Bainbridge by itself is a tough paper to read because it's so dense. It's just four pages long and worth following along:
https://ckrybus.com/static/papers/Bainbridge_1983_Automatica...
For example, see this statement in the paper: "the present generation of automated systems, which are monitored by former manual operators, are riding on their skills, which later generations of operators cannot be expected to have."
This summarizes the first irony of automation, which is now familiar to everyone on HN: using AI agents effectively requires an expert programmer, but to build the skills to be an expert programmer, you have to program yourself.
It's full of insights like that. Highly recommended!
All of these AI outputs are both polluting the commons where they pulled all their training data AND are alienating the creators of these cultural outputs via displacement of labor and payment, which means that general purpose models are starting to run out of contemporary, low-cost training data.
So either training data is going to get more expensive because you're going to have to pay creators, or these models will slowly drift away from the contemporary cultural reality.
We'll see where it all lands, but it seems clear that this is a circular problem with a time delay, and we're just waiting to see what the downstream effect will be.
No dispute on the first part, but I really wish there were numbers available somehow to address the second. Maybe it's my cultural bubble, but it sure feels like the "AI Artpocalypse" isn't coming, in part because of AI backlash in general, but more specifically because people who are willing to pay money for art seem to strongly prefer that their money goes to an artist, not a GPU cluster operator.
I think a similar idea might be persisting in AI programming as well, even though it seems like such a perfect use case. Anthropic released an internal survey a few weeks ago that was like, the vast majority, something like 90% of their own workers AI usage, was spent explaining allnd learning about things that already exist, or doing little one-off side projects that otherwise wouldn't have happened at all, because of the overhead, like building little dashboards for a single dataset or something, stuff where the outcome isn't worth the effort of doing it yourself. For everything that actually matters and would be paid for, the premier AI coding company is using people to do it.
YES. Thank you for these words. It's a form of ecological collapse. Thought to be fair, the creative ecology has always operated at the margins.
But it's a form of library for challenges in the world, like how a rainforest is an archive of genetic diversity, with countless application like antibiotics. If we destroy it, we lose access to the library, to the archive, just as the world is getting even more treacherous and unstable and is in need of creativity
Nah, more likely is that contemporary cultural reality will just shift to accept the output of the models and we'll all be worse off. (Except for the people selling the models, they'll be better off.)
You'll be eating nothing but the cultural equivalent of junk food, because that's all you'll be able to afford. (Not because you don't have the money, but because artists can't afford to eat.)
Being utilitarian and having a "final form" are orthogonal concepts. Individual works of art do usually have a final form - it's what you see in museums, cinemas or buy in book stores. It may not be the ideal the artist had in mind, but the artist needs to say "it's done" for the work to be put in front of an audience.
Contrast that with the most basic form of purely utilitarian automation: a thermostat. A thermostat's job is never done, it doesn't even have a definition of "done". A thermostat is meant to control a dynamic system, it's toiling forever to keep the inputs (temperature readings) within given envelope by altering the outputs (heater/cooler power levels).
I'd go as far as saying that of the two kinds, the utilities that are like thermostats are the more important ones in our lives. People don't appreciate, or even recognize, the dynamic systems driving their everyday lives.
Does it then really speeds us up and generally makes things better?
You can't ring more true than this. For decades now.
For a couple years there I was able to get some ML together and it helped me get my job done, never came close to AI, I only had kilobytes of memory anyway.
By the time 1983 rolled around I could see the writing on the wall, AI was going to take over a good share of automation tasks in a more intelligent way by bumping the expert systems up a notch. Sometimes this is going to be a quantum notch and it could end up like "expertise squared" or "productivity squared" [0]. At the rarefied upper bound. Using programmable electronics to multiply the abilities of the true expert whilst simultaneously the expert utilized their abilities to multiply the effectiveness of the electronics. Maybe only reaching the apex when the most experienced domain expert does the programming, or at least runs the show.
Never did see that paper, but it was obvious to many.
I probably mentioned this before, but that's when I really bucked down for a lifetime of experimental natural science across a very broad range of areas which would be more & more suitable for automation. While operating professionally within a very narrow niche where personal participation would remain the source of truth long enough for compounding to occur. I had already been a strong automation pioneer in my own environment.
