AI Trends to Watch in 2026 - The AI Show w/ Paul Roetzer & Mike Kaput

Value of the Information & Strength of the Argument

The video provides valuable insights into AI trends, drawing on the hosts’ extensive experience and observations. They reference specific research, such as METR’s scaling law for long-horizon tasks, and cite current events like the release of Gemini 3 Flash. The argumentation is coherent, with a clear structure separating technology, business, and societal impacts. However, many claims are speculative and based on personal intuition rather than empirical evidence. The hosts acknowledge this, framing their predictions as ‘instinct’ and ‘overall feeling.’ While this limits the scientific rigor, it offers a practical perspective from industry insiders.

Scientific Rigor, Source Quality, Title Accuracy

The hosts demonstrate a good understanding of the AI landscape, referencing reputable sources like METR and Gallup polls. However, they do not provide direct citations or links to these sources in the video, relying instead on verbal mentions. The description includes links to their own resources (e.g., AI Academy, newsletter) but not to external references. The title accurately reflects the content, which is a focused discussion on AI trends for 2026. The video is an opinion piece rather than a peer-reviewed analysis, but it is grounded in the hosts’ professional expertise and current industry knowledge.

The title accurately reflects the content, which is a forward-looking discussion of AI trends for 2026.

7/10

The hosts are recognized AI industry experts with substantial experience, and they ground their predictions in references to specific research (e.g., METR's scaling law) and current events. However, the discussion is largely speculative and opinion-based, lacking rigorous citations for many claims.

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

• 01:00Introduction to the episode and the use of NotebookLM to generate AI-driven insights.
• 02:30Discussion on agent-to-agent communication and commerce as a key technology trend.
• 05:00Personalization of AI assistants and its potential as a differentiator.
• 08:00Reliability of agents on long-horizon tasks, referencing METR's research.
• 12:00Other technology trends: multimodality, world models, continual learning, and on-device models.
• 15:00Business trends: moving from piloting to scaling, AI literacy, and custom evals.
• 18:00Future of work hypothesis and the impact on jobs and organizational structures.
• 21:00Societal trends: regulation, job disruption, economic impact, and potential IPOs.

• AI Academy for Marketers — Mentioned as a resource for AI literacy and training.
• The AI Show Podcast — The podcast's official page, where the full episode is available.
• SmarterX AI on LinkedIn — LinkedIn page for the company, mentioned for community engagement.
• Marketing AI Institute — The institute's website, mentioned for resources and events.
• Marketing AI Institute Newsletter — Newsletter sign-up, mentioned for weekly updates.
• Marketing AI Institute Slack Community — Slack community for discussion, mentioned for joining the community.

• METR — Referenced for research on AI task completion rates, supporting the discussion on long-horizon tasks.
• Gallup — Referenced for data on AI adoption among knowledge workers, supporting the claim that adoption is still low.
• Brookings Institution — Referenced for a study on AI's impact on jobs, supporting the discussion on job disruption.

• No direct discordant sources mentioned — The hosts do not present any opposing viewpoints or sources that contradict their claims.