AI Radioroom Episode 1 | Exploring 0G Labs’ Solutions to Challenges in AI

Summary

In this AI radio interview, Michael from ZeroG G Labs discusses the vision behind their modular AI chain, aiming to provide ultra-high performance and programmable data availability for decentralized storage networks. With a focus on AI, the project seeks to eliminate barriers between centralized and decentralized systems, ensuring transparency and efficiency, crucial for societal use cases like traffic control and manufacturing systems. The conversation also touches on the potential of on-chain AI, the importance of blockchain for societal trust, and Michael's personal journey from tech and business to co-founding ZeroG G Labs.

Takeaways

• 🚀 Michael from ZeroG G Labs is developing a modular AI chain aimed at high performance and programmable data availability and decentralized storage.
• 🌐 The project is inspired by the need for data throughputs in the tens of gigabytes per second to support onchain AI training, which is significantly higher than the current best-in-class throughputs.
• 🔍 The goal is to remove barriers between centralized and decentralized systems in terms of performance and cost, considering physical limitations like the speed of light.
• 💡 Michael's journey includes a background in deep tech, business, and finance, culminating in the establishment of a company focused on workplace well-being, which scaled to 650 people and $100 million in contract at ARR.
• 🤖 The co-founders of ZeroG G Labs are highly regarded computer scientists with whom Michael connected through a classmate, leading to the establishment of the company about a year ago.
• 🔑 The project's unique selling points are its performance, programmability, and AI focus, with a modular approach that allows for customization of data storage and security.
• 🔄 The technology is designed to be infinitely scalable, with plans to release a second phase of the main net that will allow horizontal scaling of consensus layers.
• 🛡️ Concerns about centralized control of AI models and the potential for misuse or mistraining are driving the need for transparent and verifiable AI systems, which blockchain can provide.
• 🔮 Michael believes that onchain AI is currently underhyped in the broader context but is gaining attention in the web 3 space, with societal use cases being particularly important.
• 🤖 Michael uses AI in his everyday life for tasks like writing answers for interviews, brainstorming, and automating tasks such as coding messages into a CRM system.
• 🌟 The average user would benefit from adopting a decentralized AI model for complete visibility, verifiability, and transparency, especially in use cases that require high trust and accountability.

Q & A

What is the main focus of ZeroG G Labs' project?

ZeroG G Labs is focused on developing a modular AI chain that provides ultra-high performance, programmable data availability, and decentralized storage network, aiming to bridge the performance and cost barriers between centralized and decentralized systems.

What inspired Michael to start his venture in the AI and blockchain space?

Michael was inspired by the need for a system that could handle data throughput at tens of gigabytes per second for onchain training and the desire to create an environment where AI models are transparent, verifiable, and controlled by decentralized entities.

What is the significance of the throughput capabilities of ZeroG G Labs' network?

The high throughput capabilities, tens of gigabytes per second, are crucial for supporting advanced applications like onchain AI training, which requires significantly more data transfer rates than currently offered by most data availability networks.

How does ZeroG G Labs' project address the issue of centralized control in AI?

By creating a decentralized storage network, ZeroG G Labs allows for open and transparent AI that is not controlled by centralized entities, ensuring that AI models can be monitored and verified for societal use cases.

What is the background of Michael that led him to his current project?

Michael has a diverse background, having worked as an engineer and technical product manager at Microsoft and SAP Labs, consulted for Fortune 500 companies, worked in portfolio construction at Bridgewater Associates, and built a web 2 company at Stanford.

What was the turning point for Michael to dive into the Web 3 space?

The turning point was in late 2022 when his classmate Thomas introduced him to the founders of Conflux, who wanted to start a more globally scaled project in Web 3, aligning with Michael's interest in blockchain since 2013.

What are the unique features of ZeroG G Labs' AI chain?

The AI chain offers high performance, programmability, and an AI focus with modularity, allowing users to determine storage duration, location, data type, and security levels.

How does ZeroG G Labs plan to scale their consensus layers?

They plan to horizontally scale consensus layers, making it possible to increase throughput by adding more servers, similar to adding an AWS server, which will be implemented in the pH two of the main net by the end of the year.

What are Michael's thoughts on the current hype around onchain AI?

Michael believes that within Web 3, onchain AI is starting to get overhyped, but from a broader perspective, not enough people in Web 2 are discussing it, indicating that the technology is still underhyped in the larger context.

How does Michael envision the future use of onchain AI in everyday life?

He sees onchain AI being used in various everyday scenarios, such as personal AI agents for tasks like writing responses, brainstorming, and automating processes like coding messages into a CRM system for better follow-ups.

What are the benefits for an average user to adopt a decentralized AI model over existing solutions?

Decentralized AI models offer complete visibility, verifiability, and transparency, which is crucial for users who need to know exactly what data and context their AI is using, especially for sensitive applications like educational tools for children.

Outlines

00:00

🚀 Introduction to ZeroG G Labs and AI Chain Vision

Michael from ZeroG G Labs joins the AI radio room to discuss his project's inspiration and vision. The company is developing a modular AI chain focused on high-performance, programmable data availability, and decentralized storage networks. The goal is to enable on-chain training for blockchain by providing the necessary throughput, which is currently lacking in existing data availability networks. Michael shares his background in deep tech, business, and his journey to co-founding ZeroG after a series of experiences in the tech industry and a desire to contribute to the web 3 space.

