So, regarding the productivity argument: I don't get it. It doesn't really matter (for regular employees) that you can do now in 2h what before it took 2 days. Why? Because it's not that you have the rest of the day for yourself. You still have to work 8h/day as usual. But now the pattern is different: instead of enjoying the craft digging deeper into problems in the span of 2 days, now you are rushing into some slot machine with the hope of it giving you the right answer with the right prompt.
So, if any, I would say it's worse for us. Obviously, it's the completely opposite situation for corporations and executives: they are loving the AI situation so much!
In my case, I think slower model makes it hard to manage context and tasks in parallel. I would much prefer to work in one task only, and finish it, take a break, and work on another task. Currently I have three tabs for three tasks in parallel, it is much worse than because constantly context switching is painful. I think a faster model would mean that you don't have to start a new task while waiting.
I had a friend who was CEO of a startup tell me that he typically only “worked” an hour a day, not because he was lazy but just because there was so much nonsense in his schedule. He told me he was trying to get it to two hours per day.
How successful did he turn out to be? As a CEO your days should be jam packed with brutal "chewing glass and gazing into the abyss". Is he running a lifestyle type company?
In theory, ofc. But that doesn't matter. If you were doing something that took 2 days in average, but you were doing it in half the time, then that was fine pre LLMs. Nowadays your manager knows that with LLMs you need to deliver faster no matter what, and then it's more difficult to "hide" and to slack.
I worked at a software company that made screenshot of your screen every minute. I also worked a non-software white collar job where you were expected to work non-stop for 8 hours, except for an unpaid lunch break.
People are still hiring, it's just very competitive - reframe rejection as learning opportunities returning wisdom.
In retrospect, many companies you get turned down from are likely companies you don't want to work for anyway hence the incompatibility.
It may be hard, but positive mindset will go very far towards enhancing your outcomes - you need to bring others up around you as well. Pause on this and think about the first thing that comes to mind when you respond to these words.
I saw a comment on HN a while ago. I don't remember exactly how it was worded, but roughly it was something like: if you are self-taught (which I am), you will have to do many shitty jobs before you get a good one. That is how I think of my situation. I am still doing shitty jobs, but I think that the shittiest ones are already behind, and if I had not taken them, I would not be where I am now.
> you need to bring others up around you as well
I am not 100% sure what you mean here, but I don't think that I have the authority or reputation to "bring up others." I find that telling other people what to do is futile, and the best I can do is leave them alone and let them learn from their experiences, or else you might be labelled a "rock star," which is coincidentally being discussed on lobste.rs right now:
Here’s my hot take as an elder millennial. Boomers are the absolute worst at being unable to make the distinction between time at work and time doing work. They may show up an hour before everyone else but spend the first two or three hours a day, reading the news and getting coffee and making small talk and accomplishing literally nothing. Then crow about their work ethic.
Like with any tech there are dumb ways of using it and there are smart ways. Treating it as a "slot machine giving you the right answer" is a dumb way - it may work for a bit, but it won't carry you very far because everyone else can also do this. No one is stopping anybody from digging deeper into problems than ever before using this technology - that's the smart way.
I'm amazed at how steep the AI learning curve continues to be and how people are spread so far apart on it. I think supercharged learning with AI and agents is undervalued at this point but that more people will realize its utility over time, especially as a complement to delegating work.
It also makes me think about the temptation to stop thinking with these tools, i.e. "cognitive surrender". Addy Osmani wrote a nice blog post about this: https://addyosmani.com/blog/cognitive-surrender
That's the fundamental trade off of a job where someone else gives you stuff to do and you get money. We may pride ourselves on software development being a job 'above' flipping burgers, but you're getting paid to have your butt in a chair for 40 hours a week. In exchange, you don't have to worry about the business shit. How much a burger or SaaS license costs the user isn't your problem. You take Jira tickets and implement them. You trade time for money. If, instead, you work for yourself; contracting, writing your own apps, buying lottery tickets, then you're trading results for money. If you're a freelance web developer with a stable of clients, it's a great time! What used to take a week takes hours, and you can charge your clients the same amount to build an even better website with you using AI, which means you get the choice of building a new website for additional clients, or you can take the time off and not build additional websites. But you have to hustle to continually get new clients, before AI and after AI. So it's a different life.
