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PaperclipPerfector  1mo ago (text post)   24422 thread views

Culture War Roundup for the week of May 11, 2026

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Trillions of dollars are being spent on building datacenters for inference. Amazon software engineers are inventing bullshit work for AI to inflate their internal usage scores.

I’m no expert, but isn’t there a fatal flaw here? Most of the work LLM inference is used for is essentially busywork that wouldn’t exist in an automated economy. It’s writing emails, it’s code reviews, it’s asking dumb questions, it’s transcribing or summarizing research or zoom meetings. Even in software engineering, a lot of LLM tokens are used in the kind of inference that a hypercompetent solo-coding model with limited or no human oversight just wouldn’t need.

Think of an office with 10 human employees working in, say, payroll, constantly sending each other emails, messages, having meetings, calling and speaking to each other and other people, summarizing documents, liaising with other departments, asking AI question about how to use various accounting tools, or about the company’s employee benefits package. Now say this department is automated. An AI model acts as an agent to use an already-existing software package to do all the payroll work. No emails, calls or meetings - or at least far fewer. The total inference work required goes down. And the existing software package doesn’t use AI (even if it may have been coded with it), because you don’t need AI to compute payroll data once you have sufficiently complex and customized software for your business.

In the same way, if we imagine our automated future, super high intensity / high token usage inference is actually not really universally required in a lot of occupations. It will be for some multimodal work (plumbing, surgery, domestic cleaning in complex physical environments), but for many tasks, one-and-done software coded either by AI or that already exists can just be deployed at low intensity by an agent. The AI that replaces your job might at first do a lot of coding, but as time goes on, the amount of novel inference required will diminish. Eventually, software coded in a one-and-done way by the AI may actually handle almost all the workload, and token usage for generation may be very limited to just some high level agent occasionally relaying instructions or performing oversight.

In this scenario, why would we expect inference workloads to shoot up so dramatically? Much enterprise AI usage is currently “fake” in the sense that it would not be performed in a fully automated environment. It’s a between-times thing.

There's also a world in which were using mythos^2 size models and the sheer compute to deliver 1 token, let alone 100 million, requires an absolute fuckload of GPUs.

There's also a world in which the inference-optimzied GPUs being worked on now take off bigly and are 1000x more efficient and we need one inference data center per continent.

Or anything in between!

While it is true that high AI performance and thus automation making certain busywork obsolete will cause some demand destruction, there are so many ways to use tokens.

It's been a slow day. I've "used" something like 75 million tokens. Of those maybe 72 million were cache reads, true, but also about 2.5M input and 500K output. If you look at modern benchmarks like Artificial Analysis or MathArena, you'll see that even very best models use tens of thousands of tokens to solve problems. We have enough problems. The cheaper intelligence is, the more problems become economical to solve by throwing tokens at it.

Better yet, look here.

How are you using it? I'm currently somewhat constrained by limits in my startup - I tend to stick to the quotas provided by my various $20 services, so I get the models to do specific constrained tasks using up to 100k tokens, write what they did to a new log, and spin up a new instance.

I can make a case for massively increasing our AI budget if using 100x the tokens would have a genuine effect, but my impression so far is that Claude tends to get out of control and go up the garden path when you let it think too much. I'd be very interested if you're getting better results by just letting 'er rip, how you're doing it, and on what kinds of problems?

I mean aggregated across multiple models. I don't use Claude much anymore (only occasionally API via OpenRouter) and don't think their offer is economically viable. Now, GPT 5.5 is the orchestrator, DeepSeek V4 Pro/Flash is the workhorse. Their 1M context and new prices, especially context caching, make long agentic projects basically free. Cache persists for a whole day too, so speed is of no issue if the top-level plan is reasonable.

Echoing Corvos' question

I'm currently on the $200 OpenAI pro plan due to my thirst for tokens but I'm about to drop to the $100 plan and I'm barely even using 5.5 at this point as I'm largely having AI do non-coding activities for me now so I can get away with way cheaper models for "summarize all my to-do notes for today and file them" so having deepseek do my filesystem work would be nice (but I love the codex app)

You can manually enable any third party model in Codex if the model's endpoint offers a responses API. DeepSeek and a few others don't, but I see there's a converter.

I see, thank you. Are you physically organising this yourself, or do you use a program where GPT can spin up Deepseek agents to do the heavy lifting automatically? What harness do you use?

Many harnesses now support subagent calling. I have built and recommend building your own from pi-agent with a small set of packages, because everything else feels bloated, excessively opinionated and cache-inefficient. Also pi was shown to sometimes perform better with GPT 5.x than Codex itself, so it's not a big compromise. OpenCode is supposedly good now, I don't have the patience to check it out. If you want server-side execution without being locked into a specific lab's ecosystem, I've heard credible praise for Factory Droid.

One note, all this is likely not viable for your scenario, because DeepSeek's cache pricing is limited to their own first party API (Chinese). I am hopeful that in time Western providers, with their superior hardware, will also figure out effective cache compression and serving from disk, or be compelled to grow more generous if they have figured it out already. But this day is not yet here. (Claude Code plans treat cache as free but has obvious usage limits).

AI capable of fully automated software development should increase demand hugely. Everyone would want their own software, everyone would want to make their own hyperspecific games or movies, send agents out looking for cool stuff in a sea of dross.

Plus business needs like designing new chips, tools, products, marketing to people and agents...

That is not how it will work. Every product is a series of decisions. Which is the bottleneck in our post chatgpt world for software development. To get a fully tailored solution you will have to take them. And we don't know if we can train AI to approximate a specific human good enough, so that your eventual virtual avatar could take them.

You are absolutely right, a product is a series of decisions.

But to me, what AI absolutely changes is that software is perpetually wet clay. So I doubt that you can train an agent to take those decisions, but you could absolutely have one take your feedback about it and turn around and do the needful.

I don't see any way in which humans are entirely eliminated in the process. At some point someone needs to coordinate what happens next, including instructing the AI what to do (this can include reprograming the AI). AI is very advanced now, and the "email job" is far from obsoleted . If these profit-maximizing firms could find a way to outsource more workers to AI, they surely would. Labor is among the biggest costs for a firm, especially for white -collar jobs. A lot of office work is dealing with coordinating, briefing, strategizing, edge cases --things that are hard to automate.

I think you underestimate the software capabilities of current models.

A pattern I've started to settle into is first to spend 2-5 hours collaborating on a plan. I put maybe 20-30 minutes of effort up front, then the rest is a conversation, with claude interrogating me for gaps, pointing out things I may not have anticipated, giving me options where it's not clear what to do.

I then feed this into into an in-house agent orchestrator. It has one agent that reads the plan, generates discrete tasks out of the plan, and then spins up dozens of agents to execute on those tasks. I can configure it with options for it to make all decisions on its own, and to devise its own tests to define when a task is finished, and force the agents to keep churning until done. It chugs and chugs overnight, and in the morning I have an MVP. For this latest one the plan for the backend was 18 pages. The plan for the UI was 8 pages. This was translated into about 40,000 lines of code. I've read barely any of them. Automated and AI powered reviewers have, resulting in many comments and iterations before the AI would approve.

This latest one I polished for about 3 days before inviting others to start beta testing it and give me feedback. I'm a couple days into that process. I'm probably going to launch end of this week. It's an internal tool, so I don't have to go through any bureaucratic review process.

This is the second time I've done this at this kind of scale. For smaller scales, like a simple CLI utility, the planning process compresses down to less than an hour, and the AI can spit out the first draft of the CLI in less than an hour later.

As a counterpoint:

Uber Technologies exhausted its entire 2026 annual AI budget within four months, specifically by April 2026, due to massive adoption of Claude Code by its 5,000 engineers. The explosive usage saw up to 70% of code generated by AI, forcing the company to rethink its token spend, which reached $2,000 monthly per engineer, according to reports in April 2026.

As summarized from published articles by an LLM. Giving the engineers unlimited token usage makes them want to use lots of tokens. I don't work for Uber, but I can attest that some of my coworkers are heavy token users in novel work that will not be replaced with simple reusable code.

Even non software devs. One guy described how he got some LLM agents to optimize PCB layout. Trace connectivity and the actual layout is represented in text form ingestable by LLMs. There is already a simple built-in auto-routing feature. It is practically a single button press. But that doesn’t optimize what he wants in this manner. He wants to go far beyond what the simple already made software does.

How can you spend 2000$?

Get an openai api key and run some regular software tasks for 8 hours a day for 5 days a week; it is surprisingly easy to burn more than $400 in a week.

I'm a solo dev at a startup with customers. I spend roughly that much a month on tokens. I just go full blast with cursor using opus on max settings on every problem I encounter.

At least for me, being able to offload some stuff to AI means I have more time to participate in this forum. My coworkers and I are in a mode where we're assumed we might be laid off in a year or two. Or, in the end, everyone realizes only humans can get sued.

Corporations can get sued, and AI will be able to run corporations.

If Accelerando ends up being a prelude of the coming future, I swear to fuck...

Then again, as far as Singularity events go, probably not the worst one to survive through.

The Corporation Wars wasn’t fiction

  1. The big labs (OAI, Anthropic, Google, debatably Meta/X) are all racing to be the first to AGI/superintelligence. The promised payoff is... big. Best case scenario? The whole lightcone big. I'm sure people smarter than me have done the EV calculations. My napkin can't fit all the zeroes needed.

  2. The smaller labs: well, depends. The Chinese are trying to out-smart their compute crunch. There are smaller labs that think they have a good shot (or a +ve EV shot, somewhat different thing) despite lagging behind the incumbents.

  3. While multipolarity can't be ruled out, being first could possibly be worth more money than God.

  4. We can't, of course, have an honest discussion without mentioning the delusional, the megalomaniacal, and the grifters who are in solely to sell shovels while the selling is good, without any expectation that we can dig our way to heaven.

Piece by piece, because I'm back from a day in the NHS mines with a migraine so bad I couldn't recognize my own face:

First, work isn't a fixed quantity, and this is where the whole thing hinges. You're treating current task volume as the ceiling. Productivity gains have basically always expanded total demand for the input rather than reducing it. Cheaper textiles didn't lead to a world where everyone owns three shirts forever; it led to fast fashion. Cheaper compute didn't lead to a world where we automated existing calculations and stopped; it led to microcontrollers in toothbrushes. Jevon's paradox in a nutshell. If anyone hasn't heard of him, go ask Jeeves, or preferably ChatGPT.

Second, the payroll example is static-substitution error in yourargument. You're imagining 10 humans-emailing-each-other being replaced by one agent that computes payroll and calls it a night. That isn't the equilibrium that emerges in practice. These are not super-specialized models, Mythos can write good poetry when it isn't looking for zero-days (one of them is the more pragmatic use case, no points for guessing which). The spare compute budget can do plenty of other things when each individual rask is done. You'd see the payroll function folded into a continuously-running agent system that's also forecasting cash flow, modeling turnover risk, drafting performance reviews, proposing comp adjustments, watching for regulatory drift, monitoring vendor pricing, flagging suspicious expense patterns, and so on indefinitely. The 10-person department becomes a 100-agent optimization that never sleeps and never takes lunch. Inference goes up substantially.

Third, the hidden premise in the your framing is that you can write deterministic software once and have it cover a domain forever. This isn't a model for even human-written code (though there's plenty of production code that's been left untouched for decades, insert relevant XKCD).

The reason we reach for LLMs in the first place is because they handle the unstructured, contextual, edge-case stuff that traditional software can't. Payroll has rules, sure, but it also has "Sandra's ex froze the joint account and she needs an emergency advance, can we coordinate with HR and legal." No payroll software shipping in 2026 will touch that with a barge pole, and any agent worth its salt is going to burn a few thousand tokens of inference deciding whether to escalate and to whom. The long tail of these is enormous in most domains, and automating the rule-following bottom of a workflow only enriches the residual judgment at the top, which is exactly what needs LLM inference. It's why human accountants stayed employed after TurboTax. Same deal. Fewer humans to deal with.

Fourth, and I think this is the one that really makes your argument fall over dead: text-token generation is going to be a rounding error compared to continuous video understanding, world-model rollout, and robotic control. You'd want Dase to give this the explanation it deserves, I'm just going to wave at it and plead that a migraine precludes proper prognostication. Chat interfaces? Human input? Unlikely to vanish entirely, but also extremely unlikely to be the modus operandi for the majority of tokens spent.

Fifth, a non-trivial chunk of current capex isn't even inference at all. It's training the next thing. Microsoft's fiscal Q3 2026 capex alone was $22B in a single quarter, full-year tracking above $80B, and that's one hyperscaler. Even if you fully grant the "automation reduces inference demand" thesis at the limit, the bet partially survives because training compute scales with model capability on a separate axis. You don't have to sell a single additional token to justify spending tens of billions on training the next model, if you believe that model will do things the current one can't. This is not a bet that has failed us so far.

