Strategy: ship features that 'almost work'

Lance and Noah discuss products that unlock once model capability catches up, using Cursor as an example.

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Episode notes

Welcome to the first episode of Forward Deployed (YouTube), a podcast exploring the intersection of AI, software development, and the enterprise.

We’re very excited to have you join us as we tackle the wild world of Forward Deployed engineering. We hope to make this a roughly bi-weekly show and look forward to inviting guests in the future. The idea is to dive deep into the realities of making this stuff work in companies, building on our expertise as builders both inside and outside the enterprise.

Thanks for listening, and please let us know what you think.

Thanks,

Noah & Lance

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In this inaugural episode, hosts Noah Brier (Co-founder, Alephic) and Lance Martin (Founding Engineer, LangChain) dive deep into one of AI’s most controversial ideas: The Bitter Lesson. They unpack Richard Sutton’s famous essay, debate whether LLMs truly follow its principles, and explore what this means for anyone building with AI today.

Key Topics Covered

* The Bitter Lesson: Why more compute beats clever algorithms (or does it?)

* Richard Sutton’s surprising take on why LLMs aren’t “bitter lesson pilled”

* The evolution from CNNs to transformers through Lance’s journey from Stanford to Uber’s self-driving program to LangChain

* Chain of thought prompting vs reasoning models - why your prompts might be breaking

* The real challenges of enterprise AI adoption

* Why ICs are adopting AI faster than managers

* Building for imperfection: Why optimizing for today’s models is a mistake

Timestamps

* 00:00 - Introductions and backgrounds

* 00:53 - Lance’s journey: Stanford PhD to Uber self-driving to LangChain

* 02:49 - Noah’s path from marketing to AI obsession

* 04:04 - What “forward deployed” really means

* 09:04 - The Bitter Lesson explained

* 11:31 - Why Sutton thinks LLMs aren’t following the bitter lesson

* 23:09 - Chain of thought prompting and the reasoning model revolution

* 24:19 - Building for future models, not current ones

* 45:20 - ICs vs managers in AI adoption

About the Hosts

Noah Brier is co-founder of Alephic, an AI consulting company working with enterprise clients like PayPal, EY, Meta, and Amazon on AI-powered content intelligence and competitive analysis. Previously founded and sold Percolate (marketing tech). He also runs the BRXND conference series focused on marketing and AI.

Lance Martin is a founding engineer at LangChain with a PhD from Stanford. Former computer vision lead for Uber’s self-driving truck program.

Links & References

Core References

* 📄 The Bitter Lesson by Richard Sutton (2019)

* 🎙️ Richard Sutton on Dwarkesh Podcast: “Father of RL thinks LLMs are a dead end” (September 26, 2025)

* YouTube version

* Apple Podcasts

* Dwarkesh’s follow-up reflections

* 📄 The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy (2015)

* 📄 Trading Margin for Moat: Why the Forward Deployed Engineer Is the Hottest Job in Startups (A16Z)

Related Podcast Appearances

* 🎙️ Noah Brier on Every’s AI & I: “Claude Code Can Be Your Second Brain” (September 10, 2025) - Noah demonstrates his Claude Code-Obsidian setup for research and thinking