Sarah Drinkwater
@sarahdrinkwater
Knowledge beachcomber interested in the ownership economy, flourishing communities, ecosystem building, magic, and knowledge as an end to itself.
Sarah Drinkwater
It’s interesting to think about how new generational consumer behaviors translate into new businesses. We’re seeing a slew of startups building better solutions to the housing crisis, the climate crisis, the education crisis. But there are also less obvious impacts from consumer behavior:
Rex Woodbury • Welcome to the Internet: Millennial & Gen Z Ennui
While the Web3 space may be perceived as a financialized playground for scammers, there is a small but growing subculture of leftist labor activism emerging.
Dirt • Dirt: Solid(ar)ity
I’ve started two companies that have raised $95m
today I take a seat at the other side of the table
House Capital (@housecapvc) is a completely new type of venture fund
here is what the next generation of top VC’s will look like:
jacob peterstwitter.comAre these limitations helping you be more creative?
— Exactly. It’s the most critical part. With any kind of creative work you start disabling as much as possible and narrow it down only to the necessary tools you need, and from there start making the work. That’s what I believe makes you super creative.
The founder of Teenage Engineering opens up to his creative space
Future of software and AI tooling
One question kept coming up:
Why haven’t incumbent software providers been able to leverage their valuable data assets yet?
Both giants like Salesforce and growth-stage companies with 10+ years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true?
One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks.
pocket operator™ for Pixel™
teenage.engineering
Publishing and
The way managers are taught to run companies seems to be like modular design in the sense that you treat subtrees of the org chart as black boxes. You tell your direct reports what to do, and it's up to them to figure out how. But you don't get involved in the details of what they do. That would be micromanaging them, which is bad.
Hire good people and give them room to do their jobs. Sounds great when it's described that way, doesn't it? Except in practice, judging from the report of founder after founder, what this often turns out to mean is: hire professional fakers and let them drive the company into the ground.