Sarah Drinkwater
@sarahdrinkwater
Knowledge beachcomber interested in the ownership economy, flourishing communities, ecosystem building, magic, and knowledge as an end to itself.
Sarah Drinkwater
Alternative to What? | Thomas Frank
thebaffler.com
late stage capitalism and “Alternative”
the patina of daring had begun to wear thin on the eighties’ chosen crop of celebrity-rebels. Entire new lines of insolent shoes would have to be designed and marketed; entire new looks and emblems of protest would have to be found somewhere. Consumerism’s traditional claim to be the spokesman for our inchoate disgust with consumerism was hemorrhaging credibility, and independent rock, with its Jacobin ‘authenticity’ obsession, had just the things capital required.
Out went the call for an “alternative” from a thousand executive suites, and overnight everyone even remotely associated with independent rock in Seattle—and Minneapolis, Chapel Hill, Champaign, Lawrence, and finally Chicago—found themselves the recipients of unsolicited corporate attention
German venture network 10x Founders launches with €160m, backed by 200 entrepreneurs | Sifted
sifted.eu
Aping into Progress: A Report on Crypto Philanthropy
otherinter.net
Once Upon a Time… at Bennington College on Apple Podcasts
podcasts.apple.comidea generation and
📢NEW PAPER!
Where @davidthewid, @sarahbmyers & I unpack what Open Source AI even is.
We find that the terms ‘open’ & ‘open source’ are often more marketing than technical descriptor, and that even the most 'open' systems don't alone democratize AI... See more
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.
What do labor activists, socialists, and mutual aid organizers see in Web3? Simply, a design space and set of tools to explore using towards political ends. Practically speaking, this includes an interest in transparent voting, funding of public goods, and broad-based ownership and governance of online infrastructure.