David Johnston discusses the Morpheus project, which aims to build a free future by providing individuals with their own personal AI. The project attracted significant interest and support through a fair launch, where users could stake their ETH and contribute to the project in exchange for tokens. The Morpheus network acts as a coordination platform between personalized AIs and a global network of services. The network uses a smart agent rank algorithm to recommend the best smart agents for specific tasks based on factors like reputation and usage. The fair launch model and token economics of Morpheus have attracted attention and may inspire other projects to adopt similar approaches. Morpheus Network has different buckets for rewarding contributors, including the code bucket, compute bucket, community bucket, capital bucket, and protection fund bucket. The code and compute buckets are important for providing value to the network. The code bucket rewards those who build the platform, while the compute bucket rewards those who provide computing resources. The community bucket rewards the builders of smart agents, and the capital bucket rewards those who provide capital. The protection fund bucket is for handling audits, bounties, and recovering from attacks. The network aims to strike a balance between proof of stake and proof of work. David's Twitter - https://twitter.com/DJohnstonEC Chapters

00:00 Introduction to Morpheus and its Goals

03:18 The Fair Launch Model and Attracting Support

05:16 The Role of the Morpheus Network

10:29 The Smart Agent Rank Algorithm

25:53 Understanding the Reward Mechanism

31:08 Rewarding Smart Agents and Compute Providers

34:56 Directing Rewards with Morpheus Token Holders

38:45 Creating an Ecosystem for Developers

45:01 Rewarding App Builders and Preventing Centralization

46:28 The Potential of Decentralized AI Models

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.

0G Labs is building the world's first modular AI chain that enables on-chain storage of training data, models, and the entire stack for crypto AI. Their DA layer provides high throughput and scalability, with a performance difference of 50,000 times better and 100 times cheaper than existing solutions. They aim to achieve Web2 parity on-chain for AI and promote transparency, traceability, and democratization of AI. 0G Labs is focused on making AI a public good and ensuring verifiability and alignment of models. Their roadmap includes launching the base infrastructure, introducing custom consensus, and expanding AI support and capabilities. Michael's Twitter - https://twitter.com/mheinrich

Ming's Twitter - https://twitter.com/spark_ren

Jason's Twitter - https://twitter.com/zenghbo Chapters

00:00 Introduction

17:42 The DA Layer: High Throughput and Scalability

29:13 Promoting Transparency and Democratization of AI

38:54 Differentiators of Zero G Labs

51:28 Roadmap and Future of Crypto AI

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.

GRASS (GetGrass.io) is a viral crypto AI project built on Solana that aims to scrape and validate internet data for AI training. The name 'GRASS' was chosen for its memeability and metaphors. The project addresses the scarcity of quality data and the control of web data by a few companies. It also tackles the unfairness of companies scraping data from residential networks without compensating users. GRASS is a network of 2 million devices that scrape and clean web data in real-time. The data collected by GRASS can be used for training specialized AI models, fine-tuning models, and real-time inference. GRASS is a web scraping protocol that aims to democratize access to public web data and enable the creation of AI models. The protocol allows users to download a browser extension or mobile app that scrapes data from websites and contributes it to the GRASS network. The data is then validated and stored on a decentralized network of nodes. GRASS aims to provide transparent and verifiable data, ensuring fairness and preventing bias in AI models. The protocol is built on the Solana blockchain for its speed, scalability, and innovation. Andrej's Twitter - https://twitter.com/0xdrej Chapters 00:00 - Introduction to GRASS

17:00 - Utilizing GRASS Data for AI Models and Inference

26:04 - Amassing Users and the Mission of GRASS

34:26 - Transparency and Privacy in Data Collection

44:36 - Tracking and Authenticating Data Sources

55:05 - Representing Every Region and Culture 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.

