Jellyfish

YouTube Creators Dominate AI Assistant Answers

New research from digital marketing agency Jellyfish finds YouTube creator videos are cited in more than 25% of answers given by AI assistants, rising to nearly 50% in high‑intent categories like consumer electronics and financial services. Jellyfish — part of The Brandtech Group — analyzed 27 million AI assistant responses across seven assistants and reports that independent, niche creators (especially videos longer than 10 minutes) outperform brand-owned content and celebrity influencers in AI-generated answers. In the CPG category, Jellyfish says AI assistants cite over one million unique YouTube videos daily in the U.S., highlighting how AI-driven discovery is amplifying long-form creator content as a major source of information surfaced by conversational systems.

Jellyfish Uses LLMs to Plan Ad Buys

Jellyfish, an agency owned by Brandtech Group, is promoting a product called "Share of Model" that uses large language models (LLMs) to inform media planning. The product measures how often LLMs mention a brand versus competitors, analyzes themes, sentiment and cultural context, and converts those insights into targeting signals for Google’s Performance Max (PMax) campaigns. Jellyfish cites a Project Management Institute (PMI) case where the approach drove a 20% lift in sales volume, a 45% increase in conversions and a 156% improvement in return on ad spend across a 90‑day campaign that concluded in early January. The article frames this as an example of brands moving from using AI assistants for discovery toward using LLM outputs to shape media strategy and targeting.

Tokenmaxxing Boosts Code Volume, Reduces Real Productivity

Analyses summarized by t3n and TechCrunch show that high token consumption by generative AI—so-called “tokenmaxxing”—increases raw code output but often reduces real engineering productivity and raises costs. Firms tracking engineering metrics (Waydev, Faros AI, Jellyfish) report that much AI-generated code is later revised: apparent initial acceptance rates (80–90%) can fall to effective acceptance of 10–30% after follow-up edits. Faros found code churn rose by 861% with intensive AI use; Jellyfish reported that doubling output sometimes required a tenfold rise in token costs. The article notes major tech leaders (Nvidia’s Jensen Huang, Meta’s Mark Zuckerberg) push employee AI adoption and that organisations are starting “token tracking” to measure ROI. A cited example from Kevin Roose estimated a single developer consuming 210 billion tokens in a week—equivalent to hundreds of thousands to millions of dollars at published API rates—prompting calls for closer cost monitoring.