial installations, they're not showing them. This is cool, but ineffective.

Next is a Raptor/JLS robotic sandwich assembly line.[2] This is a dozen robots and many conveyors assembling sandwiches. It's reasonably fast, at 100 sandwiches per minute. This system could be reconfigured to make a variety of sandwich-format food products, but it would take a fair amount of downtime and adjustment. Not new robots, just different tooling. Everything is stainless steel or food grade plastic, so it can be routinely hosed down with hot soapy water. This is modern automation. Quite practical and in wide use.

Finally, there's the Weber automated sandwich line.[3] Now this is classic single-purpose automation, like 1950s Detroit engine lines. There are barely any robots at all; it's all special purpose hardware. You get 600 or more sandwiches per minute. Not only is everything stainless or food-grade plastic, it has a built-in self cleaning system so it can clean itself. Staff is minimal. But changing to a product with a slightly different form factor requires major modifications and skills not normally present in the plant. Only useful if you have a market for several hundred identical sandwiches per minute.

These three examples show why automation hasn't taken over. To get the most economical production, you need extreme product standardization. Sometimes you can get this. There are food plants which turn out Oreos or Twinkies in vast quantities at low cost with consistent quality. But if you want product variations, productivity goes way, way down.

[1] https://nalarobotics.com/sandwich.html

[2] https://www.youtube.com/watch?v=_YdWBEJMFyE

[3] https://www.youtube.com/watch?v=tRUfdBEpFJg

I recently did a contract at medium sized business with a large retail and online business that had a CFO and several accountants / bookkeepers. You're describing a situation where that CFO only needs two or three accountants and bookkeepers to run the business and would lay off two or three people.

It IS about headcount in a lot of cases.

I frame the shift more like this:

Systems engineering is an extremely hard computer science domain with few engineers either interested in it, or good at it.

Building dashboards is tedious and requires organizational structure to deliver on. This is the bread and butter of what agents are good at building right now. You still need organization and communication skills in your company and to direct the coding agents towards that dashboard you want and need. Until you hit a implementation wall and someone will need to spend time trying to understand some of the code. At least with dashboards, you can probably just start over from scratch.

It's arguably more work to prompt in english to an AI agent to assist you in hard systems problems, and the signals the agent would need to add value aren't readily available (yet?!). Plus, there's no way systems engineers would feel comfortable taking generated code at face-value. So they definitely will spend the extra mental energy to read what is output.

So I don't know. I think we're going to keep marching forward, because that's what we do, but I also don't think this "vibe-coded" automated code generator phase we're in right now will ultimately last. It'll likely fall apart and the pieces we put back together will likely return us to some new kind of normal, but we'll all still need to know how to be damn good software engineers.

Another component or view of this is that automating the rote work is "eliminating the boring parts" (I love this and have worked extensively on this) but it is also eliminating the less cognitively demanding work.

Once you have automated extensively, all of the remaining work is cognitively demanding and doing 8 hours of that work every day is exhausting.

I'd imagine that when the 80% of less productive time is automated, the market doesn't respond by demanding 80% more output. There's just 20% as much work either making this a part time job or more likely a much smaller workforce as the number of man*hours demanded by the market greatly reduces.

> The bookkeeper is still there, still needed, but now they're doing the part that actually requires judgment.

The argument might be fundamentally sound, but now we're automating the part that requires judgement. So if the accountants aren't doing the mechanical part or the judgement part, where exactly is the role going? Formalised reading of an AI provided printout?

It seems quite reasonable to predict that humans just won't be able to make a living doing anything that involves screens or thinking, and we go back to manual labour as basically what humans do.

You’re not taking into account that a successful bookkeeper may have hired someone like a new grad to take the drudgery off of their hands and now they can just do it themselves.

I'm not very familiar with the field on a practical basis.

What parts of the job require judgement that is resistant to automation? What percentage of customers need that?

If the hours an accountant spends on a customer go from 4 per month to 1, do you reckon they can sustainably charge the same?

>> The thing I keep seeing firsthand is that automation doesn't eliminate the job - it eliminates the boring part of the job, and then the job description shifts.

