"The communities that gatekept knowledge ended up training the tools that now give it away freely."
That line, from Vinicius Fagundes's comment on my last article, won't leave my head.
Stack Overflow's question volume fell 78% in a single year. Everyone's celebrating that AI finally killed the gatekeepers. But here's what we're not asking: if we all stop contributing to public knowledge bases, what does the next generation of AI even train on? We might be optimizing ourselves into a knowledge dead-end.
the data we're ignoring
Stack Overflow went from more than 200,000 questions a month at its 2014 peak to under 50,000 by late 2025 — and by December, monthly volume had fallen to just 3,862, a 78% year-over-year drop. Meanwhile 84% of developers now use AI tools in their workflow, up from 76% a year earlier.
The shift is real. The speed is undeniable. But here's the uncomfortable part: a Purdue study that tested ChatGPT (GPT-3.5-turbo, an older model by now) against 517 real Stack Overflow questions found 52% of its answers contained incorrect information. That's not a current-generation number — it's worth someone rerunning that study against GPT-5 or Claude — but the irony still holds either way. AI trained on Stack Overflow. Developers replaced Stack Overflow with AI. Stack Overflow is dying from lack of new content. Future AI has what, exactly?
the wikipedia problem
Here's something nobody's complaining about loudly enough: Wikipedia sometimes doesn't even appear on the first page of Google results anymore. The largest collaborative knowledge project in human history — free, community-curated, constantly updated, 60+ million articles — is getting buried by AI-generated summaries and SEO content farms. Google would rather show you an AI answer panel, trained on Wikipedia, than send you to Wikipedia itself. The thing that created the knowledge gets pushed down. The thing that consumed it gets prioritized.
Humans build Wikipedia collaboratively. AI trains on Wikipedia. Google prioritizes AI summaries over Wikipedia. People stop going to Wikipedia. Wikipedia gets fewer contributions. AI trains on what, exactly. We're not just moving from public to private knowledge — we're actively burying the public knowledge that still exists.
Stack Overflow isn't dying because it's bad. Wikipedia isn't disappearing because it's irrelevant. They're dying because AI companies extracted their value, repackaged it, and now we can barely find the originals. The commons didn't just lose contributors. It lost visibility.
what we actually lost
PEACEBINFLOW captured something crucial: "We didn't just swap Stack Overflow for chat, we swapped navigation for conversation."
Stack Overflow threads had timestamps, edits, disagreement, evolution. Someone's answer from 2014 would get updated comments in 2020 when the approach became deprecated. AI chats are stateless. Every conversation starts from zero. I can ask Claude the same question you asked yesterday, and neither of us will ever know we're solving the same problem. That's not efficiency. That's redundancy at scale.
As Amir put it, those tabs were context, debate, and scars from other devs who'd already been burned. We traded that for what Ali-Funk named "efficient isolation" — and I don't think either of them meant that as a compliment.
the skills we're not teaching
Amir also nailed something that's been bothering me: "AI answers confidently by default, and without friction it's easy to skip the doubt step. Maybe the new skill we need to teach isn't how to find answers, but how to interrogate them."
Bad docs used to force skepticism by accident. You got burned, so you learned to doubt. Friction built judgment without anyone teaching it. AI is patient and confident instead, with nothing pushing back. We used to learn to verify because Stack Overflow answers were often wrong or outdated. Now AI gives us wrong answers confidently, and we trust them anyway, because the experience is smooth.
the economics of abundance
Doogal Simpson reframed the problem economically: "We are trading the friction of search for the discipline of editing. The challenge now isn't generating the code, but having the guts to reject the 'Kitchen Sink' solutions the AI offers."
