The Delphi Podcast

In this episode, Tommy speaks with James Seyffart, Scott Johnsson, and Hal Press about the latest developments in the long-running saga to get Bitcoin and Ethereum exchange-traded funds (ETFs) approved by the SEC.

They discuss the current filings from major players like Grayscale, BlackRock, and ARK Invest, provide analysis on Grayscale's lawsuit against the SEC, and debate the timeline and likelihood of approval.

The experts also examine the potential market impact and discuss how an ETH futures ETF approval could pave the way for a spot Bitcoin ETF.

Will the SEC finally approve these products? How would it impact crypto adoption? Tune in for this wide-ranging discussion on the nuances of ETF filings, SEC reasoning, and the uncertain road ahead.

As a disclosure the host Tommy currently holds ETHE.

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Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed ⁠⁠⁠here⁠⁠⁠.

Keywords 

bitcoin ETF, ethereum ETF, SEC approval, crypto ETF, grayscale bitcoin trust, GBTC, ethereum grayscale trust, bitcoin futures ETF, ethereum futures ETF, spot bitcoin ETF, SEC regulation, crypto regulation, bitcoin adoption, institutional crypto adoption, bitcoin price impact, crypto investing, bitcoin investing, bitcoin IRA, bitcoin in brokerage account, grayscale vs SEC, gensler bitcoin, crypto ETF race, winklevoss ETF, Barry Silbert, digital asset investing, bitcoin institutional adoption, bitcoin mainstream adoption, bitcoin passive investing

In this episode, we delve into the fascinating world of blockchain security and the intersection of artificial intelligence with the crypto space. Join us for an insightful conversation with the co-founders of Test Machine, a company dedicated to making smart contracts safer by utilizing advanced AI technology for vulnerability detection.

Matt and Andrew share their experiences, insights, and expertise in the realm of smart contract security. They discuss the challenges of identifying vulnerabilities, the potential risks of AI misuse, and the synergy between blockchain technology and AI in enhancing data reliability and security.

Whether you're a developer, investor, or simply curious about the future of blockchain, this episode offers valuable perspectives on the evolving landscape of crypto security.

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Disclosures

Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed ⁠⁠here⁠⁠.

Keywords
AI-driven security, Blockchain technology, Smart contract vulnerabilities, Test Machine solutions, Crypto security, Ethical hacking, Blockchain data integrity, AI in cybersecurity, Vulnerability detection, AI-powered audits, Crypto ecosystem, Dystopian AI scenarios, Blockchain adoption, AI-Blockchain integration, Defi projects, Ethical hacking tools, Crypto autonomy, Future of blockchain, Financial data protection, Crypto industry challenges, AI,

In this thought-provoking podcast episode, we discuss the mysteries surrounding Large Language Models (LLMs) and their implications for the future of artificial intelligence (AI) with Stephen Wolfram, creator of Mathematica, Wolfram | Alpha & Wolfram Language.

We dive into the fascinating world of Large Language Models (LLMs) and their surprising capabilities. He also discusses the underlying scientific principles behind LLMs, including the concept of computational irreducibility, and how they function as probabilistic sentence finishers. Wolfram reflects on the potential applications of LLMs in computational contracts and the challenges of aligning AI systems with human aspirations.

As the conversation delves into the future of AI governance, Wolfram explores the complexities of regulating AI and the importance of finding a balance between human intervention and autonomous decision-making. This episode offers a captivating exploration of the evolving landscape of AI and its impact on various industries and job roles.

If you enjoy the podcast, please follow us on ⁠Spotify⁠ and rate us 5 stars on ⁠Apple Podcasts⁠.

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Disclosures: This conversation is for informational purposes only and does not constitute legal or investment advice. Actual results may vary materially from any forward looking statements made and are subject to risks and uncertainties. This podcast is not investment advice. Do not buy or sell tokens based off this episode.

Podcast description

In this episode we talk to Jesse Pollak, Head of Protocols at Coinbase where he leads the development of Base, Coinbase’s L2 built on the OP Stack. Prior to leading Protocols, he led retail engineering at Coinbase for many years, building Coinbase, Coinbase Pro, and Coinbase Wallet.

