If a corporation has an incentive to make money, it will align its priorities towards making money. Question is: are "making money" and "correct priorities" synonymous?
You use "zombie companies" as a universal pejorative and suggest we should all be instead worshiping at the alter of economic efficiency, JIT-delivery, and maximizing shareholder value without really considering the critiques there.
Yes, the "zombie company" strawman is paying people to move dirt from one hole to the other and back again which is dumb, but the "efficient company" has its own strawman, one drowning in manufactured debt, peeing in pee bottles in-between amazon warehouse isles, and unable to manufacture its own medical equipment when a black-swan pandemic event hits.
Which one is "better" largely depends on if you value societal stability or shareholder profits.
Or, in the framing of the article (which is summarizing Aoki, Milgrom, and Roberts), J-style companies exceed in periods of moderate volatility where 1) things don't change so much that you need the money-above-all-else incentive that favors strong hierarchical Jobs-like leadership that finds the visionary new solution, but 2) they change enough that the money-above-all-else incentive that favors value-engineering enshittification loses out to competition. The "societal stability" is just a part of the incentive bundle that forces the adaptation called the J-style approach.
Yes, objectively these characteristics of Japanese corporations seem like inefficiencies in the "free market".
Lack of mobility across companies (no price discovery on wages), lack of specialization (no focus), age based hierarchy (anti-meritocratic). None of these sound good for a well-tuned system.
I suspect much of Japan's stagnation is due to this system.
This model is very misunderstood. It's not good at raw generation, but it has really deep world knowledge. Not just knowledge of physics, but just knowledge in general.
There are examples like providing a basic google maps view and then asking it to simulate driving from point A to point B, and it will generate landmarks from that location.
It's also great at consistency and editing. Probably SotA in editing.
Not sure what use cases will be unlocked but I sense something is there.
> LLM's know when they get information from certain places
Nah, while the companies running scrapers may log what their regular programs scraped for training data, the LLM, the document-get-bigger algorithm, isn't that type of logical system. It isn't made to have a reliable concept of fact-attribution. It can't even track which parts of an ongoing "conversation" document are supposed to be "itself" as a self-insert character, which is why prompt injection has been a recurring intractable problem.
The LLM will emit text of "X is true, and I got that fact from Y" with the same rigor that it emits text like "I am Sherlock Holmes, and I know Santa Claus was murdered by Dracula via the following deductions..."
If the LLM is used as a adapter/frontend to a search-engine, helping to craft queries, then I suppose the not-an-LLM parts of the system would "know" the what results they're serving up. However the moment you try to "summarize" the mix of all top 10 results, we're back into unreliable stochastic-bullshitting territory again.
I'm curious how the 6 months have looked from a non-programmer's perspective. What kind of co-working tools and similar optimizations have people from other fields experienced?
I am an instructor who helps deliver an apprenticeship. My new boss has been in our industry for about 20 years and is one of the most respected people in our company. They've just joined us to teach and are off doing a two week course. On the first day she was told to let AI write all of her lesson plans, and then feed the lesson plans to AI to make her slides...
Hopefully she rejects all this out of hand, but if she doesn't it'll mean that none of our trainees get the benefit of her experience, who she is as a person, and what she has to pass onto them.
We have 6 monthly reviews as instructors where we are told the same thing. "How could you use AI for your teaching?"
They don't even feel the need to justify why this would be desirable, or is needed at all. It's just pure bandwagonning. Unbelievably, most of my coworkers are extremely positive about AI, although none of them have told me they use it for anything besides preparing their lessons for them — they just use it instead of having to think, or spend time preparing...the only important thing they do at work.
I’m teaching a class at a university in Japan (on AI-related issues, as it happens). I’ve been teaching for more than 40 years, but at 106 registered students this is by far the largest class I have ever taught. AI tools are very helpful for class management, such as keeping track of attendance and homework submissions.
I have to consciously avoid using AI for more cognitive tasks, though. It would be very tempting to have Claude, ChatGPT, or Gemini summarize, classify, and grade the students’ assignments, write individual feedback, prepare my lesson plans, etc. However, I know that my engagement with the material and with the students would suffer. I also want to show the students that they are learning together with me and with each other, not with bots.
I am semiretired and have a light teaching load that gives me plenty of time to prepare for class. I can see that full-time teachers might find it hard to resist the lure of offloading their thinking to AI.