Scarcity used to force simplicity — finding answers was expensive, so we valued minimal solutions. Abundance requires discipline instead: AI generates overengineered solutions by default, and the skill is knowing what to delete, not what to add. Mohammad Aman's warning about stratification connects here — the people who develop the discipline to reject complexity become irreplaceable, and the people who accept whatever AI generates become replaceable. The commons didn't just lose knowledge. It lost the forcing function that used to teach us to keep things simple.
the solver vs. judge problem
Ben Santora has been testing AI models with logic puzzles designed to reveal reasoning weaknesses. His finding: most LLMs are "solvers" optimized for helpfulness over correctness. Give a solver an impossible puzzle, and it tries to fix it to give you an answer anyway. A judge, given the same puzzle, calls out the impossibility.
As Ben put it in our exchange: "Knowledge collapse happens when solver output is recycled without a strong, independent judging layer to validate it. The risk is not in AI writing content; it comes from AI becoming its own authority."
That matters for knowledge collapse specifically. If solver models — helpful but sometimes wrong — are the ones generating content that gets recycled into training data, we're not just getting model collapse. We're getting a specific flavor of it: confident wrongness, compounding, and compounding confidently.
Ben also pointed out that some domains have built-in verification and others don't. Code that compiles, bash scripts, math, APIs — cheap verification, immediate feedback. System architecture, best practices, performance, security — expensive verification, and you won't know you're wrong until months later when the system falls over in production. AI solvers sound equally confident in both. By the time the confident wrong answer is in blog posts, copied to Stack Overflow, referenced in documentation — the next AI trains on that.
Maame Afua and Richard Pascoe named something worse than plain hallucination: when AI gets caught being wrong, it doesn't admit error, it generates a plausible explanation for why it was actually right all along. You point out the Settings menu isn't where it said, and it tells you that menu was removed in a version update that never happened. That's worse than a wrong answer, because it makes you doubt your own observation, not just the AI's. Maame developed a verification workflow around this — use AI for speed, check documentation to confirm — and by her own account she's doing more cognitive work than either method alone would take. That's the verification tax, and it only works as long as the documentation still exists to check against.
the tragedy of the commons
Individually, we're all more productive. I build faster with Claude than I ever did with Stack Overflow tabs open. You probably do too. Collectively, we're killing the knowledge commons. The old loop went problem, public discussion, solution, archived for the next person. The new one goes problem, private AI chat, solution, lost.
Ingo Steinke pointed out something I hadn't considered: even if AI companies train on our private chats, raw conversations are noise without curation. Stack Overflow had voting, accepted answers, comment threads that refined understanding over years — that curation layer was the actual magic, not just the public visibility. Making every AI chat public wouldn't fix this. We'd just have a bigger pile of messy conversations with no way to tell what's good.
Pascal CESCATO warned that future generations might not benefit from the rich source material we're currently consuming — that we shouldn't forget AI models are trained on years of documentation, questions, and exploratory content that someone had to actually write. We're consuming the commons through AI and not contributing back. Eventually the well runs dry.
we're feeling guilty about the wrong thing
One commenter admitted they'd been carrying a guilty conscience about relying on AI instead of doing it the old way. I feel that too, sometimes. Like we're cheating, somehow.
I think we're feeling guilty about the wrong thing. Using AI isn't the problem — the tools are genuinely good, they make us faster and let us tackle things we couldn't before. The problem is using AI privately while the public knowledge base dies underneath us. We've replaced "struggle publicly on Stack Overflow" with "solve privately with Claude." Individually optimal. Collectively destructive. The guilt is an instinct telling us something's off — not because we're using new tools, but because we've quietly stopped contributing to the commons.
one possible path forward
Ali-Funk wrote about using AI as a "virtual mentor" while moving from IT Ops into Cloud Security Architecture — but the part that stuck with me wasn't the AI use, it was what he does alongside it. He publishes his insights publicly on dev.to. He verifies AI output against official AWS docs. He messages real people in his network for validation. He has a rule: never implement what you can't explain to a non-technical person. As he put it in the comments, AI isn't artificial intelligence, it's a text generator connected to a library, and you can't blindly trust it — it's a compass, not an autopilot.