We explore how Base went from idea to product inside of Coinbase, how Base and Coinbase will reconcile their differences in technical and social centralization, how Base compares to other solutions, and much more.

If you enjoy the podcast, please follow us on Spotify and rate us 5 stars on Apple Podcasts.

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Disclosures: This conversation is for informational purposes only and does not constitute legal or investment advice. Actual results may vary materially from any forward looking statements made and are subject to risks and uncertainties.

Chapters:

In this episode, we discuss the impact of AI with Andriy Mulyar, co-founder of AtlasAI and creator of GPT4All. We explore how running local private language models can provide competitive benefits over using public models like ChatGPT without confidential data. Additionally, we delve into the importance of embeddings in AI and their potential impact on understanding data semantically. Finally, Andriy shares his thoughts on crypto/AI overlap, specifically focusing on data provenance as a key area for collaboration.

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Disclosures

Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed ⁠⁠here⁠⁠.

In this episode of the Delphi podcast, host Tommy interviews Emad, the CEO of Stability.ai, an open source AI company dedicated to democratizing access to AI tech. Emad explains how his company handles the rapidly evolving field of AI and machine learning, including their curation filtering priority setting process and the importance of building all types of models. The discussion also touches on the potential threat of artificial general intelligence to humanity.

[00:01:32] Artificial General Intelligence.

[00:04:11] Misaligned AI and ISIS.

[00:07:26] Need for decentralization in AI.

[00:11:17] Optimizing data for aligned AI.

[00:13:46] Culture-specific AI models.

[00:17:53] Standardized models for intelligence.

[00:21:19] OpenAI's objectives and actions.

[00:24:07] AGI vs. productivity.

[00:30:07] The era of big models.

[00:30:51] AI's mind-boggling compression.

[00:36:37] Education with AI tablets.

[00:38:31] AI's emergent behavior.

[00:41:02] AI and Government Control.

[00:44:53] New definition of intelligence.

[00:48:45] The AI Bubble.

[00:52:23] Impact of AI on economy.

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Disclosures

Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed ⁠here⁠.

Keywords

Stability.ai, open source AI, machine learning, text to image AI, artificial general intelligence.,AGI, dangers, AIs, humans, fake news,AI, education, healthcare, productivity gains, unemployment, panopticon, centralized control, propaganda, metaverse, regulations, government control, decentralization, distribution, Internet 2.,Google, Microsoft, Meta, AI, China Social Credit Score System.,entity, alignment, national models, data sets, AGI.,information rails, Reddit, structured data sets, AI, business model,data center, cloud, partnerships, revenue, open auditable AI,Google Search, standardized models, primitive building blocks, custom model creation, Amazon SageMaker.,models, business, culture, library, AGI,education, healthcare, AGI, automation, open source A.I.,Amazon, supercomputer, petaflops, ranking, infrastructure.,technology, deals, GPUs, super compute, partners,stable diffusion, clusters, optimization, speed, size,language, AI, productivity, scalability, data,GPT-4, compute, training, language models, productivity increase.,supercomputer, AI, training, terrorism, model weights,Iran, nuclear weapon, technology, AI, government.

We sit down with Brian Retford, founder of RISC Zero, to discuss the vision and potential of zero-knowledge (ZK) technology and its impact on the world. ZK technology, although currently in its early stages and with high costs, has the potential to fundamentally change trust assumptions and enable privacy and scalability.

RISC Zero's thesis is centered around making cloud computing universally accessible, privacy-focused, and owned by users. By removing barriers imposed by profit-driven cloud players, RISC Zero aims to create an open marketplace for automation and cloud services, challenging the dominance of a few companies in the industry.

The conversation delved into the technical aspects of RISC Zero, such as the zkVM (zero-knowledge virtual machine) and its applications. The zkVM allows developers to write programs in various languages and generate proofs to verify their intended execution. RISC Zero targets a wide range of use cases, both within and beyond the blockchain space, aiming to automate manual processes, enhance trust, and ensure privacy. They also discussed Bonsai, an L2 network running on the zkVM, which enables seamless integration of ZK proofs into various blockchain protocols.