I've been a teacher (most of the time a college professor) for...a long time. Nowadays, when preparing a new course, I definitely work with AI: "Here's what I want, and who my audience is - give me a course outline".
That gives me a starting point. Of course, I modify it. Maybe I bounce back and forth to the AI for further refinements and suggestions, but ultimately I have to be happy with the result.
When prepping the individual lessons, the biggest time saver is coming up with examples to illustrate particular points. I could do this alone, but sometimes that involves staring at a blank screen for a while. It is faster to ask the AI for suggestions, pick the one I like, and refine it further myself.
Yes, but no room is made for people who see no use for it. There is a forced-consensus that this technology is useful, which I have to combat against at work.
We teach in a very different environment, but your use sounds typical of my colleagues. "I ask it for suggestions and pick one", but nobody seems to wonder about what is lost when we shrink the horizon of what we will teach to the most likely outputs from a chatbot, one of which we will use.
Maybe this makes more sense in other fields. I have to prepare people to work in the shipping industry, in extremely dangerous roles where they will be operating heavy machinery, steering ships, driving cranes etc. The fact is that AI knows next to nothing about this field because an AI cannot experience handling a ship in rough weather, has never secured a boat to a ship's side with the rain and wind in its face.
Yet, when people are brought in to instruct our trainees, they are told to "tell AI what you want and pick one of the suggestions", in the best case, or just give over everything to the AI in the worst case. And nobody seems to be able to explain why this is a better way of working than sitting with a pen and paper, brainstorming some ideas for a lesson based on your real experiences, and then delivering it. The only justification I'm ever given is your one, "I pick from a list so I am really still in control", "it's quicker and I don't have to think as hard or as long", "it's better at making slides or writing good-sounding (to management and auditors) lesson plans". No-one ever seems to justify it by saying it is genuinely a better experience for the trainees.
> Yes, but no room is made for people who see no use for it. There is a forced-consensus that this technology is useful, which I have to combat against at work.
This is the crux of the issue -- The technology is useful. Using it appropriately is probably the thing that people are ignoring, but you're conflating one and the other in your comment.
It is not useful to you in this case, and complain that it is an overall detriment in your industry. Those are fine and reasonable statements and conditions, and I see no reason to disagree with them... But your first statement, people who see no use for it? That is, to me, as off-putting an opinion as the consequence-unaware hypebeasts who are running OpenClaw with access to their trading accounts and can't see why others aren't.
I sympathise with the idea that everyone wants to use the new hammer and so is treating every problem like a nail, but hammers are still pretty good tools. (And you can ignore the ex-NFT-fans hammering on their dicks in the corner.)
I mean only that I see no use for it myself, in my own work. I'm sure there are people working in roles around me who believe they get some use out of AI doing their work for them, and they will have to answer to auditors when they find problems with their work, or when someone is killed.
To me, as a non-techie person, it feels as if people who work in software believe that because their work can be done by AI, everyone else's can, too. Or that this would be better, simply because it proposes a technological solution to human work — it is taken as read that a solution which uses cool sounding computers and data farms is better than one done by humans with a pen and a pad and life experience. They don't have to justify this belief, because the money is on their side.
I don't mean to tar you with a too-wide brush, and I feel like you have a good handle on your personal acceptance for LLM assistance. No complaint there.
I do think, maybe alternative to your view, that LLMs can provide useful feedback to graduate-level employees in most fields.
It is not that the work can be done by LLMs -- we're not there, yet, in software or otherwise -- but that LLMs as useful tutors specifically in regard to denouncing known bad ideas is largely applicable all over.
What I mean by the above is that I have yet to find a truly interesting idea spun from whole cloth by an LLM. They're mediocre at it. They're trained from the aggregate thoughts of those in every industry, and you and I both know that the aggregate of the industry is, generally, mediocre.
Conversely, though, is the hit: They won't be worse than mediocre. An indefatigable tutor who gives no great advice but will counsel you against blowing yourself up (or cutting a limb off with a rope, or falling overboard) is, to me, worth an amount.
The failure modes will get better, the advice will get better. Are we there, now? Unsure. You can tell us all better.
What does that really mean though — ten more years of data centers exploiting local communities for their resources will mean that a computer might be able to teach people to tie knots, and reliably check their work... No government would allow that to certify someone, and no company would risk the lawsuit when someone dies doing what the AI tells them, so it's a non-starter. Even if it were possible, and governments got on board with certifying training like that, would anyone think this was better than what we have now?