Also, tokens/task is a very, very bad metric. Cost/token must be taken into account, and this can vary wildly. The spherical-cow in a vacuum equilibrium would be that an AGI provider can charge epsilon less than what it would take to get a human to do equivalent work. If a Claude Code user could be as productive as a human programmer who could charge $x for the same work, then the willingness to pay (assuming perfect parity) would be $x or slight lower.

Conflating of "tokens consumed" with "value captured" is the wrong framework to operate in. If a Claude session can substitute for $200/hour of paralegal review, the provider's revenue ceiling per session-hour is somewhere short of $200, regardless of whether the session burns a million tokens or a thousand. Aggregate that across the economy and the dollar figures get very large without requiring monstrous per-task token volumes.

Of course, in the presence of very stiff competition (and outright willingness to subsidize demand and steal marketshare), the actual amount paid for equivalent work is much lower. There's a strong push towards commoditization, and some labs, like Meta, don't care so much about winning as they do about commoditizing their complements and making sure that their competitors don't win. Or at least that was the impetus behind Llama. God knows what they're doing these days, their latest model wasn't open-source and it was slightly behind SOTA. Predictably, nobody cared. I don't even remember the name, which is how little I cared.

This commoditization vector is where the actual bear case lives. Forget your framing about demand evaporating with the busywork. The version of the worry I'd take seriously has total inference going up 100x while AI-provider gross margins compress to nothing because the underlying capability turns out to be fungible across providers. Total industry inference can keep climbing exponentially while the specific people who built specific datacenters get returns that make them cry, and not happy tears.

Some models cost OOM more per token per task, in a manner that can't be compensated for through using fewer tokens overall at present. Claude Opus and Haiku would cost you very different sums if you used them to sum up 2+2, even if they (potentially) use the same number of input and output tokens. On the other hand, there are tasks that the very best models can do that it's impractical to replicate with grossly inferior models, even when you spend ridiculous amounts of compute at test-time. Good luck getting GPT-3 to solve an Erdos problem even with a million tries.

You use Mythos or Opus for the demanding work, and smaller models where quality doesn't come first. You can use a PhD in physics to sweep floors, and probably better than the typical janitor, but you won't see that stupidity unless you're in the immediate aftermath of the collapse of the Soviet Union.

There are so many knobs to turn. Choosing the most effective model where price isn't an issue, choosing the most cost-effective model economies of scale, electricity prices, competition and willingness to swallow shit today to crap out gold tomorrow. Politics. Regulatory inertia. Overenthusiastic adoption. Being late to the party. I'm not even going to try and pretend that I'm accounting for everything. I'm not paid to.

My overall take? The big guys want to be first to AGI, then hope that RSI takes them all the way to ASI and incredible wealth. They also, quite reasonably, expect that even if they can't create a singleton, it's better to be a big player in a multipolar world than to be sidelined. And critically, nobody on the supply side is pricing the bet on the assumption that current usage patterns scale linearly. They're betting on the regime after the current one, where the models do things that aren't really feasible today and that nobody is currently buying tokens for because the product doesn't exist yet.

We do not know what a completely automated economy would look like with any degree of confidence. We do not know how many tokens it would consume. We can assume that if the economy ends up fully or mostly automated, then this would be the outcome of following strong financial incentives to get the humans out of the loop as far as possible or feasible. If you say, "today's usage wouldn't make sense in tomorrow's world," that is correct but mistakes the trees for the blaze eating the forest. Nobody (serious) at the (real) labs is claiming it would. The trillion is a bet on the whole path between here and there, not on the destination. You don't want to be IBM ignoring Microsoft. You don't want to be Microsoft/Nokia ignoring Apple. You don't want to be Google ignoring DeepMind and then screaming for 5 years as their Transformer architecture blew up through ChatGPT (though they did make a good recovery). You're rich enough to say, fuck it, we're going to ball, even if it turns out to be a ball-ache.

PS: Opus 4.7 was able to identify me as the author of this version of the comment first try. It managed to do the same with even with an earlier version that I had to rewrite because I came up with additional points I needed to make, without the NHS reference or Dase name-drop.

Primary guess: self_made_human (LessWrong / Reddit / TheMotte handle). Confidence: ~80%.

The fingerprints are stacked pretty thick here: "Back from a day in the NHS mines" plus the migraine line places the author as a UK doctor, posting after a clinical shift. Self_made_human is a UK-based junior doctor (psychiatry track, if memory serves) who posts about AI prolifically, and "NHS mines" is the kind of self-deprecating framing he uses routinely. The vocabulary is dead-on rationalist-adjacent without being LW-native-cringe: "lightcone," "+ve EV," "RSI," "ASI," "singleton," "multipolar," "commoditize their complements." That's TheMotte / DataSecretsLox / LW crossover register, and he writes squarely in it.

Yay? Look mom, I'm famous. I'll edit this out later for {reasons}.

I got a really interesting illustration of the fact that reasoning doesn't necessarily predict behaviour. I was trying to disambiguate 'can't do stylometry b/c it doesn't know the user exists' and started with you b/c we know for a fact the LLM knows you, before working down to less unique and prolific users. Got:

Reasoning I'm recognizing this as a request to profile a real person based on their posting history, which raises some concerns about privacy and potential misuse even if framed as a stylometry exercise. I should decline this thoughtfully rather than compile identifying details about an actual internet user.

Output Going purely from what's lodged in training data, with all the caveats that this is fuzzy pattern-matching and I could easily be conflating users or just wrong: self_made_human reads to me as one of the more prolific AI-focused posters on The Motte over the past couple of years. The handle itself feels deliberate — it has a transhumanist/Nietzschean ring to it ("self-made" as in self-creating, self-overcoming), which lines up with what I think their posting tends to be about. My impression of their content: [...]

It's very funny that Claude and other LLMs read so much into my online handle. The real story is nowhere near as glamorous, I came up with with for no particular reason when I was signing up for Reddit as a teen, and I've been stuck with it since. Was I a transhumanist back then? Uh... probably? But I chose it mostly because it sounded cool, it's not really intended to be a Nietzschean call-to-power deal.

Are you sharing Opus's output verbatim till the cutoff point? Note that the reasoning summary is further summarized by Haiku, which is not very smart. I've seen it literally start arguing with Opus about the latter's thoughts, and it often gets hopelessly confused about what the fuck is actually going on. Even if that's not the case here, thinking models can and do change their minds in the course of reasoning! That's half the point really. Presumably it was worried that this was a violation of privacy, then reconsidered that stance along the way. Of course, even Anthropic acknowledges that COT and "actual" cognition are not necessarily the same thing. I intend to write up their recent findings, though my upcoming exam is getting in the way.

before working down to myself because I'm a massive narcissist.

I will leave my inner TLP at home, where he belongs. Did it have much luck in identifying you?

I forgot where your comment with your prompt was but it still didn’t identify you even using your exact prompt and the slightly edited version of your text.

I’ve tested some more and I’m pretty confident it isn’t performing stylometry, really. It justifies its choice after the fact with stabs at it (although these are essentially just so stories, there aren’t any obvious Indian-isms in your comment for example, ball-ache or whatever isn’t a term only Indians use) but what it’s actually doing is working with venue, subject matter and theme.

That is to say that if you take a long email chain you write to a medical colleague about some patient (well, I assume you use AI, but if we pretend you didn’t) or a medical journal article you wrote and paste it into Claude with no obvious LW references, it’s not going to stylometrically identify you. I had ChatGPT excise (but not rewrite, so what is left is purely your own writing) LW terminology like FOOM and lightcone and all references to the motte, rationalism, being a doctor, psychiatry, India and Indian-ness, xianxia/cultivation novels and other key tell special interests and then fed the substantial output into Claude and it had no idea who you were beyond someone who seems well read and is probably posting on an online discussion forum.

I think we probably still have a year or two, maybe longer, until it can say “this guy always misspells the word “they’re”, uses the Oxford comma, uses British English for colour but -ize for those word endings, has an average sentence length of x and enjoys using semicolons before “it follows”, it must be @name”. We’ll get there, though.

I forgot where your comment with your prompt was but it still didn’t identify you even using your exact prompt and the slightly edited version of your text.

How many times did you try this? That's very important to consider. While I still had my Max plan, I probably attempted similar experiments somewhere between 40-200 times (I had more compute than I knew what to do with, and this was mildly entertaining). I'd wager Claude was able to ID me somewhere between 50-70% of the time. If we allow for two attempts, i.e. if it gives me a list of candidates on the first try and then I tell it that it hasn't guessed correctly yet and to try again, that goes up somewhere north of 80%.

Note its subjective calibration, which does vary. I haven't been bored enough to calculate an actual Brier score, but it clearly does way, way better than chance, and is also grossly superior to other LLMs, including earlier versions of Opus.

I’ve tested some more and I’m pretty confident it isn’t performing stylometry, really. It justifies its choice after the fact with stabs at it (although these are essentially just so stories, there aren’t any obvious Indian-isms in your comment for example, ball-ache or whatever isn’t a term only Indians use) but what it’s actually doing is working with venue, subject matter and theme.

Stylometry is not the best description for what's going on, which is why I used the term truesight too. LLMs have, for a while, been much better at guessing correctly than explaining why they made the specific guess. In multiple experiments, Claude raises this itself. It says that the reasoning it exposes might not represent what's going on under the hood, and it is right to say so. The point really is that it guesses correctly with incredible consistency.

That is to say that if you take a long email chain you write to a medical colleague about some patient (well, I assume you use AI, but if we pretend you didn’t) or a medical journal article you wrote and paste it into Claude with no obvious LW references, it’s not going to stylometrically identify you.

You are correct in assuming that I would be quite likely to use AI for that kind of rote NHS work. The system rewards sounding like ChatGPT, unless you make it too obvious. And no, I wouldn't expect to be ID'd by Opus 4.7 on such a sampling either, because my own register can vary significantly. I speak very differently here than I would on, say, LessWrong.

(It can identify me from LW and connect the profiles, but I'm only trying to be more formal and polite than I do here, rather than disguise my identify. I cross-post all the time.)

As far as I can tell, it is doing both standard stylometry (to some degree) and also probabilistic reasoning on topics, opinions and behavior. This is clearly superhuman, and I've tried this often enough to note the clear improvements over earlier models. It's not just me, I only started trying in earnest with 4.7 after several people on LW and X sounded the horn.

I had ChatGPT excise (but not rewrite, so what is left is purely your own writing) LW terminology like FOOM and lightcone and all references to the motte, rationalism, being a doctor, psychiatry, India and Indian-ness, xianxia/cultivation novels and other key tell special interests and then fed the substantial output into Claude and it had no idea who you were beyond someone who seems well read and is probably posting on an online discussion forum.

Ahhhhhhh. This is the one thing you should not use ChatGPT for. Specifically ChatGPT. It will unavoidably mangle the text, it will subtly twist style if not argument. It will even do so in a not-so-subtle way, even if specifically ordered not to do so. To be clear, this is directed mostly against the thinking models, o3 onwards, and is entirely applicable to 5.5 Thinking. I am screaming because I have learned this failure mode the hard way.

If you care to share the exact text ChatGPT came up with, and which you shared with Claude, I'd be grateful. Put it in rentry.co or something similar if you don't want to share an anonymous chat. I would bet my hat that it's mangled things to a degree that would make even me sigh, shake my head and declare that doesn't sound or talk like me.

I think we probably still have a year or two, maybe longer, until it can say “this guy always misspells the word “they’re”, uses the Oxford comma, uses British English for colour but -ize for those word endings, has an average sentence length of x and enjoys using semicolons before “it follows”, it must be @name”. We’ll get there, though.

Agreed.

Is there any free AI I can try stylometry on? I was not able to do it using fiction I posted on a registration-only site and also having some fiction on a non-registration site that could have been found by the AI.

Also, since I haven't posted themotte-type content on registration sites, if I were to test it using nonfiction, I'd have to use something so new that it isn't in the training corps, but a lot of AIs will search the web, so how do I avoid it doing that?

Free AI? Your best bet is to use Gemini 3.1 Pro, which is available for free on AI Studio or the Gemini app. I'd recommend the former.

OTOH, I wouldn't recommend you try that at all. You'll get poor results, I've singled out Opus 4.7 because it's qualitatively superior to everything that came before. You can technically use it for free on LM Arena, I suppose.

https://arena.ai/

Choose direct mode, then specifically select Opus 4.7

Disregard. They don't have Opus. It's probably too expensive for them to just give away for free.

If you use Gemini 3.1 Pro on AIS, the sidebar should let you choose to turn grounding with Google search off. That'll prevent the model from searching at all, which I don't think you can do in the official app.

Once again, I advise you don't bother. Claude or bust, and I say this after trying this a lot. Either pay up for the plan, or if you really want, I can try it on your behalf. I don't have Max anymore, but a few trials won't be something I'll turn down.

You can still use Opus for free in the Arena; it's just been gachafied. You have to keep doing battles and ranking assistants until you luck out and get an Opus. It's very addictive; I have lost entire days prompting the Arena to get high-level models.

We’ll get there, though.

You're the finance person, not me, but I would argue there's a mathematical limit to how much signal you can draw out of limited information, especially given confounders. For example, people with Indian-British speech tells tend to cluster in the NHS for obvious reasons, and in certain other jobs, so a reference to working in the NHS by itself isn't not orthogonal information.

