When ChatGPT and similar tools exploded into the engineering workflow, a reasonable prediction was that AI would reduce reliance on peer communities. If an LLM can generate circuit suggestions, debug code, and explain technical concepts, wouldn’t engineers need forums less? The 2025 Engineering Insights Report shows the opposite. AI is making community engagement more important, not less.
56% of engineers are currently using AI in their workflows, with another ~20% planning to adopt soon.
The adoption is concentrated in conceptual design (47%), documentation (44%), and software development (35%). LLMs have appeared on the information source list for the first time in the study’s nine-year history, with 25% of electronics engineers using them during design work. But here’s the critical finding: despite this rapid adoption, communities still rank 2.6x higher than LLMs as an information source (65.4% vs 24.8%). The gap is enormous.
The reason is fundamental: AI generates answers, but communities generate trust. These serve fundamentally different functions in the engineer’s decision-making process.
An LLM can suggest a component for a low-noise amplifier design. But it can’t tell you whether that component actually works reliably in a high-vibration industrial environment. It can’t share first-hand experience with a particular manufacturer’s technical support quality. It can’t warn you about an undocumented errata that only shows up at specific clock frequencies. And crucially, it can’t be held accountable for a recommendation that turns out to be wrong.
Communities can do all of these things. Every response comes from an identifiable peer with a reputation, a track record, and a professional stake in being accurate.
AI adoption is actually creating a new category of community engagement. Engineers increasingly use LLMs to generate initial approaches, then bring those AI-generated suggestions to communities for peer review.
“ChatGPT suggested using a flyback topology for this power supply. Has anyone actually built this? Does it work at the current levels I need?” — This type of AI-to-community validation query is becoming a common pattern in engineering forums.
This pattern makes intuitive sense. LLMs are trained on internet-scale text data, which includes outdated information, marketing materials, and content of varying quality. An LLM might recommend a component that’s been discontinued, or suggest a design topology that works in theory but has known practical limitations. The community provides the reality check.
There’s a secondary effect worth noting. The proliferation of AI-generated content on the web is actively degrading search engine quality. Engineers report finding more AI-generated articles, thin summaries, and regurgitated content in search results. This content ranks well algorithmically but adds no real value.
This degradation pushes engineers further toward communities, where content is inherently human-generated, peer-reviewed, and tied to real-world experience. The more AI pollutes the open web, the more valuable curated community knowledge becomes.
As AI adoption accelerates, the content that manufacturers create becomes even more important — because it feeds both the AI systems and the community discussions that validate AI output. Outstanding application notes, reference designs, and technical documentation serve triple duty: they help individual engineers directly, they improve AI responses (since LLMs train on this content), and they fuel community conversations that drive peer validation.
The manufacturers who invest in genuinely excellent technical content will see compounding returns across all three channels. Those who produce thin, marketing-oriented content will find it ignored by engineers, deprioritized by AI systems, and absent from community discussions.
AI isn’t replacing communities. It’s making them more valuable by creating a new validation layer in the engineer’s workflow. The companies that understand this complementary relationship — and invest in both AI-friendly content and authentic community presence — will capture engineers at every stage of this evolving information journey.
SparkWire’s communities sit at exactly this intersection: 410,000+ engineers who are adopting AI tools while simultaneously relying on peer communities to validate what AI suggests. For marketers, the opportunity is to be present in both conversations — providing the technical content that AI references and the community credibility that engineers trust.
Want the full data? Download the complete report: "Where Engineers Really Make Decisions: The Engineer’s Information Journey"
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