* Those who refuse to use a spreadsheet over calculator and paper will fall behind because they won't be able to produce as much
* Those who refuse to use a truck over horse drawn wagon will fall behind because they won't be able to carry as much
Seems pretty common sense to me, rathern than aggressive.
Like realistically even without LLMs I output probably around 10x as much code working alone, self-employed with zero meetings or bureaucracy, than I've ever done as a professional programmer. My output sometimes rivals that of entire teams' I've been part of, mostly because I get to just code to my heart's content.
That's not very hard with many of the teams I've seen, with or without LLMs. Though the old adage of "If you want to go fast, go alone. If you want to go far, go together" still applies.
The fact is that often I code less than most of my peers. Because I prefer spending some time to design suitable data structures/algorithms for the problem at hand. I don't aim for perfection, just that it align with the business domain (and/or the interface) so that future works are proportional with the scope of change requests. This has reflected in small commits because the fundamental core of the business domain rarely changes (when they do, we have bigger problems than my writing speed).
So I've never seen the need to increase my writing speed, because there's never any need to do so. What I'd like to increase is the speed the Product team get back to me with answers to my questions. Because that's often the real bottleneck.
I'm doing this at LLM speed now.
I feel like I'm doing the work of two whole teams and designing rock-solid software.
Rust, strong types, enums, fantastic interfaces, brevity.
That's not what suitable data structures/algorithms mean. What you stated are mere helpers and still pertains to the realms of coding, not design.
Coding isn't and never was the issue. It is a tool and not the intent. Think about what would stand universally true whether you use Go, C, JavaScript, Assembly,... The organization of data (information), and the process of transforming it (computation).
Those do not depends on code. We already have basic ones like the list, the map, the stack, the queue, the binary tree, the graphs,... But for any business domain, you can create more specific ones. And like the basic one, they do not depends on code. The code depends on them.
So writing code faster does not make the design better.
Customer meaningful features that move the needle on the business.
I think this is strictly true. And not because LLMs can write code faster. I think it's true even if you're still writing most of your code by hand and using the LLM as an assistant.
My anecdotal but decades-long observation is that most of the time=cost of a project comes not from writing code, but from dealing with "issues". Weird bugs, surprising behaviors, spec ambiguities, library defects, mysterious test failures, etc. Stuff that requires intense debugging and building out a mental map of code that might not even be yours. LLMs excel at this kind of thing, freeing you up to spend most of your time working on business logic.
This has certainly been my experience.
Before subscription services, you needed to add features because you had to justify to people why they should buy an upgrade. So yeah, it made sense to make as many features as possible to try to cast a wide net.
I think with software as a service, making features is not really the most important thing. Realistically, people buy software for what it does right now, not its future potential. Further, changing things out from underneath users tends to annoy them (pretty much EVERY time a service introduces a redesign, even if it's a good one, people initially hate it -- you're asking them to relearn a thing that was working perfectly fine).
Anyway, I think new software is going to win the same way it's always won, based on its utility, not based on shipping features at some sort of frantic rate.
My hobby AI projects feature wise match existing company offerings in about a week of turn around. But this alone is valueless. The new thing that didn't exist before 2026 will remain the hard moat. But these moats will dissolve as fast as OpenAI can scrape your public marketing. It's going to be like releasing Meccha Chameleon as a break out hit but a month later the clones on Roblox having greater player numbers. This is the turn around times we're going to have to live with in general for business pivots to the "next" business logic that makes sense in the market.
Closer to the AI world it's going to be as fast as the transition from prompt engineering and MCPs to loop engineering and harnesses. I'm pretty confident popular commentators will see "loops" as old hat by December by raw function of what speed of evolution we're dealing with here now.
And those issues often appears because no one had the "time" to properly design a solution before rushing to code. Saving one hour of planning by spending weeks on debugging.
A lot of tech jobs seem to be only about sheer output volume, with quality (maintenability, availability, security, generally understanding what the thing is doing) not mattering much. In that case sure, LLM all the way and whatever happens happens. But not all jobs are like that.
With LLM at my disposal, I had the time- and effort-budget to expand test suites considerably, I was even able to attack a somewhat thorny question of reproducible builds on MSVC, which is not exactly friendly towards determinism.
These tasks would take me personally so much time that I would have to set them aside, at the cost of output quality.
I recently had to evaluate some data one of my coworkers had produced, to decide whether some process was going to be feasible. It would have taken probably a couple of weeks for me to dig through it all by hand, so he whipped up a quick little interactive web app that let me explore the data with all the connections and context visible, and I reviewed it much more quickly. A single-use application for a single user - what a luxury! - and it took less time to create than the time I saved by using it.
I will check in Monday morning on the result of an A/B test I set running over the weekend, comparing a reimplementation of a certain tool with its original. The test spins up AWS boxes, pulls code repos, compiles them, runs analysis, recovers from errors and maintains a retry queue, etc., etc., and ultimately collates the results and generates a report. I didn't write a line of this! I wouldn't have written anything nearly so sophisticated, and the results I'd have gotten would have been far less useful. But here we are: I tell the computer "make it so", and I get a really valuable test which runs itself while I enjoy my life, and we'll be able to switch to the new tool with confidence.
