LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it.
It's not all that different from the state of big corp software today! Large organizations with layers of management tend to lose all abiliy to keep a consistent strategy. They tend to go all in on a single dimension such as ROI for the next quarter, but it misses the bigger picture. Good software is about creating longer term value and takes consistent skill & vision to execute.
Those software engineers who focus on this big picture thinking are going to be more valuable than ever.
>Good software is about creating longer term value and takes consistent skill & vision to execute.
>Those software engineers who focus on this big picture thinking are going to be more valuable than ever.
Not to rain on our hopes, but AI can give us some options and we can pick the best. I think this eliminates all middle level positions. Newbies are low cost and make decisions that are low stakes. The most senior or seniors can make 30 major decisions per day when AI lays them out.
I own a software shop and my hires have been: Interns and people with the specific skill of my industry(Mechanical engineers).
2 years ago, I hired experienced programmers. Now I turn my mechanical engineers into programmers.
So what you are a saying is that you removed the people who can make the decisions that keep your software maintainable and kept the people who will slowly over time cause your software to become less maintainable? I'm not sure that tradeoff is a a good one.
I'm every bit as immersed in this as you are. I've been developing my own custom claude code plugins that allow me to delegate more and more the agents. But the one thing the agent is not reliably doing for me is making sound architectural choices and maintaining long term business context and how it intersects with those architectural choices.
I tried teaching all of that in system prompts and documentation and it blows the context window to an unusable size. As such the things that as a high level experienced senior engineer I have been expected to do pre-agents I am still expected to do.
If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
I'd try a different approach - mechanical engineering isn't that hard and it can benefit greatly from developing some specialized agents and fine-tuned LLMs for it. As a side benefit, that approach happens to create some software jobs too, open source for best results.
> making sound architectural choices and maintaining long term business context and how it intersects with those architectural choices.
I completely agree with you, but this is rapidly becoming less and less the case, and would not at all surprise me if even by the end of this year its just barely relevant anymore.
> If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
I mean thats totally fine, but do realize many common load bearing enterprise and consumer software products are a tower of legacy tech debt and junior engineers writing terrible abstractions. I don't think this "well how am I going to trust you" from (probably rightfully) concerned senior SWEs is going to change anything.
s
"Im not even sure we will need maintain software" (sic) - I'm not sure what your specific background is, but with a statement like that you lose all legitimacy to me.
Yes, I'd like to hear more of their background, because they seem very naive about writing software, adding to it, testing it, etc.
You can't just whip up a replacement for salesforce using claude code. Who's going to fix the bugs, who is going to have tests, manage performance testing? People will still pay for software that is tested and performant. I could get a replacement for an online spreadsheet, google docs like thing. Suppose you tell it to copy the google docs or whatever programming language. You won't know if it's buggy because you won't haev the same test coverage. You'll never know about bugs that took a long time to reveal in some combination of features.
You can create a new system with a few features together to do something. Again, not tested, not perf tested, isn't away of a compiler bug you had to work around.
But lots of simple things can be claude coded and replaced. Say something that took a photo of a person, centered it, then say put some kind of log on the pic. Something you paid $5 a month to do.
Writings on the wall, it is true, tech debt will no longer be a thing to care about.
"but who will maintain it?" massive massive question, rapidly becoming completely irrelevant
"but who will review it?" humans sure, with the assistance of ai, writing is also on the wall: AI will soon become more adept at code review than any human
I can understand "losing all legitimacy" being a thing, but to me that is an obvious knee jerk reaction to someone who is not quite understanding how this trend curve is going.
And the human downstream of this random reorganization of things at will, how do they manage it?
If its AI agents all the way down its commoditization all the way down, if humans have to deal with it there's some sort of cost for change even if its 0 for code.
Not to rain on our hopes, but AI can give us some options and we can pick the best.
a.k.a. greedy algorithms, a subject those of us on HN should be well-acquainted with. You can watch the horizon effect frequently play out in corporate decisionmaking.
AI often simply does not offer the best options, does not think strategically, and if you constrained to its choices you will often make silly mistakes.