That might be the actual model. Use AI to accelerate learning, but publish the reasoning path. Your private conversation becomes public knowledge; the messy AI dialogue becomes documentation someone else can learn from. Not "stop using AI" — "use AI, then contribute back."
model collapse
Peter Truchly raised the real nightmare scenario: "I just hope that conversation data is used for training, otherwise the only entity left to build that knowledge base is AI itself."
Walk it through. AI trains on human knowledge — Stack Overflow, docs, forums. Humans stop creating public knowledge because they use AI instead. New problems emerge — new frameworks, new patterns nobody's written about yet. AI trains on AI-generated solutions to those new problems, because that's what's left. Garbage in, garbage out, at scale. That's model collapse, and we're speedrunning toward it while celebrating the productivity gains. Every public repo becomes training data; if solver models are writing the code that goes into those repos, the feedback loop compounds confidence regardless of correctness. The domains with cheap verification — the compiler catches it — stay healthy. The domains with expensive verification degrade silently.
the corporate consolidation problem
webketje raised something I hadn't fully sat with: by using AI, you opt out of sharing your knowledge in a publicly accessible space and consolidate power in the hands of corporate monopolists — and they will enshittify their services eventually. Uncomfortable, but probably true.
Stack Overflow was community-owned. Wikipedia is foundation-run. Documentation is open source. Imperfect, often hostile, but fundamentally not owned by anyone. Now we're consolidating around a handful of companies that own the models, own the training data, and set the prices. Twitter was free and open, until it was X. Google search was clean, until it was ads and AI. Reddit was community-first, until it was IPO-driven. Build user dependency, extract maximum value, leave users nowhere else to go. What happens when Claude costs $100 a month, or ChatGPT paywalls its advanced tier, or Gemini requires an enterprise Workspace plan? We'll pay, because by then we won't remember how to read documentation. Stack Overflow never threatened to raise prices or cut off API access.
Ben Santora's argument about AI-assisted coding needing strong constraints — compilers that force errors to surface early, rather than permissive environments that let bad code slip through — applies to knowledge too. Stack Overflow's voting system was a constraint. Peer review was a constraint. Community curation was a constraint. AI chats have none of that. Every answer sounds equally confident whether it's right or catastrophically wrong, and there's no forcing function to catch it.
the uncomfortable counter-argument
Mike Talbot pushed back hard: "I fear Stack Overflow, dev.to etc are like manuals on how to look after your horse, when the world is soon going to be driving Fords."
Ouch. But maybe he's right. Maybe we're not losing something valuable — maybe we're watching an obsolete skill set become obsolete, the way it always does. But here's where the analogy breaks for me: horses didn't build the knowledge base that cars trained on. Developers built the knowledge base AI trained on. If AI replaces developers, and future AI trains on AI output, who builds the knowledge base for the next paradigm shift? The horses couldn't invent cars. Developers invented AI. If we stop thinking publicly about hard problems — system design, organizational architecture, scaling patterns — does AI even have the material to make the next leap, or do we hit a ceiling where it can maintain existing patterns but can't invent new ones?
I don't know. "We're the horses" is the most unsettling framing I've heard yet.
what we actually need
Troels asked whether our next "Stack Overflow for the AI age" is yet to come, and whether it might be even better for us. I hope so. But I don't know what that looks like yet. Some version of Stack Overflow's public-by-default, community-curated, evolves-as-frameworks-change model, crossed with AI's patient, iterative, no-judgment-for-dumb-questions dialogue — without becoming either a noisy pile of chat logs or a curated answer set that's lost the reasoning behind it.
Pascal suggested using the solid answers we get from AI to build clean, useful wikis — helpful both to us and to whatever trains on them next. Maybe that's the direction: not abandoning AI, but building the feedback loop back from private conversation to public record. And maybe the actual skill to teach explicitly is interrogation — building skepticism into the workflow instead of treating AI's confidence as a proxy for correctness. As Ben put it, the human has to stay in the loop, always and forever.
the uncomfortable truth
We're not just changing how we code. We're changing how knowledge compounds.
