TENTANG EPISODE INI
Dropping a podcast with @benfielding, Co-Founder and CEO at @gensynai
Ben started his PhD in deep learning in 2015, well before the current wave of attention around AI. In this episode I wanted to walk through how he thinks about the progress we have made, the economics of building bigger models, and what comes next.
We get into why he questions whether the money going into centralized AI will be paid back through product usage, how open models affect that business, and why having customers who keep using your product matters more than staying ahead on a benchmark.
We also walk through how Gensyn has changed. The team moved from focusing on decentralized compute to collecting truthful information from the world. Ben explains why they made that decision and how Delphi uses markets to bring information together and check it against what actually happens.
That leads into forecasting. Ben thinks predicting future events could become a much bigger use of AI. We discuss how models could learn from being right or wrong, why people need a reason to share what they know, and what it would mean to have a probability calculator helping with everyday decisions.
We discuss
- Ben's background in deep learning and how Gensyn started
- The economics of bigger AI models and whether the spending makes sense
- Open models, competition, and building products people keep using
- Why Gensyn moved away from focusing on compute
- Verifying machine learning work and making training auditable with open-1b
- Delphi, information markets, and learning from real outcomes
- Rewarding people for sharing the evidence behind their predictions
- Forecasting, better decisions, and where decentralization helps
Timestamps
0:00 – Can AI Predict the Future?
1:14 – Ben Fielding and the Origins of Gensyn
7:26 – Scaling AI Beyond Bigger Clusters
13:29 – Why Startups Can Take a Different Approach
18:49 – Trust and Verifying Machine Learning
23:59 – The Economics of Centralized AI
27:29 – Why Gensyn Moved Away from Compute
31:09 – open-1b and Auditable AI Training
35:47 – Open Models and Closed Labs
43:29 – AI Products, Customers, and Neoclouds
50:13 – From LLMs to Forecasting
53:44 – Training Models to Make Better Predictions
57:10 – Delphi and Information Markets
1:02:26 – Rewarding the Evidence Behind a Prediction
1:04:34 – Where Crypto and AI Actually Overlap
1:11:35 – The Next Wave of AI
Enjoy!
