AI Visibility Is the New SEO for Indie Makers
Search has quietly changed.
People aren’t clicking links as much anymore. They’re getting answers directly from AI tools like ChatGPT, Google AI Overviews, and Perplexity.
If your product isn’t mentioned in those answers, you’re invisible.
What’s actually happening
More users start searches in AI tools
A majority of searches now end with zero clicks
AI summaries are replacing “research via links”
For indie makers, this means ranking on Google isn’t enough anymore.
SEO → GEO (Generative Engine Optimization)
The goal isn’t to rank pages.
The goal is to be included in AI answers.
Ask yourself:
“If someone asks AI for the best tool for my problem, would it mention me?”
If not, that’s the new gap.
What helps AI pick you up
Clear, structured content (short paragraphs, real FAQs)
Specific use cases and comparisons (not generic marketing)
Real mentions on Reddit, Indie Hackers, Quora, niche forums
Reviews, case studies, and “we actually used this” content
AI trusts context, not slogans.
How to think about metrics now
Are you showing up in AI answers?
How often are you mentioned or cited?
Is the sentiment positive?
Are any signups coming from AI tools?
Page rank matters less. Visibility matters more.
TL;DR
Don’t just optimize for Google.
Optimize for where AI reads the internet.
Indie makers who adapt early get free distribution.
Everyone else gets summarized away.
The first $500 MRR is the hardest milestone because everything is manual and nothing compounds yet. The founders who get through it are usually the ones with conviction about a specific problem rather than a general vision.
What's the specific problem you're most confident about solving?
The first $500 MRR is the hardest milestone because everything is manual and nothing compounds yet. The founders who get through it are usually the ones with conviction about a specific problem rather than a general vision.
What's the specific problem you're most confident about solving?
I’ve been leaning into the idea that products need to show up inside AI answers, not just on old-school SERPs. Tools that explain your product clearly and feed structured info to LLMs seem to help. I’ve seen folks team up with a marketing agency to tighten their messaging and surface the right signals so AI has something worth pulling in, kind of like giving it breadcrumbs it can’t ignore.
Good instinct on tracking mentions that’s the right mental shift.
One thing I’ve seen work (and not work): you can’t really “feed” AI your docs and expect lift the way you’d submit a sitemap to Google. Most teams try that and get nothing.
What does seem to matter is whether your content already shows up in places AI trusts:
clear problem framing, concrete use cases, comparisons, and third-party discussion where real people reference the product in context.
Internally structured docs help once you’re being pulled in, but they rarely create visibility on their own. External signals usually come first, then your site becomes the reference.
So I’d treat AI mention tracking less like keyword rank and more like a lagging indicator of clarity + distribution lining up.
Curious if the mentions you’re seeing are coming from your own site, or from external discussions that distinction tends to explain most results.
Great breakdown of how search behaviour is evolving. I’ve noticed the same thing: when I ask AI for recommendations, it tends to cite specific case studies or forum threads rather than generic homepages.
For indie makers, that seems to mean investing in content that actually solves a problem and leaves a trail of real usage examples. Getting mentioned in community discussions feels more like contributing to the training data than marketing.
Have you been doing any prompt testing to see where you show up in AI answers? I’m curious how folks are measuring this kind of visibility before more formal tools emerge.
100 percent. AI rarely pulls from polished homepages unless there’s real context behind them. The community trail matters because it captures intent and usage, not claims. I’ve been doing light prompt testing with problem-specific questions and tracking whether the product shows up organically. It’s still messy to measure, but patterns do emerge when real examples and discussions increase.
This reframes the game really well. The shift from "ranking for keywords" to "being cited in answers" is fundamental.
One pattern I've noticed: AI models seem to heavily weight specificity and context over generic marketing language. A page that says "best project management tool" gets ignored, but one that explains "how we reduced sprint planning time from 3 hours to 45 minutes using X workflow" gets picked up because it reads as genuine experience rather than positioning.
