u/gisurfpa

Every list I find is either two years old or stuffed with affiliate links to tools that go paid after day 3. Sick of clicking through to find out "free" means 5 queries a month.

The real issue is most roundups don't separate "free tier that's actually usable" from "free, trial with a credit card attached." Those are completely different things and nobody labels them honestly.

For what it's worth, my current stack that's genuinely $0: Google Search Console for ranking and indexing data, Screaming Frog free version for crawling anything, under 500 URLs, Ahrefs Webmaster Tools for backlinks on my own verified sites, and AlsoAsked for question-based keyword ideas (3 searches a day, no card needed). I also ran Semrush's Free Tools for a quick site audit at one point and the SEO Checker flagged a few technical issues I'd completely missed. PageSpeed Insights rounds it out for Core Web Vitals.

Not every site needs a $140/mo subscription. This stack covers audits, keywords, backlinks, and performance without touching a wallet. Drop what you're actually using below.

been thinking about this a lot lately and honestly the homogenization thing feels real but I'm not sure it's inevitable. there's that MIT study showing students using ChatGPT for essays ended up converging on the same vocabulary and concepts even when they were trying to be different. and when you think about how most LLMs are trained on basically the same scraped web data, it makes sense the outputs cluster toward some kind of average. the scary part is the feedback loop, AI content gets indexed, trains the next model, which produces even blander outputs, and so on. from an SEO and GEO angle this has pretty direct implications. if 94% of marketers are running content through similar AI tools with similar prompts, we're going to end up with a web full of content that sounds identical. and AI systems like Perplexity or Google AI Mode are already pulling from that content to generate their answers. so the sources being cited are increasingly homogenous, which means the citations reinforce the same narrow range of perspectives. for brand visibility in AI that's a real problem because standing out gets harder when everything looks the same. but I reckon the prompt quality argument is legitimate too. generic prompt in, generic content out. the people I've seen get genuinely differentiated AI output are usually giving it heaps of specific context, proprietary data, unusual angles. so maybe the homogenization problem is partly a skill gap rather than a fundamental LLM limitation. curious whether others working on AI share of voice are seeing this play out, like are the brands getting, cited more often the ones doing something distinctly different with their content or just the ones with more domain authority?