Getting centimeter level GPS accuracy with Geodnet Founder Mike Horton | EP #132

Dropping a podcast with @benfielding, Co-Founder and CEO at @gensynai

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.

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/

Dropping a podcast with @mdudas, Managing Partner at @6thManVentures

Dropping a podcast with @pequityresearch Mr. P has been doing some great work breaking down the AI buildout and following where hyperscaler capex actually goes. In this episode I wanted to walk through the full stack: logic, memory, power, and networking. We get into why memory could become the largest line item in the AI bill, what old GPU rental prices tell us about compute demand, and where value is going to accrue as the physical constraints get harder to solve. At the center of the conversation is his view that AI spending cannot grow forever. Long-term contracts might change the shape of the next memory downturn, but they don’t eliminate the cycle. And signing a 10-year contract does not mean anyone can actually see 10 years of demand. We also get into why he’s excited about optics, where NAND and HBF fit as agents use more memory, and his views on Chinese open source and the future of US model development. We discuss: Why he thinks 10-year demand visibility is bullshit