Michael Burry called the subprime mortgage crisis when everyone else was buying in. Now he’s watching trillions pour into AI infrastructure, and he’s skeptical. Jack Clark is the co-founder of Anthropic, one of the leading AI labs racing to build the future. Dwarkesh Patel has interviewed everyone from Mark Zuckerberg to Tyler Cowen about where this is all headed. We put them in a Google doc with Patrick McKenzie moderating and asked: Is AI the real deal, or are we watching a historic misallocation of capital unfold in real time? — Read More

#strategy

When Google locked AlphaFold 3 behind commercial restrictions, three MIT PhD students rebuilt it in four months. Now Boltz has $28M, a Pfizer partnership, and a bet that open-source can capture drug discovery infrastructure. — Read More

Leaders of many organizations are urging their teams to adopt agentic AI to improve efficiency, but are finding it hard to achieve any benefit. Managers attempting to add AI agents to existing human teams may find that bots fail to faithfully follow their instructions, return pointless or obvious results or burn precious time and resources spinning on tasks that older, simpler systems could have accomplished just as well.

The technical innovators getting the most out of AI are finding that the technology can be remarkably human in its behavior. And the more groups of AI agents are given tasks that require cooperation and collaboration, the more those human-like dynamics emerge.

Our research suggests that, because of how directly they seem to apply to hybrid teams of human and digital workers, the most effective leaders in the coming years may still be those who excel at understanding the timeworn principles of human management.

We have spent years studying the risks and opportunities for organizations adopting AI. Our 2025 book, Rewiring Democracy, examines lessons from AI adoption in government institutions and civil society worldwide. In it, we identify where the technology has made the biggest impact and where it fails to make a difference. Today, we see many of the organizations we’ve studied taking another shot at AI adoption—this time, with agentic tools. While generative AI generates, agentic AI acts and achieves goals such as automating supply chain processes, making data-driven investment decisions or managing complex project workflows. The cutting edge of AI development research is starting to reveal what works best in this new paradigm. — Read More

This is the third in my annual series reviewing everything that happened in the LLM space over the past 12 months. For previous years see Stuff we figured out about AI in 2023 and Things we learned about LLMs in 2024.

It’s been a year filled with a lot of different trends. — Read More

… The talent shortage is brutal. Every company needs AI PMs. Few people have the skills.

OpenAI, Anthropic, Google DeepMind, and Meta all have open AI PM roles. They can’t fill them fast enough.

The hiring bar is high. You need product sense, technical depth, and hands-on AI experience. Most PMs have one or two. You need all three.

… The gap between supply and demand means comp packages keep climbing. Base salary plus equity plus signing bonuses. $500K is common. $700K+ for senior roles.

The AI PM job market dynamics show why this won’t change soon. — Read More

Last month, my college roommate Jake sent me a panicked text at 2 AM.

“Dude. ChatGPT just wrote better code than me in 30 seconds. Am I screwed?”

Jake’s a software engineer at a mid-sized tech company. Makes $140K. Has a mortgage. Two kids. He’d just spent three weeks on a feature that Claude finished in minutes.

I get it. The headlines are terrifying. Every week there’s a new story about AI “coming for your job.” Anthropic’s CEO warned that AI could replace half of all entry-level office jobs within five years. Goldman Sachs economists predict 6–7% of the US workforce could be displaced.

But here’s what nobody’s talking about: I just spent 40 hours analyzing over 2 billion job postings, academic studies, and labor market data from 2022–2025.

The truth? It’s the exact opposite of what you think. — Read More

The shape of things only becomes legible at a distance. For instance, history demands temporal distance.

… Although AI is nearing its 70th birthday, it’s been only five years since ChatGPT was launched, eight since the transformer paper was published, and thirteen since AlexNet’s victory on the ImageNet challenge, which implies the deep learning revolution is barely a wayward teenager. I think, however, that we must try to give a clearer shape to the current manifestation of AI (chatbots, large language models, etc.). We are the earliest historians of this weird, elusive technology, and as such, it’s our duty to begin a conversation that’s likely to take decades (or centuries, if we remain alive by then) to be fully fleshed out, once spatial and temporal distance reveal what we’re looking at. — Read More

In the world of AI, half a year is a very long time. Back in July, we saw LLMs being adopted faster than almost any other technology in history. Five months later we’re still seeing rapid growth, but we’re also seeing early winds of change — both in who uses AI and how they do so.

Using the latest public data,1 and a poll of US adults we conducted with Blue Rose Research, this post shares an updated picture of the state of LLM adoption. — Read More