What comes after Stack Overflow?

For more than a decade, Stack Overflow has been the go-to forum for developers seeking answers to coding questions. At its peak in the mid-2010s, the site saw more than 200,000 new questions each month. Those days are gone. Since the arrival of AI assistants such as ChatGPT, Stack Overflow’s usage has fallen off a cliff. In fact, from March 2023 to March 2024, the volume of new questions dropped from ~87,000 to ~58,800, a 32% decline in just one year. By the end of 2024, monthly questions were down 40% year over year, hitting levels not seen since 2009. Developers, who previously relied on community validation for accurate answers, now increasingly trust an algorithm’s replies, which are immediate and confident (yet not always correct).

This shift creates an ironic problem: Those large language models (LLMs) owe their impressive knowledge bases to human-generated content from platforms like Stack Overflow, creating their own cannibal’s dilemma of sorts. I’ve written at length about this problem, but the real question isn’t about how to recapture what we’ve lost, but how to move into the future. As these communities fade, what will LLMs use for future training, and how will developers ensure the accuracy and reliability of the answers these tools provide?

Paradoxically, the AI assistants displacing Stack Overflow are themselves products of Stack Overflow’s success. LLMs such as ChatGPT became “smart” by training on vast troves of human-generated content—much of it scraped from sites like Stack Overflow. Every well-explained answer, every debated best practice, and every upvoted code snippet on these forums became grist for the AI mill. In a very real sense, the data that flows from community Q&A platforms is the bedrock on which future iterations of LLMs are built. Early models were trained on data sets that included millions of Stack Overflow posts capturing the nuances of programming problems and their solutions.

This dynamic raises a concern voiced by Peter Nixey, a longtime Stack Overflow contributor: “What happens when we stop pooling our knowledge with each other and instead pour it straight into The Machine?” In other words, if developers no longer post questions and answers for others to learn from, what will tomorrow’s AI models train on? This creates the risk of model collapse, where AI increasingly recycles its outputs rather than ingesting new, human-validated insights. The accuracy and innovation in AI-generated solutions could deteriorate, and niche or emerging technologies might be sidelined if they’re absent from shrinking training data sets.

Replacements for Q&A platforms

If the classic model of social Q&A is fading, what comes next? Developers aren’t going to stop needing help anytime soon. The void left by traditional forums will likely be filled by a combination of approaches rather than a single successor.

The most obvious option is the one that is already happening whether we like it or not: LLMs are the new Q&A platforms. In the immediate term, ChatGPT and similar tools have become the go-to source for many. They provide the convenience of natural language queries with immediate answers. It’s possible we’ll see official “Stack Overflow GPT” bots or domain-specific LLMs trained on curated programming knowledge. In fact, Stack Overflow’s own team has been experimenting with using AI to draft preliminary answers to questions, while linking back to the original human posts for context. This kind of hybrid approach leverages AI’s speed but still draws on the library of verified solutions the community has built over years.

We’re also going to see developer tools increasingly integrate AI assistants. IDEs and platforms will almost certainly further integrate LLMs that can answer “how do I…?” questions on the fly, pulling in relevant code examples or documentation. We already see this with GitHub Copilot and various IDE chat plug-ins. The key challenge will be ensuring these assistants reference trusted sources.

In my own experience, it’s still a bit of a crapshoot. It’s hard to tell where LLMs pull information from, and they often cite incorrect information from less trustworthy sources than, for example, official documentation. The hope is that future iterations will increasingly cite official docs or knowledge bases in-line (much like an AI-infused search engine) so that developers can verify answers. Such tools could even use AI to help moderate and improve community content—for example, suggesting edits to a forum post for clarity or helping a user refine a vague question into a clear one. Rather than replace community, AI might become an ever-present assistant within the community.

Additionally, it’s still possible that the social Q&A sites will save the experience through data partnerships. For example, Stack Overflow, Reddit, and others have moved toward paid licensing agreements for their data. The idea is to both control how AI companies use community content and to funnel some value back to the content creators. We may see new incentives for experienced developers to contribute knowledge. One proposal is that if an AI answer draws from your Stack Overflow post, you could earn reputation points or even a cut of the licensing fee. Such systems could encourage experts to keep contributing high-quality answers, knowing they’ll be recognized and rewarded even in an AI-dominated landscape.

In short, no single platform may replace Stack Overflow in the traditional sense. Instead, its role is being distributed: part of it to AI assistants, part to more tightly knit communities, and part back to official sources. The hope is that these pieces together can provide both the efficiency of AI and the richness of human discourse.

Adapting to LLM-driven development

There may be no going back for software developers, but that doesn’t mean we should blindly accept whatever ChatGPT or GitHub Copilot churn out. Indeed, the more we rely on AI to drive development, the more skeptical we should become. To be successful, developers should consider several practices to ensure quality and accuracy.

Treat AI as a starting point and test relentlessly.

Verify critical AI-generated suggestions against official documentation or trusted sources.

Avoid blindly trusting code snippets without understanding their implications, particularly since LLMs often rely on outdated examples.

Always use linters, static analysis tools, and security scanners to put AI-generated code to the test before rolling it into production.

Pose the same question to different LLMs to expose inconsistencies.

In short, think of an AI assistant as just that: an assistant. It might be a smart assistant, but it’s informed by what it scrapes off the web. Just as you’d double-check human advice, do the same with AI-generated counsel.

On that note, keep people in the loop. Human context, mentorship, and validation remain vital in an AI-driven era. It’s important to engage regularly with peers through private or vendor-specific communities. This is perhaps one reason Reddit hasn’t seen the same decline as Stack Overflow: It’s a much more social experience that can’t easily be replaced by machines.

Unlike people, you don’t need to worry about hurting an LLM’s feelings when you provide harsh feedback. Tell the LLM when it gets things wrong. Actively engage in AI feedback loops by marking incorrect answers, suggesting corrections, requesting sources, etc. This human-in-the-loop approach should improve the tools over time. In some ways, it replicates the Stack Overflow experience.

Of course, to do any of this well, you’re going to have to invest in gaining core knowledge that will help you know how and when to trust AI output. Continuous learning and skill building remain essential, regardless of the convenience AI provides.

The future of software development will be owned by those developers who can effectively and critically prompt, evaluate, debug, and integrate AI outputs. This involves an “AI first” mindset and a commitment to continuous upskilling, particularly in areas where AI struggles, such as complex problem-solving and architectural design. It’s still an open question whether social Q&A sites will endure, but it’s a certainty that the future belongs to a combination of human and machine intelligence.