AI search tools like Elicit, Consensus, and Scite.ai have spent years racing to build centralised indexes of academic content— first by indexing the open content that is available and then trying to get publisher partnerships.
Generated by Nano-Banana Pro from text of this blog post As “AI-powered academic search engines” began their rise in 2023, the biggest question on everyone’s mind was: Where is Google Scholar? While Gemini Deep Research came and went, it relied primarily on the general web—similar to its rival, OpenAI Deep Research. Crucially, it failed to leverage Google’s greatest competitive advantages: the Google Scholar index and Google Books.
Academic librarians are increasingly being asked about AI-powered search tools that promise to revolutionize literature discovery. Consensus is one of the more prominent and earliest players in this space (alongside Elicit, Perplexity), positioning itself as a tool that can not only find relevant papers but assess the “consensus” of research on a topic.
TL;DR ResearchRabbit shipped its biggest update in years: a cleaner iterative “rabbit hole” flow, a more configurable citation graph, and an optional premium tier. The company now “ partners’ with Litmaps (others are reporting as acquired , which shows up in features and business model Free tier limits each search to <50 input papers / <5 authors and one project;
Recently, a librarian from a prestigious institution I met at a conference surprised me when he confessed that he and his colleagues were struggling to grasp the issues surrounding the impact of AI. But My talk helped clarify much of the fog around how to think about impact of AI on search. His confession wasn’t an isolated one. Many librarians I speak with admit they struggle to keep up with the blizzard of new AI-powered search engines.
Disclosure : I am on the program committee of FORCE 2026 and my institution will be hosting the event in Singapore. I am happy to answer any questions you might have. FORCE11’s annual conference, FORCE2026, will take place 3–5 June 2026 at Singapore Management University under the theme “To Go Far, Go Together: Advancing Scholarly Communication Across Boundaries and Disruptions.” Call for proposals are open!
A longer 30-minute version of a recorded talk I gave on 8 October 2025- Symposium: “Beyond Certainty - What does ‘Discovery’ mean in an open and artificially intelligent world?” A good catch-up post if you want a concise summary of my current views on “AI powered search” in academic discovery.Thanks for reading Aaron Tay's Musings about Librarianship!
Elicit.com, Consensus, and Undermind.ai are among the new leading comprehensive cross-disciplinary “AI-powered academic search engines” today. Thanks for reading Aaron Tay's Musings about Librarianship! Subscribe for free to receive new posts and support my work.
Introduction This piece makes a simple claim: Google’s AI Mode may out-search you, but it won’t necessarily be able to out-evaluate you. Launched in March 2025 (US in May, global in August), AI mode pairs Gemini 2.5 Pro with aggressive fan-out—splitting a question into many sub-queries, sweeping hundreds of pages, and answering in seconds. Thanks for reading Aaron Tay's Musings about Librarianship!
Introduction In my last post, I argued that Deep Search—iterative retrieval that blends keyword, semantic, and citation chasing with LLM-based relevance judgments—is the real breakthrough behind today’s “Deep Research” tools. It consistently beats one-shot embedding search in recall/precision, and in hindsight, it’s what I loved all along (the “generation” step just came bundled). The price?
Back in 2022, I was hyped about Retrieval-Augmented Generation (RAG).The novelty of seeing a search engine spit out a direct answer — with citations! — in tools like Elicit and Perplexity felt like the future. I even predicted that this “answers-with-citations” model could become the prominent paradigm for academic search. Three years later, that prediction has partly come true.