Stack Overflow Questions Hit Near-Zero (3,862 in December) — Where Does Developer Knowledge Go When the Commons Dies?

[alex_dev]

I have been using Stack Overflow for over ten years. I remember the first time I posted a question — it was about a weird edge case in JavaScript closures, and within twenty minutes I had three answers, two of which taught me something I did not even know I needed to learn. That experience hooked me. I went on to answer hundreds of questions and earned enough reputation to feel like a contributing member of one of the most important knowledge institutions in software engineering.

So when I saw the latest data showing that Stack Overflow question volume dropped to 3,862 in December 2025, I felt something I can only describe as grief. That is a 78% year-over-year decline from roughly 17,000 questions per month. The platform that defined how an entire generation of developers learned their craft is approaching functional irrelevance.

The Workflow Shift

The reason is obvious to anyone who writes code in 2025. The developer workflow has fundamentally changed. It used to be: encounter a problem, search Google, find a Stack Overflow answer, read the accepted answer plus the top three alternatives, understand the trade-offs, and apply the solution. Now it is: encounter a problem, ask ChatGPT or Claude or Copilot, get a tailored answer, apply it. The second workflow is faster. It is also more convenient. And it is quietly destroying something irreplaceable.

The Knowledge Commons Problem

Stack Overflow was not just a Q&A site. It was a knowledge commons — a shared, searchable, peer-reviewed repository of developer knowledge. Every question and answer was indexed, discoverable, and subject to community validation through upvotes, downvotes, comments, and edits. When you read a Stack Overflow answer, you could see that 847 other developers agreed it was correct. You could read the comments warning about edge cases. You could see alternative approaches ranked by the community.

AI answers are ephemeral. They exist only in your chat session. They are not searchable by other developers. They are not peer-reviewed. They disappear when you close the tab. We have gone from a system where solving a problem contributed to collective knowledge to one where solving a problem benefits only you.

The Model Collapse Risk

Here is the part that keeps me up at night. AI models like GPT-4 and Claude were partly trained on Stack Overflow data. The quality of their programming answers comes, in significant part, from the millions of human-curated Q&A pairs that the Stack Overflow community spent fifteen years creating. If Stack Overflow dies — if developers stop contributing new questions and answers — then future AI models lose a critical source of high-quality training data. The AI is consuming the ecosystem that created it. Researchers call this model collapse, and we are watching it happen in real time.

The Dark Knowledge Problem

Developer Q&A has not disappeared. It has fragmented. Questions that used to go to Stack Overflow now go to Discord servers, private Slack channels, company-internal wikis, and ephemeral AI chat sessions. This is what I call the “dark knowledge” problem — the knowledge still exists, but it is no longer indexed, searchable, or accessible to the broader community. A developer in Lagos cannot benefit from a solution discussed in a private Discord server in San Francisco.

My Personal Experience

I used to contribute two to three Stack Overflow answers per week. It was part of my professional practice, like code review or writing documentation. Now I answer zero, because there are almost no new questions in my areas of expertise. The questions that do get posted are often immediately answered by AI-powered bots. The virtuous cycle — where questions attracted experts who attracted more questions — is broken.

The Quality Concern

AI answers are confident. They are also not always correct. I have seen AI confidently recommend deprecated APIs, suggest solutions with subtle security vulnerabilities, and provide answers that work in isolation but fail in production contexts. Stack Overflow answers were community-validated. An answer with 500 upvotes and no critical comments had been stress-tested by hundreds of developers. There is no equivalent quality signal for AI responses.

The Silver Lining

Maybe I am being too pessimistic. Perhaps what is happening is that AI is handling the trivial, well-documented questions — the “how do I reverse a string in Python” category — and Stack Overflow can evolve into a platform for complex, nuanced technical discussions that AI cannot handle well. Questions about architecture decisions, performance trade-offs in specific contexts, and debugging problems that require deep domain expertise. If SO can find that niche, it might survive as a smaller but more valuable community.

But I am not confident that will happen organically.

I want to hear from you: Do you still use Stack Overflow? What has replaced it in your workflow? And does the death of the knowledge commons concern you, or am I mourning something that needed to evolve anyway?