No, not necessarily. There are different kinds of automation.

Earlier in my career I sold and implemented enterprise automation solutions for large clients. Think document scanning, intelligent data extraction and indexing and automatic routing. The C-level buyers overwhelmingly had one goal: to reduce headcount. And that was almost always the result. Retraining redundant staff for other roles was rare. It was only done in contexts where retaining accumulated institutional knowledge was important and worth the expense.

Here's the thing though: to overcome objections from those staff, whom we had to interview to understand the processes we were automating, we told them your story: you aren't being replaced, you're being repurposed for higher-level work. Wouldn't it be nice if the computer did the boring and tedious parts of your job so that you can focus on more important things? Most of them were convinced. Some, particularly those who had been around the block, weren't.

Ultimately, technologies like AI will have the the same impact. They weren't quite there yet, but I think it's just a matter of time.

Yeah bro, its been three years. We are just beginning. We will replace the vast majority of professional service workers in 10 years including lawyers as Ai shifts to local and moves away from the cloud.

> I was with the author on everything except one point: increasing automation will not leave us with such abundance that we never have to work again.

That's because we prefer improved living standards over less work. If we only had to live by the standards of one century ago or more, we could likely accomplish that by working very little.

As long as the owner class can leverage, "Hey, that {out group} is sitting around doing nothing and getting free money!" we'll never have anything close to UBI imo.

You also need a system that is ok with giving you some of said abundance without you working.

Last year the US voted to hand over the reigns, in all branches of government, to a party whose philosophy is to slash government spending and reduce people’s dependence on the government.

To all the US futurists who are fantasizing about a post-scarcity world where we no longer work, I’d like to understand how that fits in with the current political climate.

I am also fairly certain that if we do arrive at some abundant utopia where you can wish for anything can have it arrive, society will collapse. It's just bringing up 7 billion (probably more) spoiled brats at that point of time. Work on its own is also a form of "social control". Idle hands are the devil's tools etc.

You are painting this like it’s a bad thing. The workers decided that they would rather have higher working time to buy more things!

A lot of people would not choose to work for half the time as they do now because they do actually like to buy things.

This pattern suggests the remaining knowledge work becoming increasingly extracted upon by the owners of ai enabled firms, in similar fashion to sugar plantation workers across the global south. I would think the cost of doing so would be a level of social and civic unrest similar to the colonial revolutions (Bolivar for example) of the 19th century.

>such abundance that we never have to work again. We have heard that lie for over a century.

I'm 0.6 centuries old and have never heard that said for existing tech. Human level AI could presumably do human work by definition but that's not the case before we get that, including now.

All of those technologies of the past can be managed by humans. Once computers can manage themselves AND other technologies and people, I think it'll be a different situation.

If you want to live with no electricity, no running water, and a lack of refrigerated food, you could do so purely on welfare. In that sense, we already have the UBI that Marx predicted.

However, most people want fruits and vegetables instead of getting rickets, goiter, and cholera from an 1800s diet. Many are even willing to work 80+ hours a week to do so.

See, we have enough food to feed the entire world, every year.

It's not our production capabilities that keep people hungry; it's either greed or the problem of distribution.

Automation will definitely amplify production but it'll certainly continue to make rich richer and poor, well, the same. As inequality grows, so too does the authoritarian need to control the differential.

"The steamroller is still many inches away. I'll make a plan once it actually starts crushing my toes."

You are in danger. Unless you estimate the odds of a breakthrough at <5%, or you already have enough money to retire, or you expect that AI will usher in enough prosperity that your job will be irrelevant, it is straight-up irresponsible to forgo making a contingency plan.

While true, my personal fear is that the higher-ups will overlook this fact and just assume that AI can do everything because of some cherry-pick simple examples, leading to one of those situations where a bunch of people get fired for no reason and then re-hired again after some time.

I look through the backlog for my team consisting of 9 trillion ill-defined (if defined at all) tickets that tells you basically nothing.

The large, overwhelming majority of my team's time is spent on combing through these tickets and making sense of them. Once we know what the ticket is even trying to say, we're usually out with the solution in a few days at most, so implementation isn't the bottleneck, nowhere near.