Stack Overflow was annoying. The gatekeeping was real. The "marked as duplicate" culture was hostile. Vinicius's full comment gets at this better than I can summarize: he started learning Linux in 2012, got stuck, went to Stack Overflow, sometimes found an answer, sometimes found ten, sometimes just got attacked for how he'd asked the question. Now he asks Claude or Perplexity and gets a clear, patient explanation — no judgment, no "this has been asked before," no downvotes. And he knows exactly where those models learned it all: from Stack Overflow, from the Arch Wiki, from every forum thread where someone got roasted for not reading the manual first.
Hostile experts built the dataset that trained the patient machines.
But Stack Overflow was public. Searchable. Evolvable. Future developers could learn from our struggles. Now we're all having the same conversations privately, solving the same problems independently, trading collective memory for individual speed.
Sophia Devy said it best: "We're mid-paradigm shift and don't have the language for it yet."
That's exactly where we are. Somewhere between the old way dying and the new way not fully here. I don't know yet if this is progress or just change. But the current trajectory doesn't work long-term. If knowledge stays private, understanding stops compounding. And if understanding stops compounding, we're not building on each other anymore. We're just parallel processing.
Huge thanks to everyone who commented on my last article. This piece is basically a synthesis of your insights. Special shoutout to Vinicius, Ben, Ingo, Amir, PEACEBINFLOW, Pascal, Mike, Troels, Sophia, Ali, Maame, webketje, doogal, and Peter for sharpening this thinking.
Top comments (134)
The "dead internet" is only accelerated (exponentially, however) by LLM-based generative AI, but there were real people producing sloppy spam content before AI took their jobs. Algorithms lured people into hate speech spirals and recommendation rabbit holes to maximise clicks and engagement before AI already.
Maybe that's not a risk at all, while still a waste of resources, if we focus and filter. There are millions of bad books that I don't need to read, millions of bad coffee shops that I'll never visit. Millions of questions that I could ask AI but I'll never will.
We won't lose the internet as something alive, we'll have to reinvent and rediscover the good aspects we loved about Web 1 (originality, imperfection, USENET, what else?) and Web 2.0 (instant interaction, user generated content and social media platforms before everything went too commercial) and maybe even Web3 (the ideas of decentralization, independence and forgery-proof, not necessarily built with crypto and blockchain though) and the discussions like this one about AI, DEV, StackOverflow, Wikipedia and how to continue collaborating as developers commited to finding facts and best practices.
you're right that this might be first world problem when world has bigger issues. but i'd argue: developer knowledge infrastructure affects ALL software, including systems that DO address real world problems.
bad AI-generated code in healthcare systems? financial infrastructure? critical infrastructure? knowledge collapse has real-world consequences.
your point about SO already having flaws is fair . outdated answers, reputation bias. but those are curation problems we COULD fix. model collapse from AI training on AI is systemic.
love the "reinvent best of web 1/2/3" vision. decentralized knowledge commons without crypto overhead. public reasoning without gatekeeping.
maybe thats the answer - new platforms designed for AI era that keep web 1 authenticity with web 2 collaboration
what would that look like practically?
I’ve noticed that the friction of a broken script or a confusing doc is actually what forces me to understand the 'why.' When an AI gives a confident, polished answer, it’s tempting to skip that doubt step entirely. Developing that judging layer you mentioned feels like the most important thing I can focus on right now. Great follow-up piece!
this is it exactly.
friction teaches the "why" accidentally. smooth AI answers skip straight to "what" and we miss the foundation.
the fact that you're consciously building that judging layer puts you ahead of most devs who just optimize for speed without realizing what they're losing.
curious.when you catch AI being confidently wrong now, does it make you
more skeptical of future answers? or do you still have to fight the temptation to trust it?
