idopmstuff

> To investigate whether skills are being lost in the field of computer science, researchers at the AI firm Anthropic in San Francisco, California, designed a randomized controlled trial in which 52 software engineers were asked to perform a basic coding task3. During the exercise, all 52 participants could search the web and access instructions on how to do the task. Half of the participants were prompted to use an AI assistant as well.

> Afterwards, all of the software engineers were asked to complete a quiz about what they had learnt from the task. The participants who had used an AI assistant did significantly worse on the quiz than those who hadn’t: the average score was 50% in the AI group versus 67% in the non-AI group.

This doesn't strike me as a great test? Most engineers aren't going to learn anything from a basic coding task anyway, so I do wonder exactly what they were testing there. If it was just recall about what the issue was, then it doesn't really strike me as a problem - using AI to handle simple problems that it's clearly capable of dealing with is the right way to use it, and of course you're not going to spend time poring over the details because then you haven't saved any time by using AI.

There are other examples that don't strike me as particularly problematic, like GPS eroding people's sense of direction. It's totally reasonable to let a skill atrophy that you no longer really need because you have an ever-present tool to handle it. I'm a lot worse at doing long division than I was when I was <whatever grade one learns long division in>.

The whole skill atrophy thing seems like much less of a problem than it's made out to be. We've been letting skills atrophy for good reason long before the advent of AI. If you start at McDonald's as a fry cook and work your way up to regional manager, if you suddenly have to work a shift on the fry station you're going to be worse than you were when you were doing it all the time. MDs at investment banks almost certainly can't put together a pitch deck as well as the junior bankers who are doing that task regularly. These things are fine - part of moving up in the world and having a broader impact is being able to successfully delegate tasks, and when you delegate tasks your skill at those tasks will atrophy. No real difference whether you're delegating them to AI or not.

To be clear, there are of course cases where skill atrophy is bad. iLoveOncall posted about senior engineers in their org who have lost all of those skills and their judgment along with them. That's definitely bad! If you delegate so much that you lose the ability to even judge good work, now you can't even delegate effectively any more.

I think the real lesson with AI is that you need to be self-aware about what skills you should practice and retain vs. what skills you can let atrophy, since it's easier than ever to hand things off. I've lost most of my ability to write a SQL query, but that's fine because it was only a skill I used intermittently and AI can always do the job fine at the level of complexity I need. I have not let my skill of writing product specs atrophy (I am a PM, in case you haven't read my username), because that's critical to using AI correctly in the first place.

So many! I manage a fund that buys small e-comm brands, and at this point the whole thing runs on a combination of AI and tools created with AI. My favorite is one that scrapes my Alibaba/WeChat/WhatsApp/email supplier convos daily and uses that to build a dashboard tracking the status of my orders.

I write a Substack about the whole thing and have a pretty comprehensive list here: https://theautomatedoperator.substack.com/p/15-ways-im-using...

For the most part if you give it sample photos that have sufficient coverage of different angles, it's very good at faithfully reconstructing the product from whatever angle you choose.

The one exception I've encountered is baby mobiles. It really does not understand the physics there.

This is definitely the most interesting question in a ton of AI applications. I think folks should be really be spending a lot of time on figuring out how to deterministically check AI outputs in a way that's reliable in order to reduce the amount of work a human has to check, and to build tools that speed up the checking process.

Thinking about all of the fake citations in legal submissions that have come up of late, it seems pretty straightforward to set up a regex that captures all forms in which a cited case might be written (I could be wrong but I'd assume there's some standard variety of formats) and search those against a database (again assuming such a database exists) to ensure they all exist.

Then for the tougher problem of making sure that the cited cases say whatever the document citing them says they do, you could have an LLM run through the document, pull out the text with the case name and text about why it's being cited, then read the case and try to determine whether the reason for citing it is valid. Rather than just give a yes/no, you'd put the doc in front of the user and let them jump from citation to citation. On each citation, it'd pop up a card that shows the literal text of why it's being cited, a judgement from the LLM of whether it matches what the case says, and snippets of text from the case as evidence + deeplinks to that text within the case.

Or maybe you wouldn't even want to give the LLM's judgement since people might rely on that without reading, but there's definitely a way to speed up the review.

I believe OpenEvidence does something like this with medical papers. If you ask it a medical question, it doesn't answer so much as link you directly to the relevant papers so you can read them and determine if they're useful. Avoids all of the potential risks of using an LLM but still hugely valuable and time-saving for docs.