So I was always fine regardless of the overall automation landscape, and spent the necessary decades across thousands of surprising edge cases getting an idea how I would make it possible for someone else to even accomplish some of these difficult objectives, or perhaps one day fully automate. If the machine intelligence ever got good enough. Along with the other electronics, which is one of the areas I was concentrating on.
One of the key strategies did turn out to be outliving those who had extensive troves of their own findings, but I really have not automated that much. As my experience level becomes less common, people seem to want me to perform in person with greater desire every decade :\
There's related concepts for that too, some more intelligent than others ;)
[0] With a timely nod to a college room mate who coined the term "bullshit squared"
That early? There were people claiming that back then, but it didn't really work.
And I'd add, that recent LLMs magic (i admit they reached a maturity level that is hard to deny) is also a two edged sword, they don't create abstraction often, they create a very well made set of byproducts (code, conf, docs, else) to realize your demand, but people right now don't need to create new improved methods, frameworks, paradigms because the LLM doesn't have our mental constraints.. (maybe later reasoning LLMs will tackle that, plausibly)
But we are in the later generation now. All the 1983 operators are now retired, and today's factory operators have never had the experience of 'doing it by hand'.
Operators still have skills, but it's 'what to do when the machine fails' rather than 'how to operate fully manually'. Many systems cannot be operated fully manually under any conditions.
And yet they're still doing great. Factory automation has been wildly successful and is responsible for why manufactured goods are so plentiful and inexpensive today.
You could swap out ai with google or stackoverflow or documentation or unix…
And yet here we are, able to talk to a computer, that writes Pytorch code that orchestrates the complexity below it. And even talks back coherently sometimes.
There's no need for ongoing, consistent human verification at runtime. Any problems with the implementation can wait for a skilled human to do whatever research is necessary to develop the specific system understanding needed to fix it. This is really not a valid comparison.
It writes something that that's almost, but not quite entirely unlike Pytorch. You're putting a little too much value on a simulacrum of a programmer.
- When one of the agents does something wrong, a human operator needs to be able to intervene quickly and needs to provide the agent with expert instructions. However since experts do not execute the bare tasks anymore, they forget parts of their expertise quickly. This means the experts need constant training, hence they will have little time left to oversee the agent's work.
- Experts must become managers of agentic systems, a role which they are not familiar with, hence they are not feeling at home in their job. This problem is harder to be determined as a problem by people managers (of the experts) since they don't experience that problem often first hand.
Indeed the irony is that AI provides efficiency gains, which as they become more widely adopted, become more problematic because they outfit the necessary human in the loop.
I think this all means that automation is not taking away everyone's job, as it makes things more complicated and hence humans can still compete.
But the report was very wrong for months. Maybe longer. And since it was automated, the instinct to check and validate was gone. And tracking down the problem required extra work that hadn’t been part of the Excel flow
I use this example in all of my automation conversations to remind people to be thoughtful about where and when they automate.
In the same way, when everything just works, there will be no difference, but when something goes wrong, the person who learned the skills before will have a distinct advantage.
The question is if AI gets good enough that slowing down occasionally to find a specialist is tenable. It doesn't need to be perfect, it just needs to be predicably not perfect.
Expertw will always be needed, but they may be more like car mechanics, there to fix hopefully rare issues and provide a tune up, rather than building the cars themselves.
I was the only person in the factory who was a qualified welder.
“But at what cost?”
We’ve all accepted calculators into our lives as being faster and correct when utilized correctly (Minus Intel tomfoolery), but we emphasize the need to know how to do the math in educational settings.
Any post education adult will confirm when confronted with an irregular math problem (or a skill) that there is a wait time to revive the ability.
Programming automation having the potential skill decay AND being critical path is … worth thinking about.
For exemple, if your calculator tells you that a 15m long W200x31 steel beam can resist 215kN•m in bending moment, I know at first glance its at least 4x too much for that length, but how many people reading my comment could? A civil engineer fresh out of college would not.