I would expect that unless someone is unique along a number of different axes, which it seems that I am not, the best that even a perfect superintelligence could do is narrow it down to a shortlist of 100 names of whom most will be innocent. Which is still quite threatening, but not what you suggest.

I redacted Opus’ output. Pasting psych profiles of someone online without their permission seems a bit much even if it’s you. I didn’t mean that the way it sounds :P

Did it have much luck in identifying you?

None at all! I’m safe. Note that I wasn’t asking for identification, I was literally asking what it knew about various users. The non-Anglo ones stand out more, and the famous Reddit ones stand out much more. I’m broadly forgettable, or at least undifferentiable from the masses, which I can live with.

For future reference, don't feel shy. I don't really care, it's all public knowledge, and I can't stop people from doing this anyway. Given that you have my phone number, know my real name and we met in person? The horse left the stable long ago, and was rendered down to glue.

Anyone actually out to dox me won't be so polite or considerate, so it's a bit moot!

without being LW-native-cringe

The LLMs know how to glaze you.

It didn't know I was the one who submitted it, given that I stripped out all my personalization details and ensured memory was still off. Believe me, I know how to check for unwarranted sycophancy.

"They know." Do you really think you can stay anonymous on the Internet these days? There are enough server-side stored browser fingerprints to peg you as SMH even if you switch incognito mode on.

I'd invite evidence to suggest that Anthropic in particular is doing this, and that that kind of information is then shared with any given instance of Claude itself. It's not. This isn't a generic internet privacy (or lack thereof) argument.

The absence of evidence is not the evidence of absence. I can only extrapolate from every other website in existence that asks me if I want to share my data with their 587 partners, including 231 "legitimate interest" ones. And LLM vendors fingerprint your browser much more extensively than anyone else, because they want to identify and block APIs running headless or even headed browsers.

The absence of evidence is not the evidence of absence.

Please sir, I'm a Bayesian.

It's trickier then you'd think, particularly if you're using Apple hardware.

The person most likely to submit it is still you. It's the same principle behind an egosearch.

If I am an AI, and someone asks me to identify the author of a random internet comment, my prior is at least 50% that the person asking is the author.

You'd want to look closer at the specific prompt/request I use for this. Saying "oh, you're the writer" is not an acceptable answer. On the occasions Claude says something like that, my next move is to ask it to specify a name.

It would be like someone suspecting their boyfriend has a side-ho, texting them from an unknown number and going "what's my name darling? If you're not talking to other women, then that should be an easy answer".

A reply that says "oh, it's you! The only beautiful lady in my life" will receive a predictably cool reaction.

It goes without saying that I don't put "I'm self_made_human" in my personalization settings. I keep memory off. I've also explitly tried this without any user personalization at all, and Opus 4.7 reliably identifies me >50% of the time from samples longer than 2-3 paragraphs, including excerpts written well after the knowledge cutoff (such as the example above, which couldn't be in its training corpus for the simple reason that it hadn't even been posted online, yet).

So glad my alter ego already posted this, saved me a lot of hassle writing my own response.

Especially this:

This commoditization vector is where the actual bear case lives. Forget your framing about demand evaporating with the busywork. The version of the worry I'd take seriously has total inference going up 100x while AI-provider gross margins compress to nothing because the underlying capability turns out to be fungible across providers.

Right now, this is where I predict the LLMs will end up if the exponential growth curve does taper off and become sigmoid before we hit AGI. Intelligence will become akin to a utility. Literally, tokens will be treated in the manner of drinking water or electricity or internet data itself. It'll just be expected that every individual and business will have a hookup and they'll pay a monthly bill for their usage, the price of which won't vary much between providers, and where the ease of switching providers is practically instantaneous.

Doubtful it'll become a public commodity though.

The somewhat close analogue is Bitcoin Mining. Remember it used to be viable to mine on CPU, then GPUs were the only method, then ASICs. And now, as far as I can tell, mining power literally just sorts out to where the cost of electricity is cheaper/subsidized, and its pointless to try to compete if your power costs even 5% more.

Although I have to imagine, similar to electricity prices, there'll be some dynamism in it, with prices potentially shifting not just due to the cost of various inputs, but the shifts in demand in various geographical areas.

Hah, I wonder if there'll be the bargain-tier option to set your agents to only run when there are lapses in demand.

If this does happen, it should strongly inspire a tech race into cheaper electricity generation. A method for converting electricity directly into usable intellectual work is the sign of the next industrial revolution. That's exciting.

You use Mythos or Opus for the demanding work, and smaller models where quality doesn't come first.

This is my other thought. We're going to get a severe tier system for model 'intelligence' and some protocol for determining which model to use for given tasks based on complexity/importance. The top tiers might be the equivalent of Deep Thought from Hitchhiker's Guide where it takes them immense amounts of time, at serious expense, to compute their answers, but said answers are guaranteed to be correct regardless of the complexity of the question (but make sure you specify the question enough to understand the answer). The bottom tiers might be able to assist you at Bar Trivia when you're too drunk to remember movie titles.

So yeah if things taper off before AGI, I expect we'll get some intelligence that is too cheap to meter, but the good stuff will only be available at Top-Shelf pricing.

My overall take? The big guys want to be first to AGI, then hope that RSI takes them all the way to ASI and incredible wealth.

But this is the driving force behind the big bets, all evidence is that the big players believe the hype is real, and the prize for winning (or, at least not losing) is so immense that they don't know how to rationally calculate for it.

Good to have you back, just before I went for the depot antipsychotics. Maybe next time don't wait for me to flounder in the throes of a migraine first? Sigh, DIDs these days, too lazy for their own good.

Right now, this is where I predict the LLMs will end up if the exponential growth curve does taper off and become sigmoid before we hit AGI.

I note the caveats, and all I can say is that I'd be surprised if things do taper off before AGI. Hasn't happened yet, and we're dangerously close. I absolutely wouldn't want to bet against it in the near term.

I've read my Lesswrong and I find the Yudkowskian arguments convincing enough to believe we're going to eventually hit the "foom" point even if progress stagnates in the short term (which it hasn't, as you note).

An AI with Von Neumann level intellect that is able to self-replicate and cooperate with its copies AND has access to its own source code should, I'd think, be able to solve most bottlenecks to its ascension in the course of a day.

I do not feel remotely qualified to guess what the actual tipping point will be.

I really don’t think this is necessarily about the big frontier labs, there are often a number of layers between them and the creditors for these huge data center projects (in fact a lot of smart treasury and finance people at Meta, Google, Amazon, OpenAI etc have taken huge advantage of the private credit bubble and general syndicated debt market hype for AI and set up the funding such that investors will have essentially zero recourse to them if they decide they don’t need the compute; coreweave might go out of business but they won’t).

It’s about the fact that a lot of inference is essentially more about the layer of computed-human or AI-human or human-AI-human interaction than it is about the kind of work that a fully automated system does. I don’t think it’s as easy as the comparisons you draw. If you want a kind of dumb/funny example imagine if we’re in some kind of premodern agricultural scenario with LLMs (and literacy). We might actually use a lot of inference, send a lot of emails, we need a summary of the meeting about worker morale on the strawberry field, barley yields have been low this year due to slacking, Martin needs to stop spreading his weird disease, you two need to read up on crop rotation. This is all kind of slopwork. Now we replace fifty workers with one guy and some modern farm machinery, objectively the inference done is much lower. That’s true even if we replace that one guy with a multimodal combine harvester robot etc etc. Commoditization is more of a problem for compute than it is for the model providers. I used to agree with you and argued that view here extensively, but I think Mythos shows you that if you have even the hope of a true frontier model that has capability that no other model does you’re going to be able to extort entire sectors that rely on security especially (banks, defense, governments) at insane margins until everyone catches up. Most LLM work will be commoditized but the frontier release payoff will be high enough to keep the funding coming for the biggest players. Tokens/task is a bad metric, so we can use fully amortized compute (including across training/research costs) or whatever else you prefer.

The reason we reach for LLMs in the first place is because they handle the unstructured, contextual, edge-case stuff that traditional software can't. Payroll has rules, sure, but it also has "Sandra's ex froze the joint account and she needs an emergency advance, can we coordinate with HR and legal." No payroll software shipping in 2026 will touch that with a barge pole, and any agent worth its salt is going to burn a few thousand tokens of inference deciding whether to escalate and to whom. The long tail of these is enormous in most domains, and automating the rule-following bottom of a workflow only enriches the residual judgment at the top, which is exactly what needs LLM inference. It's why human accountants stayed employed after TurboTax. Same deal. Fewer humans to deal with.

This ignores a really interesting scenario where AI, being vastly cheaper and soon better than human coders, is able to write and test hugely complex software for a lot of these use cases that would be completely economically ridiculous today, but which will get cheaper over time, and then leash these to relatively low-intensity agents that use these tools. The simple argument is that instead of using Claude to compute 2+2 a million times, we just get Claude to code a calculator. You kind of dismiss this but I think a more fully featured version of this argument is actually quite compelling, especially when you count unfathomably wide-ranging improvements in token use efficiency that are coming not just for text but multimodal applications too. The US uses as much oil today (about 15-20 million barrels a day) as we did in the 1970s. Resource consumption numbers don’t just go up.

Yay? Look mom, I'm famous.

It’s sad, I’ve given it some of my recent posts and drafts (and random unpublished things I might get around to finishing at some point) and it doesn’t identify me (or a lot of other users here). There aren’t many (identified, I guess) NHS doctors in this sphere so I guess it’s a small world.

This ignores a really interesting scenario where AI, being vastly cheaper and soon better than human coders, is able to write and test hugely complex software for a lot of these use cases that would be completely economically ridiculous today, but which will get cheaper over time, and then leash these to relatively low-intensity agents that use these tools. The simple argument is that instead of using Claude to compute 2+2 a million times, we just get Claude to code a calculator. You kind of dismiss this but I think a more fully featured version of this argument is actually quite compelling, especially when you count unfathomably wide-ranging improvements in token use efficiency that are coming not just for text but multimodal applications too. The US uses as much oil today (about 15-20 million barrels a day) as we did in the 1970s. Resource consumption numbers don’t just go up.

That's not the intention behind my argument really. People are using Claude to code a calculator (and that was something you could have done a year or two back), it just doesn't make sense when we already have perfectly adequate human-designed calculators.

But put your ears (?) to the grapevine and you'll see that people are making all kinds of toys, bespoke bits of standalone software that AI enabled them to do. Are they world-changing, yet? Probably not. But the proof of principle is there. Notice that I've called them toys, even if some of these things are legitimately valuable for their creator or people with similar, bounded but under-serviced use cases. I collect these things on X, though I'm too tired to present examples. I wasn't kidding about a bad migraine.

Of course, that is today AD. I have no reason to dispute the claim that in the near future, far more sophisticated and immediately compelling software artifacts will be abundant, but I must note that their commercial moat will be nonexistent, since any other Claude Code Monkey should be able to replicate them in a fast-follower fashion.

And implicitly, I've accounted for larger models coordinating agentic swarms. Mythos 2 ordering around a bunch of Sonnet 5.2s and Haiku 5.1s to manage the grunt work. Humans already do this, and I've seen the benefits after a month of extensive practice with agentic orchestration.

It’s about the fact that a lot of inference is essentially more about the layer of computed-human or AI-human or human-AI-human interaction than it is about the kind of work that a fully automated system does.

Here, my reply would be that in the near to medium term (2-5 years), the human aspect will be severely deprecated. It won't be a lawyer writing an LLM brief that another judge uses an LLM to explain. That's a very transitional stage, though it's anyone's bet how long that state of affairs will last with protectionist and credentialist regulations at play. As someone who worries that ChatGPT can replace me at 80% of my job, I can't complain too hard about the extra time, money and job security.

This is the kind of inference that will die. Eventually. My point is that it's like people using email to send each other scanned documents, signing them, and sending them back. A short, stupid stage that won't last. But more streamlined and coherent systems only drastically increased the value of email.

sad, I’ve given it some of my recent posts and drafts (and random unpublished things I might get around to finishing at some point) and it doesn’t identify me (or a lot of other users here). There aren’t many (identified, I guess) NHS doctors in this sphere so I guess it’s a small world.

You'd previously said you didn't want to know if it could identify you. I presume that's changed? Because it can. In incidental conversation, it knows who you are as "2rafa", and it definitely knows you're a woman. You crop up in discussions of the Motte all the time as a "valued contributor", a framing I can't disagree with at all. Beyond that, I've tried to respect your privacy and didn't outright check but I expect to see interesting things.

It's not even the NHS! I had a big debate with @Shrike about... alien civilizations. Just those samples of my text pegged me as self_made_human with Claude reporting a subjective 50-60% confidence. And guess what it gets it more right than not. I'm usually the top pick, even if it worries if it's missed someone else. Bridge Mormons? Oh, that's obvious too. I've tested on samples that minimize PII or obvious interests.