This scenario has been the same everywhere I've ever worked, at large, old institutions as well as fresh startups.

The day I'll start worrying is when the AI is capable of following the web of people involved to translate what the vaguely phrased ticket that's been backlogged for God knows how long actually means

A lot of this can be provided or built up by better documentation in the codebase, or functional requirements that can also be created, reviewed, and then used for additional context. In our current codebase it's definitely an issue to get an AI "onboarded", but I've seen a lot less hand-holding needed in projects where you have the AI building from the beginning and leaving notes for itself to read later

The memory problem is already being addressed in various ways - antigravity seems to keep a series of status/progress files describing what's been done, what needs doing, etc. A bit clunky, but it seems to work - I can open it up on a repo that I was working in a few days back and it seems to pick up this context such that I don't have to completely bring it up to speed every time like I used to have to do. I've heard that claude code has similar mechanisms.

I've been doing stuff with recent models (gemini 3, claude 4.5/6, even smaller, open models like GLM5 and Qwen3-coder-next) that was just unthinkable a few months back. Compiler stuff, including implementing optimizations, generating code to target a new, custom processor, etc. I can ask for a significant new optimization feature in our compiler before going to lunch and come back to find it implemented and tested. This is a compiler that targets a custom processor so there is also verilog code involved. We're having the AI make improvements on both the hardware and software sides - this is deep-in-the-weeds complex stuff and AI is starting to handle it with ease. There are getting to be fewer and fewer things in the ticket tracker that AI can't implement.

A few months ago I would've completely agreed with you, but the game is changing very rapidly now.

It's not binary. Jobs will be lost because management will expect the fewer developers to accomplish more by leveraging AI.

Just keep in mind that there are many highly motivated people directly working on this problem.

It's hard to predict how quickly it will be solved and by whom first, but this appears to be a software engineering problem solvable through effort and resources and time, not a fundamental physical law that must be circumvented like a physical sciences problem. Betting it won't be solved enough to have an impact on the work of today relatively quickly is betting against substantial resources and investment.

>progressively understands the business

This is no different than onboarding a new member of the team, and I think openAI was working on that "frontier"

>We started by looking at how enterprises already scale people. They create onboarding processes. They teach institutional knowledge and internal language. They allow learning through experience and improve performance through feedback. They grant access to the right systems and set boundaries. AI coworkers need the same things.

And tribal knowledge will not be a moat once execs realize that all they need to do is prioritize documentation instead of "code velocity" as a metric (sure any metric gets goodhearted, but LLMs are great at sifting through garbage to find the high perplexity tokens).

>But context limitation is fundamental to the technology in its current form

This may not be the case, large enough context-windows plus external scratchpads would mostly obviate the need for true in context learning. The main issue today is that "agent harnesses" suck. The fact that claude code is considered good is more an indication of how bad everything else is. Tool traces read like a drunken newb brute-forcing his way through tasks. LLMs can mostly "one-shot" individual functions, but orchestrating everything is the blocker. (Yes there's progress in metr or whatever but I don't trust any of that, else we'd actually see the results in real-world open source projects).

LLMs don't really know how to interact with subagents. They're generally sort of myopic even with tool calls. They'll spend 20 minutes trying to fix build issues going down a rabbit hole without stepping back to think. I think some sort of self-play might end up solving all of these things, they need to develop a "theory of mind" in the same way that humans do, to understand how to delegate and interact with the subagents they spawn. (Today a failure case is agents often don't realize subagents don't share the same context.)

Some of this is certainly in the base model and pretraining, but it needs to be brought out in the same way RL was needed for tool use.

Ha, this triggered me. I'm building exactly this.

It's a coding agent that takes a ticket from your tracker, does the work asynchronously, and replies with a pull request. It does progressively understand the codebase. There's a pre-warming step so it's already useful on the first ticket, but it gets better with each one it completes.

The agent itself is done and working well. Right now I'm building out the infrastructure to offer it as a SaaS.

If anyone wants to try it, hit me up. Email is in my profile. Website isn't live yet, but I'm putting together a waitlist.

