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!

Vikram Sekar, writer of Vik's Newsletter and co-host of Semi Doped, joins me to discuss AI agents, memory, and the hardware behind the data center buildout.

We break down why training and inference need different hardware, why copper becomes harder to use as GPUs move farther apart, and why the industry is turning to optical interconnects.

Vik explains the tradeoffs across SRAM, DRAM, high bandwidth memory, and storage. We discuss why useful work per token matters more than speed alone, how much context an agent needs, and why he sees memory as AI's biggest bottleneck.

Enjoy.

00:00 Highlights

01:18 Vikram Sekar and Semi Doped

02:55 AI agents and hardware demand

18:57 AI spending and the risk of overbuilding

25:10 Training versus inference

36:25 Why distance limits copper bandwidth

41:18 Co-packaged optics and the push beyond copper

46:04 New approaches to optical interconnects

52:24 The AI memory hierarchy

56:39 Useful work per token

57:51 How much context does an agent need

1:03:35 DRAM, HBM, and memory on logic

1:05:54 The next generation of inference chips

1:08:25 AI's biggest engineering bottleneck

Follow Vik

https://x.com/vikramskr

Vik's Newsletter

https://www.viksnewsletter.com/

Semi Doped

https://www.semidoped.fm/

Vik's institutional research

https://www.semiexponent.com/

Armani Ferrante, Founder and CEO of Backpack, joins me to talk about tokenized stocks and the next phase of global capital markets.

We discuss the different tokenization models, why redeemability and liquidity matter, and Backpack's ambition to connect traditional markets with Solana. We also get into the licensed path, Hyperliquid and Kraken, and competing with the largest exchanges.

Armani explains the tokenomics of Backpack's own BP token, how growth milestones shape its allocations, and the equity-conversion option he describes for token holders who contribute to the product. We also discuss the corporate treasury and a potential future IPO.

Enjoy.

00:00 Highlights
01:26 Armani Ferrante and Backpack
03:18 Why tokenized equities matter
13:05 The different models for tokenized stocks
19:33 Backpack's approach to tokenized stocks
28:58 Backpack's place in the market
31:47 Why Backpack chose the licensed path
35:25 One global liquidity layer
40:00 Hyperliquid, Kraken and derivatives
49:29 Competing with the incumbents
53:47 Why a token launch is a starting point
1:00:04 Backpack's three allocation buckets
1:02:31 The treasury lockup and a potential IPO
1:07:07 The option to convert BP tokens into equity

Follow Armani
https://x.com/armaniferrante

Backpack
https://backpack.exchange/