My biggest concern isn't actually that my job can be automated (it can), but rather that the persons who need me stop valueing being right, and start valueing >being told< that they are right by an AI.
I feel my main purpose isn't that I can technically do something my clients can't, but that I know how to interpret their needs and find the solutions that fit for them.
Now they just query some AI which spits out >some< solution and they won't know that they are wrong until it can't even be tracked back to their AI.
This will result in "but the computer said" excuses all over the place.
I think we'll see an unbelievable upheaval as companies figure out that while the technical aspects of some jobs can (and should) be automated, the interpretation of those technical aspects and other higher order, but not strictly inherent processes can't. By upheaval I mean companies closing because they replace staff with AI, and lose things along the way because of it. They won't see it until it's too late, because of the pile of money they're saving.
My example comes from maintenance in machine shops. My dad's siblings were almost all employed in machining in some fashion. One uncle was maintenance for a local large company. They decided they could move maintenance from in-house specialists and contract that work to a multi-state massive company; anyone can follow a maintenance manual right? All you need are the proper certifications and everything is cool, right? The cost was less, and the number of maintenance staff on site actually went up. Great for the owners.
But what they lost was consistency and understanding in the process that eventually led to closing the company. My uncle was there every day all year long for decades and could hear and feel in his feet when something was going wrong with a machine regardless of the maintenance schedule approved by the company that manufactured the machines. The new outside company didn't pay as well, and didn't have great benefits, which meant that they cycled through staff very regularly. No one was there consistently for more than a year or two. Even though they were technically following the manufacturers recommended maintenance schedule, machines went down. Machines then stayed down for days or weeks longer than they should've, because the expertise to repair either wasn't there, or the person leading the job would quit and they would have to cycle in a new maintenance person. They lost jobs, customers took work other places. 8 years later the company closed.
There are lessons in this kind of thing, but nobody learns them.
This is a techne/metis split. You would probably like this [0] chapter about the "rightness" struggle between engineering and service at Xerox. Some paralells to A Big Little Idea called Legibility from Ribbonfarm, too.
[0] https://books.worksinprogress.co/book/maintenance-of-everyth...
Seeing Like A State is essential reading, especially for anyone working in software or operations for a corporation.
The original book that article is based on is great.
In the IT department in which I work, now the team is reduced to just me, I find myself using the phrase "Why fix a problem in 15 minutes with your own engineers, when you can take 3 weeks with a Managed-Service Provider?". Most of what I have to care for is office/internet network related.
People in other teams are slowly getting it. When I leave (or more likely "am let go"), the cold hard reality will become much more obvious during the next network problem ;)
The way this will play out will be weird, and probably not like anyone expects. Because on the one hand, you're 100% right on the value of "feel" you develop for a system when you're around it daily for a long time. But, on the other hand...
The other day I visited our old apartment and noticed the dishwasher was making loud noises. Worrying about whether it's about to fail and whether I should call in a technician, I just recorded a bit of the noise, screenshotted a spectrogram from an app, made some photos, and threw Claude at it. It proceeded to analyze the recording and match specific frequencies to components of the machine, and gave me an analysis indicating the machine is operating normally, and the noise is just the pump being louder than I'm used to due to machine being old and a bit worn out.
So we literally have that kind of capabilities in our pockets, and this will compensate for the problem of missing experience somewhat, even simply because unlike low-paid frequently-rotating staff, LLMs are not lazy and will do the detailed troubleshooting work even off a recording and "wtf?" as prompt, so if you can train your cheap, expendable staff to do just that, that's already supercharges them.
I'm also amazed at the abilities of AI when I can verify them. But, how do you know that Claude is right about your dishwasher? I mean you're not that much worse off if it's wrong (compared with a similar medical query), but isn't this a case of trusting the AI without verification, just because it looks correct and was stated confidently?
I know enough to evaluate the analysis I got; I have some general expertise and worked in fields adjacent to this, which is why I had the idea to troubleshoot a dishwasher off an audio spectrogram in the first place. And on the off chance I was wrong, this is super low risk - would cost me extra few hours of my life at a less convenient time.
