Show Notes
Applied Wizardry is a podcast for engineers and technical founders about how we build with AI: the tools, the numbers and the trade-offs, practitioner to practitioner. Hosted by Yaniv Bernstein. Episode 1, with Dave Slutzkin of Antiburn, on cutting your token burn, is out 16 October.
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Companies now use an average of 12 models per month, yet the biggest hidden variable isn't which model you pick—it's the inference provider serving it and whether your cache hit rate stays above 85%. Peter Walker from OpenRouter explains how the same open-weight model can have wildly different cost and performance depending on the provider's caching policy, latency tail, and whether a stealth provider is reselling your prompts. The conversation covers why agentic workloads broke cache economics in February 2026, how ZDR policies vary even among "zero data retention" claims, and why the frontier labs are now competing across price and intelligence spectrums simultaneously. For engineers choosing production models, this is a practical map of the dimensions that actually move the bill.
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.