Two of them:

1. ChatGPT 3.5 wrote me a script to pull some data out of Shopify and write it to a Google Sheet. Nothing remotely impressive by today's standards, but I had just commanded a computer to write code in plain English and it worked!

2. I own a bunch of e-comm brands, and with every new image model I tried to get product photography. Nothing worked until Nano Banana Pro, when suddenly I gave it a crappy iPhone pic of a product and got back a fully usable whitebox photo of it. Then I tried making the sort of infographic-style images you usually see on Amazon, and it nailed those too! In hindsight they weren't perfect, but more than good enough to use. I was about to ship that product to my photographer, and I would've had my designer make the infographic images, so that was the first time AI actually replaced a human contractor for me. Pretty big "Oh shit this is going to seriously impact employment" moment. Wrote about it here: https://theautomatedoperator.substack.com/p/ai-just-took-my-...

> Communism's failures are due to human social factors, not technological. You can't fix social problems with technology.

Yeah, that's fair. The argument would be that the core reason communism failed is because people inherently want to have greater status than those around them, so the ones who were in charge used their power to grant themselves a greater share of resources in order to demonstrate their greater status. If we have infinite non-scarce goods, whoever's in charge can still let everyone have as they need of those while demonstrating their greater status through non-scarce goods.

To be clear this isn't a prediction (I have no idea what's going to happen!), just the case I could see for this being the first version of communism that works. Though also it's not really communism, because everyone is not in fact equal; it's something like a pseudo-communist giant welfare state.

Oh yeah, to be clear I fully agree with everything you've said. My core argument is that there will be sufficient economic productivity for everyone to live incredibly well. Whether or not that happens depends entirely on the people who control both economic and political power. That keeps me up at night, and things could go horribly wrong.

I guess the optimistic side of me thinks that benevolence wins out because there's no cost to it. There is plenty of competition among the wealth for scarce resources, but food, medicine, and mass-produceable luxury goods are effectively free. Given that, it's probably just easier to give those away to everyone than to crush most of humanity by force. But that is absolutely naive optimism, because I really have no control in this situation and prefer feeling naive optimism to pessimism.

And on the communism front, I will just say that I find it some combination of deeply amusing/ironic/depressing that the people on the far left protesting AI because it'll take jobs are protesting the very technology that could, in fact, lead to the first successful incarnation of communism!

Sure, but the question is at what layer of abstraction do you have to prompt the AI?

You used to have to prompt the AI by starting to write the actual line of code you want, which it could autocomplete. Then you had to prompt it to write simple scripts or functions. The amount of scope you can prompt keeps getting bigger and bigger. Eventually, you have a PM or a CEO just telling it what features you need. Maybe it's a PM and a designer and a CEO and a CTO, but it will eventually get to the point where the number of people you need to do the prompting shrinks orders of magnitude from company sizes today. Maybe you just give the AI some money, prompt it to start a money-making business, and it goes out and does the same research and analysis that a seasoned entrepreneur would do to find an opportunity then builds out the business from there.

> the results won't be any good

Maybe, but I wouldn't bet on that. The trend over time has been that the results from prompting AI to do things have gotten better. I used to prompt it to build me dashboards and it would fail spectacularly. Now it one-shots them. Maybe the code is terrible (though doesn't matter for me, I'm the only one using it and I can verify the dashboard content is correct), but if the trend continues, it'll get better. Maybe the trend won't continue, but I've yet to come across a good explanation of why AI capabilities will just top out and cease improving forever.

Even if we start building a lot more stuff, you still don't need those construction workers. You have a GC to manage the whole project, aided by an AI who's handling scheduling/operations/logistics. You have a detailed plan to build against.

So why do you need former construction workers to manage the robots? Why can't the GC and management AI run the whole thing?

Maybe there's some scenario where you still need something like one licensed person from each skilled trade to be responsible for the robots employing those trades. But there's no way you need everyone who worked on building sites managing robots, no matter how much construction you're doing.

> Why is this time different?

That depends if AI gets to the point where it can fully replace workers, as opposed to just augmenting them. I heard Alex Imas on a podcast recently talking about how a SWE can be running 10 agents to be 10x as productive, then that SWE is more valuable so firms should want to hire more SWEs and pay them more.