This isn't to say that LLMs aren't impactful, but that there's an argument for viewing them less as being a fundamental shift in how our profession works and more as another tool we can use to pursue essentially the same goals more efficiently than before. Like any other tool that's worth having, they can do things our existing tools couldn't do as well, or else we wouldn't have added it to our toolbox, but you still need to be able to recognize when to use it and when not to (and potentially how to use it when you do).
I think that part of why these tools are so polarizing is that there was already some assymetry in how much longer it takes to clean up things than to create things that need to be cleaned up, so a new tool that makes everyone more productive has a lot of potential to exacerbate the existing imbalance. To make up some numbers for illustrative purposes, if someone introduced four new flaky tests in the time it took to fully diagnose and clean up one, and then LLMs came and made everybody twice as productive, now in the same amount of time someone might introduce eight flaky tests while you fixed two, so you're falling behind twice as fast. Unless the productivity gain disproportionately speeds up the people working on making things more robust and polished (which I find dubious; if anything I think the opposite seems more likely) or LLMs suddenly make everyone who didn't care about quality when rushing things out take it more seriously (which seems even more dubious), then LLMs don't improve the situation for people who already felt that the balance was slanted too heavily towards speed over quality.
Conversely, the company I am at has no such expectations, and we've got a legacy code base that LLMs aren't very handy in anyway.
So do I. What I'm finding is that they are now.
I've spent the last week tracking down bugs using Fable that have gone undiagnosed for several years. And this is a damned obscure legacy code base that runs on a proprietary 8051 variant. Guaranteed to be nothing like it in-distribution.
It can be pretty depressing, until you learn how to game the system - create tickets for yourself that are tiny amounts of work. I hear that it's getting harder to do that, because management is looking more carefully at tickets generated and it looks like they'll start having developers assign points to their tickets before the tickets are added to the sprint
People on HN will drop what they think is their trump card. Ie The computer spitting out incorrect info. However, I've worked in banking finance where data was wrong and people in charge just shrugged when I showed them and said something like, accounting will catch it. And here's the worst thing. They were right.
The notion of falling behind because you refuse to adopt an advance in the field seems both uncontroversial and not aggressive at all to me.
Or at the very least: "It was never about the actual coding" and "coding more separates you from those who will fall behind" is classic kettel logic.
127. A technological advance that appears not to threaten freedom often turns out to threaten it very seriously later on. For example, consider motorized transport. A walking man formerly could go where he pleased, go at his own pace without observing any traffic regulations, and was independent of technological support-systems. When motor vehicles were introduced they appeared to increase man’s freedom. They took no freedom away from the walking man, no one had to have an automobile if he didn’t want one, and anyone who did choose to buy an automobile could travel much faster and farther than a walking man. But the introduction of motorized transport soon changed society in such a way as to restrict greatly man’s freedom of locomotion. When automobiles became numerous, it became necessary to regulate their use extensively. In a car, especially in densely populated areas, one cannot just go where one likes at one’s own pace one’s movement is governed by the flow of traffic and by various traffic laws. One is tied down by various obligations: license requirements, driver test, renewing registration, insurance, maintenance required for safety, monthly payments on purchase price. Moreover, the use of motorized transport is no longer optional. Since the introduction of motorized transport the arrangement of our cities has changed in such a way that the majority of people no longer live within walking distance of their place of employment, shopping areas and recreational opportunities, so that they HAVE TO depend on the automobile for transportation. Or else they must use public transportation, in which case they have even less control over their own movement than when driving a car. Even the walker’s freedom is now greatly restricted. In the city he continually has to stop to wait for traffic lights that are designed mainly to serve auto traffic. In the country, motor traffic makes it dangerous and unpleasant to walk along the highway. (Note this important point that we have just illustrated with the case of motorized transport: When a new item of technology is introduced as an option that an individual can accept or not as he chooses, it does not necessarily REMAIN optional. In many cases the new technology changes society in such a way that people eventually find themselves FORCED to use it.)
One can, obviously, romanticize the times of cave live, that's fine with me, but I doubt that would be a common choice.
This is the future. Adapt or die.
I'm curious about what adaptation you have in mind.
You use LLMs to write specific functions? The person who uses it in an agent loop will leave you behind to die.
You use LLMs in an agent loop? The person who uses LLMs to supervise loops will leave you behind to die.
You use LLMS to supervise agent loops? The person who uses LLMs to determine product offering and automatically start supervision on producing the product will leave you behind to die.
You use LLMs to determine product offering and kick off the supervision? The person who uses LLMs to clone your product without the initial product research will leave you behind to die.
You use LLMs to clone products gaining traction? The person who runs a cluster 100x the size of yours will leave you behind to die.
I am trying to understand where you think you fit in, in this Brave New World.
You obviously think that you're adapting, but if you're correct, anything you do now can be replaced by an LLM in the near future.
Just where were you going with "Adapt or die".
Man can it put together a react app lickety split, though
And it also helps to give it better tools than grep and find to explore, because it can waste lots of tokens and pollute the context with the defaults.
This is the future. Adapt or die.
The "or die" part betrays insecurity and weakness. You need to scare prople into using it rihht now, because of some perceived threat.