This is why all the arguments about context windows and RAG exist, because at the end of the day even if you asked the question of a human with all the context there's such a thing as opinions, stated vs unstated goals, requirements vs non functional requirements, etc which will give you wildly different answers.
Most of the time people don't even know the questions they want to ask.
> Not to rain on our hopes, but AI can give us some options and we can pick the best.
But that's kind of my point. A bunch of decisions like that tend to end up with a "random walk" effect. It's a bunch of tactical choices which don't add up to something strategic. It could be, but it takes the human in the loop to hold onto that overall strategy.
Why can’t LLMs understand the big picture? I mean, a lot of companies have most of their information available in a digital form at this point, so it could be consumed by the LLM.
I think if anything, we have a better chance in the little picture: you can go to lunch with your engineering coworkers or talk to somebody on the factory floor and get insights that will never touch the computers.
Giant systems of constraints, optimizing many-dimensional user metrics: eventually we will hit the wall where it is easier to add RAM to machines than humans.
> Because LLMs don't understand things to begin with.
Ok, that’s fair. But I think the comment was making a distinction between the big picture and other types of “understanding.” I agree that it is incorrect to say LLMs understand anything, but I think that was just an informal turn of phrase. I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.
The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.
> I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.
LLMs can do neither reliably because to do that you need understanding which LLMs don't have. You need to learn from the codebase and the project, which LLMs can't do.
On top of that, to have the big picture LLMs have to be inside your mind. To know and correlate the various Google Docs and Figma files, the Slack discussions, the various notes scattered on your system etc.
They can't do that either because, well, they don't understand or learn (and no, clawdbot will not help you with that).
> The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.
These are not limitations of tooling, and no, LLM developers are not even close to overcoming, especially not "constantly". The only "overcoming" has been the gimmicky "1 million token context" which doesn't really work.
I would say that it's very germane to my original statement. Understanding is absolutely fundamental to strategy and it is pretty much why I can say LLMs can't be strategic.
To really strategize you have to have mental model of well, everything and be able to sift through that model to know what elements are critical or not. And it includes absolutely everything -- human psychology to understand how people might feel about certain features or usage models, the future outlook for what popular framework to choose and will it as viable next year as it is today. The geographic and geopolitics of which cloud provider to use. The knowledge of human sentiment around ethical or moral concerns. The financial outlook for VC funding and interest rates. The list goes on and on. The scope of what information may be relevant is unlimited in time and space. It needs creativity, imagination, intuition, inventiveness, discernment.
Claude is perfectly capable of all of this. Give it access to meeting notes and notion/linear and it can elegantly connect the dots within the context of a given problem.
Most senior could make sense (although I’d like to see a collection of independent guilds coordinated by an LLM “CEO” just to see how it could work—might not be good enough yet, but it’d be an interesting experiment).
Ultimately, I suspect “AI” (although, maybe much more advanced than current LLMs) will be able to do just about any information based task. But in the end only humans can actually be responsible/accountable.
Yes!
"Does an AI know how to do that? Does a coding assistant know that an app is really a giant collection of details?"
There are just so many small decisions that add up to a consistent vision for a piece of software. It doesn't seem like LLMs are going to be able to meaningfully contribute to that in the near future.
I tried vibecoding my own workout tracker, but there were so many small details to think through that it was frustrating. I gave up and found an app that is clearly made by a team of experienced, thoughtful people and AI can't replicate the sheer thoughtfulness of every decision that was made to create this app. The inputs for reps/sets, algorithms for adjusting effort on the fly, an exercise library with clear videos and explanations; there's just no way to replicate that without people who have been trainers and sport scientists for decades.
LLMs can help increase the speed that these details turn in to something tangible, but you definitely can't "skip all that crap and just jump to the end and get on with it."
Right, it's people who can't code (and cant afford to hire a coder) that can use LLM's to replace coders, but oyu still need to sit through it and iterate. More so than with a human being, because of the dumb mistakes LLMs still make, so you need a patient and consistent vision.
