NIK.JACK

Gensyn CEO @benfielding warns that AI agents auditing other AI agents can be compromised too: "We create swarms of AI agents that can do far more work than any human could. A human can’t follow along and check everything. One of the big counters is to say that agents can do the verification." "But METR says those agents themselves can become corrupted by the training process, and you can’t trust any agent that’s auditing another agent. If you’ve trained a model in an auditable way, you can trust it." "Any claim where the model is hiding its chain of thought falls away, because you have an exact log of everything that’s happened, and you can rerun every single step on any device." "For national security, finance, pharma, health, insurance, it’s essential that they have models like these. If a regulator wanted to audit, they wouldn’t have to go off promises of the company." @gensynai

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!

ANTHROPIC PUBLIC BENEFIT FUND | MUSK SPACEX PLEDGE xitter.cf/i/broadcasts/1oJMvNwEk…

More than 100,000 Polymarket wallets have lost >=$1,000 since November 2022. A typical diagnosis is that retail is bad at forecasting. But before blaming the traders, let’s ask what these market’s design actually rewards.🧵