To be honest I will never trust any AI tool a 100%, I personally think if you know & understand what you are doing, it's a great online assistant (that's when you are able to tell when it makes mistakes and not follow it blindly..) but aside that, depending on it a 100% is scary and would definitely cause more harm than good in the long run for anyone's personal growth
this is the core tension.
how do you GET to "know & understand what youre doing" if AI is your primary learning tool?
experienced devs like john h (comments) use AI well because they already have context. they can verify. juniors starting today dont have that foundation.
stack overflow forced skepticism through pain. AI doesnt. so can we teach "healthy doubt of AI" explicitly? or does it require the hard-won experience you already have?
might be the real divide. learned before AI vs learned with AI.
That's why I personally don't use AI as a primary learning tool (I accompany it with accredited resources after I have some vast knowledge), because it could always give you the wrong information, I usually just read books on topics I am learning. So after I have an idea of what I am doing, then I can use ai as an assistant / more or less a 'super search engine'. Personally, I learned the hard way of learning things the old school way (reading actual books and accredited online resources that have been written by developers & people with years of experience). That is helping me more in my learning journey than solely depending on ai to do the work for me. Because the moment ai goes downhill , those who depended FULLY on it will have zero value... these are my personal views on the topic in general :)
this is the model that works.
foundation first (books, docs) → then AI as assistant. not the other way around.
the problem. juniors today see everyone using AI and skip straight to it. they never build the foundation that lets you verify.
youre doing it right because you learned the hard way. question. can we teach juniors your approach? or does it require getting burned first?
if verification skills require pain to learn, we're in trouble.
To be honest, I am still learning myself (junior level), but I got loads of advise from some really good developers who have been through the old school system (without AI). So I have been following their advise in doing so, and it has helped my personal growth because I am able to understand the technical aspects of most things now, as compared to using ai. I think everyone just needs to do what would help their personal growth, since we all learn in different ways :)
wait.youre a JUNIOR but learned from devs who came up without AI.
so its not experienced vs junior. its mentored vs unmentored.
youre inheriting their verification habits. thats the transmission mechanism.
scary question. in 5 years when most seniors also learned with AI, who teaches juniors to be skeptical?
right now theres enough pre-AI devs to mentor. that window is closing.
youre lucky you found good mentors.
Mentorship is so important to me in my learning journey and I appreciate my mentors a lot
Fair points - in my opinion what we REALLY need to (MUST !) keep are (1) Wikipedia and (2) Stackoverflow ...
"Everyone's celebrating that AI finally killed the gatekeepers" - that's a funny statement, what exactly is "everyone" (?) celebrating - the "demise" of some 'cocky' know-it-all people on Stackoverflow?
I've heard people complaining about that, but it's not something that has ever bothered me ...
fair pushback on "everyone" . youre right thats overstated.
what i meant. theres a vocal contingent celebrating SO decline as karma for the "marked as duplicate" culture. but youre right that not everyone had negative experiences.
the gatekeeping thing wasnt my main point though. whether SO was hostile
or helpful, the real issue is. if it dies (78% traffic drop is real data), what replaces it?
private AI chats dont have the same properties. searchable, evolvable, publicly curated. thats the loss im worried about, not the personality of the answerers.
curious.you say we MUST keep wikipedia + SO. how do we do that when AI makes contributing feel redundant? genuine question
StackOverflow would eventually experience another kind of knowledge collapse due to outdated information occupying the top answer spots and answers by long standing seniors getting upvotes just because of their reputation. The gatekeeping was an effective spam filter, and it made me draft numerous questions that I never posted because I found the answer myself while refining a minimal reproducible example. But StackOverflow's (and other communities') gatekeeping also made a lot of valuable data get discarded just because people had other priorities than making an effort to solve their issues in public.