Greg mentions discipline and vision as determinants of successful software, which is correct but I think he misses another aspect of vision: the ability to attract and crystallize a community around their project. Arguably, most successful softwares thrive in the long term because they have a team of people that inspire each other, fill in with complementary talents, and provide continuity.
> LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it.
There’s good reason to think that they could understand the big picture just fine, even today, except that they’re currently severely constrained by what we choose, or have time, to tell them. They can already easily give a much more comprehensive survey of suitable options for solving a given problem than most humans can.
If they had more direct access to the information we have access to, that we currently grudgingly dole out to them in dribs and drabs, they would be much more capable.
Task Time horizons are improving exponentially with doubling times around 4 months per METR. At what timescale would you accept that they "can be strategic"? Theres little reason to think they wont be at multi week or month time horizons very soon. Do you need to be strategic to complete multi month tasks?
>Can an LLM give you an upfront estimate that a task will take multiple months?
>Can it decide intelligently what it would have to change if you said "do what you can to have it ready in half the time?"
Do you think ChatGPT 5.2 Pro can't estimate how long a task might take? Do you think that estimate would necessarily be worse than the estimates, which are notoriously poor, coming from human engineers?
But you can still answer my question. When an LLM can complete a task that takes a person N months or years, is it capable of being strategic?
Prompt-injection in all its forms. If the hyper-mad-libs machine doesn't reliably "understand" and model the difference between internal and external words, how can we trust them to model fancier stuff?
We can't even trust LLMs to get basic logic right, or even the language syntax sometimes. They reliably generate code worse than a human would write, and have zero reasoning ability. Anyone who thinks they can model something complicated is either uncritically absorbing hype or has a financial stake in convincing people of the hype.
I don't feel like the abstraction away from assembly language resulted in fewer software engineering jobs. Nor do I feel like Java's virtual machine resulted in fewer systems engineering jobs. Somehow I don't feel that writing in English rather than pure logic will result in fewer engineering problems either. A lot more actually. But at least we'll get the requirements out of users into something concrete faster.
What is definitely going to be abundantly clear is just how much better machines can get at creating correct code and how bad each of us truly is at this. That's an ego hit.
The loving effort an artisan puts into a perfect pot still has wabi sabi from the human error; whereas a factory produced pot is way more perfect and possesses both a Quality from closeness to Idealism and an eerieness from its unnaturalness.
However, the demand for artisan pottery has niched out compared to Ikea bowls, so that's just how it is.
> It was a nice little feature that I knew exactly how to do, but I hadn’t prioritized getting done yet because there were a bunch of other things on my plate. But with a little assist, it was quick to implement.
Exactly how I feel. AI has allowed me to work on projects that I've wanted to work on but didn't have the time/energy for.
Honestly I feel like something important was yanked from under my feet. Computers were always an escape for me, then a starting career, something that helped me get started in life. Wow, those things I learned while spending nights making things move on the computer are actually worth something. And now a lot of it is just gone, worthless, right when I started having a bit of success. That wasn't supposed to happen.
Not that I can complain much, worse things have happened to better people bla bla. But it's disorienting. I still have non-automatable skills and enjoy learning, but who says they are not going to come up with Claude Opus 4.9 or something and turns out it can do that too, ha ha.
How is a young person supposed to establish themselves in this new world?
This song bangs, looping this all day. What a throwback. Reminds me of ytcracker/digitalgangster days. Also reminds me of Das Racist. Thanks for posting this lol. Love that grime-ish electro beat.
Can you email me? Would love to chat/get more tunes from you.
> I’m starting to think that’s going to be personality and feel and polish, but turned up a notch. That’s what I used to do when I started writing apps, but in some ways I have really toned it down in favor of OS alignment.
That's all that is really required. I mean look at the Microslop fiasco. They ruined a perfectly good editor: Notepad with AI slop. But this is not reflecting in their sales. They still are showing record revenues.
Just because a competing product exists does not mean your product is suddenly obsolete. There will always be people who will want to buy (provided the market is not oversaturated). Because that is how humans do things. AI won't change that behavior overnight [1]. Look around you and you will see every product you hold in your hand has at least 5-10 competitors.
