Thanks for this post, I can truly relate. Some thoughts from a 50+ year old genx guy (EE/CS) who’s worked to stay current and keep a sharp mind in the face of these challenges plus the inevitable age-related decline:
- regular exercise has incredible benefits for the body+ mind (and the attitude that directs it). Biggest impact of everything on this list.
- Side projects that involve actual doing and not just study. e.g. I spent some money on gear for embedded development and am doing things that involve physical things and using my hands and making gadgets … phone is out of the picture while in the lab.
- Ironically, using LLMs for reviewing and refreshing knowledge you once had but now has gaps is amazing. I spend a bunch of my phone time now having in-depth chats where I know what I’m talking about to a large degree, but the LLM is helping me fill in gaps and then quizzing me on concepts and I know that the things it’s telling me are solid due to my background, then I go check, etc. A really good way to do super focused study to reload a field back into your brain. Might as well take advantage of the fact that they stole all of the world books.
- Write things down! After reviewing, do some problems with pencil and pad. After studying, doing a problem on pencil and paper is actually daunting, but then grooving through a few problems actually makes you realize you know the stuff again! And using an expensive Ampad notebook and nice mech pencil is a great sensory experience.
See Amodei’s comments regarding Iain M Banks’s Culture, his goal is benevolent machine rule. I suspect many HN folks would agree; I, for one, was rooting for the Iridians.
> If it is done responsibly, we believe automated AI research will yield models that directly enhance human welfare and advance OpenAI’s mission.
That’s what I call a load-bearing “if”.
I do not trust OpenAI or other hyperscalars to do this responsibly; and IMO it will be very difficult for government-led efforts not to result in a technocracy where a cabal of AI companies are pulling the strings. Dark times lie ahead, especially when you consider the shrinkage of true source material on the internet and the stranglehold these companies will have on information; and these AI CEOs to me are reminiscent of 19th century robber barons, none seem trustworthy.
I agree. Even though I thought this mission was interesting, to me the article massively overstates everything. NASA and the crew is SO amazingly competent, the world in recent years is SO totally devoid of competency, everyone has been thirsting for the sense of AWE that we are ALL feeling (or should be feeling now, let me list the reasons!), etc.
To me, this was irritating. True competency and things that inspire real awe encapsulate “res ipsa loquitur” — they speak for themselves. Having some internet influencer try to hype me into getting awed, and implying that “we all” are feeling a certain way as she channels our collective zeitgeist is tiresome.
And personally, IMO although the mission was nice, it wasn’t groundbreaking technically or particularly awe-inspiring.
Ironically, I left feeling a tiny bit disappointed: if everyone is truly thinking this mission is the height of awesomeness or competency, we have a low-ish bar.
I bet that when the old-timers with their starched white shirts, pocket protectors, and horn-rimmed glasses that did the 60s missions got together to watch 2026 Artemis they privately had a good laugh about how little state-of-the-art has progressed.
One way that I could imagine a human-only HN could evolve in the coming AI wasteland: motivated individuals join small local groups and are validated face-to-face at meet-ups. Local trusted leads gatekeep their chapter’s posts, and this scalable moderation works up the tree. Bad leaves get culled out reasonably fast, maybe there’s some controls at the top level that let you see more content “lower down the tree” if you’re ok with lower SNR. Latency to get a post widely distributed grows but I don’t see that as a massive problem.
> coming AI wasteland: motivated individuals join small local groups and are validated face-to-face at meet-ups. Local trusted leads gatekeep their chapter’s posts, and this scalable moderation works up the tree. Bad leaves get culled out reasonably fast,
I've been thinking the same. One way to moderate is to bring back physical consequences.
I'd also like to see an "Order of the White Lotus" community (or Fight Club if you prefer) where people who collectively agree to not use AI against each other can come together. They can still use AI (i.e. out of necessity) just not with other members knowingly.
I suspect whatever form it takes the stakes will be very high to hack yourself into and pollute the space. So the more successful the community becomes, the harder it is to keep in order.
In my recent experience, local meetups and groups are unexpectedly more prone to self promotion and low effort spamming.
Local groups have a problem where members admit their friends or pressure others into inviting their friends who are not a net positive, but it feels too impolite to refuse or to kick someone out. Meeting someone in person also develops a sense of a social bond that makes it harder to downvote or flag their posts.
Local groups have always been a haven for affinity fraud, too. Running a scam is easier when you can smile, be charismatic, and pretend to be a personal friend before springing your ask on to your victims.
This sounds like failure of leadership. Our coding meetups are already implementing what the GP suggested [0] and we also enforce our written guidelines (in this case, politely removing the bad eggs.)
Fully agree: I believe my decades of software engineering experience definitely help me fly LLM tools better than less experienced folks.
But the much more interesting question to me: as LLM coding becomes the norm, does it drive the cost of self or small-company generated software to 0?
Like many SW architects/engineers my not-so-developed work-in-retirement plan is to assemble a small team of people I’ve loved working with over the years, start an LLC, and try to make a reasonable (not posh) living doing what we love: making software to solve problems.
On the one hand, it’s clear LLM coding can accelerate and amplify our efforts, but alternately there’s many people claiming there’s no possibility of a moat, your solution/innovation can be cloned in a matter of days … ie. the value of your software is exactly 0.
Not sure which future will be closer to reality. A backup plan that seems reasonable in the 0-value case is to focus our effort on creating actual physical gadgets and systems in the embedded realm, which conceivably can be designed and prototyped by a small team… It seems like these would still be valuable.
My team has experienced this over the past 6 months for sure.
The core of the article is “ AI-assisted development potentially short-circuits this replenishment mechanism. If new engineers can generate working modifications without developing deep comprehension, they never form the tacit knowledge that would traditionally accumulate. The organization loses knowledge not just through attrition but through insufficient formation.”
But is it possible this phenomenon is transient?
Isn’t part of the presumed value add of LLM coding agents in the meta-realm around coding; e.g. that well-structured human+LLM generated code (green field in particular) will be organized in such a way that the human will not have to develop deep comprehension until needed (e.g. for bug fix/optimization) and then only for a working set of the code, with the LLM bringing the person up to speed on the working set in question and also providing the architectural context to frame the working set properly?
In my view with current LLMs: they still produce far too much bloat and unclean solutions when not targeting them at very specific issues/features, making LLMs essentially a requirement for any debugging or features for the lifecycle of the product/service.
- regular exercise has incredible benefits for the body+ mind (and the attitude that directs it). Biggest impact of everything on this list.
- Side projects that involve actual doing and not just study. e.g. I spent some money on gear for embedded development and am doing things that involve physical things and using my hands and making gadgets … phone is out of the picture while in the lab.
- Ironically, using LLMs for reviewing and refreshing knowledge you once had but now has gaps is amazing. I spend a bunch of my phone time now having in-depth chats where I know what I’m talking about to a large degree, but the LLM is helping me fill in gaps and then quizzing me on concepts and I know that the things it’s telling me are solid due to my background, then I go check, etc. A really good way to do super focused study to reload a field back into your brain. Might as well take advantage of the fact that they stole all of the world books.
- Write things down! After reviewing, do some problems with pencil and pad. After studying, doing a problem on pencil and paper is actually daunting, but then grooving through a few problems actually makes you realize you know the stuff again! And using an expensive Ampad notebook and nice mech pencil is a great sensory experience.
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