The Adversarial Testing Podcast

How to Train a Frontier LLM — The Full Pipeline

24 min · 15. touko 2026
jakson How to Train a Frontier LLM — The Full Pipeline kansikuva

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A technical walk-through of the entire training pipeline for a modern frontier large language model, from raw data curation through pre-training, mid-training, GRPO reasoning RL, safety alignment, and deployment monitoring.

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jakson Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools (Abstract, Introduction & Conclusion) kansikuva

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools (Abstract, Introduction & Conclusion)

The abstract, introduction, and conclusion of NBER Working Paper No. 35275 by Mert Demirer, Leon Musolff, and Liyuan Yang (May 2026). Using data on more than 100,000 GitHub developers and their AI usage telemetry, the paper traces how the productivity effects of AI coding tools evolve across three generations - autocomplete, sync agents, and async agents - and asks how much of those task-level gains reach final output. Each generation sharply increases coding activity, but the gains attenuate steeply across the production hierarchy: large effects on lines of code shrink to small effects on releases, consistent with a weak-link model in which human review and integration remain the binding constraint.

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