That works for a while, but what if AI gets to the point where it can manage the 10 agents as well as the SWE? Of course you could say the SWE can now manage 10 agents who each manage 10 agents so he's even more valuable, but that has to break down eventually. You don't need 1,000 SWEs each managing 10,000 agents - you hit a bottleneck in the ability to give them work fast enough (even if you need the SWE at the top at all).

I think it's easier to think of from the perspective of blue collar labor. It's further out there time-wise, but let's assume we get a humanoid robot that can do any labor a human can do. It costs $25,000 and maybe a couple grand a year to operate. Works 20 hours a day when it's not charging.

The construction worker it replaces isn't going to start managing a team of robots construction robots - there's already a GC doing that, and you can't scale building nearly the way you can scale writing code because of physical constraints. When the robot I've described exists, a huge swathe of the population is going to be unemployed. There's no competitor to hire them because the competitors just get robots too.

The problem with the studies is that they're cases in which specific groups within the broader economy lost jobs. Those aren't really comparable to the (theoretical) path of job displacement of AI for a couple of reasons:

1. Those people didn't get substantial, ongoing financial assistance. If we end up in a UBI world, particular one where the UBI people get is high enough to get more or less anything that's not very scarce (e.g. land in coastal cities), the negative economic component of job loss is removed. 2. Everyone else still had jobs. When you lose your job and everyone else continues to work and be successful (or at least you perceive that to be the case), there's a big hit to the status and meaning in your life. If everyone is affected in the same way, then your relative status to others remains unchanged, and everyone collective needs to reorient society to find their meaning.

I'm not saying it will go well, but I do think there's a theoretically possible path where there is large scale unemployment but because we have nigh infinite productivity, everyone has access to unlimited non-scarce resources (including luxury cars and fine foods and whatever medical treatment they need), and we end up with an enormous number of competitive leagues of everything, events centered around music and arts, dinner parties and all manner of other social activities that are what give people meaning.

My feeling is it's not as bad of a metric as people think. Companies don't fully know the best way to use AI and things are changing rapidly, so you want people using a lot of tokens even on stuff that seems maybe kind of dumb on the surface, because if you find one useful thing and share it in the org that makes up for a lot of failures.

But I do think you also need to say, "To be clear, don't game the system. Any token usage that is even remotely justifiable as useful for the business is fine, and we will give you a lot of latitude. But if you're in the top 10% of token users, we are going to review your token usage, and if we find that you have a dozen agents perpetually running writing slam poetry, you're going to get fired."

Great stuff. My favorite genre of writing about AI is seeing how it can be practically applied to non-tech jobs/businesses. Wish we had more of this.

I'm curious about the 60% automation of financial/forensic analysis - what's missing? Is it stuff that's purely blocked by model capabilities, or are there places where scaffolding is likely to bridge the gaps?

Also curious about the workflow - is this more individual, LLM-driven features or agentic workflows? Looked like the former from the product video but there wasn't a ton of UX shown there.

I ask largely because this seems like the sort of thing where you could really start to string these features together in such a way that you start with a description of the case and whatever files you have, and then an agent does its analysis of the docs, spins up action items (get missing docs, confirm that X ambiguous doc is what the AI characterized it as, etc.) and tracks the progress of all of them, leaving your forensic accountant there in a supervisory role, managing and providing expertise.

It feels like that's the way a lot of expert analysis jobs like this are headed. I've been working on the same sort of flow to use agents to manage my business. Started with LLM skills that could be used to handle tasks I used to do myself, and since then I've increasingly been having AI use those skills on its own without me invoking them and chain things together into full blown workflows. Some parts I'm still supervising closely, but others that have been working consistently for a while I now don't really watch unless Claude flags something for me to review on my dashboard.

100% agree with this. I think takes like OP's would be much more interesting if they staked out a position in the future. I think it's pretty uncontroversial to say that someone with a great deal of technical expertise is going to be a hugely more effective LLM user today.

The question that really matters is whether that will continue to be the case. My guess is that technical expertise matters less over time, and the ability to specify the desired outcome is eventually the only thing that becomes important. But I could be wrong! The direction this all goes is pretty fuzzy in my mind.

I think the problem with this logic is it's based on the capabilities of LLMs today and really fails to address the prospect that they will continue to improve.

I used to be a PM and am technically literate enough but can only very minimally write code. I have been using LLMs to build (or try to, at least) internal tools for my business since GPT-4.