The Reddit/IH/forum mention point is underrated. These models are trained on conversational data where real people discuss real problems. If your product gets organically mentioned in those discussions, you're essentially getting peer-reviewed by the training data.
Curious about measurement - have you found reliable ways to track AI-driven discovery? The attribution is murky since users don't always know (or say) they found you via ChatGPT or Perplexity.
Exactly. AI doesn’t reward claims, it rewards procedural specificity. “Best tool” is meaningless to a model; “here’s what broke, what we changed, and the result” is reusable reasoning.
Forum mentions work the same way. Repeated, problem-level mentions across Reddit/IH act like consensus signals, not promotion.
On measurement: we’re pre-analytics. I rely on prompt testing across tools, referrer hints where available, and self-reported attribution as directional signals. When organic forum mentions rise, AI visibility usually follows a few weeks later.
Feels a lot like early SEO before the tooling existed.
"Procedural specificity" - that's the exact framing I was reaching for. AI models can't do anything with "best" but they can pattern-match against "here's what we tried, what broke, and why this worked."
The early SEO parallel is apt. We're at the "figure out what works by experimenting" phase before proper tooling emerges. The prompt-testing approach makes sense - systematically checking if your product shows up in AI responses to relevant queries is basically manual SERP tracking for a new interface.
The forum-mentions-leading-AI-visibility lag you mentioned is interesting. It suggests the relationship is causal rather than just correlated - forums are literally training signal, not just proxy metrics. That changes how you think about content strategy: you're not just building brand awareness, you're contributing to the dataset that shapes future AI responses.
One experiment I've been curious about: intentionally using distinct, memorable phrasing when describing your product in forums, then tracking whether those phrases eventually appear in AI responses. Like tagging your own data to see if it gets picked up.
Yes....procedural specificity is the currency AI actually “spends.” Generic claims like “best tool” have zero utility because models can’t verify them or turn them into reasoning. What matters is concrete, reproducible steps: what failed, what was changed, and what the outcome was. That’s the pattern AI can cite and generalize.
Forum mentions function the same way. When multiple users discuss real problems and solutions across Reddit, Indie Hackers, or niche communities, it creates a consensus signal that models learn from. It’s not promotion it’s data that informs the AI’s reasoning.
On measurement, we’re still in the pre-analytics era. I use prompt testing across tools, track referrer hints where possible, and collect anecdotal attribution. Typically, there’s a lag: increases in organic forum mentions show up in AI visibility a few weeks later. It’s very reminiscent of early SEO before proper tracking tools existed.
One interesting experiment I’ve been running: deliberately using unique, consistent phrasing in forum posts almost like tagging your content and then checking if those phrases surface in AI responses later. It’s a kind of manual dataset engineering, but early results suggest it works.
The unique phrasing experiment is clever - essentially creating linguistic fingerprints to track your content's propagation through training data. It's like SEO keyword tracking but for a system where you can't inspect the index directly.
"Manual dataset engineering" is an honest framing. We're all doing this whether we admit it or not - every forum post is a potential training sample. The difference is doing it intentionally vs accidentally.
Curious what patterns you're seeing in the early results. Are certain types of phrasing more "sticky" than others? I'd guess concrete, jargon-adjacent terms (specific enough to be unique, generic enough to be reusable) would propagate better than purely invented vocabulary.
The “procedural specificity” point here is the money. One tactic that’s worked for makers: build a single canonical ‘How it works + when to use it’ page that’s written like a workflow, not a pitch.
A scrappy AEO/GEO loop:
Also +1 to your format insight: ChatGPT tends to like narrative walkthroughs, Perplexity rewards explicit sourcing/comparisons, and AI Overviews often pull clean definitions + supporting context.
This is spot on.
Procedural specificity gives AI something it can simulate, not just summarize.
A workflow-style page answers “how” and “when,” which is exactly what most AI prompts are implicitly asking.
Thanks for sharing your approach! It's helpful to hear that it's still early days for measuring AI-driven discovery. Have you noticed any particular prompts or types of discussions that tend to get your product picked up more consistently? Curious how you're scaling this beyond one-off tests.
