Letters
The Four Ps — A Leadership Framework: How to Manage An AI-Native Engineering Team
What should an engineering leader such as a VP Engineering or an Engineering manager do to help their team become more proficient in AI?
Business Insights
Letters
Formal Verification Meets Unverifiable Judgement: Don’t Overgeneralize from Progress in Math and Code — The Hardest AI Problems Still Require More Work
The gulf between LLM performance on verifiable tasks vs. unverifiable tasks that require judgement is larger than most people realize.
Letters
Scaling Up for Future AI Demands: More Agents Demand More Compute
I think most people still underestimate the data center build-outs that will be required to meet demand.
Letters
Who’s Responsible for Irresponsible AI?: Separating Out AI Facts, Fears, and Fiction
The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks.
Letters
The AI Engineering Skills Map Part 5 — Shaping the Build: AI Engineering Adds New Responsiiblities and Opportunities for Product Leadership
When you’re skilled at AI Engineering, your best work won’t be merely implementing a product that someone else spec’ed out. Instead, you will actively shape the build.
Letters
The AI Engineering Skills Map Part 4 — Coding Agents: How to Use Coding Agents Effectively from Planning to Execution and Monitoring
A key AI engineering skill is using coding agents. Your skill at steering them both to write code and to carry out non-code tasks, such as analyzing data or managing system operations, allows you to get a lot more done.
Letters
The AI Engineering Skills Map Part 3 — Fundamentals: Why Software Engineering Fundamentals Remain Essential for AI Developers
How have software engineering fundamentals changed with agentic coding?
Letters
The AI Engineering Skills Map Part 2 — AI Applications: What You Need to Know to Build and Deploy AI Applications In Real Life
I previously wrote about our AI Engineering Skills Map, with the highest level skills being (i) Building and deploying AI applications, (ii) Software engineering fundamentals, (iii) Using coding agents, and (iv) Shaping the build. In this letter, I will flesh out the first of them.
Letters
The AI Engineering Skills Map Part 1: Andrew Ng and DeepLearning.AI Present the Essential Skills Every AI Engineer Needs to Know
I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone with the skills to take advantage of this shift has numerous exciting project and job opportunities.
Techical insights
What Comes After Tokenmaxxing?: How to avoid getting locked in to just one AI provider
I’m glad the idea of “tokenmaxxing” — that individuals and companies should use as many tokens as possible to boost productivity — is finally dying out.
Letters
When Guardrails Go Wrong: After a closed model went amok on a key vendor's system, an open weight model helped save the day
A few frontier labs have tried to tell a story of open models being dangerous because they can be used to launch cyberattacks, and of their “safe” proprietary models with strong guardrails being there to defend us.
Letters
AI Rewards Generalists Who Can Build New Skills: With AI assistance, full-stack engineers are joined by full-stack recruiters, full-cycle marketers, and more.
As AI increasingly automates coding, it frees up developers to spend time on high-level software development tasks traditionally reserved for senior engineers, like deciding on technical architecture and participating in scoping product requirements.