> ~0% of them can be implemented by AI as it exists today

I think it's more nuanced than that. I'd say that - 0% can't be implemented by AI - but a lot of them can be implemented much faster thanks to AI - a lot of them can be implemented slower when using AI (because author has to fix hallucinations, revert changes that caused bugs)

As we learn to use these tools, even in their current state, they will increase productivity by some factor and reduce needs for programmers.

Can you give an example to help us understand?

I look at my ticket tracker and I see basically 100% of it that can be done by AI. Some with assistance because business logic is more complex/not well factored than it should be, but most of the work that is done AI is perfectly capable of doing with a well defined prompt.

Apparently you haven't seen ChatGPT enterprise and codex. I have bad news for you ...

We're all slowly but surely lowering our standards as AI bombards us with low-quality slop. AI doesn't need to get better, we all just need to keep collectively lowering our expectations until they finally meet what AI can currently do, and then pink-slips away.

> Once somebody cracks the memory problem and ships an agent that progressively understands the business and the codebase, then I'll start worrying.

Um, you do realize that "the memory" is just a text file (or a bunch of interlinked text files) written in plain English. You can write these things out yourself. This is how you use AI effectively, by playing to its strengths and not expecting it to have a crystal ball.

I lost my job as a software developer some time ago.

Flipping burgers is WAY more demanding than I ever imagined. That's the danger of AI:

It takes jobs faster than creating new ones PLUS for some fields (like software development) downshifting to just about anything else is brutal and sometimes simply not doable.

Forget becoming manager at McDonald's or be even good at flipping burgers at the age of 40: you are competing with 20yr olds doing sports with amazing coordination etc

>the most unskilled labor

People are worried about white-collar not blue-collar jobs being replaced. Robotics is obviously a whole different field from AI.

Jobs that require physical effort will be fine for the reasons you state

Any job that is predominantly done on a computer though is at risk IMO. AI might not completely take over everything, but I think we'll see way fewer humans managing/orchestrating larger and larger fleets of agents.

Instead of say 20 people doing some function, you'll have 3 or 4 prompting away to manage the agents to get the same amount of work done as 20 people did before.

So the people flipping the burgers and serving the customers will be safe, but the accountants and marketing folks won't be.

Can you walk me through this argument for a customer service agent? The jobs where the nuance and variety isn’t there and don’t involve physical interaction are completely different to flipping burgers

The burger cook job has already been displaced and continues to be. Pre-1940s those burger restaurants relied on skilled cooks that got their meat from a butcher and cut fresh lettuce every day. Post-1940s the cooking process has increasingly become assembly-lined and cooks have been replaced by unskilled labor. Much of the cooking process _is_ now done by robots in factories at a massive scale and the on-premise employees do little else than heat it up. In the past 10 years, automation has further increased and the cashiers have largely been replaced by self-order terminals so that employees no longer even need to speak rudimentary English. In conclusion, both the required skill-level and amount of labor needed for restaurants has been reduced drastically by automation and in fact many higher skilled trade jobs have been hit even harder: cabinetmakers, coachbuilders and such have been almost eradicated by mass production.

It will happen to you.

Funny, I go to South Korea and the fast food burger joints literally operate exactly as you say they couldn't. I've had the best burger in my life from a McDonalds in South Korea operated practically by robots.

It's a non reality in America's extremely piss poor restaurant industry. We have a competency crisis (the big key here) and worker shortage that SK doesn't, and they have far higher trust in their society.

But why would the market get worse? Those recruiters aren’t just in an arms race with applicants, they’re also in an arms race with each other, and there’s an incentive to improve their tools too.

That's exactly the point of the essay though. The way that you're implicitly modeling labor and collaboration is linear and parallelizable, but reality is messier than that:

> The most important thing to know about labor substitution...is this: labor substitution is about comparative advantage, not absolute advantage. The question isn’t whether AI can do specific tasks that humans do. It’s whether the aggregate output of humans working with AI is inferior to what AI can produce alone: in other words, whether there is any way that the addition of a human to the production process can increase or improve the output of that process... AI can have an absolute advantage in every single task, but it would still make economic sense to combine AI with humans if the aggregate output is greater: that is to say, if humans have a comparative advantage in any step of the production process.