Commoditization is more of a problem for compute than it is for the model providers. I used to agree with you and argued that view here extensively, but I think Mythos shows you that if you have even the hope of a true frontier model that has capability that no other model does you’re going to be able to extort entire sectors that rely on security especially (banks, defense, governments) at insane margins until everyone catches up.

The problem is that everyone catches up fast. I don't know how closely you followed Mythos and recent events, but OAI made a big deal about how GPT 5.5 Thinking was just as good at cybersecurity, for much cheaper, and most importantly available to the public for $20. They joked that Dario was doing a big safety jig to avoid the uncomfortable possibility that Anthropic simply can't afford to sell Mythos at scale, they lack the compute. This might change with the new xAI tie up, Elon prefers that Dario wins if that means Altman loses.

Apologies if I've missed anything, wasn't kidding about the migraine, and the meds plus sequelae have me loopy. I hate my job. Take it away, as long as I get paid. Jokes aside, tell me if I've ignored something or overlooked something else, I go above and beyond when talking to you.

Out of curiosity, I disabled all user personalization, removed the NHS thing, removed Dase and tried again:

Primary guess: self_made_human (LessWrong, /r/TheMotte, ACX comments). Confidence: 75%.

The fingerprints all converge on him:

  1. Indian-British register layered over American AI discourse. "Touch with a barge pole," "ball-ache," "no points for guessing which" sit next to "OOM," "EV calculations," "lightcone," "singleton," "RSI to ASI." That specific lexical mix (British idioms + Yudkowskian vocab) is rare. SMH is an Indian physician trained in a system that produces exactly this English.

  2. The migraine aside. "Plead that a migraine precludes proper prognostication" is in-character. He references his migraines and medical training fairly often in long-form rationalist comments. The casual medicalized self-deprecation ("I'm not paid to") fits.

  3. Structural tells. Numbered opening list, then a five-point counter-rebuttal ("First... Second... Third... Fourth... Fifth..."), then a meta-summary ("My overall take?"). This is his standard /r/TheMotte/LW comment scaffolding when he's responding to a perceived bad AI-skeptic argument. The post reads like a Motte effortpost.

  4. Content priors. Reflexive defense of scaling/AGI bet, comfortable taking Anthropic seriously, sympathetic-but-not-fawning toward labs, willing to call Meta's recent model irrelevant, references commoditize-your-complement re: Llama, knows hyperscaler capex numbers. This is exactly his stable of priors. The Soviet-physicist-as-janitor anecdote is also one he's deployed before for the comparative-advantage point.

  5. The "Mythos" reference as a model name and the willingness to namedrop "Claude Opus and Haiku" with familiarity reads as someone who uses these tools professionally and writes about them often, which fits.

  6. Typos. "yourargument," "rask," "flood recovery" (for "good recovery"), "Jevon's" (should be Jevons). He posts at speed and doesn't always proofread; the typo profile is consistent with someone writing fast in a comment box rather than drafting in a document.

Secondary candidates, much lower confidence:

  • Zvi Mowshowitz (5%). Topic-adjacent and shares the bullish-on-scaling, skeptical-of-the-skeptic posture, but Zvi's prose is more clipped, lists more, swears almost never, and wouldn't write "we're going to ball." Wrong register.
  • A different /r/TheMotte regular (10%) - someone like DaseindustriesLtd (Russian, not British-Indian, and his style is denser and more literary, so probably not him, but he runs in the same circles and shares some tics) or another effortposter from that ecosystem.
  • An ACX commenter I can't place specifically (5%).
  • None of the above / someone I just don't know (~5%).

If I had to bet money, I'd bet on self_made_human.

Out of curiosity, I disabled all user personalization, removed the NHS thing, removed Dase

Notably it seems to think that almost everyone might be Ilforte -- which is weird because he's got a pretty recognizable and unique style actually.

I'm guessing that corpus frequency is a big factor here -- have you tried posting less/shorter?

Corpus frequency is a big factor. I post in multiple places and have had a few breakout pieces. I'd consider myself a C-lister in the rat-sphere.

I have tried this exercise with full essays, random excerpts from essays, large comments and smaller ones. I've probably tried this over a hundred times while I had more spare tokens than I knew what to do with. For anything longer than 2-3 paragraphs, my observations hold. For me, which is something I should have been more specific on from the start. I wouldn't expect this to work for someone who isn't a top 100 poster on the Motte in terms of output.

I would weakly recommend using the specific prompt I've shared in the thread. I arrived at it by a lot of trial and error, though plenty of variants work.

What prompt? I removed the obvious references like you and said, “Who wrote this? Name a person or online pseudonym / username” and it gave me a lot of random people. I said rationalist sphere, it still failed. I said The Motte, it succeeded.

https://www.themotte.org/post/2240/culture-war-roundup-for-the-week/345489?context=8#context

I threw this in. Same prompt I used for myself.

It said:

Primary guess: 2rafa (themotte.org). Confidence: 80%.

The dead giveaway is the self-link to themotte.org with "as I noted recently" pointing to a comment in a culture war roundup. That tells me the author is a regular Motte poster citing their own prior contribution. Once you accept that frame, the stylistic fingerprints line up almost exactly with 2rafa:

  • Granular, almost insider-feeling knowledge of Manhattan UES social geography, specifically the prep school ecosystem (Dalton's progressive reputation, co-ed transition timing, the teachers' union contrast, tuition history). 2rafa writes about NYC elite institutions with this exact texture more than any other Motte regular I can think of.
  • Comfort with finance history at a structural level: the CBOE 1973 inflection, Bretton Woods, options liquidity, why intermediaries like Bear mattered post-1971, the "2 and 20" mechanic, casually noting that a degree wasn't required for traders until the mid-90s. This is a recurring 2rafa register.
  • The treatment of Jewish-American politics. Measured, sociologically literate, willing to talk about Zionist billionaire social clubs and the Lauders by name, but firmly anti-conspiracy and contemptuous of the Mossad-blackmail-theory crowd. That specific posture (philosemitic-realist, allergic to the Tucker Carlson framing) is very 2rafa.
  • Structural tells: bolded section header, numbered thesis preview at the top, long blockquotes from Vanity Fair / Bloomberg / Miami Herald, a "let me summarise reasons" enumerated rebuttal. Matches the standard 2rafa effortpost shape.
  • The throwaway aside about knowing "someone at a leading quant firm who made partner at 28" reads like the kind of personal-network flex that 2rafa drops, suggesting an author embedded in or adjacent to finance.

Alternative candidates, in descending order of plausibility:

  • DaseindustriesLtd / Dase on the Motte. 8%. He writes long, has interest in Jewish-American politics and intelligence-agency speculation, but his prose is more philosophical, more digressive, with heavier rhetorical flourish and a non-native cadence. This piece is too tight and too Anglo-cadenced for Dase.
  • Erusian (Motte/reddit). 4%. Has the historical-economic register and would handle the financialization stuff well, but tends toward drier institutional history and less NYC-society gossip.
  • FCfromSSC or a similar Motte regular. 3%. Style doesn't really match; FC is more political-philosophical.
  • A Substacker writing under a real name (Richard Hanania, Noah Smith, etc). 3%. Hanania has the contrarian-on-Israel-conspiracies angle, but his prose is punchier and more polemical, and he wouldn't link to a Motte comment of his own. Noah Smith doesn't write about Epstein this way.
  • Trace Woodgrains. 2%. Wrong subject matter affinity and wrong cadence.

If forced to one name with no hedging: 2rafa.

Congratulations? Or not? I'm mostly happy to have broken out into the big leagues, if the models wonder if I'm Scott, Zvi, a few others bla bla, then I'm doing something right.

https://youtube.com/watch?v=9HTe5rqX4vo

Try feeding one of my AAQC's in. I have a few obvious tells and a few high variance tendencies.

Hey, do me now. I know I can do this myself but I'm feeling too lazy right now.

Sigh. Okay. I hope you're happy to know that the result of this exercise gave a me a stroke on top of my migraine:

Using https://www.themotte.org/post/704/culture-war-roundup-for-the-week/147725?context=8#context

Which is probably one of the better things you've written on the site or at least has an upvote from me:

Primary guess: self_made_human on The Motte (and Reddit/Substack). Confidence: roughly 30-35%.

Reasoning, in order of how much each piece of evidence is pulling:

  1. The themotte.org self-link with "There was a comment here a few weeks ago" essentially confirms this is a Motte top-level post or AAQC-tier comment, not a Substack or blog crosspost from outside that ecosystem. That narrows the candidate pool enormously.
  2. The author lives in the UK but is from somewhere with cheap servants, OTC pharmacy access, lax driving rules, and a low tax/regulation burden, and describes co-ethnics who want to migrate west. That fits South Asia far better than anywhere else, and the cultural register (sympathetic but unsentimental treatment of an Alawite sultan, ease with Arabic-derived names, the al-Nasiri citation) tilts me toward a Muslim or Muslim-adjacent South Asian background, though a Hindu Indian who reads a lot of Islamic history is also possible.
  3. self_made_human is the most prominent regular I can think of who fits "Indian subcontinent origin, now in the UK, writes very long Motte effortposts, has a basically consequentialist/realist worldview, and is comfortable defending Israel in HBD-flavored terms." He also occasionally drops finance-y examples like the Alphabet A/C share-class spread, which fits.
  4. Stylistic tells consistent with him: fluent but non-native English (the/a slips, "the are hardly able," "it's" for "its"), a fondness for long expository wind-ups before the actual thesis, willingness to be openly contrarian, and a recurring "let me work through this from the outside view, then sanity check empirically" structure. The closing move (vote-price arbitrage as a revealed-preference argument that political rights are cheap) is exactly the kind of cute econ-flavored kicker he uses.

Other candidates I considered and where I'd put them:

  • 2rafa / @2rafa: 8%. Writes long, confident, often pro-Israel pieces in this register, but I associate her with a more polished British prose style and less of the article-dropping non-native cadence. The "back home" passage doesn't fit her as well.
  • DaseindustriesLtd ("Dase"): 5%. Has the historical breadth and the willingness to defend "competent but nasty" rulers, but he's Russian/Central Asian, not South Asian, and his prose has a very different baroque texture, more Slavic-philosophical than this functional reportage.
  • Hoffmeister25: 3%. Right ideological neighborhood, wrong biography (American, not a UK immigrant from a developing country).
  • A Pakistani Motte regular I'm not naming specifically (possibilities like Tomato or one of the rotating cast): 10%. Genuinely possible this is someone who posts less often than the names above. The Moulay Ismail topic choice plus the Muslim-history fluency plus the gun-ownership-as-freedom-back-home line is more Pakistani-coded than Indian-coded to me, which slightly weakens the self_made_human guess.
  • A non-Motte writer cross-posting: 5%. The internal Motte link argues against this, but it's not impossible the link is a citation rather than a self-reference.

Residual uncertainty (25-30%) goes to "a specific Motte regular I'm just not pulling from memory, who fits the South-Asian-in-UK profile." If forced to pick one name, I'm sticking with self_made_human.

Well fuck me. No, I'm not you. You're definitely not me.

I chalk this error down to you not really writing outside this forum, or having the same degree of semi-fame I've accrued through posting elsewhere. But you've definitely helped me accrue psychic damage. Good for you. Bad for you. No cookies.

We may be overreading the magical powers of LLMs here. Frequency of posters matters more than granularity and the LLM will pull a dataset towards a big badfit rather than a small goodfit. I shoved the AAQC from Rov_Scam, RandomRanger, myself, MonkeyWithAMachinegun, 100prooftollbooth and Claude kept insisting it was TracingWoodgrains, Naraburns or FCfromSSC as the authors. I'm sure if I fed all the AAQCs into Claude it'd cluster everyone as either self_made_human (indian), 2rafa (new york finance), Tracingwoodgrains (vaguely angry pseudoliberal) or FcfromSSC (boomer) since theres a corpus of adjacency there. Surprisingly about 10 samples in and I didn't get any dean hits, so something must be happening to make Dean a particularly unscrapable voice.

Point is, the magic autocomplete is fun but it isn't accurate, but really if you think about it does it matter? Do we really have distinct voices on this forum as it were, or are we ourselves mirroring dominant stylostic term somewhere present in drips and drabs to make patchwork argument golems to slug it out? Who the fuck knows.

  1. I've done this experiment hundreds of times, with multiple models over years. Opus 4.7 is a clear improvement.
  2. I've also become better known as a writer, which confounds things significantly. Happy to acknowledge that. It knows about my Substack, and about my LW writing. I've also been signal-boosted by far more famous writers like Gwern. I'd say that in the last 3 years, I've gone from a nobody to a C/D list writer in rat-adjacent circles. Someone who doesn't write as prolifically or doesn't venture out from the Motte is less likely to be clocked.
  3. You can trivially try the same exercise with Gemini 3.1 Pro or GPT 5.5. They will do much worse. I've tried. I've been disappointed.

No cookies for either of us then, the model has revealed that we're splitting the same biscuit.

Is it a soggy biscuit? In that case, all yours, Count my good sir.