What are the likely use cases in my industry then? That AI is used to bodge the important paperwork that protects lives; is used to draft legislation; is used by both employees and management to do things like personal development reports.
Is anyone meant to be impressed? Is this worth communities having their water stolen from them?
I appreciate I am skeptical, but it is hard not to be when the world spends all day telling you a piece of technology is going to fundamentally change the world, and in real life you only see people use it to blag CVs, personal reports, and lesson planning.
> "What does that really mean though — ten more years of data centers exploiting local communities for their resources"
That is purest hyperbole. Data centers use a lot of electricity, but they are hardly looting local communities. The water issue is wildly exaggerated, unless a data center is located in a desert, because most water is recirculated.
And why do you think no one will allow an AI to certify someone on certain topics. Their knowledge at the moment is roughly the average of people in the field. Is an average person in your field not able to certify others? In any case, AIs are improving very rapidly, so what is not possible today will be possible tomorrow.
As an example, let me point out the Tesla FSD. On a per-mile basis, self-driving Teslas have a massively lower accident rate (less than 20%) than human-driven vehicles. That is a very physical activity being handled by an AI.
- pre GPT-5.4: very limited use; some smart people got some mileage out of the models, but it always required serious work and a very suitable problem. Of course the models could solve homework problems, but that felt more like a downside to us who teach.
- since GPT-5.4 (Mar 2026): the "wow" release; suddenly answering MathOverflow-level problems that have previously been stumping experts. Still prone to hallucinations, but smart enough to use the built-in Python skill to verify its claims on small examples when possible. Probably a lot better at formula-heavy math than at the abstract "philosophical" kind.
- GPT-5.5: gave me a fascinating, significantly nontrivial and highly instructive "proof from the book" on an MO-hard problem that I'm in the process of writing up. Might have been luck and good prompting, though. Didn't really feel like a qualitative leap from 5.4, but I take quantitative any time. Still requires suitable problems, but it's much harder to rule out suitability from the get-go.
Claude and Gemini have been also-rans the whole time and still are. I use Claude for secretary-like tasks; occasionally it finds an easy proof too, but usually because I've missed something obvious.
Oh, and GPT, and to a lesser extent Claude, are great at hunting errors in maths. Probably 90% of my prompts so far have been for proofreading my writings.
I work at a company that deploys AI to enterprises
The average office worker is amazed at Copilot (not in the IDE - but the app bundled with Windows), and they mostly copy paste material into their enterprise provided ChatGPT / Gemini, and get tips from Facebook / Instagram on their top 5 best prompts for work productivity
Showing them agents that automate work at scale is a very magical experience
And then everyone that has to deal with their copy pasted output is too nice to say how bad it is and how much work it just offloads to the next person that’ll probably get frustrated and have an agent handle it.
Claude in Office was a tipping point for nontechnical folks around me. Everyone’s slides decks are immaculate now. Finance isn’t needing nearly as much BI help. It’s pretty impressive.
I find it really troubling finance are relying on LLMs (word generators!) for financial analysis - I mean I guess it means there will never be any annoying gaps in the data.
I use it a lot now for knocking up grafana charts etc. It’s not so much that the LLM is feeding the numbers through. You can still use real tools to analyse and summarise the numbers, it’s just much quicker at driving them.
As ever with data analysis, two things will continue to be true. Real insights come from spotting something that looks off and digging into it deeper. Secondly, it’s really easy to connect data in a misleading way.
I’ve had a Claude analysis handed to me this morning including a summary list of actions we’re going to take next which falls into this very trap.
The insights you’ll get from your data will only be as deep as the curiosity of the person at the helm.
Sure it depends how it is done but for most uses I'd say they are not appropriate - building tools with them is ok if you double check (though how many people will when the answers seem good enough at first?).
I'd find it really troubling if financial analysts are using them without knowing the deep limitations of the tooling (which the companies selling them will not highlight for you). They don't actually count or reason so they are liable to just make up figures based on their training dataset, not the data you give them.
Using them for actual financial analysis and generating reports based on data will lead to hallucinated figures which conform to what was asked for, not what the data says and silently fills in gaps in the data. It's extremely dangerous and not something they are good at at all.
Don’t get me wrong, I very much agree with the danger. As I highlighted - I saw it this morning when someone used Claude to draw the wrong conclusions.
I’m saying there is a way in which they can be used where there isn’t scope for numerical hallucinations at all. They can write sql queries, for example, without ever being allowed to even see numbers.