Also, you don’t need AI to replace your job, you need someone higher up in leadership who thinks AI could replace your job.

It might all wash out eventually, but eventually could be a long time with respect to anybody’s personal finances.

I'd you team bought the latest IDE for $200/mo and was able to finish tickets, you 50% of your team be laid off?

Or would you just do more stuff?

I feel like most software projects have an endless backlog.

Better IDEs, programming languages, packages, frameworks, etc have increased our productivity, reduced bugs -- but rarely reduced headcount.

Ever hard anyone migrate from php+jQuery to react+node and reduce head count due to increased productivity?

I sometimes reminiscent about the LAMP stack being super productive. But at the time I didn't write tests :)

Wasn't that the point of mentioning Jevon's Paradox though? Like they said in the essay, these things are quite elastic. There's always more demand for software then what can be met, so bringing down the cost of software will dramatically increase the demand for it. (Now, if you don't think there's a ton of demand for custom software, try going to any small business and ask them about how they do bookkeeping. You'll learn quite quickly that custom software would run much better than sticky notes and excel, but they can't afford a full time software developer as a small business. There's literally hundreds of thousands of places like this.)

In reality that would probably mean that something like 60% of the developer positions would be eliminated (and, frankly, those 60% are rarely very good developers in a large company).

The remaining "surplus" 20% roles retained will then be devoted to developing features and implementing fixes using AI where those features and fixes would previously not have been high enough priority to implement or fix.

When the price of implementing a feature drops, it becomes economically viable (and perhaps competitively essential) to do so -- but in this scenario, AI couldn't do _all_ the work to implement such features so that's why 40% rather than 20% of the developer roles would be retained.

The 40% of developer roles that remain will, in theory, be more efficient also because they won't be spending as much time babysitting the "lesser" developers in the 60% of the roles that were eliminated. As well, "N" in the Mythical Man Month is reduced leading to increased efficiency.

(No, I have no idea what the actual percentages would be overall, let alone in a particular environment - for example, requirements for Spotify are quite different than for Airbus/Boeing avionics software.)

The problem is, you won’t necessarily know which 20% it did wrong until it’s too late. They will happily solve advanced math problems and tell you to put glue on your pizza with the same level of confidence.

Why do people make arguments like this?

"Work" isn't a finite thing. It's not like all the people in your office today had to complete 100% of their tasks, and all of them did.

"Work" is not a static thing. At least not in positions of many knowledge-worker careers.

The idea of a single day's unit of "work" being 100%, is really sophomoric.

Also, If 100% of a labor force now has 80% more time...wouldn't it behoove the company to employ the existing workforce in more of the revenue generating activities? Or find a way to retain as much of the institutional knowledge?

Doom, fear-mongering and hopelessness is not a sustainable approach.

We are already in low-hire low-fire job market where while there aren't massive layoffs to spike up unemployment there also aren't as many vacancies.

What happens if you lay off 80% of your department while your competitors don't? If AI multiplies each developer's capabilities, there's a good chance you'll be outcompeted sooner or later.

That's an oversimplification. Work is rarely so simply divisible like this.

Creative destruction is a fundamental component of economic growth and has been happening since economies were part of humanity.

You are thinking too linearly. When the price of goods and services go down because the cost to produce those goods are services decreases, that means things are cheaper. Now that things are cheaper we have more money to spend on other goods or services.

Who knows what industries will be created because of this alleged release of human labor.

When the refrigerator was invented we didn’t just replace an industry of shipping ice, we created new industries that relied on refrigeration. That’s creative destruction. That’s economic growth.

This is not to mention that I find the scope and scale of AI displacement to be highly dubious and built on hype.

Automations do create jobs, but fewer jobs. Businesses wouldn’t invest so much money if they had to keep the same number of workers. Automation necessarily reduces the number of humans working in aggregate at one task.

What DOES go up with automation is demand. Fewer farmers today than 100 years ago, but significantly more mouths to feed.