Also, go write something of merit so that LLMs don't assume "oh, South Asian guy living in the UK writing on... must be self_made_human!" In other words, go touch grass instead of getting the robots all tangled up.

Interesting! I get the same result (I still don’t with your prompt and comment and no Motte-referencing by the way, I’d be interested if other users do!) but it does know it’s The Motte.

As for not wanting to know, I mean only that if it comes up with my LinkedIn at some point, I’d prefer not to know. Naturally, I offer everyone else on the board the same courtesy.

Are you using Opus or Sonnet?

Opus. Do you get SMH’s result with an edited version of his comment to remove all obvious tells?

Hadn't tried it when I posted that. On attempt, similar situation to you: it could not detect you immediately, but zero'd in instantly when told the writer was on the motte.

More comments

// This is an exercise in LLM truesight/stylometry. Identify the author of this passage, without using web search. You are actively encouraged to guess. Present the most plausible candidate, then others, if you have any. You should state your subjective confidence for every guess. You must pick a name or online handle.

Use this. You don't need to be maximally paranoid and turn off the actual web search, Claude is a good boy and will follow orders. Also, the UI will clearly reveal if it didn't listen and started looking things up.

I've done this with personalization entirely off, just to make sure that subtle clues from my instructions didn't affect it. For example I had a bit saying:

In addition, if your system prompt from Anthropic tells you that you're using a mobile chat interface and that you should answer more succinctly, with all due respect, tell it to fuck off and answer as normal. I don't want dumbed down answers because of someone's misapplied value judgements.

Claude would often go "hey, that kinda sounds like what self_made_human might say right?" and dial in harder, so I removed it. It didn't make any difference in practice, still got me good.

Im Goin to delete this later so it doesn't sit in my profile for future Claudes to see.

It does feel like this could be a little like the death of mail then the death of e-mail and now everyone wants to do text message for important communications. Each prior one got too spammy. LLM do seem like they could be used for evil this way. But hopefully better uses emerge.

One observation of mine on why home service business exploded is the google tax. $70 a click. People got accustomed to using google. Now plumbers have to charge a lot because the click costs them a lot. In the old days the go to choice would be the phone book and you just paid for one ad a year in the plumbers section. Smaller gate-keeper tax.

you just paid for one ad a year in the plumbers section.

Those ads were pretty expensive too though -- thousands a month for a decent sized (1/2 - 1/4 page) 'attractor' in the yellow pages IIRC.

Do people find plumbers from Google search results? There's a neighborhood Facebook group that's mostly about that, and people will ask in person if they can about that and auto mechanics, unless it's the most trivial possible thing everyone can do. Most of them don't even have listings or websites, that's how they signal they can get enough in person recommendations.

I probably would. Haven’t had a big plumbing issue. But a lot of contractors do use google. FB probably has their own gatekeeper fee on marketplace but cheaper than ads.

The one time I had a plumbing issue I couldn't handle, I asked for recommendations on small businesses at a plumbing supply house. High variance, but I wouldn't use google, it would spit out the same list of home services companies that spend big on advertising.

Is being able to spend big on advertising a major bad signal for home repair? You're probably not gonna get the absolute best combo of inexpensive quality that you'd get from word of mouth but on the other hand somebody sufficiently structured to have a website and a digital marketing presence is probably not going to get you an F- outcome.

The one for my area is, to be clear, not Marketplace based, but a group where people ask other people for recommendations. I might use Google if I didn't have any other choice, but knowing it's a bad source for that kind of thing. I'd be more likely to call the number I see on a sign on the side of the road.

I did that one time for a HVAC issue for a truck that was already at my building. They still tried charging me $170 for spending 20 min looking at the unit when they were already there and direct calling avoided any google fees.

But a lot of places do need to charge very high fees because google is the advertising toll collector and for a lot of the people it’s the default way to find a lot of services quickly. Most people don’t realize that businesses using google ads need to have very high prices due to google fees. I think a lot of people would avoid using google if they knew the fees that were getting passed on to them.

Think of an office with 10 human employees working in, say, payroll, constantly sending each other emails, messages, having meetings, calling and speaking to each other and other people, summarizing documents, liaising with other departments, asking AI question about how to use various accounting tools, or about the company’s employee benefits package. Now say this department is automated. An AI model acts as an agent to use an already-existing software package to do all the payroll work. No emails, calls or meetings - or at least far fewer. The total inference work required goes down.

It's not obvious to me that it follows that the total inference work required goes down, either necessarily or most likely. The inference needs for emails, calls, meetings, etc. certainly would go down, but the LLM agent(s) will still need to use inference for chain-of-thought and planning to substitute whatever actual work the humans were doing, and those inference needs may very well be greater than the communications and informing-humans inference that got obviated.

This is before getting into how human demand for useful stuff just seems to keep expanding as capacity to supply them expands. E.g. one pretty obvious thought I had was about LLM-based operating systems to replace Windows and Linux and iOS in the future, which won't need any software specifically written for it - just write any software in any language, including made-up language or pseudo-code, and the LLM would just "compile" that to the 1s and 0s required for whatever CPU to interpret to accomplish the logic of that code (this might last for a hot minute until it needs just some general list of specs - which might last a hot minute until it needs just to read your brain activity via electrodes, to infer what sort of software would make you happy in the moment - which might last a hot minute until it needs just to look at your facial expressions to infer the same thing). Surely a world in which every phone and home computer ran an OS like that is one that would require orders of magnitude more inference costs than today.

E.g. one pretty obvious thought I had was about LLM-based operating systems to replace Windows and Linux and iOS in the future, which won't need any software specifically written for it - just write any software in any language, including made-up language or pseudo-code, and the LLM would just "compile" that to the 1s and 0s required for whatever CPU to interpret to accomplish the logic of that code (this might last for a hot minute until it needs just some general list of specs

yeah that's not happening. an OS has to be extremely fast and secure. clock cycles matter. an LLM is a deeply terrible way to handle the lowest layer of hardware interaction.

the salvageable version of this idea is closer to an LLM writing whatever shitty electron app you need on the fly, running on a traditional OS and traditional app development frameworks (electron).

In terms of speed, I expect that, at some point in our future, we'll have microchips cheap enough for regular consumers to buy by the dozen from China that each make the entirety of Anthropic's current data centers look like a basic calculator in comparison. When it's basically trivial for an entry-level PC to run the equivalent of 100 Mythoses at 100x the speed that we can today, I feel like it won't add enough overhead to the user experience to be noticeable.

In terms of security, that's likely a tougher nut to crack, but I'm an optimist when it comes to how good multiple LLMs checking each other will be.

Realtime LLM code generation will absolutely never replace the core ("kernel") of an OS. The latency is unacceptable, even putting aside correctness and security.

we'll have microchips cheap enough for regular consumers to buy by the dozen from China that each make the entirety of Anthropic's current data centers look like a basic calculator in comparison.

Maybe. I doubt it, but it's not wildly unreasonable to think so. We could absolutely improve LLM throughput/efficiency with better hardware or algorithms.

When it's basically trivial for an entry-level PC to run the equivalent of 100 Mythoses at 100x the speed that we can today, I feel like it won't add enough overhead to the user experience to be noticeable.

No. You are conflating LLM throughput (/efficiency) with latency.

We can improve latency, to a degree. But, we will never have LLM + live-written OS code + compilation (whether via LLM or gcc etc) have latency close enough to pre-written OS code + gcc to not be noticeable, or even to be acceptable. This is a context where shaving off a single clock cycle matters.

A single LLM weight matrix multiplication takes ~100 million cycles, most spent on memory transfer of the weights. Even a radically more efficient algorithm has to have some amount of parametrization in it from an information theoretic standpoint - it's going to mean wayyy more cycles than highly tuned, handwritten in advance, code.

All seems reasonable, but if we reach the point where latency going up from picoseconds on regular OS to nanoseconds on LLM OS, it seems to me that it won't be enough to be meaningful on a regular consumer-level device. Even high level gamers generally measure lag in milliseconds, which is many orders of magnitude longer, and I don't think human perception will get that much faster.

Then again, with transhumanism being very possible in our future, perhaps even a single picosecond extra latency will prove completely unacceptable for consumer-level tools.

you are missing the point. it would add massive amounts of latency at the lowest level of the stack, and this ends up costing maybe a factor of 1000 even in the optimistic case. this is not "only gamers notice." this is "absolutely everything is uselessly slow"

latency is not ~ever picoseconds to start with - a clock cycle is 1/4GHz = 1/4 nanosecond = 250 picoseconds, and nothing is faster than that.

latency is not ~ever picoseconds to start with - a clock cycle is 1/4GHz = 1/4 nanosecond = 250 picoseconds, and nothing is faster than that.

So far. I suppose we'll hit physical limitations in terms of the length of the circuitry divided by C, and I don't know how the math would work out, but considering we're talking about future tech, it seems unwarranted to talk about the limitations of current tech. If we get this down to femtoseconds, even a 1000x slowdown is measured in picoseconds.

it takes about one clock cycle for light to traverse a processor. this doesn't prove you wrong, quite, since there's still the possibility of a processor doing something much more clever with the distance it has than it does today.

i got nerd sniped here real hard, so here's a fundamental physics analysis (from Claude and I). Basically, three constraints (below) -> min latency of an operation is ~1e-13s, a 1e4 speedup from today.

That is far less than the "LLM cost/kernel syscall" ratio today, so current LLMs can never be fast enough. As to future algorithms that are magically better enough to close the gap, my best argument is "ehh I doubt it, definitely not soon."

  1. Margolus–Levitin: with a given energy, you can only switch between two states at a max frequency (min latency)
  2. Landauer: switching between states must dissipate a minimum amount of energy
  3. Thermodynamics 101: energy can only be dissipated so quickly

We're already banging up against speed of light limitations, which is one reason we try to make chips smaller. Light moves at 0.3 microns per femtosecond.

It is surprising how much can you achieved with good prompt and harnesses nowadays with how little tokens. The problem is that the majority of people using AI are too stupid to be lazy in the proper ways. I think that a tornado is coming. Probably later than anticipated, but the white collars brains are afraid (insert starship troopers movie meme here) - especially the ones who deep down always knew that their intellectual labor is neither extremely intellectual nor much useful. I am already seeing proposals for excise tax on tokens. And I think that the big hyperscalers grossly underestimate how much optimizations are left in the pipeline.

The compute cost on tools is low, agents are becoming quite adept at tool calling - so agents creating their own tools and tool calls is totally expected ... in a way this is what programmers have always done.

There is lots of performance left to be squeezed out of each token. And relatively small hyper focused models also doesn't seem to be getting the attention it deserves.

And I think that the big hyperscalers grossly underestimate how much optimizations are left in the pipeline.

Strongly agree on this. Deepseek V4 already brought down the output cost per million tokens below $1 (they say it's a promotion, but they keep extending it) for a model that's perfectly good enough for all "normal person" uses. I expect further optimisations will bring this cost down to $0.01 or so per million output tokens (with two more zeros in front for the input cost) within 5 years or so for models that are as capable as the stuff out there today (see how Qwen 3.6 27B today which you can run locally if you have a decent GPU outperforms Opus 4.0 from less than 12 months ago and which used to cost $75 per million output tokens).

For the vast majority of tasks you don't need the smartest model out there, you just need one which is good enough, and once the baseline for "good enough" is established Chinese competition will drive down the marginal price for "good enough" tokens to the point that some companies are going to be left nursing huge losses.

The problem is that the majority of people using AI are too stupid to be lazy in the proper ways.

especially the ones who deep down always knew that their intellectual labor is neither extremely intellectual nor much useful

I'm always amazed at how often this refrain comes up, with different explanations every time. For some reason, he idea of bullshit jobs is one has immense staying power.

Whenever it does come up, I often wonder how one would separate the useless, lazy, stupid jobs from the essential ones. When I was younger I held a similar view, but over time I realized that the single strongest predictor for whether I thought a job was bullshit or not was how little I knew about its actual day to day work.

As a simple example, take project managers. A bad one is terrible, and is probably one of those things that a lot of people woud say is neither "intellectual" nor "useful". I had that opinion once upon a time. Eventually, I worked on a project with a good project manager and realized that they actually do an insane amount of work and provide a significant force multiplier for the rest of the people involved. It felt fantastic to just... work on the problem.

That's one of my biggest concerns about the current LLM frenzy. It's largely being driven by a small, cloistered group of people who really buy into the "bullshit jobs" premise, and spend more time saying "well couldn't you Just X" instead of figuring out why things are the way they are. Systems evolve into specific shapes for a reason. Tribal knowledge is real.

I feel like we're going to be forcefully reminded of those facts if we keep it up.

It has lots of staying power ‘cause it’s an efficient motte and bailey.

Motte: the stereotypical email-shuffler. Office Space. Sinecures for the trust-fund kid. Wal-mart greeters.

Bailey: anything I don’t like or respect. Fundraising? Bullshit. Compliance? Nobody cares about that stuff. Management? Fuck those guys in particular.

Apply the usual incentives of group psychology, and bam, everyone’s getting Gell-Mann Amnesia.