Josh, the co-founder of Astria, discusses the journey from data availability layers to building a shared sequencer network. He explains the concept of shared sequencing and its advantages, such as amortizing the cost of engineering and providing a competitive experience for rollup developers. He also addresses the trade-offs and constraints of using a shared sequencer, including the block time and potential lock-in. Josh highlights the target market for shared sequencers and the potential value accrual in a world where multiple rollups tap into the same shared sequencer. Astria is focused on building a shared sequencer for rollups, which allows for faster and more cost-effective transactions. The market is still evaluating the cost and security trade-offs of shared sequencers versus centralized providers. The architecture of a shared sequencer relies on a distributed network, but it remains to be seen if it can be cost-competitive in the market. There is also an ideological question of where to draw the line between a developer building an app-specific rollup on a centralized sequencer and writing to a base layer. The landscape of optimistic rollups versus ZK rollups is constantly evolving, with ZK technology progressing significantly. Base sequencing refers to rollups that are purely dependent on the block producers of the L1, while shared sequencing involves a separate sequencing layer. Astria's go-to-market strategy involves vertically integrating and building their own rollups on top of the shared sequencer to demonstrate its viability. ⁠Josh's Twitter - https://twitter.com/Jskybowen Chapters 00:00 Introduction and Background 17:14 Target Market for Shared Sequencers 25:11 Value Accrual in a World with Shared Sequencers 32:41 The Evolving Landscape of Optimistic Rollups and ZK Rollups 44:11 The Definition and Challenges of Base Sequencing 50:41 Astria's Go-to-Market Strategy: Vertically Integrating and Building Their Own Rollups 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.

Integral is a financial operations and accounting platform for companies with Web3 assets. It simplifies financial workflows and provides real-time access to financial information. The platform saves businesses time and frustration by automating tasks such as bookkeeping, payroll, and payments. Integral enables companies to manage their crypto assets, track transactions, and generate financial statements easily. It also facilitates global operations, complex money flows, and multiple asset classes. The platform is part of the larger transformation in capital formation and the redefinition of companies in the Web3 era. Integral is an accounting software for the future of composable Web3 companies. It aims to facilitate the operations of businesses beyond just accounting, including tax, payments, payroll, and financial planning. By providing accurate and real-time data on business finances, Integral enables founders and large corporations to make better decisions and manage their assets more effectively. The platform also offers benefits such as reducing the need for internal controllers, automating processes, and improving efficiency. Integral serves a range of clients, including startups, large brands like Nike, and VC firms. The company is also exploring the use of AI to enhance legibility and provide proactive recommendations.

• Gui's Twitter⁠
• Avi's Twitter
• Drew's Twitter

Chapters

00:00 - Introduction and Background

08:39 - Overview of Integral and its Benefits

31:25 - The Composable Nature of Integral in the Web3 Era

45:13 - Enhancing Legibility and Providing Proactive Recommendations with Integral

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.

io.net is a distributed training and inference project built on Solana. They aim to solve the problem of the GPU shortage in the AI industry by building a decentralized network that connects underutilized GPUs from multiple sources. Ionet uses clustering technology to combine GPUs from different geographic locations, allowing for more efficient and cost-effective AI compute. They are attracting both web2 and web3 customers, with a focus on inferencing, which makes up the majority of the market. The goal is to decentralize the AI ecosystem and prevent big tech companies from controlling all aspects of AI. io.net is a decentralized AI network that provides GPU compute power for AI workloads. They are focused on solving the compute aspect of decentralized AI and offer a network of choice for users to perform inference, fine-tuning, and training. The team is driven by a sense of urgency and executes quickly, following operational best practices. They have a disciplined go-to-market approach, targeting Series A to seed-generated AI companies. io.net aims to be the currency at the center of decentralized AI and is exploring the possibility of building a decentralized model marketplace and expanding into other areas like gaming and zero knowledge.

• Tory's Twitter

Chapters

00:00 Introduction to Ionet

01:03 Solving the GPU Shortage

13:24 Attracting Web2 and Web3 Customers

27:51 Building a Decentralized Model Marketplace

29:47 The Role of Crypto in Incentivizing Participants

32:37 Easy Onboarding for GPU Workers

34:00 Organic Demand and Onboarding Sales Process

37:20 Choice and Flexibility in Compute Options

43:22 Conquering the Three Key Stakeholders in Decentralized AI

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.

