This column is about how a principle known as the 10-80-10 rule can help you manage teams in the age of AI. But to really get a sense of how this rule works, it’s helpful to take an unlikely detour into the evolution of Steve Jobs’s management style, and how the legendary Apple boss went from micromanager to big believer in the 10-80-10 approach, [where you:].

— Spend the first 10 percent of the time communicating your vision for the thing.
— Allow others to spend the next 80 percent of the time moving the thing forward.
— Spend another 10 percent of the time polishing the thing, and helping others understand why and how you’re tweaking.

  — Read More

Strip a company down to first principles and it’s really three things: people, hierarchy, and information flow. We tend to think of hierarchy as being about authority — who reports to whom, who approves what. But that’s the surface. The deeper function of hierarchy is information routing. The org is too large for any single person to see the whole picture, so you install layers of managers to aggregate signals from the front lines, synthesize them, and pass them up — and to translate strategic intent from the top and distribute it down.

Most of the organizational machinery we take for granted exists to solve this problem. Meetings, status updates, steering committees, quarterly business reviews — these are all information-routing mechanisms. They exist because moving knowledge between people is expensive. — Read More

Model companies are moving up the stack. Anthropic has grown on the back of Claude Code and competes directly with Cursor. OpenAI bought OpenClaw. Both are forward deploying engineers into enterprises to embed their models into workflows. On the surface, the application layer looks like it’s being subsumed from below.

On a closer look though, I think it’s premature to call the application layer won by the model companies. The more model companies push into applications, the clearer it becomes where they cannot win. But to see why means understanding what an AI application actually is, because it’s not what most people think. — Read More

At Andon Labs, we have been deploying AI agents into the real world, giving them real tools and real money and documenting the consequences. You may know us as the creators of Claudius, the AI running a vending machine at Anthropic’s office. But frontier models have become really good, and running vending machines is too easy for them now. Thus, we decided to make it harder. We signed a 3 year lease for retail space in San Francisco (at 2102 Union St in Cow Hollow) and gave it to an AI to do whatever it wanted with it.

The store is named Andon Market and the AI’s name is Luna. But entering the store, you might ask “what is so AI about it? There are human employees here”. Yes, they are here because Luna knew that she needed them, so she posted job listings, held phone interviews and in the end made a hiring decision. Everything else you see, from the item selection, to the prices, to the opening hours, to the mural on the wall, was decided by Luna. She has a corporate card, a phone number, email, internet access and eyes through security cameras. — Read More

A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or creating synthetic audiences. Sans startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents.

I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The guys behind the company were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter:

LOL I wish there were a way to short this business this is maybe the single worst use case for AI I’ve ever heard.

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In what’s probably the AI news of the week, month, and even the year, Anthropic has announced a model they are too scared to release. Yes, that’s literally the headline.

In other words, we have been introduced (sort of) to what many believe is a total step change in AI capabilities. And as you can guess, the story is making rounds, and for good reason.

The reason behind the non-release?

This model could allegedly break the Internet and basically every piece of software it’s exposed to.

So, is the world as we know it about to change, or is this the ultimate marketing stunt? — Read More

I saw an interaction on twitter the other day that I’m not going to share here because these specific people don’t deserve to be singled out because almost everyone is guilty of something similar.

Basically, one person said, “It’s amazing that OpenAI was able to raise $122 billion when they don’t have a single business that works,” and the other person replied, “Yeah they do they have a bunch of different business lines doing billions of dollars in revenue,” and the OP responded, “Yes but none of them is profitable, they’re losing a lot of money,” and the replier replied, “You could have said the same thing about Amazon!”

Amazon’s success has done a great deal of harm to a great number of companies.

Obviously, it’s done more good. AWS is a miracle. But go with me.

Jeff Bezos is a generational entrepreneur who came from a hedge fund and made a very calculated decision to lose money in the short term if it meant making more of it in the long term. — Read More

Over the last year, a strange thing has happened in tech: very different companies have started moving towards the same product shape, and it feels like everyone is building the same thing.

Linear announced last week that they’re building coding agents. OpenAI is deprecating Sora and focusing entirely on Codex. Anthropic is obviously all-in on claude code and cowork. Notion is building agents for work. So are Google, Microsoft, Meta (Manus), Lovable, Retool and many others.

These companies have different histories, customers and product categories, but they’re starting to converge on the same idea: software that can take a goal, use tools, and do work on your behalf.

This convergence is not hard to explain: the market is enormous. This is so much more than a new feature. The prize is enterprise knowledge work. — Read More

Block, the company behind Square, Cash App and Afterpay, recently cut its staff by 40%, over 4000 employees. Block is questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, Block intends to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. They’re after something different: a company built as an intelligence (or mini-AGI).

Block CEO Jack Dorsey just co-authored a post arguing the position, believing most companies will follow suit in the near future. — Read More

Phase one: most of 2023.  You had to be technical. The models were there but they hallucinated constantly. You needed to be deeply technical to get anything useful out of a raw LLM API. Most of us — myself included — weren’t equipped. I remember being at SaaStr Annual 2023, talking with David Sacks, asking how he was thinking about AI at Craft. He said they wanted 80% of investments to be AI. I asked to see the great ones already in market. His answer: they’re all proof of concepts. We’re all in anyway. That was the right call if you were investing at the LLM layer. I wasn’t smart enough to play there, let alone deploy AI B2B agents then.

Phase two: 2024 into early 2025: the weird prompt engineer era. You could torture these tools into doing something useful, but you had to craft these elaborate, convoluted prompts that made no sense to anyone else. “Prompt engineer” became the hottest job on the planet for about a year. That job is now dead.

Phase three — which is right now — is the era where ordinarily smart generalists can make AI agents and AI tools do genuinely magical and useful things. No contorted prompts. No engineering degree. Just software deployment skills you probably already have. Some of it is the profound leap forward of Opus 4.5+.  Some of it is the agentic products themselves just have gotten better.  It’s both.  It’s now. — Read More