Discussion

dsabanin: I'm convinced that at some point looking like being productive and being productive becomes the same thing.

quater321: So what is important is not that 10 or 20 times the work can be done, but that you are stressed out and exhausted while doing your work?

caprock: I find value in learning some things deeply but not all things.The ability to be more selective about where I attend deeply, while leveraging fast shallow learning to complete other tasks... That seems like a potential benefit and a nice choice to have in the toolbox.

functional_dev: trick is maintaining enough domain expertise... so we can actually audit those shallow outputs.If the baseline knowledge drops too low we cannot tell when the AI is being lazy or wrong

softwaredoug: I have some algorithms I absolutely must know. So I’m hand coding them and asking the agent to critique me.I do a very similar thing in writing - I need feedback, don’t rewrite this!In both cases I need the struggle of editing / failing to arrive at a deeper understanding.The future dev will need to know when to hand code vs when to not waste your time. And the advantage will still go to the person willing to experience struggle to understand what they need to.

atomicnumber3: I don't think it's all that bad. There's definitely vibe coding that is "copy paste / throw away" programming on ultra steroids. But after vibe coding two products and then finding them essentially impossible to then actually get to a quality bar I considered ready to launch, I've been working on a more measured approach that leverages AI but in a way that simply speeds up traditional programming. I use it to save tons of time on "why is pylance mad about X" "X works from the docs example but my slightly modified X gives error Y" "how do I make a toggle switch in css and html" "how am I supposed to do Python context managers in 2026 (I didn't know about the generator wrapper thing)" all that bullshit that constantly slows you down but needs to be right . AI is great at helping you kickstart and then keeping you unblocked.I've been using Gemini chat for this, and specifically only giving it my code via copy paste. This sounds Luddite but actually it's been pretty interesting. I can show it my couple "core" library files and then ask it to do the next thing. I can inspect the output and retool it to my satisfaction, then slot it in to my program, or use it as an example to then hand code it.This very intentional "me being the bridge" between AI and the code has helped so much in getting speed out of AI but then not letting it go insane and write a ton of slop.And not to toot my own horn too much, but I think AI accelerates people more the wider their expertise is even if it's not incredibly deep. Eg I know enough CSS to spot slop and correct mistakes and verify the output. But I HATE writing CSS. So the AI and I pair really well there and my UIs look way better than they ever have.

nis0s: What’s important? That bridges get built and stay up, or that they’re built only after toiling X amounts of hours. AI will change the nature of work, it’s going to make a lot of people uncomfortable. But more importantly, it’s going to let people who understand things faster get the info they need to be productive.

bluefirebrand: AI does not currently build bridges that stay up

phil21: I have a feeling we would all be terrified if we knew how much AI had a role in building bridges at the moment.TBD if they stay up, I suppose.The stories I hear from various white collar professions not related to tech are... interesting, to say the least. There is a whole lot of unsanctioned shadow IT going on regardless of policy.

acmerfight: Pure 'vibe coding' is essentially technical 'tittytainment'. Using AI for the horizontal spread while you enforce vertical architectural depth is true deep work.

elgertam: I have a nearly total opposite take. I can't tell you how many times I've read a book, a paper or something else and been confused by some ambiguity in the author's prose. Being able to drop the paper (or even the book!) into an LLM to dig into the precise meaning has been an unbelievable boost for me.Now I can actually get beyond conceptual misunderstanding or even ignorance and get to practice, which is how skills actually develop, in a much more streamlined way.The key is to use the tool with discipline, by going into it with a few inviolable rules. I have a couple in my list, now: embrace Popperian falsifiability; embrace Bertrand Russell's statement: “Everything is vague to a degree you do not realize till you have tried to make it precise.”LLMs have become excellent teachers for me as a result.

acmerfight: We actually don't disagree at all—you are perfectly illustrating my point.Applying strict epistemic discipline (Popper, Russell) to resolve ambiguity and accelerate actual practice is the very definition of deep work. You aren't using AI as a shortcut to skip thinking; you're using it as a Socratic sparring partner to deepen it. This is exactly the paradigm shift I'm advocating for.

