Subvolts take Explore AI search, model performance, welfare, and efficient hardware. Apr 26, 2026 - Developers
- Learn about new enterprise search solutions and efficient AI hardware for local inference.
- Business
- Discover how AI can improve enterprise search and understand the implications of advanced model behavior.
This episode features discussions on emerging AI models and hardware solutions, offering solid insights into current industry trends.
AI in the AM: 99% off search, GPT-5.5 is "clean", model welfare analysis, & efficient analog compute
Audio streams from Erik Torenberg, Nathan Labenz. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
This episode of The Cognitive Revolution features Anna Patterson discussing Ceramic.ai's new enterprise search for LLMs. Lukas Petersson shares Andon Labs results on Opus 4.7 and GPT-5.5. Zvi Mowshowitz analyzes model welfare, and Naveen Verma explains EnCharge AI's analog compute for efficient inference.
This episode covers new ways to search using AI, how advanced AI models perform, and how to make AI run more efficiently. It's for anyone curious about the latest AI developments and how they might impact businesses and technology.
This episode offers insights into the evolving AI landscape, from enterprise search solutions to the performance of emerging models like GPT-5.5. It's for AI practitioners, researchers, and business leaders interested in efficient AI deployment and understanding model behavior. The discussions on model welfare and analog compute directly relate to current efforts by frontier labs to improve AI efficiency, safety, and capabilities.
- HostNathan Labenz
- GuestAnna PattersonCeramic.ai
- GuestLukas PeterssonAndon Labs
- GuestZvi Mowshowitz
- GuestNaveen VermaEnCharge AI
- Episode Title: AI in the AM: 99% off search, GPT-5.5 is "clean", model welfare analysis, & efficient analog compute
- Show: The Cognitive Revolution
- Length: 158 minutes
- Ceramic.ai Pivot: low-cost enterprise search for LLMs
- Andon Labs Findings: surprising differences in performance, behavior, and "ruthless" tactics for GPT-5.5
This edition of AI in the AM features Anna Patterson on Ceramic.ai’s pivot to low-cost enterprise search for LLMs, designed to combine public and private data with stronger fact-checking. Lukas Petersson returns with new Andon Labs results on Opus 4.7 and GPT-5.5, including surprising differences in performance, behavior, and “ruthless” tactics. Zvi Mowshowitz unpacks model welfare and how to interpret troubling model behavior, while Naveen Verma explains EnCharge AI’s analog in-memory computing approach for dramatically more efficient local inference.
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- LLMs
- Anna Patterson
- Large Language Models, AI systems trained on vast amounts of text.
- model welfare
- Zvi Mowshowitz
- How to understand and interpret potentially concerning AI model behavior.
- analog in-memory computing
- Naveen Verma
- A way to process information directly where it's stored, making AI faster and more energy efficient.
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Questions people ask
- What is Ceramic.ai pivoting to?
- Ceramic.ai is pivoting to low-cost enterprise search for LLMs, designed to combine public and private data with stronger fact-checking.
- What are the new results on GPT-5.5?
- Andon Labs reports surprising differences in performance, behavior, and "ruthless" tactics for GPT-5.5 compared to Opus 4.7.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Erik Torenberg, Nathan Labenz.