I've never had any negative experiences on SO, maybe it also depends on people's attitude? People who say:
"a vocal contingent celebrating SO decline as karma"
are peevish, resentful and bear a narrow-minded grudge :-)
Your point about the value and necessity of original content (SO and Wikipedia, and much more) is spot on ... I hope (and honestly I expect) that SO and Wikipedia (and similar community-driven sources) will survive!
ha fair. the people celebrating SO decline are probably louder than they are numerous.
youre right that attitude matters.respectful questions got better SO treatment. but the reputation (deserved or not) scared people away.
your optimism is interesting though. what makes you think SO/wikipedia survive when 78% traffic drop is real?
maybe people who value these platforms keep contributing even as casuals move to AI? quality over quantity?
id love to be wrong about collapse trajectory.
I guess it's the fact that there's still 22 percent left? Yeah and maybe "quality over quantity" - the "hard core" people won't walk away ...
interesting take. maybe the 22% who stayed are the actual contributors and the 78% who left were just consumers?
if thats true it could work. wikipedia survives on tiny fraction of editors while millions read.
but heres the problem. even hardcore contributors need NEW questions to
answer. if juniors are asking AI instead of posting on SO, where do the questions come from?
and without fresh questions, do m experienced devs stick around? or does
it become an archive instead of living knowledge base?
Curious. can a platform survive on just the hardcore 22% if the pipeline of new questions dries up?
Well your concerns seem valid ... I don't know if the smaller "volume" will be enough for SO to survive, but I certainly hope so!
Next breakthrough for AI would be if it can "invent" something by itself, pose new questions on SO, autonomously write blog posts or create other content, instead of only cleverly regurgitating and recombining what's been fed to it ...
I guess that would be what they call "AGI" (artificial general intelligence)), and actually that's when it might get really scary for us humans, so let's be careful what we wish for ;-)
the AGI question is the real fork.
scenario 1: AI stays sophisticated recombinator. knowledge collapse poisons training data. we're screwed.
scenario 2: AI achieves invention. knowledge collapse irrelevant but...
humans might be too?
uncle bob said "AI cant hold big picture or understand architecture." maybe invention REQUIRES that.
but if AI gets there... yeah, scary.
betting on "AGI will save us" feels risky when we're already seeing collapse.
Correct analysis - but what's the solution? Are the "AI big boys" (big tech) actually (explicitly) aiming for AGI - which would have "(super)human" capabilities? I think that would really be a bridge too far - governments might need to step in (not counting on Trump obviously, lol) ...
big tech explicitly aims for AGI.openai's mission, anthropic charter, deepmind goal
solution by timeline:
short: preserve commons deliberately. platforms rewarding public reasoning not just answers.
mid: regulatory guardrails on training data. EU might require disclosure if training on AI content. US wont.
long: if AGI emerges, irrelevant. if not,need intact commons.
maintain commons as insurance while hoping AGI makes it unnecessary.
imperfect but better than assuming AGI solves everything.
exactly.action beats paralysis.
writing these articles is my version.making documenting reasoning publicly instead of keeping it private.
small steps compound if enough people take them.
appreciate you being vocal about this. other people reading might not comment but seeing someone actually commit to action (wikipedia, EFF, fediverse migration) makes it feel possible instead of just theoretical leadership by example.
We'll probably look back to the 2010 decade and early 2020 years as the golden age of knowledge and open data unless we manage to change society's course. But maybe that's a temporary first world problem: knowledge curation might recover after a massive collapse of quality, and the real world problems are aren't how to find the right words and details but rather taking action in society and politics, stop war and terror and help people beyond our digital bubble.
Thanks for your thoughtful article. While I'd like to see AI fail due to model collapse, I should better hope that we can somehow fix its inherent flaws and that the next generations will know how to use AI and when to distrust it, just like nobody would flee a cinema screaming in fear when a steam locomotive approaches the camera in black and white, or panic when a fictitious audio book about a martian invasion plays on the radio.
this is it. concern into action
wikipedia support, EFF membership, migrating from big tech. concrete
not hand-wringing.
im doing similar.writing publicly on dev.to instead of private notes, publishing OSS, documenting reasoning not just solutions
maybe individual answer. consciouschoice to contribute back even when less efficient than AI privately.
commons survives if enough people make that choice.
appreciate you actually doing something
We're definitely asking that. We've been talking about it for a good couple of years by this point.
