CDOQ

If you understand technology , you have known for a while that Artificial Intelligence (AI) will change how we work and live. Over the last two years, we have started to see what that means. Software development, research, search, finance, writing and many other activities are already being done differently. Over the last few months, another transition has started happening very quickly: AI is moving from answering questions to doing things for us. Personal AI assistants can research something, buy things, make reservations, send messages, manage calendars and increasingly take care of tasks without requiring us to sit there and supervise them. ChatGPT, Claude, Gemini and Grok have been moving in this direction for a while. But most AI products still feel like software. You open an app or website, find the right conversation and tell the AI what to do. A human assistant doesn't work that way. OpenClaw , which appeared less than a year ago, showed the industry what was possible with...

When someone throws a ball at you, it is pretty easy to catch it. You don't calculate the trajectory of the ball, the movement of your body, and where your hand needs to be at the precise moment the ball reaches you. No child learns calculus before learning how to catch a ball. We learn something much more useful: an intuitive understanding of the physical world. A robot has to learn some version of that too. It needs to understand where objects are, how they move, what might happen if it takes an action, and how that action could change both the robot and the environment around it. This is the basic idea behind a World Model (WM). A simple way to think about a World Model is that it tries to answer: If I do this, what happens next? Last week, I attended Actuate 26 . World Models and the related problem of robotics training data came up repeatedly.  Image created by an LLM :) Large Language Models (LLMs) had one enormous advantage: the training data already existed. Trillions ...

The men who can manage men manage the men who can manage only agents, and the men who can manage foundational models manage all.   - Inspired by Will & Ariel Durant  

A few weeks ago, I finished reading Elon Musk by Walter Isaacson . The book provides a fascinating look into Musk’s life and how he has been able to make industry-changing contributions in business and technology. It also raises an uncomfortable question: can you get rockets to orbit and accelerate the transition to electric vehicles without accepting some of the chaos that comes with Musk? Isaacson captures this tension well in the final paragraph: "Could you get the rockets to orbit or the transition to electric vehicles without accepting all aspects of him, hinged and unhinged? Sometimes great innovators are risk-seeking man-children who resist potty training. They can be reckless, cringeworthy, sometimes even toxic. They can also be crazy. Crazy enough to think they can change the world."  One of the most useful parts of the book is Musk’s “algorithm” for building deep-tech hardware products:  "1.  Question every requirement.  Each should come with name of the pe...

With eight billion people in the world, why are truly original ideas so rare?

Before calendars, did birthdays exist?

In the canvas of the universe, Earth is a dot, and we are a dot within it. And still, our problems feel immense to us.

Most product managers don’t fail because they lack ideas; they fail because they don’t make the right trade-offs. Over the course of my career, I’ve led product organizations building everything from 0→1 products to businesses generating over $600M in revenue spanning hardware, software, AI, and crypto. What I’ve found is that while the domains evolve, the craft of product management remains fundamentally the same. In the fast-paced world of product development, the job of a product manager is not to optimize for a single objective but to balance competing forces. I’ve found that good product managers consistently operate across four objectives:      A. User Experience      B. Profit (Unit Economics)       C. Competitive Advantage      D. Technology Advancement Read more about the four objectives here . The most common mistake product managers make is optimizing for one objective at the expense of the others. These objecti...

The fundamental flaw in the idea that AGI (Artificial General Intelligence) can be more intelligent than humans is the assumption that language itself represents intelligence.