skyberrys: That's a different take than I've been considering AI to be genuinely useful. I try to not use it for deep work, infact I try to use it minimally but frequently for short checks on my own understanding.Using your research paper reading example, I would read the research paper, but then ask an AI tool specific questions about the work, frequently in new chats. Then at the end I might ask it to implement my description of the paper. I guess it's your 'debate with me' conclusion, the I ly difference is I would try to have multiple short conversations.

agumonkey: We need to allocate some % of our AI use to tackle this problem. Help us learn and find better abstractions and methods.

peteforde: Several weeks ago, I spent about a week fully reverse engineering a Stereomaker pedal. It accepts a mono signal and produces a stereo field using a 5-stage all-pass filter to mess with the phase without the use of delay (which sounds cheesy and creates a result that doesn't mix well back to mono).I've not really worked with audio circuits previously, and I'd been intimidated to approach the domain. My journey was radically expedited by iterating through the entire process with a ChatGPT instance. I would share zoomed photos, grill it about how audio transformers work, got it to patiently explain JFET soft-switching using an inverter until the pattern was forced into my goopy brain.Through the process of exploring every node of this circuit, I learned about configurable ground lifts, using a diode bridge to extract the desired voltage rail polarity, how to safely handle both TS and TRS cables with a transformer, that transformer outputs are 180 degrees out of phase, how to add a switch that will attenuate 10dB off a signal to switch line/instrument levels.Eventually I transitioned from sharing PCB photos to implementing my own take on the cascade design in KiCAD, at which point I was copying and pasting chunks of netlist and reasoning about capacitor values with it.In short, I gave myself a self-directed college-level intensive in about a week and since that's not generally a thing IRL, it's reasonable to conclude that I wouldn't have ever moved this from a "some day" to something I now understand deeply in the past tense without the ability to shamelessly interrogate an LLM at all hours of the day/night, on my schedule.If you're lazy, perhaps you're just... lazy?Anyhow, I highly recommend the Surfy Industries Stereomaker. It's amazing at what it does. https://www.surfyindustries.com/stereomaker

acmerfight: This is a phenomenal example of exactly what I am advocating.Notice you didn't ask the AI to 'just design a stereo pedal for me.' You interrogated it, reasoned about netlists, and forced the concepts into your brain through intense friction. That is pure deep work.

great_psy: Does this post feel AI generated to anyone else ?But to actually answer the question: I’ve been putting research paper pdfs in notebook llm , and turning them into ~40 minute podcasts which I listen to on my walks. Yes it’s shallow learning, and it might have some hallucinations in there but I wouldn’t have read some of those otherwise.

acmerfight: Ironically, what you described is exactly using AI to help with deep work. You do the heavy lifting (reading), and use AI strictly for stateless verification and testing your mental model. That is the ideal synergy.

SoftTalker: Weird post given it looks like an LLM wrote it.

dminik: If you're not sure what something is saying, how can you be sure that the AI had picked the correct interpretation?

Arainach: I'm sick of hearing about AI, but I'm significantly more sick of anyone who knows how to write English prose at a level higher than "typical rural American" being accused of using AI to write.

SoftTalker: Well that's the world we live in now.

nis0s: Not really, there’s a lot it does right. But any automated tool or calculator will be as good as its operator.

atrettel: The issue, in my experience, is that there is a lot of productive work that does not look productive at first glance. Long term work may not look productive for years until it suddenly is tremendously productive. And there is a lot of quiet and often thankless maintenance work that goes on largely unnoticed that helps others do their jobs well. Both have value despite superficially looking unproductive at times. I'd argue that both look productive at long time scales but unproductive at short time scales.

sagarm: LLMs absolutely let you explore ideas and areas you wouldn't have otherwise...but does your new design actually _work_?I'm curious whether the "knowledge" you gained was real or hallucinatory. I've been using LLMs this way myself, but I worry I'm contaminating my memory with false information.