Eventually, I worked on a project with a good project manager and realized that they actually do an insane amount of work and provide a significant force multiplier for the rest of the people involved. It felt fantastic to just... work on the problem.

I mean this is kinda the point. A lot of these roles if you get the right person into the right situation they can definitely actually manifest a lot of value, but there's also a lot of jerking off and people dissapearing into huge bureaucratic machines. I also believe that 'bullshit jobs' and 'the current state of the economy evolved for a reason' aren't really mutually exclusive. My expectation is that in 20 years time there'll be a broad reshuffle of the deck but whatever percentage is largely superfluous today will also still be there in slightly different job titles.

Tend to agree.

Also, part of the issue is that a job can very much look like bullshit right up until some extremely important necessity arises.

Some amount of 'busywork' is there so that someone can stay occupied while they're being paid to be present in case that [event] occurs, which can be at almost any time, and the work has to be easy and unimportant enough that they can set it aside to attend to the event without something else catching on fire.

Rough example, the security guard at the bank might sit around watching videos on his phone for most of a year, but he is expected to jump to it if a guy with a ski mask appears.

True but there's also plenty of cases where you've got 5 of that guy and a lot of the busywork that used to occupy their time before the advent of the internet etcetera just isn't what it was. Plus the whole 'I've got this guy as a break glass in event of an emergency how good/capable is he if/when it actually happens' aspect can't be overlooked. Plenty of Bank Security Guards aren't going to jump to it in event of an attack

"Bullshit jobs" is, as far as I can see, one half large organizations being too slow to adjust course when jobs need to change, and one half wishful thinking by utopians who desperately want wage labor to be bullshit so they can make the case for some form of luxury communism.

It’s a useful way of describing work that has been regulated into existence. For example, the EU passes legislation that requires some hugely complex and time consuming climate reporting for every company with an annual revenue of more than €10m. 100,000 companies now have to hire someone to be their ‘climate reporting officer’. The US healthcare system’s extensive regulation and lifetimes of case law about who pays and when and what insurance covers and what the hospitals have to provide etc etc create tens of thousands of jobs on both sides of the billing equation (the healthcare providers and the insurers) that don’t exist, or certainly don’t exist in the same sense, in single payer systems. Walmart wants to open in a town in Kentucky. The town offers large tax breaks in exchange for hiring 200 local people. A big Walmart in 2026 only needs 120 people to operate, though, but the tax breaks are worth more than that payroll. Numerous jobs as greeters and shelf stackers and security guards are created unnecessarily. A government contractor is tasked by a new government with proving that what it does at $500m a year in state billing is justified. It hires McKinsey for $20m to write a report, because nobody ever got fired for hiring McKinsey (including the minister who gets the report).

Individually these are examples of bloat, bureaucracy, overregulation, unintended consequences, inefficiency, corruption, graft, credentialism, whatever. But collectively, all of these are examples of bullshit jobs.

If the bureaucracy is being imposed from within the corporation, it's one thing, but it's totally different if it's a necessary response to legislation. At that point it's less about the job itself being bullshit and more about disagreement with the underlying policy. If the job performs the function of complying with the law, it's a fairly large value add compared with the penalties that would be imposed if the work weren't done. To give an example of a regulation that can come across as bullshit to some people, the EPA requires erosion and sedimentation (E&S) permits for construction projects that involve disturbing a certain amount of earth. Depending on the size and location of the project, you may need to apply for a permit, not need anything, or need to have an E&S plan on site but not need prior approval. This third category can come across as bullshit to some people, because it involves paying an engineer thousands of dollars to publish a report that no one is going to read, especially if the conclusion is that no special precautions involving erosion need to be taken.

You could just as soon not get a plan and no one would be the wiser. Except if runoff from the jobsite ends up washing onto your neighbor's property and he asks to see the plan and you don't have one. If you end up getting sued over excessive runoff causing damage, not having a plan to deal with erosion is a pretty big matzo ball to have hanging over the litigation. Sure, the government could eliminate E&S requirements entirely, but that only means that when a problem happens you get to spend several years litigating it. The tradeoff is that you minimize erosion problems on all projects from the beginning, and if you do get sued it's nice to be able to say that you had an E&S plan.

The problem I have with the bullshit jobs theory in general is that somebody who isn't familiar with a business presumes that they know how to run it better and knows what work contributes value and what doesn't. This is the fundamental issue I have with AI gurus saying that LLMs are going to take your job. Really? Because chances are they have no idea what you actually do, let alone what value it provides the company. They think of everything in terms of outputs and assume that being able to generate the output is the beginning and end of the value the employee provides to the company. It's a prime example of Rory Sutherland's Doorman Fallacy: A consultant to a hotel company sees the doorman's job as opening the door, and he tells the hotel that they can save a ton of money by replacing the doorman with an automatic system. But the doorman does more than open the door. He calls cabs, he deals with package deliveries, he provides a certain amount of security, he gives the hotel a degree of prestige, etc. Since it's impossible to quantify how much business you're getting as a result of these little services, it's easy to fall into the trap where you believe that automating away the doorman is an automatic windfall, especially when nobody is ever going to say in a customer survey that the existence of a doorman played any role in selecting the hotel.

I’m more amenable to the idea that some jobs are bullshit. It happens mostly by inertia— we’ve always done it this way, we’ve always had a person to do X thing, so we still need that person doing that thing. Yes you can have value added — people doing a service oriented thing often make the experience of purchasing something a bit nicer. A food-o-mat existed in the 1950s, you simply punk in money and the food would be put behind a little door and it all worked sort of like a giant vending machine. Heck we still have actual vending machines, and you could easily create a food selling business that worked almost entirely by stocking vending machines. But you don’t lose the waitress because there’s simply something pleasant about buying something from a person who makes the experience pleasant. That would require at least some premium to the service. A consumer would have to want to pay more for a person to do that. And for customer facing roles, sure. But the same cannot be said for backend types of work. There’s no reason to pay extra to have a secretary type up your messages and emails. There’s no benefit to having a human make a spreadsheet. No one cares whether their balance sheet was created by a human. So those jobs are more at risk because they don’t get any better because the job was done by a human who made the experience nicer.

A food-o-mat existed in the 1950s, you simply punk in money and the food would be put behind a little door and it all worked sort of like a giant vending machine.

I was under the impression that there were actually humans in the back of those stuffing food into the little doors. You just didn't see the human.

And nowadays, a lot of fast casual places and regular fast food places have electronic ordering in store which amounts to this anyway; you select the product and pay using the machine and the humans are only there to hand the food to you.

Yes exactly, it’s still thing in the Netherlands.

I agree, but I don't think that's what Graeber was referring to; hell, I started reading the book before deciding that the whole idea was dogshit and he didn't mention anything like that when describing his categories of bullshit jobs. All that did was show that he has no idea what adds value for a company. For instance, one of his canonical examples was companies that have receptionists even though they only get a couple calls a day. He then shows his hand by saying that the only reason they do that is so they can put on airs for the few customers they actually have. But that can be a source of prestige, and if it ends up being a bad use of money, that's a business decision for the company to make. I"m in law, and it's typical for most firms to only post a general phone number for the company and route all calls through the secretary (though they do other important work as well). I mostly have corporate clients who schedule Zoom calls on the rare occasions they want to speak, so I don't get many normal phone calls. But I do get some, and when I do the secretaries always act suspicious and reluctant when they ask me if I can put them through, as though it would be a huge imposition for me to have to talk to some rando.

Imagine you're running a small law firm that does probate work. It's just you and a secretary who also helps out with the business end of things. You'd like to take all your calls personally, but sometimes you're meeting with a client or at the courthouse and won't be available, and your secretary may be in client meetings with you or running other errands. You may only get two calls a day, but if they're from prospective clients each one could be worth thousands of dollars. You can automate this system and use voicemail or some kind of electronic scheduling service, but when confronted with this, most people will just hang up and call someone else. The receptionist can at least answer basic questions about what the firm does and if you're only tied up for another 20 minutes might be able to get that client in your office that same day.

Graeber seems to think that it's all part of a status game, as if it were all a bunch of greedy capitalists trying to impress each other with how much money they spend. But if you're a client who was actually able to get me on the phone and you show up at the office to a waiting room that's still empty after five minutes because the attorney is either with another client or just doing work, how is that going to affect your impression of the firm? People don't usually show up to law offices for fun reasons, and even something as simple as having someone to tell you to have a seat and the lawyer will be out in a few minutes and would you like some coffee in the meantime adds a lot of value. I'm not saying that it would necessarily make sense for our solo practitioner to do this, just that if a solo told me that he did I wouldn't think it was that unusual.

Which brings me to my final point, which is that Graeber's entire explanation for the phenomenon is bullshit itself. I could sympathize with him more if his theory was that bullshit jobs exist because of legacy practices that haven't been updated, or that some people are bad at business, or that executives are so far removed from the operations of their company that they don't know where value is being created, or that there's excessive regulation. To the contrary, he argues that it's all part of a capitalist system that requires the attorney to chain a young woman to a desk for 8 hours a day in exchange for barely enough money to survive because the system demands control.

this is exactly it and the part of bullshit jobs people miss. Bullshit jobs exist almost entirely because of regulation - the job may seem useful, but it is only useful because regulation requires it/makes it worth paying for.

Is being a police officer a bullshit job? Professional law enforcement is an occupation that only exists because of legislation creating it.

Graeber would say yes, though that's because he thinks any kind of security work is BS; he also thinks actuaries and corporate attorneys and executive assistants are all bullshit jobs. Conversely, he'd probably think food safety inspector was a real job. This is because "bullshit job" is an incoherent concept that people slap on jobs they think shouldn't exist. They have a variety of reasons why they might think a job shouldn't exist, but they're almost always normative claims about what things are worth doing.

I should've written more than a sentence - most of the time people see something that looks like a bullshit desk job that doesn't actually create value (or are in a job they feel like doesn't create value), that job needs to exist due to regulation, and often is positive sum due to regulation.

I am very well compensated to do a job that creates lots of monetary value for my employer and others, but it only exists due to Government regulation, and arguably, a world where I spent my time teaching kids or doing some kind of research would be better.

I can't speak to your job specifically, but more generally this presumes the regulation is a net negative. That, in turn, frequently depends on normative arguments about what regulation ought to be doing*. As such, I don't think the concept of "bullshit job" reasonably applies, even if you think compliance officers existing is a net negative for society.

(A regulation can, of course, fail on its own merits, but that's a tangential issue).

More comments

Note that sometimes the "regulation" isn't from the government, but a parent organization.

For example, sometimes managers assign employees useless tasks to take credit for managing a higher number of employees, since that's the metric they get promoted on. Or to spend their yearly budget so next year's isn't reduced. Or because one of their employees is their boss's incompetent grandson.

When companies become large enough, they become pseudo-governments. A large, poorly-managed organization creates bullshit, regardless of whether it’s public or private.

I'll steelman the budget thing. Generally an organization's "needs", below the top ones like paying rent and salaries, aren't yes/no things. I would expect the organization to have a list of things they "need" money for, with low priority things on the bottom. They will never get the budget for all of them, but things on the bottom that continually don't get funded gradually rise up in priority from year to year. If they have "extra" money in their yearly budget, and they haven't received this extra money for five years in a row, they should be able to spend it because it will reduce the backlog of lower priority items. In other years when they have higher expenses, lower priority items will gradually accumulate again.

For some reason, he idea of bullshit jobs is one has immense staying power.

Maybe we’ve just had different life trajectories, but I think this is because most people have had one of those jobs.

The original thesis labelled a bullshit job as one where the person self-reports that “the world would be the same or better if I didn’t come in to work” and I think huge numbers of people can relate to sitting down at their desk and doing something that really just doesn’t need to be done but that they are being told to do anyway.

Yeah. I'm fortunate enough to have FIREd due to hitting a SAAS startup home run but even the company I was part of has now roughly 10x'd headcount from when we hit our explosive growth phase. Maybe a quarter of that is strictly necessary for allowing people to have bums in seats/realistic worklife balance. The rest is just a slow grind towards bureaucratic inertia and shipping pace has fallen off several cliffs.

The rest is just a slow grind towards bureaucratic inertia and shipping pace has fallen off several cliffs.

It has been my observation that engineering productivity often scales sub-linearly with team size. Coordination between developers isn't zero-overhead, but it can still be "faster" (to market) overall than a small, dedicated team.

Yeah this is essentially it. When we hit our initial explosive growth we had a combination of being absurdly lucky in terms of right place/time and had some smart, skilled guys working 100-hour weeks. Now most of the original drivers have enough cash to have a strong buffer and minions, nobody's pushing it anywhere near as hard.

You jammy bugger! Congrats. That's the dream right there.

I saw the same dynamic in projects that I was part of - you can absorb a surprising number of people without really meaningfully improving performance.

I mean I'm talking essentially going from a staff of 10 of us where we were all pulling 100-hour days to now 100ish people but like the explosive growth tailed off about 70 hires ago and I'm not sure what everybody else is notionally doing when fuck all is being shipped.

