As models become more useful, demand for compute grows I strongly believe inference demand will continue to outstrip supply, pushing GPU rental prices higher Open source or closed source, both need chips, memory, and power. This is bullish for continued AI capex investment as payback periods get faster and faster
Temporal team are cracked devs You should start following them as the squad pushing forward Solana’s microstructure
Most liquidity will consolidate on one chain. We've been saying this at Frictionless for a while The bar is that chain's market structure has to work for the whole world, not just a few jurisdictions
Congrats @anza_xyz team! Massive update to the network
This is a pretty crazy stat when you think about it And this is just spot. The opportunity gets much bigger as perps and other derivatives move onchain
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!
Dropping a podcast with @mdudas, Managing Partner at @6thManVentures A crypto business can make money without that money reaching its token holders. In this episode I wanted to talk with Mike about what gives a token value and who benefits when the business grows. We get into how he built 6MV, why he invests in applications, and how he thinks about returning money to investors when a business might not stay ahead for long. A big part of the conversation is about tokens and equity. Mike thinks teams need to explain what people are buying, where the revenue goes, and how token holders benefit. We talk about buybacks and how much control holders really have. We also get into why trading apps need better assets and fewer ways for users to lose money, his views on Solana, Hyperliquid, and Ethereum, and where crypto fits into AI. We discuss - How Mike built 6MV - What makes a crypto business last - When to sell and return money to investors - Stablecoins, lending, and prediction markets - Social trading, slippage, and keeping users - Solana, Hyperliquid, and what gives ETH value - Tokens, equity, buybacks, and what holders actually own - Why Mike calls XRP and Cardano “memes for suits” - AI agents, payments, and compute markets - Whether AI companies are worth their valuations Timestamps 0:00 – What Gives Tokens Value? 0:58 – Mike Dudas and the Origins of 6MV 6:20 – Liquidity and Returning Capital to Investors 10:16 – Real Adoption Beyond Crypto Token Prices 16:56 – Crypto Apathy and the Rise of AI 21:43 – Why 6MV Changed Its Consumer Investing Thesis 26:34 – Social Trading, Slippage, and Better Assets 36:25 – Solana, Hyperliquid, and the Chain Landscape 39:37 – The Problem with ETH’s Value Proposition 43:16 – Tokens, Equity, and Where the Money Goes 46:21 – Governance Tokens, XRP, and Cardano 49:40 – Where Crypto Meets AI 55:38 – Are AI Valuations in a Bubble? 58:42 – The Next Five Years of Crypto Enjoy!
The token is the beginning, not the end.
I'm going to QT because I want to keep the discussion going You can break it down into two core issues - Market Structure - The Total Addressable Market of Global Finance Lets talk about market structure first because I think its actually the more important one, because this directly translates into better products It should be no surprise to anyone that has studied the financial system that over time as we moved from paper trading to electronic trading, systems got faster. As a result, physical proximity to the matching engine gave trading firms a unique advantage from being physically colocated in the same data center or having a direct fiber link to the matching engine If you haven't gone down this rabbit hole, I encourage you to read the history of Flash Boys. There is a reason trading firms spend so much money optimizing networks, microwave links and private fiber lines, because milliseconds in latency can translate into billions of dollars NYSE even normalizes fiber lengths for the same connection service inside its Mahwah data center, regardless of which hall or cabinet a firm uses. The goal is to remove the advantage of simply having a closer rack. This is described in ICE’s colocation specifications Remaining thoughts here: https://t.co/8gs2vq9IdJ
