CDOQ

Imagine an AI agent causes $10 million in damages. Its owner argues that the agent, not the owner, is responsible. But the agent has no assets and no insurance. Have we created accountability or simply given humans and corporations another way to escape it? Before we give agents more autonomy, we need to answer three questions: Who are they? Who is responsible for their actions? And would making them legal persons improve accountability? My starting point is simple: an agent should not get to act independently while everyone behind it gets to disclaim responsibility. 1. Identity: Who are you? Consider an AI agent contacting your company's purchasing agent and offering a 20% discount on equipment. How does your agent verify that the seller's agent actually represents the company it claims to represent? A name is not enough. Neither is a convincing conversation. The seller's agent should present a credential signed by the company, verifiable through a source your company alre...

The theorem doesn't care who proved it, but careers run on whodunit.

Most businesses that survive remain small by choice. In hi-tech, that choice can be deadly. 

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.

My agent often answers better than I do—not because it’s more intelligent, but because it never forgets and can hold more context at once.

If humans are genetically programmed to reproduce, how do some people consciously choose not to have children?

Why do we realize the finite nature of life only when we’re closer to the end, and not in our youth?

Why do children repeat the same question, even when the answer never changes?

Human life begins like E = mc²—setting aside c, our energy grows with our mass. Eventually, the equation seems to flip: energy wanes while mass lingers.

Technology evolves more through human belief than through natural laws.

Some things don’t mix well, no matter how good they are individually—like Mexican huevos rancheros and French sourdough bread.

Music doesn’t recognize generational boundaries.

A leader’s empathy for employees should not exceed the team’s empathy for customers.

Your cost structure is not your customer’s problem. 

Business school teaches you the theory of business — the frameworks, the financial models, the case studies. But real understanding comes from seeing how ideas collide with the messy, unpredictable world of people, markets, and technology. Over the years, I’ve found a handful of books that open your mind beyond the classroom. They explore the mental models, systems thinking, and creative problem-solving that shape great companies and great decision-makers. Here are eleven that I think business students will find useful:  Charlie Munger 1. Poor Charlie's Almanac   - Timeless lessons on decision-making and mental models from Charlie Munger. 2. Making things work   - Insight into the systems-thinking approach that underlies modern technology and organizations. 3. The Snowball  - A deep dive into Warren Buffett’s philosophy and long-term business perspective. 4. Fooled by randomness  - A brilliant exploration of luck, probability, and human bias...

Normal is what a lot of other people like you do. 

People who love what they do age more slowly.

Climbing a hill from a standstill is a grind. With momentum, it’s a glide. But from a standstill, changing direction is easier than when you’re already in motion. 

Imagine teaching a lion to speak perfect English. Every word pronounced flawlessly, grammar impeccable. Yet, a conversation with it would still be impossible. Its frames of reference are so alien to ours that true understanding cannot occur—just as Ludwig Wittgenstein noted in Philosophical Investigations : ‘If a lion could speak, we could not understand him.’ Now imagine a counterpart trained on the vast body of human knowledge: GenAI. Here, conversation is not only possible but also delightful, because its frame of reference aligns with ours. GenAI can reason, explain, and answer across countless domains. On the surface, it seems like a mind without limits. Conversations But there’s a catch. Unlike a lion, GenAI can confidently deliver falsehoods. Without deep understanding of the topic in discussion, you may never notice. Knowledge alone isn’t enough. Context, experience, and critical thinking remain irreplaceable—even when conversing with a machine that seems to know everything.

The world works because not everyone wants the same thing at the same time. 

If you’re not consistently making things better, they don’t stay the same—they get worse. 

Driving down Hwy 101 in Silicon Valley yesterday, I spotted this billboard (See image). It reminded me that while the Bay Area overflows with technical brilliance, tech companies often stumble when it comes to marketing and communication. The ad boldly declares: “DEPLOY BRAVELY.” Deploy Bravely? In software, deployment is the last place you want bravery. A good deployment should be boring—built on testing, quality assurance, monitoring, and the ability to roll back instantly if things go sideways. Sonos once deployed bravely and left some of its speakers effectively unusable.  The bravery resulted in the company’s market cap declining by half. I have no idea what Prisma Airs is—and the ad doesn’t make me interested in finding out more. Can you tell what it’s about?

I met a software engineer yesterday at a conference — let’s call him Ted (not his real name). He has worked at well-known Silicon Valley companies like Square, Google, and Yelp. Ted told me that culture varies widely across companies. I asked, “But how does that affect your job? You’re a software engineer — you just have to code to the requirements.” He smiled and said, “Culture defines how good the requirements are, how good my code is, and how happy the customer will be with the application.” Then Ted shared a practical test for culture: measure how much time you spend convincing others. If most of your energy goes into persuasion instead of building, the culture is working against you. In a healthy culture, things just flow — and you spend your day building, not convincing. In other words, in good cultures, it takes less energy to get things done.

Don't be so productive that you lose the joy of work. 

In investment banking, exploiting regulatory loopholes is often called innovation. 

Both Charlie Munger and Warren Buffett have an extraordinary gift for simplifying the complicated. They taught me that the most powerful ideas are often the clearest. I have since carried that lesson into my own work. At Moki, a company that provides software for remotely managing hardware in retail environments, we faced a common challenge: overcoming CIOs' fear of losing control. Instead of delving into the technical depth and cybersecurity of our product, we focused on a single, compelling promise: " Total Control of their devices." This simple framing cut through the complexity and directly addressed their primary concern. It worked.   An often-cited (but unverified) framework, attributed to Einstein, outlines five levels of intellect: 1. Smart: Possesses vast knowledge (e.g., a McKinsey consultant; whatever you think of them, they do know a lot). 2. Intelligent: Applies existing knowledge effectively (e.g., Michael Dell, who adapted the direct-to-consumer model for...