[1] Think about all the things that are still not computerized and which requires you to fill some or the other form of paperwork. We have had computers for over nearly 6 decades now. We STILL have physical forms that we fill from time to time. Computerization was touted to revolutionize this and yet here we are. Still not achieved 100% digitalization. The same will happen with AI as well. There is this initial burst of excitement (which is the phase we are in) until reality sets in and that's when people will learn how to best use the technology. What you are seeing today (vibe coding et all) is NOT IT.
LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it.
It's not all that different from the state of big corp software today! Large organizations with layers of management tend to lose all abiliy to keep a consistent strategy. They tend to go all in on a single dimension such as ROI for the next quarter, but it misses the bigger picture. Good software is about creating longer term value and takes consistent skill & vision to execute.
Those software engineers who focus on this big picture thinking are going to be more valuable than ever.
>Good software is about creating longer term value and takes consistent skill & vision to execute.
>Those software engineers who focus on this big picture thinking are going to be more valuable than ever.
Not to rain on our hopes, but AI can give us some options and we can pick the best. I think this eliminates all middle level positions. Newbies are low cost and make decisions that are low stakes. The most senior or seniors can make 30 major decisions per day when AI lays them out.
I own a software shop and my hires have been: Interns and people with the specific skill of my industry(Mechanical engineers).
2 years ago, I hired experienced programmers. Now I turn my mechanical engineers into programmers.
So what you are a saying is that you removed the people who can make the decisions that keep your software maintainable and kept the people who will slowly over time cause your software to become less maintainable? I'm not sure that tradeoff is a a good one.
This might have been true pre-agent AI programming, but honestly the code seems better than ever. It finds edge cases better than me.
I know... I know buddy. The world changed and I don't know if I'm going to have a job.
I'm every bit as immersed in this as you are. I've been developing my own custom claude code plugins that allow me to delegate more and more the agents. But the one thing the agent is not reliably doing for me is making sound architectural choices and maintaining long term business context and how it intersects with those architectural choices.
I tried teaching all of that in system prompts and documentation and it blows the context window to an unusable size. As such the things that as a high level experienced senior engineer I have been expected to do pre-agents I am still expected to do.
If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
I'd try a different approach - mechanical engineering isn't that hard and it can benefit greatly from developing some specialized agents and fine-tuned LLMs for it. As a side benefit, that approach happens to create some software jobs too, open source for best results.
> making sound architectural choices and maintaining long term business context and how it intersects with those architectural choices.
I completely agree with you, but this is rapidly becoming less and less the case, and would not at all surprise me if even by the end of this year its just barely relevant anymore.
> If you are eliminating those people from your business then I don't know that I can ever trust the software your company produces and thus how I could ever trust you.
I mean thats totally fine, but do realize many common load bearing enterprise and consumer software products are a tower of legacy tech debt and junior engineers writing terrible abstractions. I don't think this "well how am I going to trust you" from (probably rightfully) concerned senior SWEs is going to change anything. s
Finding edge cases is completely orthogonal to creating maintainable software. Finding edge cases ~= identifying test suites. Making software maintainable ~= minimizing future cost of effective changes.
Ignoring future maintenance cost because test suites are easier to create seems like disjointed logic.
[flagged]
"Im not even sure we will need maintain software" (sic) - I'm not sure what your specific background is, but with a statement like that you lose all legitimacy to me.
Yes, I'd like to hear more of their background, because they seem very naive about writing software, adding to it, testing it, etc.
You can't just whip up a replacement for salesforce using claude code. Who's going to fix the bugs, who is going to have tests, manage performance testing? People will still pay for software that is tested and performant. I could get a replacement for an online spreadsheet, google docs like thing. Suppose you tell it to copy the google docs or whatever programming language. You won't know if it's buggy because you won't haev the same test coverage. You'll never know about bugs that took a long time to reveal in some combination of features.
You can create a new system with a few features together to do something. Again, not tested, not perf tested, isn't away of a compiler bug you had to work around.
But lots of simple things can be claude coded and replaced. Say something that took a photo of a person, centered it, then say put some kind of log on the pic. Something you paid $5 a month to do.