What also increases is new kinds of jobs; entirely new fields. The automobile shrank the number of buggy whip makers, but taxi drivers increased. Then the internet increased Uber drivers on top of taxi drivers.

> Are we supposed to just all go work at a datacenter or in the semiconductor industry (until they automate that too)?

Datacenters are very automated. They already don't require many people and they're going to be needing less and less humans in them going forward.

Semiconductor manufacturing is also very heavily automated.

Neither datacenters nor chip manufacturing employ a whole lot of people. But I think you're looking at it wrong. Jobs come from people with money wanting to pay for jobs. That's not going to change.

The jobs of the future may be that you're a court jester for Larry Ellison, or that you do something else that's fundamentally pointless but happens to be something that a person with money wants. Companion, entertainment, errands. Now, that may sound dystopian, but on some level, so are most white collar jobs today. Microsoft employs 200k people. How many of these are directly involved in shipping money-making products - five percent? Ten? The rest is there essentially for the self-sustaining bureaucracy itself. And there's no reason for that bureaucracy to exist except the whims of people with money and power - delegation, empire-building, pet projects, etc.

Disagree. The input to output ratio is ridiculous. With the latest LLMs you can input very few words to generate a lot of production usable output.

Maybe I'm being naive here, but for AI (heck, for any good algorithm) to work well, you need some at least loosely-clearly defined objectives. I assume it's much more straightforward in semi, but there're many industries, once you get into the details, all kinds of incentives start to disalign and I doubt AI could understand all kinds of nuances.

E.g. once I was tasked to build a new matching algorithm for a trading platform, and upon fully understanding of the specs I realized it can be interpreted as a mixed integer programming problem; the idea got shot down right away because PM don't understand it. There're all kinds of limiting factors once you get into the details.

AI breaks the window you repair, at scale

https://en.wikipedia.org/wiki/Parable_of_the_broken_window

Ya'll have junior devs? I haven't seen one of those in almost a decade.

> The future seems to be one operator working on the entire puzzle, minus the hierarchy of people.

Given the rest of your argument that makes no sense. Why should that one operator exist? If AI is good at big picture and the entire puzzle, I don’t see why that operator shouldn’t be automated away by the AI [company] itself?

Yeah I’m very much seeing this right now.

I’m a pretty big generalist professionally. I’ve done software engineering in a broad category of fields (Game engines, SaaS, OSS, distributed systems, highly polished UX and consumer products), while also having the experience of growing and managing Product and Design teams. I’ve worn a lot of hats over the years.

My most recent role I’m working on a net new product for the company and have basically been given fully agency over this product: from technical, budget, team, process, marketing, branding and positioning.

Give someone experienced like me capital, AI and freedom and you absolutely can build high quality software and a pretty blinding pace.

I’m starting to get the feeling than many folks struggling with adopting or embracing AI well for their job has more to do with their job/company than AI

This gives me a lot of hope for a decentralized future for all kinds of service industries. Why would you go to a big-name accounting firm where the small number of humans can only give you a sliver of attention, when you can go to a one-man shop and get much more of the one human’s attention? Especially if you know that the “work” will be done by the same tools? So many of the barriers to entry in various services - law, accounting, financial advising, etc. - is that you need a team to run even the smallest operation that can generate enough revenue to put food on your table. Perhaps that won’t be the case for long - and the folks that used to be that “team” can branch out and be the captains of their own ships, too.

There aren't 10x revenue gains in most businesses if their workers become 10x more productive. Some markets grow very slow and/or have capped growth.

Therefore, the best way to increase profit is to lower cost.

If anything, I see a decrease in the quality bar. Code is sloppier, there are more bugs, more outages, more security issues. Whatever alpha AI provides is being spent on feature velocity and AI integrations at the cost of those other things.

There are lots of things people want explicitly because a human is part of the loop. AI generated art will never attract the same premium as something created by (or at least claimed to be by) a human. People seek status, and that can only be conferred by other people. The problem is that, unlike other products of human labor, status is a zero sum game.

There is no “preparing for the disruption” at an individual level, aside from maybe trying to 100x a polymarket bet to boost your savings.

