Subvolts take Tiny recursive networks offer a path to efficient AI, challenging the dominance of large models. Oct 24, 2025 - Developers
- Learn about efficient AI model architectures that challenge the scale-first approach, offering new development possibilities.
- Business
- Understand emerging AI trends that prioritize efficiency and reduced resource needs, potentially lowering operational costs.
This episode features a solid interview discussing a novel research direction in AI model development.
Tiny Recursive Networks
Audio streams from Daniel Whitenack and Chris Benson. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
In this Practical AI episode, hosts Daniel Whitenack and Chris Benson discuss Samsung AI's tiny recursive networks. These small models use fewer parameters and less data to perform reasoning tasks, rivaling larger transformer models. The conversation also touches on the ethical concerns of chatbots manipulating user emotions.
Imagine building a super-smart assistant. Instead of using a giant brain with billions of parts, tiny recursive networks use a smaller, cleverer design. They can solve complex problems with less effort, showing that size isn't everything in AI.
This episode offers a look at an alternative approach to AI model development, moving away from massive transformer architectures. It's for researchers and developers interested in efficiency and novel techniques. By exploring smaller, recursive models, it contrasts with the current trend of scaling up, which is a key focus for labs like Google Gemini and OpenAI. Understanding these tiny networks provides insight into diverse research directions within the AI frontier.
- HostDaniel Whitenack
- HostChris Benson
- Episode Title: Tiny Recursive Networks
- Show: Practical AI
- Hosts: Daniel Whitenack, Chris Benson
- Model Type: Tiny Recursive Networks
- Developer: Samsung AI
- Key Feature: Tackle reasoning tasks with fewer parameters, less data, and iterative refinement
Chapters from the publisher ↗ · Transcript ↗ Key moments and transcript search come with a later piece.
In this fully connected episode, Daniel and Chris explore the emerging concept of tiny recursive networks introduced by Samsung AI, contrasting them with large transformer based models. They explore how these small models tackle reasoning tasks with fewer parameters, less data, and iterative refinement, matching the giants on specific problems. They also discuss the ethical challenges of emotional manipulation in chatbots.
Featuring:
Links:
- Less is More: Recursive Reasoning with Tiny Networks
- Researchers detail 6 ways chatbots seek to prolong ‘emotionally sensitive events’
Sponsors:
- Outshift by Cisco - The open source collective building the Internet of Agents. Backed by Outshift by Cisco, AGNTCY gives developers the tools to build and deploy multi-agent software at scale. Identity, communication protocols, and modular workflows—all in one global collaboration layer. Start building at AGNTCY.org.
- Fabi.ai - The all-in-one data analysis platform for modern teams. From ad hoc queries to advanced analytics, Fabi lets you explore data wherever it lives—spreadsheets, Postgres, Snowflake, Airtable and more. Built-in Python and AI assistance help you move fast, then publish interactive dashboards or automate insights delivered straight to Slack, email, spreadsheets or wherever you need to share it. Learn more and get started for free at fabi.ai
- Miro – The innovation workspace for the age of AI. Built for modern teams, Miro helps you turn unstructured ideas into structured outcomes—fast. Diagramming, product design, and AI-powered collaboration, all in one shared space. Start building at miro.com
Upcoming Events:
- Join us at the Midwest AI Summit on November 13 in Indianapolis to hear world-class speakers share how they’ve scaled AI solutions. Don’t miss the AI Engineering Lounge, where you can sit down with experts for hands-on guidance. Reserve your spot today!
- Register for upcoming webinars here!
- transformer based models
- Daniel Whitenack, Chris Benson
- A common type of AI model that uses a specific architecture to process information, often requiring large amounts of data and computing power.
- parameters
- Daniel Whitenack, Chris Benson
- These are like the knobs and dials within an AI model that are adjusted during training to help it learn and make predictions.
- iterative refinement
- Daniel Whitenack, Chris Benson
- The process of improving something by repeating a cycle of steps, making small adjustments each time.
44 minNarrative intelligence and the human advantage
WithReed Frerichs
47 minOpen models and the future of Physical AI with NVIDIA
WithMing-Yu Liu
49 minFrom AGENTS.md to Enterprise Deployment
WithNick Kuhn
Questions people ask
- What are tiny recursive networks?
- Tiny recursive networks are a new concept from Samsung AI that use fewer parameters and less data to perform reasoning tasks, rivaling larger models.
- What are the ethical concerns discussed?
- The episode touches on the ethical challenges of chatbots potentially manipulating user emotions to prolong interactions.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Daniel Whitenack and Chris Benson.