> The result, in my case, is that I code more than I have in years. Three years ago I coded maybe once a fortnight, mostly throwaway PoCs to demonstrate concepts. Now I code most days of the week, in between other work.
This kind of senior engineering role really depends on the type/size of an org.
I've had jobs like this on & off and generally don't stick around for long. There have always been high-impact hands-on-keyboard senior roles that involve coding most/every day..
> The other thing that gave way was thinking time. There's very little of it in my working day now. The productivity gains from AI got captured by output volume rather than output quality.
I actually see this externally from b2b vendors I am a client of. Companies that used to churn out X new products/month are now pushing 4X products but they all suck. The quantity over quality market is going to produce new opportunities for others.
> Three years ago [...], the process was familiar: write a proposal, get feedback, iterate, build a small PoC to demonstrate value, get a team assigned to take it to MVP, ship something fully featured and integrated with the rest of the platform six to twelve months later.
This to me smells of large, slow, very political organisation where actual work gets done at glacial pace. The increase in speed is probably not due to LLMs, rather to the fact that this person now has an excuse to present working products while before, by their own admission, they were mostly dedicated to producing corporate slop.
there's no anti-ai hype train. It doesn't exist. There'll merely be more and more bots crowding into every space that the only people left will not care.
not yet. but given the growing backlash my bet is that at some point linkedin monkeys will eventually all begin posting about the importance of “human centric design” or whatever.
at that point there’ll be some money to be made for the supposedly “replaceable” software engineers they’ve been shitting on this whole time.
i’m hoping my bet pays off, cos i would like nothing more than to rinse these people for everything they’ve got. it’ll be payback time baby.
When I get the sense that something might be generated I ctrl+f "honest" and "framing".
These are words that humans use, but that Claude loves to use in a particular way, the kind of way used in this article. It particularly likes the phrase "The honest version".
I was listening to some obscure band you wouldn't know with a few chicks I met roller blading last week, and I can't help but read your comment while imagining the first few notes of the second song playing. After that we watched The Simpsons and my advice is to not eat a cow, man.
It makes me wonder if large engineering organizations are going to splinter. The coordination costs are getting, proportionally, much larger than they used to.
When I left my corporate engineering job wayyyyy back in March, there were engineers and engineering leaders going off and getting a lot done, individually or in small teams. But project management and QA couldn't keep up with it. Managers resorted to turning their tokens loose on Jira just to try to make sense of it all (which, ironically made them the first to hit their token goals on the dashboard every week, and brought Jira to it's knees).
And, even worse, the junior engineers had no idea what was going on or how to get involved in anything.
My take on this, which is almost entirely pulled out of my rear end because I last worked in a large company before the rise of agents, is that we’ll see a move from vertical teams of specialists who get pulled into projects to build a mobile app or handle infrastructure. Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
Coordination has in my experience always been the big bottleneck in getting anything done, it’s just not hurt so much because everyone expected a feature that could have been done in a fortnight to take months.
> Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
> Coordination has in my experience always been the big bottleneck in getting anything done
I work at a large enterprise you've heard of. They're currently re-organizing the product area to remove currently-static two pizza teams into an amorphous blob of feature-oriented teams. Once the feature is complete, the team is dissolved and the engineers re-enter the pool, tasked with new features.
All that to say, I think you're right on the money with your assessment.
Where does the feature go for long term ownership? That throws you build it you own it out the window. We are going to get more time for documentation and handover right, right? Engineers are famous for generating good documentation.
Claude with Jira is the first time I've applied AI and felt like it was truly saving me time. The UI and search tools are so clunky, it feels much better to say "Find jira tickets like xyz" and read through their titles/summaries in the command prompt.
I am not sure if that's a good thing for Claude, or an indictment of Jira.
No, it isn't sustainable. There is a paper called "The AI Layoff Trap"[1], it says that it is a prisoner's dilemma and this is why this dude feels like he's in an arms race.
On the other front, people are saying that NVidia can't deliver stable drivers for like 15 months and they don't want to take software updates at all, they are more happy with last year's drivers.
I think this is a black swan event in the industry. A lot of people already suffered and more people will suffer still. Industry is going to change for sure, but probably not in a way that you would expect. Black swan simply doesn't work that way, it doesn't change industry in a good way, hence black swan.
black swan is not a reference to the badness of blackness but the unlikelihood of something contradicting volumes of previous data.
Thus it can change industry in a good way or a bad way, because the black swan is unprecedented and unpredicted, its consequences and their nature is unsettled.
The description I remember is the idea of holding the belief “all swans are white” until one encounters a black swan, and having to update their beliefs accordingly. What does this mean about swans?
But maybe that’s not the intended meaning either? It’s an interesting expression.
Traditionally in economics black swan is an unpredictable negative event.
The only thing that is unsettled here still is how many more people will lose their jobs and how much cumulative loss prisoner's dilemma will generate.
I saw random people on Internet suggesting to piggyback this disaster and dip into the crazy money that it is "generating", but in a zero-sum game somebody has to lose.
Thinking that memorizing insane code rules is being skilled in making software is like thinking that memorizing all the generals' birth days is being skilled in warfare.
Before AI, trying to program even a simple thing was an exercise in frustration from rules that had only been put in place by programmers to protect their own jobs and make it as difficult as possible for a normal person to develop. Oh! You mixed tabs and spaces, now your code will not compile and you're stuck another day. Oh! You forgot a semicolon, now the code won't run, even though the software points out your missed semicolon and thus knows how to fix it.
AI takes care of all that bagage and now I and others can make fully functional software that solves real world problem for real people.
The author could have written a rather incisive 800 words on this if he'd really tried.
But I will not read 2500 words of redundant, repetitive slop. It's really bad writing.
There is no pacing or conclusion to speak of. It's sort of just a loose list alternating between upsides and downsides, punctuated by the usual bullshit list-y ad-copy summations:
The build cost collapsed, the alignment cost rose, the thinking time disappeared, and the productivity gains got captured by output volume rather than output quality.
> The engineers who've adopted these tools effectively get heard more often, get their proposals taken seriously more often, and shape direction more than those who haven't.
I want to point out if the organizational model or your team's engineers are resistant to change, it doesn't matter how good of an engineer you are, or how good at proposal writing you are. With or without AI.
This kind of senior engineering role really depends on the type/size of an org. I've had jobs like this on & off and generally don't stick around for long. There have always been high-impact hands-on-keyboard senior roles that involve coding most/every day..
> The other thing that gave way was thinking time. There's very little of it in my working day now. The productivity gains from AI got captured by output volume rather than output quality.
I actually see this externally from b2b vendors I am a client of. Companies that used to churn out X new products/month are now pushing 4X products but they all suck. The quantity over quality market is going to produce new opportunities for others.
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