A New Vision for Human Work After Automation
Frontier models are trained to satisfy the average user, which is why their outputs look impressive in a demo and fall apart the moment you apply them to a project you care about. Dan Shipper closes Thesis: 2027 with a vision for human work after automation—showing how we can build and work with AI that extends our perception and agency rather than replacing them.
TL;DR
- Frontier models are trained for average users, leading to performance issues in specific projects.
- Dan Shipper's Thesis: 2027 envisions human work enhanced by AI, extending perception and agency.
- AI progress is predicted to create more work for humans, not less.
- An event on November 5 will feature speakers from OpenAI, Notion, Sequoia, and Microsoft to discuss human work after automation.
- Registration offers eligibility for a giveaway of $20,000 in credits and benefits from Notion, Anthropic, and OpenAI.