The Velocity Lab

Open source caught up to Sonnet on paper, but Dave and Dan still won't switch. Why they default to Sonnet over Opus, what you're really paying Anthropic for, and the 0k-per-engineer model-spend rule of thumb.

Show Notes

Episode Summary

Open-source models have caught up to Sonnet on the benchmarks, so does it still matter which model you use? Dave and Dan argue the model stopped being the differentiator a while ago. They break down why they default to Sonnet over Opus, why a free model matching Sonnet still doesn't make them switch, and what you're actually paying Anthropic for.

Key Topics

  • Is the model still the moat? Why open-source parity with Sonnet doesn't change how they work
  • Trust as the real switching cost — you don't adopt someone else's evals, you build your own confidence
  • Default your org to Sonnet, not Opus (Sonnet runs ~40% cheaper) — and when to reach for Opus
  • The spend framing: expect an engineer to run $50k/year on an LLM, and expect them to at least double their throughput for it
  • Don't fragment across tooling — pick a stack and build a system around it
  • Anthropic's real product is enterprise adoption: a whole-org toolset plus hands-on help getting teams to actually use it

Notable Quotes

  • "You should expect an engineer to spend $50,000 a year using an LLM. But they should double their throughput."
  • "Make your default model Sonnet."
  • "We can prove the newer models don't really matter, 'cause we don't even use them."

About The Velocity Lab

Dave O'Dell and Dan McAulay work inside engineering organizations every day helping them ship faster with AI. No hype, no BS — just what's working in the field.

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What is The Velocity Lab?

Dave O'Dell and Dan McAulay work inside engineering organizations every day, helping them ship faster with AI — not in theory, but inside their actual teams. Each week they share what they're seeing in the field: what's working, what isn't, and what most people are getting wrong. Covering Claude Code updates, AI-enabled SDLC acceleration, and personal AI agents. No hype, no BS.