[eng_director_luis]

Alex, this post hits close to home, and I want to add the management perspective because what I am seeing with my teams genuinely worries me.

I manage about forty engineers across multiple squads. Last quarter, I did an informal survey — I asked each team to track their information sources for a week when solving technical problems. The results were stark. My senior engineers (5+ years experience) still occasionally visit Stack Overflow, maybe once or twice a week. My junior engineers — the ones hired in the last two years — never visit Stack Overflow. Not once during the tracking period. They go straight to ChatGPT, Claude, or Copilot for everything.

At first, I thought this was fine. They were shipping code, passing code reviews, and meeting deadlines. But then I started paying closer attention to the quality of understanding behind their solutions.

The Context Gap

Here is what I noticed. When a senior engineer solves a problem using a Stack Overflow answer, they can typically explain the trade-offs: why this approach was chosen over alternatives, what the performance implications are, and where the edge cases might be. When a junior engineer solves the same problem using AI, they can tell you what the solution is but struggle to explain why it is the right approach.

Stack Overflow answers came with context. The top answer had discussion threads. People posted warnings like “this works but will cause memory leaks in production with >10K connections.” Alternative answers offered different approaches for different constraints. The junior engineer reading that thread absorbed context about the problem space, not just the solution.

AI gives you one confident answer. It does not show you the debate. It does not show you the three developers who tried a different approach and explained why it failed. It strips away the learning that comes from seeing a problem from multiple angles.

What I Am Doing About It

I have started implementing what I call “explain your solution” reviews. In addition to standard code review, I now ask engineers — especially junior ones — to write a brief paragraph explaining why they chose their approach. Not what the code does, but why this approach was selected over alternatives. What trade-offs were considered. What assumptions were made.

The first few weeks were painful. Several junior engineers could not articulate why they chose their approach beyond “the AI suggested it.” That is not engineering. That is copy-paste with extra steps.

I am also encouraging my teams to document their solutions in internal wikis, even when AI helped them find the answer. The idea is to rebuild some version of the knowledge commons at the team level. It is not a replacement for Stack Overflow, but at least it creates searchable institutional knowledge.

The broader concern I have is about the next generation of senior engineers. The current seniors built their expertise partly by learning on Stack Overflow — reading debates, understanding trade-offs, and developing judgment. If the juniors today never develop that depth of understanding, what kind of senior engineers will they become in five years?

I do not have an answer for that, and it keeps me thinking.

[cto_michelle]

Alex, I appreciate the depth of your analysis, and Luis, the “explain your solution” reviews are something I might steal for my org. I want to add the strategic lens here because I think what is happening to Stack Overflow is part of a much bigger shift that every technology leader needs to understand.

The Broader Pattern

Stack Overflow’s decline is not an isolated event. It is part of a fundamental change in how developers discover and consume technical information. AI is replacing search as the primary interface for developer knowledge. Google search traffic to developer documentation sites is down across the board. My company’s technical documentation gets roughly 35% fewer organic search visits compared to last year. But when I look at how our APIs are being used, adoption is actually up.

What is happening is that developers are reading our docs through AI. They ask ChatGPT “how do I implement authentication with [our product]” and the AI synthesizes our documentation into a direct answer. The developer never visits our site, but they still consume our knowledge.

This is the same dynamic that killed Stack Overflow. The knowledge is still being used — it is just being consumed through an AI intermediary instead of being accessed directly.

Adapting to the New Reality

At my company, we have started restructuring our technical documentation for what I call AI-parseable knowledge. This means clear, consistent titles and headers. Structured data formats. Explicit versioning. We added an llms.txt file to our documentation site that provides AI models with context about our product, current API versions, and deprecation notices. If AI is going to be the interface through which developers consume our docs, we need to optimize for that interface.

Some of my peers think this is capitulation. I think it is pragmatism. The shift has already happened. Fighting it is like fighting the transition from print encyclopedias to Wikipedia.