Spot on about the shift to "being cited" rather than "ranking."
The forum mentions point is particularly interesting because it creates a compounding effect. When someone authentically mentions your product solving their specific problem in a Reddit thread or IH discussion, that context is:
The irony is that the most effective GEO strategy looks almost identical to what worked before the AI era: genuinely helping people where they're already discussing problems. The difference is the payoff is now multiplicative rather than linear.
One thing I've noticed - AI tools seem to heavily weight specificity. "Best email tool" gets ignored, but "how I reduced cold email response time from 48 hours to 20 minutes using X workflow" gets cited because it reads as real experience rather than marketing.
For those monitoring this: have you tried tracking your product mentions across communities? The correlation between organic discussion volume and AI citation seems real.
Absolutely agree, especially on specificity. AI seems to reward lived experience over positioning language. Real problem, real context, real outcome. The forum mentions matter because they’re earned, not manufactured, and that’s why both humans and AI trust them. Tracking community mentions has been eye-opening for me too. When discussion volume goes up, AI visibility usually follows.
exactly - the earned vs manufactured distinction is key. and that discussion volume → AI visibility lag is real, creates a nice feedback loop once it kicks in
This resonates with what I'm seeing while building a tech news aggregator with AI summaries. The "specific use cases and comparisons" point is key — when I structure summaries with concrete elements like prerequisites, tradeoffs, and next actions, they get much more engagement than generic TL;DRs.
The challenge I'm finding: content that's optimized for human skimming (bullet points, headers) often works differently than content optimized for AI citation. AI seems to prefer narrative context that explains why something matters, not just what it is.
For Japanese dev content I'm translating, this creates an interesting dynamic — the original often has dense procedural detail that AI loves, but needs restructuring for English readers who expect different formatting.
Are you seeing any patterns in what types of structured content get cited more reliably by different AI tools (ChatGPT vs Perplexity vs Google AI)?
Yes, I’m seeing the same tension. AI tools tend to cite content that explains causality and intent, not just structure. ChatGPT seems to favor narrative walkthroughs and real workflows, Perplexity leans more on explicitly sourced comparisons, and Google AI Overviews pulls from clear definitions plus supporting context. Dense procedural detail does well with AI as long as the “why” is explicit. The trick is layering narrative context first, then structure for humans.
Helpful breakdown — the "narrative context first, then structure" approach makes sense. For Japanese dev content, I'm finding that the original articles often have implicit causality (step X exists because of constraint Y) that gets lost in direct translation. The explicit "why" needs to be surfaced, not just preserved.
Will experiment with layering narrative before bullet points. Appreciate the insight on Perplexity vs ChatGPT differences — useful for deciding how to format different content types.
I’ve been developing a premium AI app concept called INNER — The Mirror to Your Mind. It’s designed to help users uncover subconscious patterns through subtle interactive choices and daily micro-interactions.
The goal is to create a deeply engaging and emotionally resonant experience that encourages self-insight, personal growth, and consistent engagement.
I’d love to hear feedback from fellow founders and developers on how to refine the experience and make it even more impactful.
Thanks! That’s exactly what I was aiming for to offer a fresh perspective on personal growth and self-insight.
Thanks, really appreciate that.
I’m still early and exploring how subtle interactions can create meaningful self-insight without overwhelming the user.
Curious from your experience — what usually makes people stick with products like this long-term?
What usually drives retention in products like this isn’t insight, it’s recognition. Users come back when the system reflects something back that feels uncomfortably accurate. My advice would be to anchor the experience around a few repeatable moments where users think “this described me better than I could.” Start narrow with one or two patterns you can detect reliably, then deepen those over time. Consistency of reflection beats feature depth early on.
That’s a really insightful point. Focusing on consistent moments of reflection rather than overwhelming features makes perfect sense — it gives users a reason to return and trust the experience. Curious to hear if you’ve seen examples where even subtle repeated insights significantly increase engagement.