Agents, adoption, enterprise systems, and what changes when AI enters real companies.
I have a 14,000-word document I maintain manually, to add into ChatGPT queries I make related to my role as CTO. It covers a lot of ground about Pactum: * An extremely basic description of who we are and what we do * Company strategy, long-term goals, financial model, and progress towards those * Our target market, ICP, personas, value
Here's a model I use to think about how AI will change work. Some parts of every activity will cost 1,000x less than they do when a human does it. For engineers it might mean reading and typing code, for radiologists reading X-ray images, etc. But it's not 100% of the time a human
There seem to be two views of what will happen with engineers' jobs given the new efficiencies from using advanced AI coding tools (Cursor, Claude Code, etc). The first view is partial rebound. Fewer engineers will be needed in total: if engineers are 20% more effective per hour, there will be some downsizing of the total engineering bill. Perhaps
Anthropic, OpenAI and possibly others are doing one in the US with 1,000 participants from public sector research institutes. It's called an "AI Jam" and the companies are obviously doing it out of commercial interest. Even so, the government sector must have such obvious use cases. Just running a bureaucracy consists of processing and summarizing
Writing this post I got heavy AI assistance, starting off with my raw notes of the competition. I know it's not up to my usual quality – this was the only way I could find time to share this. Overall I consider the result not good enough, but I will keep experimenting with different kinds of AI assistance if
White House's new chip rules seem to not have huge impact right now, in the "tier 2" countries like Estonia. It is unlikely we ever would have been able to build a $1B data center anyway. But this restriction could still become significant. I don't fully understand but I assume the per-country limit
With LLMs now multimodal and extremely cheap, they can do much of what a human can, when limited to only the virtual. This rapid progress in the world of bits, however, is a sore reminder of how slowly things improve in the physical world. I need an AI that can: * plan meals, manage food inventory, cook, wash dishes, clean up
Context: first thoughts on Pause Giant AI experiments. I will refine my thinking over time. * I had not thought about AI safety much since ~2017, after thinking a lot about it in 2014-2017. In 2017, I defended my MSc thesis on an AI-safety-inspired topic (though very narrow and technical in nature), but decided to take some distance
Microsoft recently held an event where they announced the "Office 365 Copilot". It was extremely impressive to me, to the extent that (when this launches) I will consider switching from Google's work suite to Microsoft's. Why is this announcement so impressive? In one word, integration. Pretty much every Office app can refer to documents,
"What's the Moat of your AI company?" That seems to be top of mind for founders pitching their novel idea to VCs -- and the most common question they get. But I think it might not be the right question. Right now, nobody seems to have a good answer. a16z doesn't. At the same time,
