I recently experienced some cognitive decline due to chronic health issues. This is unfortunate timing because I am shipping a game with online multiplayer for the first time. So "brain works properly" is kind of a basic job requirement! But you work with what you got...
Some day I have the energy to do programming "manually", but on many days, the difference between "tasks get done" and "I am just staring at the screen reading the same code over and over again" is "ask the LLM to do it."
So I have had to shift my identity from "the guy who does the thing" to "the guy who ensures the thing has been done."
That being said, I do check the diffs carefully. (I tried a more hands-off approach a few months ago, and that was a pretty bad idea.) And I've learned to make the diffs as small as possible: more digestible for me, and less room for the AI to go off the rails.
Nice bonus to small, well defined changes is that small, fast, cheap models can handle them. (Having a great time with Luna lately, even on Low reasoning effort, which I wasn't expecting at all.)
I think that the industrialization of software development already happened decades ago. The common professional software development methodologies, both waterfall and sprint-based, ARE the industrialization of software development. Isn't modern "agile", sprint-based software development methodology directly influenced by Japanese manufacturing methods? I don't know when software development actually was a guild field, and my personal experience doesn't reach back this far, but based on what I've read at least, it seems to me that even back in the 1990s it was already not a guild field.
In fact the four-year degree you'll get from school is getting increasingly distant from the skills I actually want out of a new grad. It's not impossible to bridge the gap or anything but my transition into the commercial realm in the early 2000s was a cakewalk compared to the sheer number of things I'm asking a new grad to learn as soon as they're settled in at their desk... source control, CI/CD, bug trackers, devops, and that's just the beginning of that list not the end.
Those skills are easily taught. But in every run of the mill project I’ve been on, I’ve been really happy about all the maths and other science stuff I’ve been taught at college. It’s easier to grasp the web DOM, React, and git when you already know about trees and the related algorithms. Easier to learn SQL after opening some books on relational theory (basically the first chapter on database theory) and learning about projections. And definitely easier to learn the nature of distributed systems.
Learning how to use Bash and Ansible is a walk in the park after that.
For similar aesthetic reasons, have only dabbled with code-gen LLMs. Im happy to vibe code css and html but not, well actual code. I'm aware of my own double standard, Im fine with median-reverting banal css and html.
I do worry about some aspects of LLMs :
- google giving AI code snippets so easily, prevents traffic to sites like stack-overflow, where discussion happens
- vibe coding means less people hit the actual hard parts of coding, which result in learning to think as a developer. Thence where the next generation of developers who understand code ?
- code will become a magical, inscrutable black box where no-one understands how it works. DNNs and LLMs have this feature already. At least with LLM discovered math, the proof in lean exists, which is independent at how it was arrived at / searched for.
- if I write a novel, and the DNN AI detects it as AI slop with 68% probability, then what recourse do I have to prove its human written ?
- circular training : subsequent rounds of LLMs trained not on human input, but on LLM slop of the previous generation [ the %ge of which increases ]
- you will own nothing : fortunately it looks like self-hosted open weight LLMS will remain a thing, but the massive spend by hyperscaler companies on Datacenters/GPUs/RAM has driven down supply and driven up prices of CPU, GPU, RAM for enthusiasts / gamers / home AI nerds / garage startups .. which might stifle innovation.
https://getdx.com/blog/ai-productivity-gains-are-10-percent-...
But I spent a lot of time in the second half of this week dealing with friction with a team that is very annoyed that I'm moving fast and using an agile methodology so I can't tell them the exact, precise REST calls that I'm going to have for them in six months designed to a tee and signed off in triplicate before they start development against it. Manifesting that increase in code production as real value to the business is going to take more from me than just spewing the code out more quickly.
AI isn't creating this problem. I would have had this problem anyhow even if I were writing all the code by hand again. I know, because I've been there before. But the increased velocity is manifesting in increased organizational stress and not just increased velocity.