The conversation explores the concept of Layer 3 (L3) blockchains and their potential impact on the crypto industry. L3s offer unlimited customizations with near-zero gas fees, allowing for limitless creativity and experimentation. The discussion focuses on Degen, a community built on the L3 chain, and its journey from a tipping functionality on Forecaster to launching its own L3. The founders of Syndicate, the team behind Degen's L3, explain the need for L3s and the benefits they provide to different types of applications. The conversation also touches on the future of L3s and the potential for further customization. Degen Chain, an L3 solution built on top of Ethereum, offers low gas fees and customization options for developers. The ability to customize blockchains is seen as a powerful feature, and Degen Chain aims to pull customizations from startups specializing in that space. L3s are designed for customized functionality, while L2s are for scaling. Interoperability between L3s is a vision that many in the space are pursuing, and there are different approaches to achieving it. The security and value capture of Ethereum are important considerations, but the focus should be on usage and adoption. Degen Chain is just the beginning, and there is excitement about the future development and growth of on-chain communities.

• Ian's Warpcast
• Will's Warpcast
• Jacek's Warpcast

Chapters

00:00 Introduction and Disclosures

01:06 Understanding Layer 3 (L3) Blockchains

11:50 Benefits of L3s for Different Types of Applications

29:18 Degen Chain: Low Gas Fees and Customization

31:06 L3s vs L2s: Customized Functionality vs Scaling

32:04 Interoperability: A Vision for L3s

34:32 Usage and Adoption: The Key Metrics

36:30 The Future of On-Chain Communities

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 conversation, Jeff from Nous Research discusses their work on open source models and the BitTensor subnet. They address the challenges of ranking models on platforms like Hugging Face and the need for a more reliable and commercially viable model ranking system. Nous Research has developed Nous Hermes, an open source model that can be fine-tuned to follow specific instructions. They also created the BitTensor subnet, which allows developers to submit their models and rank them against synthetic data generated by GPT-4 and Claude. The goal is to approximate the state-of-the-art models and incentivize the creation of high-quality models. The conversation explores the potential of synthetic data generation, the future of model selection, the world simulator and amorphous applications, the intersection of AI and crypto, concerns and risks in the crypto AI space, the inevitability of AGI, the role of crypto in AI, regulation and over-regulation in crypto AI, the potential interaction between AGI and centralized systems, proof of personhood and verifying authenticity, and Nous Research's mission and focus.

• Jeffrey's twitter

Chapters

00:00 Introduction to Noose Research and Open Source Models

07:12 Overview of Nous Hermes and Fine-Tuning Models

26:30 Challenges with Open Source Models and the Need for BitTensor

32:10 Ranking and Reputation in BitTensor Subnet

34:27 Approaching GPT-4 with BitTensor

37:42 The Potential of Synthetic Data Generation

39:07 The Future of Model Selection

41:29 The World Simulator and Amorphous Applications

44:48 The Intersection of AI and Crypto

48:32 Concerns and Risks in the Crypto AI Space

51:17 The Inevitability of AGI

53:35 The Role of Crypto in AI

56:01 Regulation and Over-Regulation in Crypto AI

58:52 The Potential Interaction Between AGI and Centralized Systems

01:05:24 Proof of Personhood and Verifying Authenticity

01:10:04 Nous Research's Mission and Focus

01:12:26 Attracting AI Developers to Nous Research

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.

Recorded Live at ETH Denver 2024, this conversation explores the intersection of crypto and AI, discussing the need for decentralized AI models and the challenges of achieving this. The participants highlight the importance of keeping AI open and driven by people, rather than organizations. They discuss the potential of blockchain and Web3 technology to enable new business models and address the limitations of centralized AI companies. The conversation also touches on the role of incentives and tokens in driving innovation in the crypto AI space. The discussion concludes with an examination of BitTensor and the potential for future advancements in crypto AI. The conversation explores various themes related to the intersection of crypto and AI. It discusses the value of outside judgment in rating AI systems, the concept of a hive mind, and exciting use cases in the crypto and AI space. The projects AGI Guild and Morpheus are highlighted, along with the decentralized data warehouse Space and Time. The potential of utilizing unused data in DeFi protocols and the decentralized chat GPT platform MyShell.ai are also discussed. Concerns about OpenAI's dominance, the collaboration between Web2 and Web3, and the concept of zero party data are explored. The conversation also touches on the potential of data DAOs and crowdsourcing, the idea of minimum viable centralization, and the inflection point of decentralized AI and compute. Tangible innovation in ZKML and decentralized data, the importance of edge compute, and the need for decentralized inference are further examined.