My point isn't that AI can substitute for expertise in general, not just yet, but that if you're competent enough to evaluate it, AI can absolutely do hours of work in minutes even for things involving troubleshooting on factory floor (or home appliances). And, that this is a lever where personnel on the floor may have sufficient expertise to evaluate diagnostic work, but not enough motivation or space to do the diagnostic work themselves.
At that point of low risk, why not just wait till it breaks and then get a replacement?
Seems the AI analysis added no value, but took, up time...
Do you always wait for things to break completely before considering getting repair or replacement?
For my rental properties where tenants don't necessarily take good care of things, most definitely wait until the appliances break to replace them.
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I believe that the imprtatnt bit that many will come to understand ,maybe after costly mistakes is that 'tasks can be automated, jobs cannot(yet)' one still needs the discernment to know what the sequence of tasks lead up to meaningfully.
It kinda reminds me of the story of the Toyota process , while their car product lines were/are heavily automated the process still relies on the smalll teams on the line to spot/solve and iterarte over mistakes/inefficiences then add the refinements back onto the automated tasks.
Similarly althose stories of radilogists going jobless have not panned out , as interpreting slieds is a 'task' that a radiologist does.
Semantics , yeah but it does shape the way of thinkings regarding jobs/economy and not to mention the whole anthropomorphizing by the AI labs and boosters. In essence the wordings can/could introduce a healthy dose of reality/skepticism to the wildest predictions regarding job losses etc.
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The right proxy is GDP not the employment rate. The Industrial Revolution didn’t result in a decrease in employment.
This is already an issue I face both with clients and personally where I can say something authoritatively, but they’ll always go and just ask an LLM.
Sometimes it’s in my favour. LLMs estimate very high prices and long timelines for software projects.
If it’s a legal / regulatory issue it’s usually wrong.
It's still mildly amusing when I ask what kind of work is involved for a change, and it gives me some estimate of 3-6 weeks or whatever, and then I say "no I mean like what changes" and then it spits out the complete change in 30 seconds.
That has been my experience as well. One-shot time and budget estimates are always ridiculously off.
If you hook it up to legal databases like Lexus Nexus and let it ONLY search proper places for law (instead of Wikipedia) these systems are superhuman at law too. A lawyer acting as a meat proxy for Opus 5.5 will smoke a lawyer without AI systems on average these days.
Yes, well, a typical client has a free tier of ChatGPT and just voice-to-text’s questions into that.
Was anyone doubting LLms are good at searching and summarising text?
Some internal engineer is doing god's work by creating a system skill for the LLM that multiplies all estimations by five. /s
On a more serious note, the pre-LLM internet had very little useful detailed information regarding software estimation addressing effort for building something in specific domains.
Most writing on software estimation tried to tackle a general approach that could apply to all domains. So you had a "applicable to all, useful to none" situation going. And since crud apps were the most ubiquitous apps, software estimation techniques were perceived to give more accurate results with respect to those type of apps.
I'm sure internally big AI companies are accumulating vast amounts of estimation training data by documenting how long it takes their LLM to complete specific app building tasks. I don't think they would be in a rush to push that data into public models though as it would be like beating a dead horse that is the current human workforce anyway.
Your concern about higher-ups valuing being told they are correct vs actually being correct is exactly what I saw at my last job (one of the largest companies in the states) and now at another smaller firm. It was really disheartening because I do use qwen a lot for personal stuff and claude at work, and have been able to multiply my productivity by only having to understand a ticket and then code review instead of doing all the intermediate steps, but a lot of people have let it go full self driving and just tell it "implement ticket ABC-123 and raise a PR" without checking the output at all.
I wholeheartedly agree. To clarify my position, I don't think having these amazing models mean we should replace everyone, quite the opposite.
I do think they are close enough to the stage where people will be replaced, even though it's not the right or correct thing to do, and that we need to move on from the "denial" phase quickly enough so our societies can adapt / regulate / legislate while not too late.
This, 100 times over. And it's already happening.
Which isn't necessarily a bad thing. Smart leaders / entrepreneurs will realize this and then use human input where required. The rest will be cleared out after the computer has made enough mistakes for them.
>I know how to interpret their needs and find the solutions that fit for them.
Without additional explanation this is indistinguishable from the role of a technical product manager.
A capable developer will at some point become a capable people manager :)
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