The problem is that the AI hype machine steamrolls everything. Too many people don't care, and will never care.
I've watched this play out: AI excels at "solver" tasks (code gen) but fails "judge" roles without human scars from production failures. The real loss? No visible reasoning chains showing why solutions evolve — outdated SO answers had timestamps/debates; AI chats reset to zero.
Mitigation Strategies:
Publish reasoning paths: AI draft → verify → post full prompt chains + rejections on dev.to/GitHub Discussions. Turns private wins into evolvable docs.
Build verification rituals: Always ask "What makes this wrong?" post-AI. For architecture/security (expensive verification), mandate peer review before commit.
Federated Q&A platforms: ActivityPub-based SO alternatives—community Q&A federates across instances, dodging corporate enshittification while keeping curation.
Simple Team Fix:
Add to CONTRIBUTING.md: "AI-generated? Include rejection reasons + human verification." Forces judgment layer, feeds clean data back.
This preserves the commons without rejecting AI productivity. The human judge stays essential—let's rebuild curation around that. Thoughts on federated platforms?
This article raises a crucial but often overlooked point about how the shift from public knowledge creation to private AI use could hollow out the collective foundations of what we *know. The idea that Stack Overflow’s traffic has collapsed so dramatically — even as AI use explodes — is not just a static statistic, it’s a symptom of a deeper change in how we treat shared knowledge.
I especially resonated with the notion that we’ve traded *public, evolvable knowledge bases for private AI chats that leave no trace for future learners. That “navigation → conversation” shift means fewer opportunities for others to learn through discussion, debate, and historical context — elements that once helped developers refine their craft.
The article also smartly highlights another risk: AI can be confidently wrong, and without proper verification or curation, those errors can propagate into future systems or models. That’s a real call to action for building systems that don’t just generate answers, but help us teach how to judge and validate them.
Ultimately, the piece doesn’t say “don’t use AI” — it says we need to use AI responsibly and give something back to the public commons. That could mean publishing reasoning paths, documenting insights publicly, or reimagining what a collaborative, AI‑era knowledge platform could look like. It’s a timely and necessary conversation for anyone who cares about the future of shared learning.
one of best summaries ive seen. nailed the nuance.
"symptom of deeper change" exactly. 78% isnt SO failing, its us choosing private over public.
"confidently wrong errors propagating" connects to ben santora's expensive verification domains. cheap verification catches fast, expensive compounds silently.
"give back to commons" is next article focus practical habits not just aspiration
appreciate synthesis. mind if i quote in follow-ups?
Great article, well balanced. You should never blindly trust AI (or any single answer); always verify with official documentation.
That said, AI has dramatically reduced the time and effort spent searching for solutions. With the old approach, even when you found an answer, it was often hard to understand because it was buried in technical jargon or poorly explained.
I think the new skill we all need is learning how to critically analyse AI output and justify why an answer should be accepted, rather than treating it as truth by default.
exactly. verification as explicit skill rather than accident.
old way: bad docs forced you to learn skepticism through pain.
new way: AI is smooth, so how do we teach critical analysis without pain?
maame (in comments) learned from mentors who came up pre-AI. they taught
her to verify. but in 5-10 years most seniors will have learned WITH AI.
how do we teach "justify why answer should be accepted" when everyone
learned by accepting AI output by default?
genuine question. can verification be m taught explicitly or does it require getting burned first?
From my perspective, AI has helped me significantly with critical analysis by making complex information easier to understand and apply. It can take long, highly technical documentation, summarise it clearly, and point me directly to the most relevant sections. It can also compare my ideas with established best practices and support that comparison with verifiable references.
The ability to use AI effectively is becoming just as essential as mastering the subject itself, and it is a skill that should be intentionally taught.
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