In the early days, I'd get a little ways, then the LLM would start breaking things, and I'd try but fail to get it to fix things. But over successive generations, I was increasingly able to get it unstuck by offering suggestions on where it may have gone wrong. With Opus 4.7, I don't even really have to do that - if something isn't working it's usually sufficient to just tell it what's broken. It can figure out how to fix it without my input. And of course fewer things are broken in the first place.

So I think I'm very well positioned to understand how these things are improving - better able to get the LLM to do what I want than the post OP quoted from /vibecoding (though I am 99% sure that post is actually AI slop), but less so than most of the people posting in this thread. As they've improved, whatever ability I have to guess at the causes of problems based on my experience having seen things go wrong with products I've PMed has become less necessary to getting the right outcome.

I expect that trend to continue - increasingly the LLM won't need the guidance of people with a great deal of technical expertise. I basically no longer have to attempt to diagnose problems in order to get them fixed, though with the caveat that I am building internal tools for which I am the only user, so certainly much simpler in scope than the stuff OP is talking about.

> Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture.

The crux of what I'm trying to say here is that I absolutely believe that this line is 100% true today, but I would be deeply cautious about assuming that it will continue to be true given the improvements in LLMs over the past few years.

If we assume that there will be an AI that is perfect in terms of ability to find vulnerabilities, cheap to run and widely available to everyone, then anyone can run it on any piece of software before deploying it. All vulnerabilities get found before they can be exploited.

One of the big challenges with cybersecurity is that attackers only need to find one exploit, while defenders need to stop everything. When you have a large surface area and limited resources, it's much easier to be the side that only has to succeed once. AI eliminates the limited resources problem.

Nope, wrong again - SOTA LLMs get the car wash thing right.

If you're going to have strong opinions on a topic, it would behoove you to keep up with it.

I'm not. I understand the difference and also that through improvements to the core models as well as harnesses, LLMs are able to handle an increasing share of tasks. I also understand that these things will continue to improve until AI can automate entire jobs.

You, on the other hand, are confusing LLMs from the past with current SOTA LLMs, which can tell how many rs are in strawberry.

It seems likely to me that doctors whose job is almost or entirely about making diagnoses and prescribing treatments won't be able to keep up in the long run, where those who are more patient facing will still be around even after AI is better than us at just about everything.

If I were picking a specialty now, I'd go with pediatrics or psychiatry over something like oncology.

Interesting, thanks for testing.

I feel like a more detailed prompt and/or some scaffolding to have it extract experience, put it in a structured format, give numerical ratings against specific criteria then use all of that would be able to consistently get the right result, but I am too lazy to actually test.

Even if we take this to be true, I'm not sure that it really matters?

It's comparing two resumes with the same information and picking one of the two. That's obviously a situation that would never occur in actual hiring. This doesn't demonstrate anything at all that indicates that LLMs would incorrectly preference LLM-written resumes in the real world.

It'd be interesting to do the same thing but with two resumes that are almost identical. One is slightly better (an extra year of experience or a specific note of some skill that is relevant to the role), and the other slightly worse one is written by an LLM. If the reviewing LLM picks the worse one in that case, you're potentially establishing a bias that would matter. As it stands this experiment just seems contrived and pointless.

Some people talk like skill atrophy is inevitable when you use LLMs, which strikes me as pretty absurd given that you are talking about a tool that will answer an infinite number of questions with infinite patience.

I usually learn way more by having Claude do a task and then quizzing it about what it did than by figuring out how to do it myself. When I have to figure out how to do the thing, it takes much more time, so when I'm done I have to move on immediately. When Claude does the task in ten minutes I now have several hours I can dedicate entirely to understanding.

> 'Or none' is ruled out since it found the same vulnerability

It's not, though. It wasn't asked to find vulnerabilities over 10,000 files - it was asked to find a vulnerability in the one particular place in which the researchers knew there was a vulnerability. That's not proof that it would have found the vulnerability if it had been given a much larger surface area to search.

The funny thing is that the author uses your exact logic when he finds evidence that goes against his hypothesis. He made posts that asked questions about things that Satoshi definitely would've known? Misdirection! Somebody else does it? Strong evidence against them!

I am watching DTF St. Louis (which is not a terrible reality show about a third tier city like the title implies, actually a Jason Bateman kind of dark comedy/whodunit), and Peyronie's features in the story. The show also has the first commercials I've ever seen for a Peyronie's treatment, and apparently it's an official ad partner of the show. I wonder if some enterprising show exec decided to go pitch the perfect sponsorship or if the company making a treatment commissioned a show...

