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Did AI Put SEO on Notice?
Two years ago, search marketers were up against two challenges: more frequent and supposedly “helpful” Google algorithm updates, and the growing din around AI search. Industry voices warned content teams to experiment with AI-assisted workflows or risk watching a $90‑billion SEO industry collapse under its own weight. Google stoked the fire with a demo at its 2023 I/O conference, unveiling an AI-powered “Search Generative Experience” that answered complex queries in full paragraphs, leaving little reason for users to click through.
If Google did the “heavy lifting” of reading and summarizing content, then why have publishers?
As analysts pointed out, Google’s new search model could only work if it could pull from the already existing content ecosystem. Its demo used information from the National Park Service, tour guide websites, and countless other sources.
That means without those sources, the impressive generative answers would have nothing to draw from.
For companies like Aura — already a recognized name in the identity theft protection space and search engine results pages (SERPs) — the stakes were clear. Starting in late 2021, Aura doubled down on organic content and by 2023 was seeing more than 400% year‑over‑year traffic growth and twice the number of referring domains. Back then, Aura’s content ops were entirely human-powered, running on bi-weekly sprints. A small in-house team, supported by freelancers, handled content, off-site growth, and reporting.
But as AI began eating up traffic, Aura needed to start codifying its content operations.
Alina Benny: The Operator Behind Aura’s Content
“This year, content ops looks different: we’re a smaller pod working closer with internal teams, and solving specific problems instead of chasing broad rankings,” said Alina Benny, head of content and SEO at Aura.
Alina’s perspective is based on a decade of being in the SEO trenches. At Sales Hacker, she ran content until its acquisition by Outreach. At Nextiva, she pushed traffic up 500%. Now at Aura, her focus is on driving organic conversions, lowering CAC, and building AI-informed content systems.
She also advises companies like Figma, Gong, and Hotjar, mentors through First Round Capital’s Fast Track, and sits on juries for the Digiday and Women in Content Marketing Awards. Her track record is building lean content operations tied to revenue, not vanity metrics.
Where AI Fits in
The principle behind this shift is simple: give AI the repetitive work — metadata, internal link suggestions, content refreshes — so humans own research, topic selection, and accountable claims. That line matters in YMYL spaces. Just this summer, The Verge flagged how Google’s own Med-Gemini model invented a non-existent brain structure, and NPR reported on lawyers sanctioned for filing AI-generated briefs full of fabricated citations.
For Aura, which operates in identity protection and other YMYL categories, these examples underline why human oversight isn’t optional. Alina’s team front-loads the human work into detailed briefs and documentation, then uses AI for steps like outlines, metadata, and formatting shortcodes. Her team remains responsible for reviewing and approving final drafts.
Every AI-assisted piece is tagged, tracked, and measured against human-written content. AI does not handle sensitive topics like scam alerts or security research. That responsibility remains with Aura’s internal team.
Alina’s team is also working through several unresolved questions that will shape how they run content ops going forward:
llms.txt: There’s no sign that major LLMs recognize or use it, and crawlers don’t seem to treat it consistently. In Alina’s view, it isn’t worth the effort right now.Schema and AI search: Some SEOs argue structured data helps with AI visibility, but there’s little evidence to back it up. During tokenization, schema markup gets broken down into the same basic tokens as normal text, which strips away its structure. Aura is still pro-schema because it could still matter in retrieval-augmented generation (RAG) workflows.Freshness: With GPT-5 built for reasoning over memorized knowledge, SEO relevance may be stronger than ever. Adding to this is the fact that 40–60% of sources cited by AI change within a month. Over longer periods, the drift climbs toward 70–90%. That level of churn means Aura’s team needs systems that account for constant change and not one-off optimizations.
Search Isn’t Collapsing — It’s Expanding
One final misconception worth addressing is that AI assistants will replace Google. Recent data actually shows the opposite: people who adopt ChatGPT tend to keep using Google just as much, sometimes more.
Instead of substitution, overall search behavior is expanding. Alina and her team believe SEO and what some call Generative Engine Optimization (or “GEO”) are not in competition with one another, but go hand in hand. Google remains a pillar, and AI search is an additional surface to consider.
This perspective shapes how they size up new tactics. If an experiment has value in both traditional and AI search — like publishing clear, useful content, getting cited by trusted third-party sites, or participating in relevant online communities — it’s a safe investment. If a certain adjustment only seems to matter for AI search, with no benefit in traditional search (like llms.txt), it may not be worth the effort.
In Alina’s words, the team prioritizes efforts that are helpful for users even if it doesn’t do much for AI search.
“We’re going to keep our SEO foundations strong, add AI surfaces where it makes sense, and avoid chasing shiny objects,” says Alina. As always with SEO, there are no permanent rules.