daniel’s theories

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  Article·Symplectic·10 June 2026

  The Holy Grail Was Always the CV

  How AI is helping us solve a challenge that has limited research information management for more than two decades

  • AI
  • research information management
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  Writing·DavidWorlock.com·15 May 2026·Password required

  From the Information Annullus to the Impact of AI: an interview with Dr Daniel Hook, CEO, Digital Science.

  In this interview, Dr Daniel Hook discusses the growing influence of AI, the challenges of information overload, and the future of digital research and innovation. Watch the interview: www.patreon.com/davidworlock/

  • AI
  • Infrastructure
  • Innovation
  • Interview
  • research information management
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  Article·Holtzbrinck·8 April 2026

  The Tools That Shape Us

  Language is a technology we rarely examine — a cage that shapes thought from the inside. Every communication tool rebuilds that cage: the printing press, keyword search, and now LLMs. But where earlier cages had visible bars you could trace and interrogate, the walls of a model's training data are invisible. Fluency feels like liberation. The cage has always had architects; what's new is that we can no longer see them.

  • AI
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  Article·Physics World·31 May 2024

  Ask me anything: Daniel Hook – ‘The skills I learned as a researcher are applicable and helpful in any walk of life’

  Daniel Hook, chief executive of Digital Science, reflects on his career developing information tools for researchers

  • Interview
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  Article·Digital Science·2 May 2024

  Barcelona: A beautiful horizon

  Digital Science CEO Daniel Hook explores how the Barcelona Declaration will push forward openness and transparency, as well as innovation to benefit the scholarly record.

  • AI
  • Innovation
  • research information management
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  Article·Digital Science·30 October 2023

  Specificity versus synthesis: An uncertainty principle for Large Language Models?

  Large Language Models are known to hallucinate facts. While there is an active debate on whether this is a bug or a feature, the fact remains that we don't understand AI to the level where we can get LLMs to trace back to their motivation for making a specific pronouncement. Thus, LLM providers who work in the science space are faced with a challenge - they want to leverage the new capabilities of LLMs but need somehow to create references back to the original work, but in forcing an LLM to work in a way that creates referencability, one destroys its ability to create a synthesis from multiple sources, creating a fundamental playoff between specificity and synthesis.

  • AI