Writings on the wall, it is true, tech debt will no longer be a thing to care about.
"but who will maintain it?" massive massive question, rapidly becoming completely irrelevant
"but who will review it?" humans sure, with the assistance of ai, writing is also on the wall: AI will soon become more adept at code review than any human
I can understand "losing all legitimacy" being a thing, but to me that is an obvious knee jerk reaction to someone who is not quite understanding how this trend curve is going.
New account... singing the praises of how AI "changed everything" in the past few weeks... my money is on this being a shill.
They were a skeptic 3 weeks ago... fast forward today and they've oriented their company's strategy around Opus 4.6 agents and vibe-coding.
This would be funny if it were not exactly what appears to be happening where I work...
And the human downstream of this random reorganization of things at will, how do they manage it?
If its AI agents all the way down its commoditization all the way down, if humans have to deal with it there's some sort of cost for change even if its 0 for code.
AI often simply does not offer the best options, does not think strategically, and if you constrained to its choices you will often make silly mistakes.
This is why all the arguments about context windows and RAG exist, because at the end of the day even if you asked the question of a human with all the context there's such a thing as opinions, stated vs unstated goals, requirements vs non functional requirements, etc which will give you wildly different answers.
Most of the time people don't even know the questions they want to ask.
> Not to rain on our hopes, but AI can give us some options and we can pick the best.
But that's kind of my point. A bunch of decisions like that tend to end up with a "random walk" effect. It's a bunch of tactical choices which don't add up to something strategic. It could be, but it takes the human in the loop to hold onto that overall strategy.
Why can’t LLMs understand the big picture? I mean, a lot of companies have most of their information available in a digital form at this point, so it could be consumed by the LLM.
I think if anything, we have a better chance in the little picture: you can go to lunch with your engineering coworkers or talk to somebody on the factory floor and get insights that will never touch the computers.
Giant systems of constraints, optimizing many-dimensional user metrics: eventually we will hit the wall where it is easier to add RAM to machines than humans.
> Why can’t LLMs understand the big picture?
Because LLMs don't understand things to begin with.
Because LLMs only have access to aource code and whatever .md files you've given them.
Because they have biases in their training data that overfit them on certain solutions.
Because LLMs have a tiny context window.
Because LLMs largely suck at UI/UX/design especially when they don't have referense images.
Because...
> Because LLMs don't understand things to begin with.
Ok, that’s fair. But I think the comment was making a distinction between the big picture and other types of “understanding.” I agree that it is incorrect to say LLMs understand anything, but I think that was just an informal turn of phrase. I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.
The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.
> I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.
LLMs can do neither reliably because to do that you need understanding which LLMs don't have. You need to learn from the codebase and the project, which LLMs can't do.
On top of that, to have the big picture LLMs have to be inside your mind. To know and correlate the various Google Docs and Figma files, the Slack discussions, the various notes scattered on your system etc.
They can't do that either because, well, they don't understand or learn (and no, clawdbot will not help you with that).
> The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.
These are not limitations of tooling, and no, LLM developers are not even close to overcoming, especially not "constantly". The only "overcoming" has been the gimmicky "1 million token context" which doesn't really work.
I would say that it's very germane to my original statement. Understanding is absolutely fundamental to strategy and it is pretty much why I can say LLMs can't be strategic.
To really strategize you have to have mental model of well, everything and be able to sift through that model to know what elements are critical or not. And it includes absolutely everything -- human psychology to understand how people might feel about certain features or usage models, the future outlook for what popular framework to choose and will it as viable next year as it is today. The geographic and geopolitics of which cloud provider to use. The knowledge of human sentiment around ethical or moral concerns. The financial outlook for VC funding and interest rates. The list goes on and on. The scope of what information may be relevant is unlimited in time and space. It needs creativity, imagination, intuition, inventiveness, discernment.
LLMs are fundamentally incapable of this.
Claude is perfectly capable of all of this. Give it access to meeting notes and notion/linear and it can elegantly connect the dots within the context of a given problem.
It routinely can't "connect dots" on a 10 kloc project with design notes right there in the same project.