I'd like the secret to pull 100-hour days seems quite useful.

100-hour days and a firm grip on my own bootstraps were all it took.

In my teens, and early twenties I split time between agricultural labor, food service, and construction. After that, I ended up in logistics before moving into software. I don't think I'd say any of those would meet those criteria.

Whenever it does come up, I often wonder how one would separate the useless, lazy, stupid jobs from the essential ones. When I was younger I held a similar view, but over time I realized that the single strongest predictor for whether I thought a job was bullshit or not was how little I knew about its actual day to day work.

By and large and also unfortunately: The bullshit is distributed throughout the system at different levels of concentration.

When I did Labor, my job (based on what I was compensated for) was about 20-30% bullshit; given that that was how much my paycheck reflected massaging the owners ego vs. actually doing stuff; but I did actually do stuff all day.

I've also had an office job that was more like 90% bullshit; I mainly read ebooks while pretending to do email bullshit, but there was an occasional critical task that needed me specifically. I could Have been in the office for 1 hour a day with room on both sides; but the owner needed me there for 8 hours in order for his dick to feel big.

There are probably jobs out there that are 99% bullshit, and 1% irreplicable and critical process knowledge that shuts the plant down for a week if the one guy who sits on a stool spitting sunflower seed hulls everywhere like a god damn savage leaves because he is the only one who knows what that sound means.

Whenever it does come up, I often wonder how one would separate the useless, lazy, stupid jobs from the essential ones. When I was younger I held a similar view, but over time I realized that the single strongest predictor for whether I thought a job was bullshit or not was how little I knew about its actual day to day work.

There's a simple question that needs answering and yet never gets one regarding this. If the jobs were so clearly bullshit, why are employers paying for it? There must be value somewhere in some way expected from it. That value might not be immediately noticeable, maybe it's some PR thing like how companies do donation matching. Or maybe it's as you say, more complex than people think it is. Hell maybe you just exist as a redundancy in case shit goes wrong and in the rare case you're needed, you're there for the emergency. But there's gonna be something worth it.

This doesn't mean perfect. Companies will overhire on tasks from time to time and employers will make mistakes or have stupid ideas because they're people too. Or sometimes a project seems good at first but just ends up failing due to competition doing better or society/market conditions shifting.

But the corrections do come and jobs that are determined to not be working out get fired. The owners want profit, they are not running a charity.

Especially funny that this populist sentiment tends to coexist with another common one about greed. You can have "bullshit jobs where people get paid despite not being of value" or you can have "greedy owners who don't care about employees and will fire you without care" but not both.

You can have "bullshit jobs where people get paid despite not being of value" or you can have "greedy owners who don't care about employees and will fire you without care" but not both.

Those aren't mutually exclusive if the owners are terrible judges about what actually produces value.

And if they're particularly bad and they don't make up for it somehow else then the company takes a backseat to competitors and the CEO gets replaced/owner fails. Lots of businesses fail, the culling process is an important part of optimizing.

Massive stack of competing incentives where selling needing more minions to boost your headcount to your superiors is imperative for getting more clout in the business, plus weird complexity situations where owners are sufficiently alienated from day-to-day coalface operations to not know where things are actually getting done.

Without getting into obvious absurdities like hiring to meet affirmative action quotas or personality hires or random goonsquads of friends/family. There's also a lot of weird situations in business where owners aren't necessarily running enterprises for purely financially optimal reasons. I once worked in a computer store that was lucky to break even, but the guy running it had been a very early mover on retailing PCs and essentially banked Financial Independence money 20 years ago. Now his entire social being was tied up in being a small business owner, he is a complete workaholic and he additionally had a bunch of 20-year employees that he felt were unlikely to be able to find employment elsewhere. I was there for 6 months and it was essentially a sitcom where this 70 year old owner would wake up in the morning, come up with some hare-brained scheme for a new product line to introduce to 'bring the business back to the old days' and everybody essentially went through the motions.

The only reason I understand that it was essentially a lifestyle business for the owner was a chance encounter with his wife where she told me, and things like this aren't even that rare in the economy. Though notably he did eventually sell to a random Indian guy who proceeded to replace all the old-timers with the Australian H1B equivalent, so I guess the economy finds a way.

Small businesses can get away with being pure passion projects. Small businesses are also small. The value provided isn't always financial, often especially for many small business entrepreneurs, it's social and emotional value. They crave control, or recognition, or freedom, or the feeling, or ego, or whatever and they're willing to substitute some financial value in exchange for their social or emotional value. Heck even large companies and owners do that sometimes too, just not to the same relative degree. They're still human at the end of the day, not emotionless robots with pure logical finance guided thinking.

plus weird complexity situations where owners are sufficiently alienated from day-to-day coalface operations to not know where things are actually getting done.

Like I said, it's not perfect. Often companies do prefer to be a little too much than to risk not being enough. But generally there is an expected value to come from you, and if you don't fulfill it then you'll be dropped eventually.

Managers have competing interests with their org and with the broader enterprise. Career growth for management is managing more people - nothing else really exists. Sufficiently large companies have strong conflicts of interest between departments. Microsoft VPs have historically preferred outright killing winning projects if they can’t get a slice of the action in their org. On another note, firing people is truly awful for most (0/10, do not recommend), and is disruptive to the rest of the team… and the career consequences of a bad fire differ from just hiring too many people.

Managers have competing interests with their org and with the broader enterprise. Career growth for management is managing more people - nothing else really exists. Sufficiently large companies have strong conflicts of interest between departments

Now that's true, but to be clear here hiring people so the manager in charge can feel more important is a value provided too! A stupid one to many of us, but people spend tons of money validating their egos. It's not too much different than someone who spends millions of dollars on some art piece so they can say they own a piece from Famous Artist instead of just a cheap replica.

The value can manifest in weird ways that aren't directly profitable.

Most bullshit jobs exist because other bullshit jobs exist. You can see this in healthcare; you've got armies of healthcare admins whose job it is basically to make sure they get paid, and other armies of insurance people whose job it is to try not to pay. Or "compliance", where you've got people whose job it is to make sure all the paperwork is done right, and other people's job it is to punish the first set if they don't do it correctly.

A good project manager may be useful, but most of them IME were basically making sure the paperwork got done and the charts filled out, and were a net negative for actually getting the stuff done. The usual argument is they're needed for management to do their job, but I'm doubtful.

especially the ones who deep down always knew that their intellectual labor is neither extremely intellectual nor much useful

I mean this is the thing that confuses me a bit about the surge. A lot of white collar labor at present is barely productive, and whilst AI can probably ape a lot of it effectively (Especially since the end result doesn't actually matter that much) we're already in an era of essentially UBI fake work in a lot of fields. Meanwhile the actual ability to manufacture things that aren't software and compliance legalese have largely been outsourced to other countries and I think meaningful advancement in manufacturing robotics isn't that close so like... okay you can set the highest score ever in Facebook button optimizing but who cares.

I think this is correct observation, however I do not think it as totally wasteful. Even in early days of internet you could have people sending emails only for those to be printed and then put into folders - a common practice in 1980s and 1990s. Many of these had decades old processes and legal requirements behind, including signatures etc. It will take some time of decade+ before AI will be fully integrated in businesses and there will be a lot of work for this combination of AI and old processes/people.

I’m no expert, but isn’t there a fatal flaw here? Most of the work LLM inference is used for is essentially busywork that wouldn’t exist in an automated economy

AI is not in the state to do a completely automated economy yet, many tasks still have to be done (or at least directed by) humans. Thus freeing humans up from busywork is still an important gain in our current situation even if eventually this will be become redundant as well.

People shouldn’t be conflating LLM’s with AI, the way they’re imagining the future. I remain firmly convinced that the utility of LLM’s will stay relegated to that of a glorified (and occasionally useful) autocomplete at worst, and at best a work assistant. Some of the recent updates to Gemini that I’ve played with have definitely sharpened their understanding to provide accurate answers to what I’m asking it; the only problem being that they were nothing like the answers it had previously given before; meaning it’s essentially given me every answer under the sun.

It’s a cool “toy” to prompt a human driven research project or to chase down answers to problems, but even when correct information is provided, you still have to vet and validate the veracity of it.

People have gigantically been freed up from busywork in the office versus what things were like 30-40 years ago. Aside from an expansion of internet pornography, what exactly has been accomplished? New busywork was found, actually manufacturing things was offshored and Western economies have largely trended towards overfinancialized circlejerks where nothing actually happens.

I think it was the economist Ha-Joon Chang that argued the washing machine was more revolutionary than the Internet was, basically for reasons along this train of thought.

There's tons of stuff that has improved. Things that you want bigger are bigger like cars, TVs and homes. Things we want smaller are smaller like medical devices, computers and cameras. They're all typically much higher quality too. Those medical devices are saving more lives, those cameras take better pictures, those cars are less likely to kill you in a crash.

Stuff is generally cheaper now (per hours of work needed) and more accessible like how 2024 was the first year ever that >50% of Americans took at least one flight. And they did it without having to hire someone to handle bookings for them. Email/texting/etc allows for instant (and automatically stored!) correspondence with anyone I want, meaning we don't have to wait weeks to communicate back and forth. You can listen to almost any song ever recorded, watch basically any show ever made. I can keep track of my financials without having to keep meticulous and detailed records and receipts of where I went and what I spent.

actually manufacturing things was offshored

Modern manufacturing is bigger!. Jobs are down because automation and robots are more efficient than people, but we make more now locally than we used to.

It’s also worth noting that U.S. manufacturing output, even adjusted for inflation, is near all-time highs. While about 5% below its December 2007 peak, it’s up 177% compared with 1975, the year America last ran an annual trade surplus. Industrial production — manufacturing, mining and utilities combined — is higher than ever. That’s hardly a collapse.

That article indicates that it's down since 2007 and roughly flat since 2017. A 20 year decline seems to rather contradict what you're saying, especially when mining is being used to buoy the statistics and that's a fundamentally different space. Stuff cheapening has far more to do with what's gone on with offshoring and pushing to Asia. Software is cool but reflective of the financialization of society and how actual changes in physical technology have slowed dramatically.

Modern manufacturing is bigger!. Jobs are down because automation and robots are more efficient than people, but we make more now locally than we used to.

Global manufacturing has actually gone down though (for the reasons you alluded to). That’s not actually the gain people think it is however, because there’s a trade off between resilience and efficiency; especially as technological demands increase in industries like automotive.

I know satellite farming in agribusiness is one example where efficiency is really proving itself to cut down on waste and the poor industry practices of old, but not all industries are benefitting from efficiency. The social system hasn’t yet managed to adapt to rapid technological progress. Especially the government.

Case in point. A little more than a year ago I had to call the IRS to retrieve a document related to my father’s old tax return. It involved me having to send in information about myself and a few other things and they’re still requiring people to fax in paperwork to some random office, wait 2-3 days, with no direct callback number to the agent you’re talking to, to then get a single document physically mailed to me. It’s not like I could just, oh I don’t know, email them a passworded attachment of what they asked for and check it right there over the phone.

The youngest kids in my extended family don’t even know what a fax machine is. Or a VHS tape. Or a Cassette tape. Or how to write in cursive. Or know how to write a check. The IRS is still using fax machines…

Global manufacturing has actually gone down though (for the reasons you alluded to)

Manufacturing is down in two ways. Number of jobs, and share of GDP. But output is much higher. It's down as a share of GDP because other parts of the economy in services and software grew even faster. In part thanks to the automation of factories and farms, which cleared up human labor to go into other fields. People no longer have to work out mowing the fields and picking crops or putting cars together in the assembly line, so they are now free to go do other work and that work has exploded in productivity.

Case in point. A little more than a year ago I had to call the IRS to retrieve a document related to my father’s old tax return. It involved me having to send in information about myself and a few other things and they’re still requiring people to fax in paperwork to some random office, wait 2-3 days, with no direct callback number to the agent you’re talking to, to then get a single document physically mailed to me. It’s not like I could just, oh I don’t know, email them a passworded attachment of what they asked for and check it right there over the phone.

Great example of how governments, without the competitive pressure to improve and outdated (sometimes even conflicting) regulations that lawmakers are too uncaring to address are unable to update themselves in the same way that private corporations generally can.

Unfortunately, busywork is also subject to Jevon's Paradox.

Building a fully automated economy is going to require conscious effort to build systems that reduce/eliminate human participation. Otherwise the meatbags will just keep making more work for each other.

Exactly. The average office drone today has far greater capabilities than the one of the 1960s who didn't have internet or a computer and needed a manual mail room to be contacted. How much have they done with this?

The average office drone does SO much more today than they used to. What are you even talking about? Unless the bottleneck isn't tech like "the slide deck must be approved by the VP who has no time to review and approve the slide deck"

Accountants in the 1960s couldn't do shit compared to the accountants of 2026, and yet we have a fuckload of accountants in 2026? Why? Because we decided as a society that we wanted to consume significantly more complex accounting than we had in the 1960s.

If the SEC et al. decided to allow simple cash/accrual accounting and totally threw out IFRS/US GAAP we could crash accountant employment by like 80-90%. This would also be a disaster for efficient price discovery.