The podcast explored the interplay between ZK technology and artificial intelligence (AI), highlighting how ZK can serve as a check on automation and support authenticity, privacy, and generative art. While acknowledging the mathematical complexities involved in ZK, Brian emphasized RISC Zero's goal of making it accessible and user-friendly, allowing users to focus on the possibilities it offers rather than the underlying intricacies. The AI angle was discussed in terms of leveraging ZK to enable new forms of social networking that emphasize privacy and individual responsibility.

In conclusion, the podcast provided insights into RISC Zero's mission to democratize cloud computing, the potential of ZK technology to revolutionize trust assumptions, and the broad range of applications enabled by the zkVM. The conversation also touched on the future implications of ZK in AI, identity and privacy. RISC Zero aims to overcome the challenges of ZK adoption and create a more open and accessible computing landscape.

This summary was written with the help of ChatGPT.

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Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed here.

Additional disclosure: Delphi Ventures is invested in RISC Zero.

In this captivating episode of the podcast, we are joined by Jake Brukhman, an expert in OpenAI and large language models (LLMs), to discuss the interplay between AI and crypto. We dive into various thought-provoking topics, starting with the concept of moats in AI and whether they are sustainable in the long term. Jake shares his insights on the limits of LLMs and their future, highlighting their impressive ability to continue text but also shedding light on their inherent limitations.

We then explore the intriguing dynamics between established tech giants like Microsoft and Apple and the disruptive potential of AI. Jake shares his perspective on how these incumbents (and OpenAI) may leverage their massive distribution channels and data to maintain their dominance in the AI space. The conversation takes an interesting turn as we discuss OpenAI's cautious approach towards AI progress and their concerns about its potential dangers, contrasting with Stability.AI's more adventurous perspective.

Delving into the intersection of crypto and AI, Jake provides valuable insights into the current and future possibilities of AI technology in the crypto landscape. From there, we move into fascinating discussions on far-fetched AI ideas, the potential of AI-driven crypto projects, Paradigm's transition to a Crypto and AI fund, and the intriguing concept of Worldcoin's solution for sybil resistance.

Finally, we explore the impact of AI on the traditional job market and the potential for unlocking a greater form of humanity. Jake reflects on his past predictions in the AI field, sharing what he got wrong and how his views have evolved over the years. Join us for an engaging and thought-provoking conversation with Jake Brukhman, where we explore the fascinating world of AI and its interplay with crypto.

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Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed here.

Sei is a Layer 1 blockchain built for trading. It was co-founded by Jay, who was inspired to create a decentralized trading platform after working at Robinhood during the GameStop trading fiasco. Sei is designed to offer the best trading experience possible, with fast finality, high throughput, and specialization for its core focus.

Sei achieves its goals through a number of innovative technologies. These include frequent batch auctions, twin turbo consensus, and parallelization. Frequent batch auctions prevent front running and ensure that everyone gets the same trade price. Twin turbo consensus achieves faster time to finality and eliminates the risk of re-orgs. Parallelization increases throughput and allows for more transactions to be processed per second.

Sei is still in its early stages, but it has the potential to revolutionize the way we trade. It is a powerful new tool that could give traders more control over their assets and make the markets more fair and transparent. 

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Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed here.

As an additional disclosure Delphi Ventures is an investor in Sei. 

In this episode, Jose Macedo offers an inside look into the world of Delphi Labs' recently launched Incubator and valuable advice for startups.

Episode Overview

Jose provided valuable insight into the decision-making process behind selection projects for incubation, touching on first principles thinking and inbound ideas from founders. He also discussed how Labs balances incubating projects with near-term profit potential versus those focusing on long-term, game-changing technologies. 

Jose shared his unique views on the future of capital formation, the importance of founders building up their EQ to handle the highs and lows of the startup world, and Labs' multi-decade approach that gives founders confidence that they will be a part of a long-lasting brand.

Additionally, we delved into the topic of how Delphi Labs compares and contrasts with Y-Combinator's successful incubator model, considering aspects to emulate or avoid. Jose shared his thoughts on building a supportive network for startups, including the importance of adapting and evolving the incubator's model, resources, and support structures to stay relevant in the ever-changing startup ecosystem. He also discussed the technical process behind guiding incubated projects to be the most successful versions of themselves. 

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Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed here.

As an additional disclosure the host has ownership interests in Delphi Labs but is not a day to day operator in this entity.