It routinely cannot read files more than 2k lines long.
You can't even provide detailed CLAUDE.md instructions because "file is too large and will affect context".
But sure. "Just give it access to a magnitude more info and it will be able to do stuff".
yes, its just a matter of capability, not skill
Yeah, it's strange to me that the default assumption is that current LLMs are already human-level AGI.
I basically just posted the same response. I generally agree with everything you said.
Only thing to add, maybe we have the most senior of seniors verifying the decisions of AI.
Most senior could make sense (although I’d like to see a collection of independent guilds coordinated by an LLM “CEO” just to see how it could work—might not be good enough yet, but it’d be an interesting experiment).
Ultimately, I suspect “AI” (although, maybe much more advanced than current LLMs) will be able to do just about any information based task. But in the end only humans can actually be responsible/accountable.
They just can't yet. They'll tell you themselves they can only take in a certain amount of a context window.
Yes! "Does an AI know how to do that? Does a coding assistant know that an app is really a giant collection of details?"
There are just so many small decisions that add up to a consistent vision for a piece of software. It doesn't seem like LLMs are going to be able to meaningfully contribute to that in the near future.
I tried vibecoding my own workout tracker, but there were so many small details to think through that it was frustrating. I gave up and found an app that is clearly made by a team of experienced, thoughtful people and AI can't replicate the sheer thoughtfulness of every decision that was made to create this app. The inputs for reps/sets, algorithms for adjusting effort on the fly, an exercise library with clear videos and explanations; there's just no way to replicate that without people who have been trainers and sport scientists for decades.
LLMs can help increase the speed that these details turn in to something tangible, but you definitely can't "skip all that crap and just jump to the end and get on with it."
Right, it's people who can't code (and cant afford to hire a coder) that can use LLM's to replace coders, but oyu still need to sit through it and iterate. More so than with a human being, because of the dumb mistakes LLMs still make, so you need a patient and consistent vision.
Greg mentions discipline and vision as determinants of successful software, which is correct but I think he misses another aspect of vision: the ability to attract and crystallize a community around their project. Arguably, most successful softwares thrive in the long term because they have a team of people that inspire each other, fill in with complementary talents, and provide continuity.
> LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it.
There’s good reason to think that they could understand the big picture just fine, even today, except that they’re currently severely constrained by what we choose, or have time, to tell them. They can already easily give a much more comprehensive survey of suitable options for solving a given problem than most humans can.
If they had more direct access to the information we have access to, that we currently grudgingly dole out to them in dribs and drabs, they would be much more capable.
>LLMs can't be strategic because they do not understand the big picture
While I do tend to believe you, what evidence based data do you have to prove this is true?
> While I do tend to believe you, what evidence based data do you have to prove this is true?
IMO the onus is to prove that they can be strategic. Otherwise you're asking me to prove a negative.
Task Time horizons are improving exponentially with doubling times around 4 months per METR. At what timescale would you accept that they "can be strategic"? Theres little reason to think they wont be at multi week or month time horizons very soon. Do you need to be strategic to complete multi month tasks?
Can an LLM give you an upfront estimate that a task will take multiple months?
Can it decide intelligently what it would have to change if you said "do what you can to have it ready in half the time?"
>Can an LLM give you an upfront estimate that a task will take multiple months?
>Can it decide intelligently what it would have to change if you said "do what you can to have it ready in half the time?"
Do you think ChatGPT 5.2 Pro can't estimate how long a task might take? Do you think that estimate would necessarily be worse than the estimates, which are notoriously poor, coming from human engineers?
But you can still answer my question. When an LLM can complete a task that takes a person N months or years, is it capable of being strategic?
Saying the tiger has to prove it can eat you is not a great strategy to survive a tiger attack.
Well so far the tiger faceplants in an embarrassing fashion every time it tries to eat someone. So I'm not really worried about that.
Gary Marcus: "LLMs will never be able to a..... wait, what do you mean they can already do that?"
Prompt-injection in all its forms. If the hyper-mad-libs machine doesn't reliably "understand" and model the difference between internal and external words, how can we trust them to model fancier stuff?