AI is perhaps even helping solve it to some degree, though far from totally. I have written before about how people eventually learned not to play the "oh well we can't do this until we have documentation" card on me [1]. This week they played the "well, I see you have docs but they aren't in our precise format". Guess what AI can do in about 15 minutes really well? You may recall the term "style transfer" getting tossed about a lot 3-4 years ago, and it is still something AI is extremely good at, and "take these docs in this format and convert them to that format" is just a style-transfer problem. AI really does chew at the "oh but we need docs" old-school card... and they can't even complain about the quality of the AI docs because in order to do that, they'd have to actually read them, and that is not the point of the "but we need docs" card, you see....
I don’t really agree that AI can’t make development faster, though. What the author describes as the negative AI outcome is blind vibecoding. There are many other ways to use it, and even the basic “enhanced autocomplete” is a net benefit, especially since that functionality is dirt cheap or free.
We have been programmed to believe that MBA wet dreams are inevitable because "$x hundreds of billions invested can't be wrong" but they very often are.
The thing about complex systems is that they are not predictable. Right now the answer is whatever you think is right, and a lot of scenarios seem plausible.
It is a fun intellectual moment trying to make sense, but maybe only time will tell how the dynamics work out.
Doesn’t really support the rest of the argument. If you’re a skeptic and think it’s all hype, there’s nothing to worry about.
Especially if you replace people with robots that don't need days off and have the people train their replacements.
That might be because of some lag, but another explanation might be that the tools are really effective at increasing someone's perception of their productivity while net productivity gains measured over long time windows are low or zero. Another explanation might be that the incompetent/negligent users are causing enough harm to nearly neutralize the gains brought by proficient / effective users. I don't know what the explanation is, but there is a mystery in the divergence between users' perceptions of their own productivity, and the observed productivity gains in the economy.
I’m in the final prep for ship phase of a project that started February. It’s a bottom-to-top rewrite of a project that’s been shipping for two years, and took two years to write, initially.
I’m deliberately doing it all with the $20/month ChatGPT sub. After it ships, I’ll move to the $100/month sub; but I want to be able to say that the entire project was done on a low-tier LLM subscription.
Yes. But.
Industry can miss its revenue targets, Oracle can go bankrupt (looking more and more likely) OpenAI, Anthropic and SpaceX might find "true value" at about 10% of current valuations (that feels right to me), all that can happen with the associated economic chaos... and still out of it comes world shaping new technology
Both things can be true
There are things that LLMs remain uniquely suited for, while the echo chambers go for everything.
There's no requirement to use new technology. Maybe what you do, or how you do it is just fine, and that's OK.
Skepticism today can remind me of someone who went into a cave for 3-4 years with a dated take of LLMs on day 1 and not remember software improves, and LLMs are software.
There are no shortage of uniquely working solutions with LLMs if sone seeks to find them, and also the self-validating echo chamber that says that it doesn't if that's desired. Which parts of software development, or how software development may better happen differently with the involvement of AI remains to be seen.
I don’t think it can be denied that the models do show an ability to find security vulnerabilities that may have otherwise been missed
You have to stop thinking like an employee or a worker bee, and start thinking like a boss.
Guess what, the work you give your boss isn't 100% gold either. And sometimes you are preoccupied, or tired, so what you deliver isn't the best of all possible deliverables. Sometimes you hallucinate, i.e. you are dead wrong when you think you are dead right.
This is the job of a boss: take fallible employees and turn what they produce into something actually valuable. The boss lets the grunts do the grunt work, and then they combine the output together, and do the due diligence, etc etc.
It's an entirely new skillset. But learning the new skillset isn't the real blocker--the real blocker is that you've got to stop thinking like a grunt and start thinking like an executive.
WHAT is it that you really want to do? Frame that question as clearly and as concretely as you can. Then farm out thee work to human or AI grunts, and be prepared to do your due diligence on the result.
Why not replace all mid-execs with LLMs trained on every MBA textbook and linked-in blog post ? why stop at grunt coders, graphic designers and phone marketeers ?
Can we reduce the role of CEO to the following prompt : "make money, by any legal means"