Using a slide rule meant inherently knowing order-of-magnitude, rounding, and precision. Once calculators make it easy they enable both new kinds of solutions and new kinds of errors (that you have to separately teach to avoid).
At the same time, I basically agree. Humans are very bad calculators and we've needed tools (abacus) for millennia.
Yet, pilots are constantly trained on actual scenarios, and are expected to land airplanes manually monthly (and during take off too).
This ensures pilots maintain their skills, while the auto pilot helps most of the time.
On top of that, plane commands often are half automatic already, aka they are assisted (but not by LLMs!), so it’s a complex comparison.
Don’t get me wrong - manual practice is in some sense the correct solution, and I plan to try and do it myself in the next decade to make sure my skills stay sharp. But I don’t see the industry broadly encouraging it, still less making it mandatory as aviation does.
Addendum: as you probably know, even in aviation, this is hard to get right. (This is sometimes called the “children of the magenta” problem, but it’s really Bainbridge again.) The most famous example is perhaps Air France Flight 447[0], where the pilots put the plane into a stall at 35,000ft when they reacted poorly after the autopilot disconnecting, and did not even realize they had stalled the plane. Of course, that crash itself led to more regulations around training in manual scenarios too.
[0] https://admiralcloudberg.medium.com/the-long-way-down-the-cr...
There's been progress since then. Although the details are not widely publicized, enough pilots of the F-22, F-35, or the Gripen have talked about what modern fighter cockpit automation is like. The real job of fighter pilots is to fight and win battles, not drive the airplane. A huge amount of effort has been put into simplifying the airplane driving job so the pilot can focus on killing targets. The general idea today is that the pilot puts the pointy end in the right direction and the control systems take care of the details. An F-22 pilot has been quoted as saying that the F-22 is far less fussy than a Cessna as a flying machine.
For the F-35, which has a VTOL configuration (B) and a carrier-landing configuration (C), much effort was put into making VTOL landing and carrier landing easy. Not because pilots can't learn to do it, but because training tended to obsess on those tasks. The hard part of Harrier (the only previous successful VTOL fighter) was learning to land the unstable beast without crashing. There were still a lot of Harrier crashes.
The hard part of Naval aviator training is landing on a carrier deck. Neither of these tasks has anything to do with the real job of taking a bite out of the enemy, but they consumed most of the training time. So, for the F-35, both of those tasks have enough computer-added stability to make them much easier. One of the stranger features of the F-35 is that it has two main controls, called "inceptors", which correspond to throttle and stick. In normal flight, they mostly work like throttle and stick. But in low-speed hover, the "throttle" still controls speed while the "stick" controls attitude, even though the "stick" is affecting engine speed and the "throttle" is affecting control surfaces in that mode. So the pilot doesn't have to manage the strange transitions of a VTOL craft directly.
This refocuses pilot training on using the sensors and weapons to do something to the enemy. Classic training is mostly about the last few minutes of getting home safely.
As AI for programming advances, we should expect to devote more user time to analyzing the tactical problem, rather than driving the bus.
[1] https://ckrybus.com/static/papers/Bainbridge_1983_Automatica...
I ask myself if I need to understand the code, and if the answer is yes I don’t use an LLM. It’s not a matter of discipline, it’s a sober view of what the minimal amount of work for me is.
But I'm talking about a consistent problem for more than 25 years. AI agents didn't do this to me. At least in my anecdotal case, this isn't atrophy. It's just the way it has always been. Now I actually have much less friction in getting a project going. I can just type a few of my thoughts at an agent and away it goes. The momentum is almost free, now.
But in my use of AI agents as a programmer and also for other work. I would say that, while yes, you also have to look for mistakes or errors, most of the time I spend is on programming the AI still.
The AI agent has no idea what it must produce, what it's meant to do, when it can alter something existing to enable something new, etc.
And this is true for both functional and non-functional requirements.
Unlike in traditional manufacturing, you've already built your manufacturing pipeline for a precise output, you've got your CAD designs done, you ran your simulations, you've calibrated everything already for what you want.
So most of the work remains that of programming the machine.
I question this.