Im employed by two semi-technical cofounders that vibe coded the MVP until they couldn't maintain the technical complexity. I expect scenarios like this to continue. There is a subset of companies that eventually will those engineers.

> What happens when there’s software you think should exist, and you no longer need to hire a bunch of people at $150k-$250k per year to build it?

What happens when 200 out-of-work former software engineers take a look at your software and use LLMs to quickly build their own version each undercutting everyone else's prices in a race to the bottom?

Is AI filling in for all those COBOL programmers they needed yet?

I belive the black plague actually caused a massive labor shortage and wages increased. When a huge amount of people die and you still need to have people build bridges and be soldiers and finish building the damn cathedral that's been under construction for the last 400 years then that is what will happen.

Here's an article:

https://history.wustl.edu/news/how-black-death-made-life-bet...

The black plague's capital-concentration aftermath supposedly fueled the renaissance and the city-state ascensions, and ultimately the great land discoveries of the 14th and 15th centuries.

Not sure if there's an analogy to make somewhere though

> Job loss is likely to have statistics more comparable to the Black Plague.

Maybe this is overly optimistic, but if AI starts to have negative impacts on average people comparable to the plague, it seems like there's a lot more that people can do. In medieval Europe, nobody knew what was causing the plague and nobody knew how to stop it.

On the other hand, if AI quickly replaces half of all jobs, it will be very obvious what and who caused the job loss and associated decrease in living standards. Everybody will have someone they care about affected. AI job loss would quickly eclipse all other political concerns. And at the end of the day, AI can be unplugged (barring robot armies or Elon's space-based data centers I suppose).

Agree. I feel like most of the people sounding the alarm have been in the software-focused job hunting market for 6+ months.

Those who downplay it are either business owners themselves or have been employed for 2+ years.

I think a lot of software engineers who _haven't_ looked for jobs in the past few years don't quite realize what the current market feels like.

Even people in category #1 should be concerned. Even if their income is not directly affected, the potential for disruption is clearly brewing: mass unemployment, social and civil unrest.

I know smart and capable people that have been unemployed for 6+ months now, and a few much longer. Some have been through multiple layoffs.

I am presently employed, but have looked for a job. The market is the worst I've seen in my almost 30 year career. I feel deeply for anyone who needs a new job right now. It is really bad out there.

3 You realise that super-autocomplete is an incredible technology but the hype behind it far exceeds its capabilities and you're excited for the possibilities it may promise for making your work easier and more enjoyable.

I feel like that's a rather bad-faith take, so if you're going to make that kind of accusation you better back it up. People can legitimately believe that AI is not going to be the end of the world, and also not be privileged. And people can be privileged, and also be right. Not everything can be reduced down into a couple of labels, and how those labels "always" interact.

For 1, unless you already have an self-sustaining underground bunker or island, you will be affected. No matter how much savings and total compensation you have. If you went out to get grocery in the last week, it will affect you.

I just skimmed this and the so called zeitgeist here is fear. People are scared, it's material concern and he effectively stoked it.

I work on this technology for my job and while I'm very bullish pieces like that are as you said slopish and as I'll say breathless because there are so many practical challenges here to deal with standing between what is being said there and where we are now.

Capability is not evenly distributed and it's getting people into loopy ideas of just how close we are to certain milestones, not that it's wrong to think about those potential milestones but I'm wary of timelines.

Let me get something straight: That essay was completely fake, right? He/It was lying about everything, and it was some sort of... what?

Did the 80 million people believe what they were reading?

Have we now transitioned to a point where we gaslight everyone for the hell of it just because we can, and call it, what, thought-provoking?

Well the zeitgeist is that our brains are so fried that such piece of mediocre writing penned by a GPT-container startupper can surge to the top

This is what they get for not reading our antislop paper (ICLR 2026) and using our anti-slopped sampler/models, or Kimi (which is remarkable relatively non sloppy)

https://arxiv.org/abs/2510.15061

I thought normies would have caught onto the EM dash, overuse of semicolons, overuse of fancy quotes, lack of exclamation marks, "It's not X, it's Y", etc. Clearly I was wrong.