Hah, thanks but unfortunately I quit and started a business a couple of years ago, in no small part because I didn't want to spend my time maneuvering to kill stupid ideas.

Hahaha yes this is absolutely true but often times so much more work.

Yeah, I don't really accept the argument that AI makes mistakes and therefore cannot be trusted to write production code (in general, at least - obviously depends on the types of mistakes, which code, etc.).

The reality is we have built complex organizational structures around the fact that humans also make mistakes, and there's no real reason you can't use the same structures for AI. You have someone write the code, then someone does code review, then someone QAs it.

Even after it goes out to production, you have a customer support team and a process for them to file bug tickets. You have customer success managers to smooth over the relationships with things go wrong. In really bad cases, you've got the CEO getting on a plane to go take the important customer out for drinks.

I've worked at startups that made a conscious decision to choose speed of development over quality. Whether or not it was the right decision is arguable, but the reality is they did so knowing that meant customers would encounter bugs. A couple of those startups are valuable at multiple billions of dollars now. Bugs just aren't the end of the world (again, most cases - I worked on B2B SaaS, not medical devices or what have you).

As a former PM, I will say that if you want to stop something from happening at your company, the best route is to come off very positive about it initially. This is critical because it gives you credibility. After my first few years of PMing, I developed a reflex that any time I heard a deeply stupid proposal, I would enthusiastically ask if I could take the lead on scoping it out.

I would do the initial research/planning/etc. mostly honestly and fairly. I'd find the positives, build a real roadmap and lead meetings where I'd work to get people onboard.

Then I'd find the fatal flaw. "Even though I'm very excited about this, as you know, dear leadership, I have to be realistic that in order to do this, we'd need many more resources than the initial plan because of these devastating unexpected things I have discovered! Drat!"

I would then propose options. Usually three, which are: Continue with the full scope but expand the resources (knowing full well that the additional resources required cannot be spared), drastically cut scope and proceed, or shelve it until some specific thing changes. You want to give the specific thing because that makes them feel like there's a good, concrete reason to wait and you're not just punting for vague, hand-wavy reasons.

Then the thing that we were waiting on happens, and I forget to mention it. Leadership's excited about something else by that point anyway, so we never revisit dumb project again.

Some specific thoughts for you:

1. Treat their arguments seriously. If they're handwaving your arguments away, don't respond by handwaving their arguments away, even if you think they're dumb. Even if they don't fully grasp what they're talking about, you can at least concede that agents and models will improve and that will help with some issues in the future.

2. Having conceded that, they're now more likely to listen to you when you tell them that while it's definitely important to think about a future where agents are better, you've got to deal with the codebase right now.

3. Put the problems in terms they'll understand. They see the agent that wrote this feature really quickly, which is good. You need to pull up the tickets that the senior developers on the team had to spend time on to fix the code that the agent wrote. Give the tradeoff - what new features were those developers not working on because they were spending time here?

4. This all works better if you can position yourself as the AI expert. I'd try to pitch a project of creating internal evals for the stuff that matters in your org to try with new models when they come out. If you've volunteered to take something like that on and can give them the honest take that GPT-5.5 is good at X but terrible at Y, they're probably going to listen to that much more than if they feel like you're reflexively against AI.

I don't know that people are saying code is dead (or at least the ones who have even a vague understanding of AI's role) - more that humans are moving up a level of abstraction in their inputs. Rather than writing code, they can write specs in English and have AI write the code, much in the same way that humans moved from writing assembly to writing higher-level code.

But of course writing code directly will always maintain the benefit of specificity. If you want to write instructions to a computer that are completely unambiguous, code will always be more useful than English. There are probably a lot of cases where you could write an instruction unambiguously in English, but it'd end up being much longer because English is much less precise than any coding language.

I think we'll see the same in photo and video editing as AI gets better at that. If I need to make a change to a photo, I'll be able to ask a computer, and it'll be able to do it. But if I need the change to be pixel-perfect, it'll be much more efficient to just do it in Photoshop than to describe the change in English.

But much like with photo editing, there'll be a lot of cases where you just don't need a high enough level of specificity to use a coding language. I build tools for myself using AI, and as long as they do what I expect them to do, they're fine. Code's probably not the best, but that just doesn't matter for my case.

(There are of course also issues of code quality, tech debt, etc., but I think that as AI gets better and better over the next few years, it'll be able to write reliable, secure, production-grade code better than humans anyway.)