What invariably does and will happen though is they’ll inner join instead of left join and some data will get missed. Or there will be some missing context (users in this set already have a certain class of property by virtue of some selection bias and that will be mistreated as some signal etc).
Interesting. I don't have to use PowerPoint much, but I hate it when I do. I don't want the llm to write the words but I do want it to make things look nice. So does this work well now?
My pipeline for this is vscode + prompts + markdown templates + GitHub copilot -> markdown docs -> pandoc to produce.docx -> copilot in word for “nice” formatting -> copilot in ppt for nice decks. LLMs all the way down.
I find it’s easier to version control and diff the .md artefacts, those remain my authoritative source.
I was doing something like this, and then realized at least with claude that it’s so much better at HTML that it’s better to get an HTML-first deck together, which could then be turned into a PPT template and/or PDF directly, depending on needs.
It saved me a fair number of design-tweak steps in the md -> pandoc part of the workflow. Realistically, hand editing claude’s HTML is also easy in most cases, so I didn’t feel like I lost much (for the generative cases). Similarly if it’s mostly what I’ve written directly that’s the source it’ll be in markdown, and I’ve found it’s a faster path to have md -> (LLM-translated HTML deck) -> pdf.
If you don't want an LLM to write the words, surely you also want to decide on the data and graphs to show by yourself? Isn't that 90% of a presentation? The "looking nice" part doesn't matter as much, it could be black text on a white background and it would be fine.
The important part is the presentation matching your presenting cadence, which is something LLM generated presentations never get right. I don't have a problem with people generating presentations, but most of the time they just end up reading whatever is on the screen when presenting.
With a little bit of work, it works very well. You can generate powerpoint directly with Codex or Claude Cowork. There is also Canva support for these tools and it has its own AI integration. Another useful tool in this space is the Gemini integration in Google slides.
If you are a bit technical, reveal.js is actually really nice for this. I one shotted a pdf export for that uses a headless browser. I've used that a few times now.
What works well for me is to take an existing presentation and then some raw input and generate a new presentation in the same style as the old one from the raw input. After that, I can go in and tweak individual slides.
Another thing I did recently was take somebody's existing pitch deck and fix it with a one line prompt: "this deck is a bit meh, pimp it!" that worked unreasonably well. I like using shitty prompts like that. Codex often manages to do the right thing if you don't overthink your prompts.
Classic deck of somebody that used way too much text and only bullets. It did a great job on that presenting the content in a more simple and better structured way. Pulling out key facts and highlighting those, simplifying text, etc. Doing that manually would have taken hours.
In business: using coworking tools to review and propose filing of emails; manage my files and folders; on a daily basis scour the intranet for interesting and relevant content.
Personal: my wife tutors in her native language to non-native primary and high school kids. They are all using these tools now generate fresh content for practice based on school lesson plans. The kids are improving much more quickly now than they were just a few months ago.
Copilot Cowork in the M365 ecosystem. It inherits all the permissions from my account, has access to exchange to send me emails, and OneDrive to save each day’s summary for posterity and future refinement.
As a former data scientist, I started to use code agent 3 monthes ago. Before that, I use chat completion on web. Now, I nearly do everything which outputs documents with code agent.
I’m not him, but I’ve started using them to do the analysis (SQL, Python etc.) and then output the report as Quarto HTML which can be hosted on GitHub Pages. It works well for this analysis style work.
Once I was going to send some figures to leadership so I checked the queries myself and not only had it done it correctly, but it had also included a lot of sanity checks with other places in the database which as a human I doubt I’d have had the time or inclination to do.
Even for modelling work it can be good to check your ETL queries, or write one itself and then check it etc.
I am responsible for all the contents but the process of those essays and reports are first generated by prompts that embody my ideas, thoughts and facts I encountered.
I think Claude Cowork through the Microsoft thing which was copilot but is now named M365 (or something?) is likely creating every powerpoint resentation within our organisation at this point.
We have whatever AI is in teams transcribe every meeting, and it's scaringly good at it. It's also extremely good at sumerizing or finding things from pervious meetings when tasked. One disadvantage in this, is that I can see how stupid I sound on writing. I'll go "yeah, hmm, yeah, that's, yeah", but it really is pretty good.
