OpenAI leads the general AI space, especially GenAI, but a range of AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research. Perhaps you have faculty or students asking you to present these tools to classes. (Also: this entry is intended to help librarians and other information professionals learn about AI. It is not, in itself, meant to be seen as promotion of AI.) Many tools are free with premium tiers for advanced features. For the latest developments, tools like these often update based on models from underlying providers (e.g., OpenAI, Anthropic).
Major AI Companies
In 2025, several AI companies produce GenerativeAI chatbots, and are putting major resources into AI for web searching such as:
Google https://ai.google: 1.5 billion users monthly; develops Gemini models and Knowledge Graph; tools provide conversational, summarized search results, integrating with Google’s ecosystem, which librarians might appreciate for its broad web indexing and accessibility.
Note: Will Google Scholar survive the rise of AI-powered searching?
Microsoft https://microsoft.ai: 40 million daily users; integrates its AI assistant, Copilot, into Bing, using OpenAI’s GPT-4 for conversational search with citations. Its deep integration with Microsoft 365 is being integrated into enterprise and library knowledge management.
OpenAI https://openai.com: 1.1 billion queries daily; powers ChatGPT Search, offers conversational search with real-time web access. Librarians might test its ability to handle complex queries conversationally, though it requires a Plus subscription for advanced features.
Perplexity AI https://www.perplexity.ai/: valued at $9–18 billion; 780 million queries monthly; known for its conversational search engine using semantic search and provides cited, summarized answers. Focus on reliable sources and research-oriented interface may align with librarians’ focus but more testing is needed.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) Focus: Robotics, human-computer interaction, data science. https://www.csail.mit.edu
Meta AI (Facebook, Inc.) Focus: Self-supervised learning, AI ethics, robotics. https://ai.meta.com
Microsoft Research AI * Focus: AI in healthcare, conversational AI, AI ethics. https://microsoft.ai
OpenAI Focus: Advanced AI research, large-scale machine learning models. https://openai.com
Partnership on AI to Benefit People and Society Focus: AI best practices, societal impacts of AI. https://partnershiponai.org
"...The idea that we should outsource academic authorship to LLMs rests on the assumption that writing is (only) a mechanical, predictable or reductive process which, with the right prompts, can be replicated with ease." — Masters, 2025.
Bottom line: For health sciences librarians, AI tools may or may not support their work with health professionals but so many of the underlying processes for locating and processing information raise concerns for anyone interested in scientific accuracy, transparency and rigour. HSLs, like other academic librarians, are also not that enamoured of Silicon Valley broligarchies and their hegemonic power. Incidentally, librarians make a distinction between searching for sources and searching for answers. This much is true: so much of AI provide the second while hiding the first; transparency is not their strong suit. Note information provided to you on this page is changing, so check for current information (or discuss with a librarian).
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
