Ai

Catching up on some reading today and came across this:

[Claude Code] has the potential to transform all of tech. I also think we’re going to see a real split in the tech industry (and everywhere code is written) between people who are outcome-driven and are excited to get to the part where they can test their work with users faster, and people who are process-driven and get their meaning from the engineering itself and are upset about having that taken away. (This is not to say that there aren’t many issues with AI aside from these things, of course.)

It wouldn’t surprise me to see more artisanal teams, startups, and small businesses spring up to give that second set a home. But I think we’ll also see more startups and projects created by the first set, too.

That’s Ben Werdmuller (emphasis mine), responding to Simon Willison’s recap of the year in LLMs (Simon also published some predictions for 2026)

Going to start collecting different AI design metaphors. Drew Breunig described Gods, Interns, and Cogs. Geoffrey Litt is thinking about Copilots and HUDs.

Would love to see more documentation, like this March 2025 post from Google, educating content creators on how to live in the AI search future.

Learning about DeepSeek today …

• This great FAQ from Ben Thompson digs into why this is big deal, why it might be good in the long run, and also provides a nice background on different reinforcement learning approaches.
• You’d be right to think DeepSeek, an AI model from a Chinese company, doesn’t want to talk about some things.
• DeepSeek was surprisingly cheap to train and is very cheap to use (compared to similar models from OpenAI and Anthropic).

Drew Breunig:

After plenty of discussions and tons of exploration, I think we can simplify the world of AI use cases into three simple, distinct buckets:

1. Gods: Super-intelligent, artificial entities that do things autonomously.
2. Interns: Supervised copilots that collaborate with experts, focusing on grunt work.
3. Cogs: Functions optimized to perform a single task extremely well, usually as part of a pipeline or interface.