If you split the tasks for the AI in small chucks you keep the architectural control and it's not a slot machine anymore. You still read code and occasionally you write code too. Not much but it's the price to pay for the extra speed.
If you start the AI on something big and come back after one hour then yes, you might discover that you wasted an hour and got nothing.
You can dig deeper into problems with AI. For me, it supplements my knowledge in domains I don’t fully understand. It also helps me learn. So I can tackle problems I wouldn’t otherwise.
I’m excited for ultrafast AI. It likely means less temptation to multi-thread and deeper flow in single sessions.
Not OP, but: I guess in a similar fashion to when I google things or read other websites: I don’t, but I use my instinct, judgement, experience…
Very often I do catch LLMs, even the best such as Opus, confidently saying wrong things about areas in theory I know little of. And sometimes I fail to catch them and only realize that later on….sort of like…how I learned my whole career? So many wrong abstractions, tools, and so many hard earned lessons. With LLMs it’s the same, but the process is much faster. For critical decisions I don’t blindly trust an LLM, for example.
Some things are verifiable. Before coding agents, if I encountered an issue with a library or a framework, my first hunch would be to find a GitHub issue with a suggested workaround. Nowadays, I can ask an agent to really dig into it and often it does surface the root cause. For example, the other day I got a test hangup after updating to Angular 22, and the agent managed to find the bug and suggest a very trivial workaround compared to what I originally planned to go with. I reported the issue and it was fixed the next day, more or less along the lines of what I'd do.
I trust AI to surface general information and best practices on established knowledge domains. For example: best practices for securing my VPS.
For domains whete SoTA is constantly changing like AI, I use LLMs to aggregate and interact with my own research from trusted sources ala Karpathy LLM wiki.
I don’t generally trust everything I read on the internet whether its AI generated or not. I do my own research for the things that matter to me.
I think of it as a genetic algorithm loop. The LLM is basically a mutator function within the loop. If you can define the end shape you're looking for using tests and specification then you can throw the LLM at the problem and have it converge on the solution. It generate some code, it gets run, the LLM is fed the result back, and it iterates. If you can run the LLM at a really high throughput, then you can iterate on the solution faster. This can largely compensate for the overall capability of the model. Instead of hoping it gets the right solution in a few shots, you can just have it try a whole bunch of things until you get a useful result.
I dig into problems way, way deeper with AI than without. I can also add a lot more polish to features, add more test coverage, write more documentation, explore multiple approaches rather than go with gut-feel, and so on.
>instead of enjoying the craft digging deeper into problems in the span of 2 days, now you are rushing into some slot machine with the hope of it giving you the right answer with the right prompt.
If you're treating it like a slot machine you're doing it wrong. It will give you exactly what you ask for if you ask clearly, i.e. write a clear, detailed specification, not just "do X!". The nondeterminism comes from vagueness in specification.
I was saying that AI is going to make software development cheaper as in the salaries of software engineers will go down because some of that salary will now be redirected to AI companies and the fact that the world will need to absorb twice-(x10?) the amount of the development power.
its not obvious to me that salaries go down, my hunch was that salaries go up but the bar is higher. Software becoming easier to produce (still hard to verify and make useful fwiw) raises the ambitions of software projects, and we don't seem to be close to the ceiling of demand for software systems
There's a limit to what the demandXsupply curve can absorb. It really depends if there's twice as many developers or 10 times more. I think we have enough software development jobs to where we can absorb productivity doubling rather easily, not so sure about anything beyond that.
I think due to how leveraged software is, the top % of software developers are more desired (and compensated) than ever, and the bottom % will have difficulty finding a role, and there are structural barriers to entering that top % (intelligence, location, etc). Companies have infinite demand for the cream of the crop talent
I can actually back this up, most job offers I get actually come from people I happened to work with that never get a public job listing and are only obtainable via being highly regarded by others. I was told that my friend in their department where the role opened up got an email about a senior position and to reply if they have a recommendation.
