Value of Smaller Task-Specific Models

Patrick McKenzie (patio11) and Philip Kiely, early employee at Baseten, discuss the inference stack: the critical layer of software and hardware that sits between a model’s weights and a user’s prompt. They cover inference engineering, how intermediate layers are evolving over a technical stack that is changing every six months, and how sophisticated organizations are actually consuming LLMs beyond just writing their questions into chatbot apps.
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Full transcript available here: www.complexsystemspodcast.com/inference-engineering-with-philip-kiely/

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Links:

• Download Inference Engineering: https://www.baseten.com/inference-engineering/ 
• Philip's website: https://philipkiely.com/ 
• Stripe's Emily Sands on Complex Systems: https://www.complexsystemspodcast.com/episodes/the-past-present-and-future-of-ai-with-stripe/ 
• Des Traynor on Complex Systems: https://www.complexsystemspodcast.com/episodes/des-traynor/  

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Timestamps:
(00:00) Intro
(00:30) The AI deployment pipeline
(03:04) Evolution of abstraction layers in engineering
(05:14) Defining inference and model weights
(08:45) Architecture of language and diffusion models
(10:11) AI adoption in the broader economy
(11:30) The shift toward agentic workflows and RL
(14:55) Function calling and real-world actions
(20:10) Sponsors: Mercury | Meter
(22:59) Technologies for agentic tools: MCP and skills
(25:32) The craft of writing a harness
(29:56) Using AI for automated proofreading and tool creation
(34:12) Balancing LLMs with deterministic code
(37:31) Observability and chain of thought reasoning
(39:31) Sponsor: Granola
(41:21) Observability and chain of thought reasoning
(50:45) Speculative decoding and hidden states
(55:37) The value of smaller, task-specific models
(59:55) Internal competencies versus buying solutions
(01:09:27) Self-publishing a technical book in record time
(01:23:20) Wrap