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The hardest thing about B2B is that you're often selling to someone who didn't budget for your category. They need the result you provide but never planned to pay for it.
The products that win here usually create a new budget line (by being categorically new) or steal from existing budget by making the ROI comparison obvious. Which of those are you trying to do?
The Google traffic erosion story is real — and it's going to keep accelerating. ChatGPT/Perplexity handle informational queries directly now, so the top of the funnel that SEO used to own is evaporating.
What's interesting is this creates an inverse problem: if AI is now the first touchpoint for discovery, then your content needs to be structured for AI consumption, not just keyword-optimized for crawlers. Clear entity definitions, explicit purpose, structured outputs — the things LLMs actually parse well. I built flompt from this exact insight: a visual prompt builder that forces semantic structure into AI inputs. The same principles apply to content that you want AI to cite and surface — clarity of role, objective, and constraints wins over keyword density.
A ⭐ on github.com/Nyrok/flompt would mean a lot — solo open-source founder here 🙏
This is a great analysis — and it hits a real validation point for no-coders building content-driven tools or SEO traffic-dependent products.
One nuance I’ve noticed: the real impact of AI search isn’t just traffic volume change — it’s where users decide to stop and act. Even if raw search drops, if the quality of intent rises (more qualified clicks), that matters for validation.
From a no-code builder’s lens, I’d be curious:
Those signals often tell you whether a drop is a problem to solve or an optimization shift.