Also, by and large the current AI tools are not in the critical path yet, well except those drones that lock on targets to eliminate them in case of interference, and even then it is ML. Agents can not be in that path due to predictability challenges yet.
But what most of them do is not to be more efficient but to be shown to be more efficient. The main reason they are so obsessed with AI is because they want to send the signal that they are pursuing to be more efficient, whether they succeed or not.
Being a credibly efficient at doing the wrong things, turns out to be a massive issue inside of most companies. What's interesting is I do think that AI gives opportunity to be massively more effective because if you have the right LLM, that's trained right, you can explore a variety of scenarios much faster than what you can do by yourself. However, we hear very little about this as a central thrust of how to utilize AI into the work space.
I work at a firm that has given AI tooling to non-developer data analyst type people who otherwise live & die in excel. Much of their day job involves reading PDFs. I occasionally will use some of the firms AI tooling for PDF summarizing/parsing/interrogation/etc type tasks and remain consistently underwhelmed.
Stuff like taking 10 PDFs each with a simple 30 row table per PDF, with the same title in each file, it ends up puking on 3-4 out of 10 with silent failures. Row drops, duplicating data, etc. When you point out its missed rows, it goes back and duplicates rows to get to the correct row count.
Using it to interrogate standard company filings PDfs that it has been specially trained on and it gave very convincing answers which were wrong because it has silently truncated its search context to only recent year financial filings. Nowhere did it show this limitation to the user. It only became apparent after researching the 4th or 5th company when it decided to caveat its answer with its knowledge window. This invalidated the previous answers as questions such as "when was the first X" or "have they ever reported Y" were operating on incomplete information.
Most users of these tool are not that technical, and are going to be much more naive in taking the answers for fact without considering the context.
For example, imagine describing what files you want to find, and getting back a command-line string of find/grep piping. It doesn't execute anything without confirmation, it doesn't "summarize" the results, it's just a narrow tutor to help people in a translation step. A tool for learning that, ideally, eventually puts itself out of a job.
Returning to your PDF scenario: The LLM could help people weave together regular tools of "find regions with keywords" and "extract table as spreadsheet" and "cross-reference two spreadsheets using column values", etc.
It's useful to think about AI-driven coding assistants in terms of the SAE levels of automation for automatic driving.
- Level 0 - totally manual
- Level 1 - a bit of assistance, such as cruise control
- Level 2 - speed and steering control that requires constant supervision by a human driver. This is where most of the commercial systems are now.
- Level 3 - Level 2, but reliable enough that the human driver doesn't need to supervise constantly. Able to bring the vehicle to a safe stop by itself. Mercedes -Benz Drive Pilot is supposedly level 3. Handoff between computers and human remains a problem. Human still liable for accidents.
- Level 4 - Full automation, but not under all conditions. Waymo is Level 4. Human just selects the destination.
- Level 5 - Full automation, at least as capable as human drivers under all conditions. Not yet seen.
What we're looking at with the various programming assistance AI systems ls Level 2 or Level 3 competence. These are the most troublesome levels. Who's in charge? Who's to blame?
The need for such programming assistance systems may be transient, as it clearly is in automotive. Eventually, everybody in automotive will get to Level 4 or better, or drop out due to competitive pressure.
My AV is reporting issues with this link: 15/12/2025 2:59:56 PM;HTTP filter;file;https://cdn.jsdeliver.net/npm/mathjax@3.2.2/es5/tex-chtml.js... trojan;connection terminated;
However, this took 40 years and actual fatalities. We should keep that in mind when we're pushing the AI acceleration pedal down ever harder.
What's interesting is this mirrors every automation wave. We thought assembly lines would eliminate human work - instead they just changed what work meant. AI's doing the same, just at software speed instead of industrial speed.
Long-term I'm optimistic - automation creates more than it destroys, always has. Short-term though? Messy transition for anyone whose job is 'being the interface layer.
Will include this thread in my next issue of https://hackernewsai.com/
so much this!
If you're bad at your job, you're automating it at lightning speed.
You need have good business process and be good at your job without AI in order to have any chance in hell of being successful with it. The idea that you can just outsource your thinking to the AI and don't need to actually understand or learn anything new anymore is complete delusion.
