Product Management

Product managers decide what should be built to create and keep customers; and coordinate activities from R&D through marketing to ensure the overall success of product(s).

All this starts with understanding the outcomes that customers want, mapping that downstream to positive impacts for the company (ROI and growth), and mapping that upstream to input levers that their team can control. To do this product managers must have customer, business, and technical savvy.

Since product companies face tough competition, product management is fundamentally strategic. 7 Powers is a strong introduction to business strategy. To add to this, Ben Thompson’s Statechery blog analyzes tech news with a strategic lens.

Two truisms of software are change and speed, therefore software product managers operate under high uncertainty. Marty Cagan’s Inspired describes how to make smarter bets, and is the canonical book on solving this core problem. Furthermore, the organization must be setup to operate with agility.

Much of my career has been at start ups. From the perspective of an investor-founder, Michael Seibel breaks down building products at start ups with great clarity.

Lastly, product managers are judged in the end by what gets shipped, High Output Management describes how to create productive teams. Making Things Happen gets into the nitty gritty of software project management, which product managers must be capable of.

A couple interesting product managers to follow are: Shreyas Doshi, and Lenny’s Newsletter.

Writing

Good writing produces good thinking.

The Sense of Style defines style and teaches intuitively how to develop a classical style. If you have less time, watch the talk.

Writing Down the Bones by Natalie Goldberg, taught me about the habit of writing.

Paul Graham is sharp about many things including writing, and he thinks writing will only become more important with AI.

David Perell translates writing into the 21st century, follow his twitter.

Artificial Intelligence

AI is complex, but it is not impenetrable.

For the non-technical, here is a fun illustration of how Random Tree predictive models work. This is not the type of “modern AI” that powers ChatGPT or self-driving cars. But this illustration is an approachable way to intuit how computers find patterns in data, which is the essence of machine learning.

For those who want a deeper mathematical intuition of how “modern AI” works, 3blue1brown is a great source for this, done without any actual numbers.

Karpathy is one of the luminaries of the field and he has a passion for teaching, his Intro to Large Language Models is a perfect mix of simplicity and detail. He also has a course on neural nets called Zero to Hero.

If you don’t yet know how to code, python is the way to go for AI. Learn python the hard way follows the philosophy that the hard way to learn to code, literally typing stuff, is actually the easy way. They’re right.

Andreessen Horowitz is an important venture capital fund that has kept a close eye on AI for years, they’ve done a good job of collecting what they consider the canonical documents on modern AI as of 2023. Many useful resources there.

The theoretical possibilities of LLM agents, and the proof point of coding agents is creating an explosion of interest in AI agent technology. This Language Agents tutorial provides a good foundation from which to distinguish what is real from what is hype.

Lastly, AI technology is powerful and will reshape society in good ways and bad. Most thinking on AI ethics is shallow, Vervaeke is anything but.