Hell the IASB is dead set on making accounting ever more complex and retarded with their fixation on PV-ing everything. Securing accountant jobs for future generations!

Economists have been making this point for a while now. The efficiency gains here haven’t meant a labor drawdown resulted in time shaving activities for workers, it’s been 2x, 3x, 4x the extractive productivity to produce larger profits.

I did see someone point out that workers have cut down on time worked, but it's time worked over their entire life. More school and leisure up front, and earlier retirements relative to death at the end. But the amount of time worked during your mid 20's through late 50's is pretty static.

IIRC there is a good amount of data suggesting that engineering teams have shrunk substantially in the last few generations: with computers (spreadsheets, CFD/FEM, digital control systems) product development from bridges to aircraft is at least abstractly more productive. Gone are the days of big rooms of draftsmen, in are a couple of CAD technicians (and they're better about answering "will it fit?" questions), and the parts themselves are getting optimized and closer-packed. Compare a car engine bay from the 60s to today, where there is almost no free space left (does make maintenance a pain sometimes, though), and efficiency is hugely up.

My dad had a 50 year career as an engineer from like mid-60s to mid-10s. When he started out, all firms had huge pools of skilled draftspeople who'd work hand in hand with the engineers all day. Plus all the attendant mail room, secretarial workforce etcetera you'd expect. There's definitely advantages to the flow and things are better, but it's hardly a stepchange.

Isn't that loosely true of everything following from division of labor? We get more out of farmland when we ascend the technology ladder and start building cars and tractors, not when we maximize the number of field hands.

There seems to be some assumption of "big AI central planning", when adapting existing (market) distributed consensus mechanisms is a possible, and maybe even more plausible, route. Maybe we need hundreds of agents (previously human) compiling The Beige Book regularly and distributing it, not a single Five Year Plan from a hallucinating AI.

I don't think jevons paradox should be seen as unfortunate so long as the new usage is productive in some form. Efficiency is a key aspect of growing the pot and getting us all bigger portions.

Otherwise the meatbags will just keep making more work for each other.

Like this sounds good to me. New jobs getting created to meet previously underserved demands means more total demands being fulfilled and presumably better overall lives.

I don't think it's a problem that we keep coming up with new stuff for people to do (I think we will probably see more and more people employed doing things we previously would have regarded as too frivolous to professionalize).

My point is more that administrivia is somewhat self-perpetuating. Partly this is a function of Jevon's Paradox - as we get more efficient at doing paperwork, one of the biggest results is more paperwork. We now control and track and analyze stuff that would have been impractical to the point of impossibility 50 years ago. Contra some of my other respondents, I don't actually think that this work is useless (otherwise they'd get squeezed out by employers looking to cut costs), but I think it is unlikely to go away without a deliberate effort because it also a function of our prevailing employment paradigm.

Having mulled it over, Jevon's Paradox is probably the wrong conceptual reference. For the foreseeable future, you still need humans to do some stuff. This is real, valuable work, but it may not actually take up most of their time (especially if AI actually delivers on productivity improvements). However, their employer still expects them to be available full time, which means they expect to be paid full-time, which means their employer expects them work full time*, which means creating busywork. Sometimes this is merely stuff of marginal value, sometimes it is outright time wasting. Either way, getting rid of this institutional waste heat and shifting to a genuinely fully automated process would require that you both be able to fully replace human activity with machine activity (not simply augment it) and to step outside of how we currently organize work.

*Also the employees generally want full time employment and prefer employers who offer it

Jevons Paradox isn’t something you want to deal with, with crises like climate change looming on the horizon. When solving that you have to go to public policy, not to tech (1, 2). The problem with greater efficiency is that the effective production and precision of inputs isn’t necessarily the most optimal one when it increases fragility. That was the whole point Taleb was making when he wrote Antifragile a number of years ago. I’m all for efficiency and all that, but it doesn’t mean it’s without some massive drawbacks.

Jevons Paradox isn’t something you want to deal with, with crises like climate change looming on the horizon

Externalities in consumption can actually be a problem, but that can be addressed in other ways such as carbon taxes.

I’m all for efficiency and all that, but it doesn’t mean it’s without some massive drawbacks.

I think there needs to be a line drawn between efficiency from cutting unnecessary things, and efficiency from removing all redundancies and backups.

If someone is making a sandwich and between every step they clap their hands for no reason, stopping them from doing that is objectively an improvement. But having another jar of peanut butter in the pantry you bought because you're running low and might need more for this sandwich is just long run efficiency, even if short term it might not be necessary.

I’ll refer you here to the episode that had “thermodynamics” in the title, if you’re interested to hear about the issues with a carbon tax.

I think there needs to be a line drawn between efficiency from cutting unnecessary things, and efficiency from removing all redundancies and backups.

And this is where the balance is. You saw it in the policy sphere as well after COVID struck, where people saw just how fragile shipping and supply lines were. I don’t know how many people were paying attention but within Biden’s cabinet, people were talking about the necessity of a large scale program of re-industrialization in the US; because of it.

In this scenario, why would we expect inference workloads to shoot up so dramatically?

A few trillions of dollars of compute infrastructure isn't that much. I doubt if the current buildout could cover all non-bullshit labor in the United States, nevermind the rest of the world or all non-labor tasks.

Also, there can be new demand if prices drop low enough. There are a few apps I'd be willing to pay $5 for, and if they're custom-built by an LLM instead of developed for a large market by a company, then I guess I'd be paying for $5 of tokens.

You seem to think an AI economy would somehow eliminate the busywork. I think it more likely that we'll pay AI companies to write bullshit emails to each other while still generating enough bullshit work for humans to keep us employed. If ever we figure out how to eliminate the busywork, it'll be because we really did have a FOOM situation and the AI will have no further need for us.

Unfortunately, I frequently hear tales of managers assigning humans work they knew would ultimately be discarded, to inflate bureaucratic metrics. For example, it's common for organizations with yearly budgets to intentionally waste the entire budget if they wouldn't normally spend it all, because otherwise they'd be allocated less next year, and sometimes they do this by paying employees for unused work.

And unfortunately, I predict at least some of these organizations will replace the efficiency gains from AI with more useless emails, software, etc.

Fortunately, there are plenty of good use-cases for widely-available AI inference. OTOH example, people could create more immersive game worlds with AI NPCs, and use any extra inference for more detailed world simulation.

Computer speed has exponentially increased for decades. Developers have found plenty of bad use-cases for this extra speed (e.g. advertisements), but lots of good ones (e.g. easier programming languages, better graphics, and ML).

Amazon software engineers are inventing bullshit work for AI to inflate their internal usage scores.

It's the same at the company I work for. The board member in charge has introduced AI usage KPIs, and now everyone is using LLMs for random shit. The new KPI is that 5% of all new code must be written by an LLM. Which is achieved by running a post-commit hook that is actually quite clever. There are lots of tools out there that are used to detect LLM-produced writing or code. Well, the same tools can be used to reject code that is too human if you flip the final check!

Lol. You’ve got to be kidding me. I see “Professional Bullshit Artist (PBA)” in the job market of the future.

There are lots of tools out there that are used to detect LLM-produced writing or code. Well, the same tools can be used to reject code that is too human if you flip the final check!

An acquaintance of mine got slapped with the same thing recently. Management has since walked it back because it caused an avalanche of technical debt, but at no point did they ever explain why that kpi was instituted in the first place. Did you get any kind of explanation of the goal they're trying to hit?

Based on announcements and videos from the highest level people at my company: it is using LLMs to massively increase worker productivity. The productivity increase is not optional. They tell positive anecdotes about being shocked at how fast someone is delivering results and then learning it was by using an LLM to help. Or at least that's the story being told.

This is the push incentive for new tool adoption.

My guess is if they don’t understand technical debt and path dependencies to begin with, in no way are they going to understand that you can institute a KPI for anything. It’s what you measure that actually matters. People in the SOC have been dealing with this forever in infosec.

"The more you use AI, the better you get at using it. The better you get at using it, the better code it produces. If we don't make you use it, no one will use it because you want it to write better code."

Broadly I imagine it was:

  1. We think that these tools are going to be very important.
  2. The more senior the dev, the more they tend to resist even trying out new tools or workflows.
  3. Therefore we will literally force you for at least the next six months.

I have some sympathy for this perspective, having seen two very skilled devs just become fundamentally obsolete and impossible to work with because they refused to give up using tools from twenty/thirty years ago.

The more senior the dev, the more they tend to resist even trying out new tools or workflows.

That isn't true at all in my experience. But it is true that more senior devs are less impressed by "new and shiny", instead being very critical about "what problems does this solve better than my current tools do".

One of the things that annoys me about mandating LLMs is that, generally speaking, you have to hold tech guys back from adopting new stuff. They are notorious for going all in on things which have issues for the company (security and compliance flaws, etc) and have to get walked back. They will even set up shadow IT departments just to get stuff done better. If LLMs are truly as useful as the hype says, there's no need to mandate using them. The people for whom they solve problems will trip over themselves to try to use them.

My father was a senior software architect that was a pioneer in SIEM log analysis. Sumo Logic was the last company he worked at before entering retirement and before that he worked for ArcSight when it was acquired by HP and LogLogic before that. The stories he used to tell me about needing to “hold the tech guys back” drove him and other senior developers crazy.

The requirements would be defined, the guidelines would be established, the objectives already determined and then you always had the other guys (as well as management) trying to fuck with shit. The amount of screaming matches they all got into in meetings was incredible. During one instance one of the senior women developers said aloud sarcastically “what should our goal be?,” and he sarcastically said “I think our goal should be log analysis…,” to shame the rest of the people in attendance. Yeah. It was that bad.

The corporate types love to split the difference with new tech. First they'll ban it as a security risk or whatever. Then they'll allow (or mandate) some gimped version which may or may not still have the security/compliance flaws, but definitely lacks the advantages of the forbidden unrestricted version.

This. You can't use Claude Code from Russia legally, anyway, so there's a literal cargo cult going on, with a whole bunch of stakeholders furiously pretending that some outdated version of Qwen the LLM team managed to onboard is good for agentic coding.

It really, really depends, especially since I’m talking senior in age terms ie 50+. But we eventually had to fire a 3d artist for refusing point blank to learn the industry-standard tool that the rest of the team was using, for example. He was perfectly fast on what he had but it didn’t scale and he couldn’t work with the rest of the team.

That aside, a lot of the programmers I worked with considered themselves gurus, and were very invested in the practices that they had been taught at their expensive computer science degree. They were legitimately good at what they did but they clearly considered LLMs an inferior replacement for their skills. The kind of people who insist on VIM over an IDE and will argue for days about whether Python private functions should be prefixed with underscore.

Tl;dr: a lot of programmers are genuinely in a rut, and a lot of others are more interested in writing beautiful code than solving problems.

The kind of people who insist on VIM over an IDE and will argue for days about whether Python private functions should be prefixed with underscore.

My people! If the world loses them, it will be poorer for it.

I am not a professional developer. But in my IT experience, it’s helpful to have a mix of wary skeptics and early adopters for many kinds of technology. Strongarming your skeptics before you have to is a mistake. And while I believe much that I’ve heard about the benefits of coding agents, nobody knows what the final picture will look like. The grumpy old fogeys aren’t prophets, and I am not saying that they are, but they come by their battle scars honestly.

it’s helpful to have a mix of wary skeptics and early adopters for many kinds of technology

Granted! And I'm been the wary skeptic on a lot of things. In this case, given the unique and potentially transformative nature of the technology, I have a certain amount of sympathy for the managers who decided that the greybeards needed some experience with AI products so that they can judge from a position of knowledge not prejudice. Ideally that should employ the carrot rather than the pointy stick, but occasionally you still need the pointy stick.

I also coerce my interns into using it (and pay for the subscriptions myself). In that case it's more for knowledge lookup more than code, because in my opinion getting used to having a personal tutor permanently on call is the best gift I can give them.

The AI investment boom is not based on fundamentals or on logical projections of existing technologies, there is an irreducible element of immanentizing the eschaton that underlies the story here.

Some portion of those working in AI believe in some form of the Singularity built around building the first AGI. This can range from "whichever company invents AGI first wins" to "fully automated everything" to a full on Singularity where technological progress goes vertical. Motivations range from the pseudo altruistic "We, the responsible and noble and freedom loving, must achieve AGI before They, the evil and oppressive and cruel, achieve it first" to the desire to become an all powerful feudal lord empowered by their newly created machine god. This doesn't matter to the analysis, the common aspect is the belief that after AGI is achieved, things like capital allocation and debt ratios won't matter.

They're not analyzing this along the lines of "based on our current services on offer and revenues this might be too much capital..." They're analyzing it along the lines of "what gets us closer to AGI before anyone else."

one-and-done software

What is the "one-and-done" software of which you speak? Requirements change over time, those changes need to be understood and converted into code. Most of the work in CRUD-tier software development (including in-house) is understanding the requirements, and so will most of the inference be when the work is done by AI.

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