However, software development is funny in a way where you don't need a job in order to be successful. I've never worked at a company and I'm pretty up there on the ladder, but I am not quite sure what will happen in next few years when ever possible thing that can be made in software is already explored to the fullest especially with singular developers launching 3 to 7 projects a month.
> with the hope of it giving you the right answer with the right prompt.
Consider that our ability to evaluate quality of the output is falling further behind our ability to produce it. The “right answer” is not the most likely outcome.
Sure but if you're really unhappy with your employer employeeing you for 8 hours a day you can also harness this power on your own personal projects to help break free from the 9-5 grind if you so desire.
A huge class of problems are just toil and drudgery. Maybe ai will give you even more time to dig into juicy problems that are too complex for it to solve, by letting you bypass all the pure toil problems.
I’m digging into deeper / more complex problems, now. On top of that, I’m also building products faster for our startups, so I am filling in much more of a product role than merely an engineering one. But, really, it is both — and I’m absolutely loving it!
Also, with the added speed I can produce things more in line with the quality I’ve always wanted to add (many more tests, for example).
The thing I really love about working with computers is when I achieve something. That's the thing that makes me figuratively, and sometimes literally, throw my fists into the air and go "Yeaaah!"
With the AI tooling, I'm getting those more like a couple times a week.
Plus, I'm using AI to attack the things in my day that are "a drag", and getting them done too.
The highs are more frequent and the lows are not so low.
Oh, sure, I can make things with it. But I have an extraordinarily hard time saying that I made something.
It feels like it cheapens the whole thing. Maybe I'm just old, because I remember people saying the same thing about code completion in Visual Studio back in the late 90s.
This is so much more than code completion, though.
Exactly how I feel. I didn’t make a damn thing. I essentially asked a chatbot to.
Did I ask for better things with some important concepts pre-rolled? Yeah, of course. But that’s so, so much less interesting than having actually made a thing.
I try to remind myself that the output of my projects have nothing to do with who I am, but the honest truth is they always mattered to me.
Now that’s dead, and it’s never coming back. It ain’t exactly existential dread, but it is something I’ve lost.
I did a deep binge on two or three projects I would never do, and like five small ones that would have consumed months.
It felt like that, kinda, for a bit. Now whenever it does something for me I get nothing. I didn’t do it… the chatbot did. What’s for me to celebrate? How can there be any real pride or satisfaction for a thing that was just handed to me because I asked for it?
If anything it diminishes my satisfaction looking back on previous projects. They’re “a few hours with a chatbot”, now.
The things I had to learn and the informed decisions I had to make? All pointless trivia, now. A child could do it.
The magic and possibilities parts just all wore off after a heavy run, and I don’t know if that’s ever coming back.
I hear what you and the other sibling comment are saying. I, thankfully, somehow, am able to focus more on the results than the process. Having fun playing a game (that AFAIK no longer exists) with my family is still having fun. Having people using a new apt cacher that fixes problems with existing ones, and also can survive the recent DDoS, is still a really great thing.
But, I'm not going to yuck your yum. I appreciate the people who do jointery using hand tools, even if I'm out here with a track saw and a router.
> The things I had to learn and the informed decisions I had to make? All pointless trivia, now. A child could do it.
Probably this is a hyperbole. Did you do the experiment? I expect that the child won't be able to do it. Ask an adult. Same thing. Ask an expert of the domain. Maybe but not as fast or as good as you.
I feel like I spend a lot more time reviewing and fixing the output of it and debugging parts it can't debug, so to me a faster model is optimizing the part that is already pretty fast. If my job were greenfield stuff I would probably YOLO it more, but when you're working on a launched product with a lot of users..
Generally, I agree because what happens is the messaging around AI is doing more, faster. Not using AI to deliver at a higher quality level, etc. But I think it boils down to incentives and discipline. So given the incentives we have today at most workplaces faster AI will just be used to produce more slop.
So, if any, I would say it's worse for us. Obviously, it's the completely opposite situation for corporations and executives: they are loving the AI situation so much!