We can't even trust LLMs to get basic logic right, or even the language syntax sometimes. They reliably generate code worse than a human would write, and have zero reasoning ability. Anyone who thinks they can model something complicated is either uncritically absorbing hype or has a financial stake in convincing people of the hype.
I don't feel like the abstraction away from assembly language resulted in fewer software engineering jobs. Nor do I feel like Java's virtual machine resulted in fewer systems engineering jobs. Somehow I don't feel that writing in English rather than pure logic will result in fewer engineering problems either. A lot more actually. But at least we'll get the requirements out of users into something concrete faster.
What is definitely going to be abundantly clear is just how much better machines can get at creating correct code and how bad each of us truly is at this. That's an ego hit.
The loving effort an artisan puts into a perfect pot still has wabi sabi from the human error; whereas a factory produced pot is way more perfect and possesses both a Quality from closeness to Idealism and an eerieness from its unnaturalness.
However, the demand for artisan pottery has niched out compared to Ikea bowls, so that's just how it is.
> It was a nice little feature that I knew exactly how to do, but I hadn’t prioritized getting done yet because there were a bunch of other things on my plate. But with a little assist, it was quick to implement.
Exactly how I feel. AI has allowed me to work on projects that I've wanted to work on but didn't have the time/energy for.
> AI has allowed me to work on projects that I've wanted to work on but didn't have the time/energy for.
So let's flood the world with projects nobody, including their authors in the first place, cared for enough to dedicate time and energy to.
Honestly I feel like something important was yanked from under my feet. Computers were always an escape for me, then a starting career, something that helped me get started in life. Wow, those things I learned while spending nights making things move on the computer are actually worth something. And now a lot of it is just gone, worthless, right when I started having a bit of success. That wasn't supposed to happen.
Not that I can complain much, worse things have happened to better people bla bla. But it's disorienting. I still have non-automatable skills and enjoy learning, but who says they are not going to come up with Claude Opus 4.9 or something and turns out it can do that too, ha ha.
How is a young person supposed to establish themselves in this new world?
> I don't feel like the abstraction away from assembly language resulted in fewer software engineering jobs.
Given the models are unlikely to stop getting better, I think it is fair to say the human contribution is going to keep getting "leaner".
That is going to change the job, but also head count.
But I agree harnessing models opens up opportunities for better product design, ... but only ... everywhere.
The people who design the most usable software have always been in a minority. They will be valuable for some time.
Are you the wallflower from the legendary fugg efnet rap video
https://www.youtube.com/watch?v=evBderwVKKE
This song bangs, looping this all day. What a throwback. Reminds me of ytcracker/digitalgangster days. Also reminds me of Das Racist. Thanks for posting this lol. Love that grime-ish electro beat.
Can you email me? Would love to chat/get more tunes from you.
Sure what is your email? Mine is ccarterdev@gmail.com I watch this comment more closely though :)
Mob currently controls my email
Emailed! Mines in my profile.
> I’m starting to think that’s going to be personality and feel and polish, but turned up a notch. That’s what I used to do when I started writing apps, but in some ways I have really toned it down in favor of OS alignment.
That's all that is really required. I mean look at the Microslop fiasco. They ruined a perfectly good editor: Notepad with AI slop. But this is not reflecting in their sales. They still are showing record revenues.
Just because a competing product exists does not mean your product is suddenly obsolete. There will always be people who will want to buy (provided the market is not oversaturated). Because that is how humans do things. AI won't change that behavior overnight [1]. Look around you and you will see every product you hold in your hand has at least 5-10 competitors.
[1] Think about all the things that are still not computerized and which requires you to fill some or the other form of paperwork. We have had computers for over nearly 6 decades now. We STILL have physical forms that we fill from time to time. Computerization was touted to revolutionize this and yet here we are. Still not achieved 100% digitalization. The same will happen with AI as well. There is this initial burst of excitement (which is the phase we are in) until reality sets in and that's when people will learn how to best use the technology. What you are seeing today (vibe coding et all) is NOT IT.
So now we're supposed to become the idea guy? Goddamnit.