To find some of the most effective people that others may not know about, I make sure to leverage my network. Before hiring for a new role, I ask people who they consider to be the top 1% of performers for a certain function. I then reach out to the people who were recommended to me for calibration meetings, but my secret goal is to actually hire t... See more

What I learned from reading “Situational Awareness” is that the next generation of privilege-blind Silicon Valley investors are even more out of touch than expected. It’s really sad and feels like a cosmic joke that the world’s most affluent class of people who legitimately have the resources to change the world for the better are living and thinki... See more

A-players create success while B-players participate in success.

The death of the press release Why launching products on social media is the only way to go Recently I've been working with a lot of early startups as part of A16Z SPEEDRUN — our accelerator focused on the intersection of TECH x GAMES — and many of these teams end up launching… Show more

The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed,... See more

Do less, then obsess.

The 8-hour work day, weekends, time off—hard-fought and won by the labor movement!—seem to have been triumphs for human health and well-being. Why should we assume that stopping here is right? Why should we assume that less work was better in the past, but less work now would be worse?

We have machines now that we can basically talk to like humans. It’s a remarkable testament to the human capacity to adjust that this seems normal, that we’ve become inured to the pace of progress.

A rough analogy to the current LLM process is that making a new model is like baking a cake. You figure out your data and algorithms—like mixing the batter—and then you pretrain the model, that is, run it on a large number of computers for several months—like putting it in the oven—and then at the end you do some “post training”—like frosting and d... See more