• Casey's Twitter
• Colin Gagich
• Vassilis Twitter

Chapters

00:00 Introduction and Background

01:54 The Need for Crypto and AI

06:29 Challenges of Decentralizing AI

09:18 Building Better Models with Web3

13:31 Innovation and Value in Crypto AI

19:11 BitTensor and the Future of Crypto AI

24:18 The Value of Outside Judgment

25:14 The Concept of a Hive Mind

25:43 Exciting Use Cases at the Intersection of Crypto and AI

26:33 AGI Guild and Morpheus Projects

27:32 Space and Time: Decentralized Data Warehouse

29:26 Utilizing Unused Data in DeFi Protocols

29:53 Decentralized Chat GPT with MyShell.ai

30:48 Concerns about OpenAI's Dominance

34:00 Web2 and Web3 Collaboration

35:11 The Concept of Zero Party Data

37:30 The Potential of Data DAOs and Crowdsourcing

38:06 Minimum Viable Centralization

40:29 The Inflection Point of Decentralized AI and Compute

43:45 Tangible Innovation in ZKML and Decentralized Data

45:35 The Importance of Edge Compute

46:30 The Need for Decentralized Inference

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, Tommy and Santiago interview Ethan Sun, the co-founder of MyShell, a decentralized platform for creating and staking AI native applications. They discuss the evolution of AI over the past decade, the importance of consumer-facing applications, and the potential for AI to enhance daily life. Ethan explains how MyShell empowers creators to easily build AI experiences and the role of incentives in the platform's ecosystem. They also explore the future of MyShell, its competition, and the scalability challenges in the AI crypto space. In this conversation, Ethan Sun, the co-founder of MyShell, discusses the future of AI agents and the potential for personalized AI companions. He explores the idea of AI agents that understand individual needs and can perform tasks without explicit instructions. The conversation also touches on the improvement of underlying models and the role of human feedback in reinforcement learning. Ethan shares his thoughts on the differences between OpenAI and Crypto AI and the potential for crypto to empower the open source community. He discusses MyShell's end game and the importance of creators in the platform. The conversation concludes with a discussion on the challenges of incentivizing different parties within the system.

Takeaways

• MyShell is a decentralized platform for creating and staking AI native applications.
• The focus of MyShell is on consumer-facing applications that enhance daily life.
• The platform makes it easy for non-technical creators to build AI experiences without coding.
• Incentives play a crucial role in the MyShell ecosystem, rewarding creators and users for their contributions. The future of AI agents lies in personalized companions that understand individual needs and can perform tasks without explicit instructions.
• Improving underlying models and gathering human feedback are crucial for enhancing AI agents and reinforcement learning.
• Crypto AI has the potential to empower the open source community and provide incentives for creators to monetize their work.
• MyShell aims to create a platform that enables creators to build personalized AI applications and attract a wide range of users.
• The hardest party to incentivize within the system is the creators, who play a crucial role in unifying models, users, and stakeholders.

Chapters

00:00 Introduction and Background

03:01 The AI Stack and MyShell's Focus

07:01 Creating AI Experiences on MyShell

09:47 Easy Creation of AI Experiences

12:45 Applying Incentive Models to AI

16:34 Incentivizing Creators and Users

27:41 Competition and Differentiation

32:41 Web2 vs Web3 and Scalability

39:42 The Future of AI Agents

42:23 Improving Underlying Models and Data

45:04 Creators' Ideas and Applications

48:42 OpenAI vs. Crypto AI

51:09 Crypto's Impact on Open Source

56:02 MyShell's End Game

01:00:08

AGI and Human Feedback

01:04:19 Ratio of Creators to Users

01:06:40 Excitement and Concerns about Crypto AI

01:09:47 Incentivizing Supply and Demand

01:10:16 The Importance of Rules and Restrictions

01:12:04 Blockchain Infrastructure and Deployment

01:14:17 Hardest Party to Incentivize

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.

As an additional disclosure, Delphi Ventures is invested in MyShell.AI