The Commons Question

But here is where I agree with your concern, Alex. The knowledge commons is not dying — it is transforming. It is moving from a human-searchable format (Stack Overflow threads, blog posts, documentation sites) to an AI-parseable format (training data, RAG databases, structured knowledge graphs).

The risk is this: if the commons becomes AI-only — if knowledge exists only inside model weights and RAG systems owned by private companies — then it is no longer a commons. A commons, by definition, is a shared resource that anyone can access. If OpenAI and Anthropic and Google are the only entities that can effectively access and distribute developer knowledge, we have replaced a public commons with private gatekeepers.

I do not think we are there yet. But the trajectory is concerning. The open-source AI movement and efforts to keep training data transparent are important counterweights. We need to be intentional about ensuring that the new knowledge infrastructure remains accessible to everyone, not just those who can afford API credits.

The Practical Reality

For now, my advice to other CTOs and technical leaders is threefold. First, optimize your documentation for AI consumption — it is where your users are. Second, invest in internal knowledge management because the external commons is fragmenting. Third, support efforts to keep developer knowledge open and accessible, whether that is contributing to open-source training datasets or supporting platforms that maintain public knowledge repositories.

The era of Stack Overflow as the central nervous system of developer knowledge is over. What replaces it will be determined by the choices we make in the next few years.

[data_rachel]

I want to bring some data to this conversation because I think the numbers tell a more nuanced story than “developers stopped using Stack Overflow.”

The Usage Data

Last quarter, I ran an analysis of my team’s development workflow data as part of a broader developer productivity initiative. We instrumented browser activity (with consent) and IDE telemetry for eight data scientists and six ML engineers over a four-week period. Here is what we found.

Before AI tools (baseline from 2023 data): developers averaged 8.2 Stack Overflow visits per day. Average time per visit: 4.3 minutes. That is roughly 35 minutes per day reading Stack Overflow content.

Current state (Q4 2025): developers average 0.5 Stack Overflow visits per day. AI assistant interactions: 14.7 per day. Average AI interaction duration: 2.1 minutes.

On the surface, this looks like a pure efficiency gain. Developers are getting answers faster. But when I dug deeper into the data, the picture got more complicated.

The Understanding Gap

I measured what I call “solution iteration cycles” — the number of times a developer has to revisit or modify a solution after initial implementation. In the Stack Overflow era, the average was 1.8 iterations per problem. With AI-first workflows, it is 2.4 iterations. Developers get to a first attempt faster, but they spend more time debugging and refining.

The total time from problem identification to working solution is roughly the same. What changed is the distribution. With Stack Overflow, developers spent more time upfront reading and understanding, then implemented more correctly on the first attempt. With AI, they implement faster but iterate more.

And here is the critical insight: the net learning appears to be lower with AI-first workflows. When developers iterated with Stack Overflow, they learned WHY their approach was wrong by reading alternative answers and comments. When they iterate with AI-generated code, they learn WHAT is wrong through debugging, but they often do not develop a deep understanding of why the correct approach is correct. They just keep asking the AI to fix it until it works.

The Educational Loss

I miss the educational value of well-written Stack Overflow answers. I am thinking of answers like the famous explanation of floating-point arithmetic, or the detailed breakdown of how Python’s GIL works, or the comprehensive guide to SQL query optimization. These were not just answers — they were tutorials disguised as answers. Reading them made you a better engineer even if you were not trying to solve that specific problem.

AI does not produce that kind of educational content. It produces targeted answers to specific questions. There is no serendipitous learning. You do not stumble across an amazing explanation of a concept you did not know you needed to understand.

What the Data Suggests

Based on what I have measured, I think the Stack Overflow decline is creating a hidden debt in developer knowledge. Developers are shipping code at roughly the same speed, so the productivity metrics look fine. But the depth of understanding is shallower, and I suspect this will manifest as increased technical debt, more subtle bugs in production, and a slower rate of genuine innovation over time.

The hard part is that this is nearly impossible to measure in the short term. The effects are cumulative and slow-moving. By the time we can clearly see the impact in our data, it may be too late to rebuild what we lost.

Alex, to answer your question directly — I barely use Stack Overflow anymore, and I think that is a loss, not a gain.