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Dave Slutzkin, co-founder of Antiburn, joins Yaniv to break down what actually drives token costs in agentic development and how to bring them down. They cover how caching differs between Anthropic and OpenAI, when to reach for which model and thinking mode, and why the habit of compaction matters more than any other optimisation. If you're spending real money on AI-assisted development, you'll come away with concrete tactics for cutting token burn without cutting capability.

Key takeaways

Context window depth is the dominant driver of token cost; compacting actively at 200,000-300,000 tokens matters more than any other single action.
Cache expiry behaviour differs materially between providers: Anthropic's cache expires at exactly 1 hour, OpenAI's Codex cache is best-effort and often drops in 5-20 minutes, but OpenAI subscriptions do not charge for cache writes while Anthropic subscriptions effectively do through quota.
Using a frontier model with a simple prompt to delegate to cheaper subagents is a practical pattern that balances capability and cost without requiring complex harness engineering.
High thinking modes often produce worse results than medium or low because models overthink, invent requirements, and fix unrelated bugs; the benchmark sweet spot is typically medium.
Skills files, MCPs, auto-memories, and system prompts silently bloat context windows; regular clean-up every month or two reduces both cost and quality degradation.
Fast mode costs 2.5x per token but can improve output quality through tighter interaction loops; it is best used deliberately rather than left on by default.

Chapters

00:00 Introduction
00:38 The Token Burn Problem
03:12 Three Stages of Adoption
07:03 Model Choices Multiplying
11:14 The Cardinal Mistake: Depth
18:13 Worst-Case Usage Patterns
22:13 Picking Models by Feel
30:23 Thinking Modes and Overthinking
35:23 Fast Mode Trade-offs
48:46 Caching: Anthropic vs OpenAI
52:29 Context Window Ceiling
56:32 Cleaning Up Context Bloat
56:46 Where to Find Dave

Links and tools mentioned

Antiburn: https://antiburn.com
Anthropic Claude: https://www.anthropic.com
OpenAI Codex: https://openai.com/codex
Harvey: https://www.harvey.ai
Legora: https://legora.com

About Dave Slutzkin

Co-founder of Antiburn, a company that helps developers understand and optimise their token spend for AI-assisted coding.

This episode is presented by SparkLaunch and Dam Secure.

SparkLaunch (https://sparklaunch.io/wizardry): Applied Wizardry listeners get an extra 20% off.
Dam Secure (https://damsecure.ai/wizardry): Applied Wizardry listeners get their first month free.

Credits

Justin McArthur, Executive Producer and Video Editor: https://www.linkedin.com/in/justin-mcarthur/
Steph Hefferan, Editor: https://www.linkedin.com/in/steph-heff/

Applied Wizardry: Cutting AI coding token costs, with Dave Slutzkin

The Startup Podcast

Applied Wizardry: Cutting AI coding token costs, with Dave Slutzkin