Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026

Value of the Information & Strength of the Argument

The talk provides valuable insights by demystifying AI and placing it in a historical context. The argumentation is solid, built on a chronological narrative that connects technological developments to business and societal impacts. Campbell effectively uses the Gartner Hype Cycle as a framework to analyze the current state, and he supports his claims with concrete examples, such as the failure of Bing to gain market share despite a better interface, and the unexpected demand for radiologists after AI-assisted imaging. He also addresses common misconceptions, such as the belief that AI will replace all jobs, by showing how automation often increases demand. The speaker’s experience and balanced perspective add credibility, though some points are based on personal anecdotes rather than empirical data.

Scientific Rigor, Source Quality, Title Accuracy

The talk demonstrates a high level of scientific rigor in its historical accuracy and technical explanations. Campbell references well-known events and publications, such as the neural scaling laws paper and the ImageNet competition, without providing formal citations. The sources cited in the description are limited to conference links, which are not directly related to the content. The title accurately reflects the content, which is a critical analysis of AI hype and future prospects. The talk is well-structured and the speaker’s expertise is evident, but the lack of explicit references to academic papers or industry reports slightly reduces its scientific rigor.

The title accurately reflects the content, which separates hype from reality and discusses future directions.

8/10

The talk provides a well-structured historical overview of AI, with accurate references to key events and figures (e.g., ImageNet, GPT models, Gartner Hype Cycle). The speaker demonstrates deep industry knowledge and balanced skepticism. However, some claims are anecdotal and not all sources are explicitly cited in the talk.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 02:00Introduction to the talk and the problem of AI's name and history.
• 10:00Discussion on human tendency to anthropomorphize AI and pareidolia.
• 20:00History of neural networks and the ImageNet breakthrough.
• 30:00The founding of OpenAI and the development of GPT models.
• 40:00Application of the Gartner Hype Cycle to AI and the current investment frenzy.
• 50:00Early successes in software development and protein folding, and the future outlook.

• NDC Conferences — Conference organizer and context for the talk.
• NDC Copenhagen — Specific conference where the talk was recorded.

• Gartner Hype Cycle — Methodology that supports the speaker's analysis of the AI hype cycle.

• ndccopenhagen.com