Token Maxxing Is Over and Here Is What Comes Next

What happens when you give 200 engineers who have barely touched AI a coding assistant and a six-month deadline to rebuild their platform? Pepijn Senders, a principal engineer who previously led AI enablement at HelloFresh, has done exactly that, and he chose Sonnet over Opus on purpose.

His reasoning is what he calls "Sonnet maxxing." A model that reasons less and stops sooner keeps developers involved, and that matters a lot when the codebase is banking software full of hard business rules. The result was 800,000 lines of code, about 60% of them written by Sonnet, shipped by a team that never stopped thinking for itself. Pep also explains how to justify the spend once the token-maxxing era ends and the CFO starts asking questions. He covers a scatter chart that plots token spend against output, the uncomfortable question of whether a 1% better home screen was worth $20K in tokens, why he measures gains in headcount rather than 2x or 3x multipliers, and where he fell short when trying to prove ROI at HelloFresh.

We also get into a codebase test you can run yourself, why AI recommendations quietly narrow your choices, and multiplayer refinement sessions where the whole team works through the agent's questions together. Pep shares an experiment where designers and engineers got the same one-shot prompt, and it showed how words like "scalable" and "maintainable" steer a model. We finish with how his team built a meta harness on top of Claude Code, after finding that 85% of their code followed the same path: refinement, specification, implementation, verification.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm

Pepijn Senders: https://www.linkedin.com/in/pepijn-senders

Timestamps:

[0:00] Can you still read your codebase

[1:15] Meet Pep from HelloFresh

[2:35] What is Sonnet Maxing

[3:41] Rebuilding a platform in six months

[5:02] Why Sonnet made adoption easier than Copilot

[6:37] Proving the ROI of AI coding

[8:50] Fighting for AI ROI at HelloFresh

[11:02] Token spend vs output

[13:24] Tinder pickup lines on the company dime

[14:13] Measuring ROI in headcount saved

[18:26] Keeping humans in control of AI code

[22:26] When Opus and Fable stop speaking English

[24:19] Multiplayer refinement sessions with AI

[28:16] Magic words that steer the model

[32:32] Inside the company Meta harnesses