I assume we're going to see a massive increase in AI with this Cowork inside the Microsoft client. We actually have a better tool available through a librechat where you can create and configure your own agents with the same filesystem access to your one drive, and a lot more tools and models than just Claude. Almost nobody has been capable of figuring out how to use it though, so they've been using the regular office365 copilot and it sucks so bad that a lot of people stopped beliving in AI.
It's ironic that Microsoft fumbling the ball on AI, but being very good at enterprise customers (especially non-IT) means that they'll likely be the company which is going to sell us AI tools that people will actually use. I have no idea why it's so hard for people to pick up the Librechat tool we're given access to through our equity fund. It's quite litterally a copy of ChatGPT where you can point-and-click configure an agent, but we're seeing that even employees who use a lot of ChatGPT privately don't use this tool professionally. Meanwhile everyone has been capable of using the Microsoft thing (that I personally think is less user friendly since you will need to add your configuration files to every promt).
"I have no idea why it's so hard for people to pick up the Librechat tool we're given access to through our equity fund"
That's because M365 is integrated with the whole Office/Exchange environment, especially in terms of security policies, etc. MS also guarantee that the data are private, this is very important for many companies both from the IP protection perspective and the liability to expose some users/customers data (think of GDPR regulations is Europe).
I don't know who is behind Liberchat, probably some good and friendly folks, but when it comes to privacy/security Microsoft has much more to loose and if shit happens it is easier to sue them than some random VC-financed company from the USA.
At work the tools handed to most are still essentially chatbots. Getting access to coding tools is an uphill battle because there isn’t really a good way to manage risk yet. Hard enough to keep a coding agent in check locally and ensure it does rm -rf anything. Scale that to thousands of people with limited skill and it doesn’t really work. So currently they just don’t.
That’s in a finance shop. I’d imagine it’s different in programming shops where handing people Claude code is a bit more plausible
Purely anecdotal, but in my team of 20 data analysts, we've seen a bunch of them become quite productive in producing tools and apps. These are analysts with mostly domain knowledge, and not so much programming knowledge - meaning that they knew the basics to write scripts, and wrangle data programmatically, but not enough to actually engage in software engineering.
Some of these are now contributors.
I also have a friend (beware, N=1 study) with zero prior programming knowledge that has released his first app.
They lag behind because we build for ourselves first. We are rolling out Claude to the biz team this week and they will get access to Cowork, which is still preview aiui.
Sales will be another big user of agent automations, for better or worse. Poor usage by Google to craft emails and slides for us is why the suits are getting an Anthropic sub. Stay human in the loop my friends!
I've always been a "power user", making little python programs and figuring out new ways to do things with seemingly unrelated systems. My knowledge is shallow, but very broad.
A year and a few jobs ago I was genuinely up against a wall I could not see breaking through, not if I wanted to ever sleep again. Hundreds of completely bespoke customers. Hideous archaic tooling. Two of us. It was bad times. So I started paying for Claude - desperation move, to try and vibe my way out. Honestly, it's been a little bit like having superpowers.
Not just code generation, which has been great, but gaining knowledge and understanding with incredible velocity - sort of like how RSS felt back in the day, or when Google stopped being worthless in the very end of the 20th C. When Wikipedia started.
So where am I now? Well, I ditched the hell job (I didn't really drink the koolaid of their "Enterprise Solution" anyway), and got a regular day job in my core competency. I guess I do a lot of what is called "vibe coding", all kinds of utilities, what I call my "extracurriculars". A graph view for Asciidoc in VSC to show includes, xrefs, partial includes. Graph view for everything actually - it's surprisingly insightful for PDM and config management. Analysis tools for sensor faults based on Python open source astronomy tools. All sorts of converters and aggregators and cleaners for a devil's piss bucket of enterprise systems. A bazillion new MapTools macros for gaming, making complex RPG systems nearly pushbutton. A little harvest of local LLM systems doing all sorts of things, like my "Reviewinator" for copy edit. I could type the rest of the day and wouldn't come close to the end of the list.
So, pretty amazing. Very interesting systems with what must be some N-dimensional geometry underlying, maybe a signal to an underlying principle of emergence. Who knows?
In the long term, it's going to be Enterprise Software that eats the big losses from these systems. For all sorts of reasons, but mostly because Enterprise is where software goes to die. It's all bespoke to hell, it's all ancient, no one is working there because they want to. So a domain expert, with AI assist and a little know how, is probably going to whip up a superior set of tools in a short enough time to make it really worthwhile. Watch that space: SAP, Siemens, Teamcenter, SalesForce. Watch their consulting revenue.
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