05:00

🌐 Modular AI Chain: Performance, Programmability, and AI Focus

The conversation delves into the unique aspects of ZeroG's project, emphasizing performance with the ability to scale to tens of gigabytes per second, programmability that allows users to customize data storage solutions, and an AI focus that aims to develop on-chain model training. Michael explains the technical barriers and the company's plan to overcome them, highlighting the importance of efficiency and the potential for blockchain to provide transparency and verifiability in AI systems.

10:01

🔮 The Intersection of AI and Web 3: Potential and Overhype

Michael discusses what drew him to the intersection of AI and web 3, expressing concern over centralized control of AI models and the potential for misuse or runaway models. He believes blockchain is ideal for creating transparent AI systems, especially for societal use cases. The discussion also touches on whether the buzz around onchain AI is overhyped, with Michael suggesting it's overhyped in web 3 but under-discussed in web 2, and he envisions a future where different types of agents are used based on use cases.

🤖 Everyday AI Use Cases and the Benefits of Decentralized AI

The final part of the script explores Michael's personal use of AI in his daily life, including using AI to write responses, brainstorm ideas, and automate tasks. He also addresses why an average user might prefer a decentralized AI model over existing solutions, emphasizing the importance of transparency, verifiability, and the ability to understand the context and inputs of AI models, especially for critical applications.

Keywords

💡AI Radio

AI Radio is presumably a platform or show dedicated to discussing topics related to artificial intelligence. In the script, it is the setting where the interview takes place, indicating a focus on AI-related discussions and news.

💡ZeroG G, Labs

ZeroG G, Labs appears to be the company or project that Michael is associated with. It is likely involved in the development of AI technologies, as it is the subject of the interview in the script.

💡Modular AI Chain

A modular AI chain refers to a system designed with interchangeable components or modules that can be easily updated or modified. In the context of the video, ZeroG G, Labs is developing a modular AI chain that aims to provide high performance and programmability.

💡Data Availability Networks

Data Availability Networks are systems designed to ensure that data is accessible and can be retrieved when needed. The script mentions that current networks have limited throughput, which the project aims to improve significantly.

💡Throughput

Throughput in this context refers to the rate at which data can be transferred through a network. The script discusses the need for higher throughput to support advanced applications like onchain training of AI models.

💡Onchain Training

Onchain training implies training AI models directly on the blockchain, which requires significant data throughput and computational resources. It is a goal for the ZeroG G, Labs project to enable such capabilities.

💡Decentralized Storage Network

A decentralized storage network is a system where data is stored across multiple nodes, rather than a centralized server, offering benefits like increased security and availability. It is a foundational component of the ZeroG G, Labs project.

💡Programmable Data Storage

Programmable data storage allows users to define parameters such as storage duration, location, and security levels. It is highlighted in the script as a key feature of the ZeroG G, Labs project, enabling customization for different needs.

💡Blockchain

Blockchain is a distributed ledger technology that provides a secure and transparent way to record transactions. In the script, it is the underlying technology for the AI project, ensuring transparency and verifiability.

💡Web 3

Web 3, or Web 3.0, refers to the next generation of the internet, characterized by decentralized applications and technologies, including blockchain. The script discusses the potential of Web 3 in the context of AI development.

💡Decentralized AI

Decentralized AI refers to AI systems that are not controlled by a single entity but are distributed across a network. The script emphasizes the importance of decentralized AI for creating transparent and accountable AI models.

💡Onchain AI

Onchain AI is a concept where AI functionalities, such as model training and decision-making, are conducted on the blockchain. The script discusses the potential of onchain AI and its current state of development.

💡Transparency

Transparency in this context means the openness and clarity of processes, particularly in AI models. The script mentions the importance of transparency for building trust in AI systems, especially for societal use cases.

💡Verifiability

Verifiability is the ability to confirm the accuracy or truth of something, such as the data and processes used in AI models. The script discusses how blockchain can enable verifiability in AI systems.

💡Provenance

Provenance refers to the origin or source of something, which is important for understanding the context and history of data and AI models. The script highlights the role of blockchain in providing provenance for AI.

Highlights

Michael from ZeroG G Labs joins the AI radio to discuss the future of data availability and decentralized storage networks.

ZeroG aims to provide ultra-high performance programmable data availability and decentralized storage, targeting throughputs of tens of gigabytes per second.

The vision is to remove performance and cost barriers between centralized and decentralized systems, enabling on-chain AI training.

Michael's background includes engineering and technical product management roles at Microsoft and SAP Labs.

He has also worked in business consulting for Fortune 500 companies in technology and finance.

Michael's experience at Bridgewater Associates involved managing a $60 billion portfolio construction division.

After graduate school at Stanford, Michael founded a company focused on workplace well-being, which scaled to 650 people and raised $125 million.

The ZeroG project was initiated after Michael's classmate Thomas introduced him to Conflux and the opportunity to work on a global scale project in web 3.

Michael believes in the importance of open, transparent AI for societal use cases to prevent misuse and ensure ethical governance.

ZeroG's modular AI chain is designed to be infinitely scalable and programmable, allowing users to customize data storage and security.

The project's focus on AI and blockchain aims to create an environment where AI models are verifiable and transparent.

Michael discusses the potential of on-chain AI for societal use cases, such as traffic control and manufacturing systems.

He sees the current buzz around on-chain AI as overhyped in web 3 but under-discussed in the broader context of web 2.

Michael shares his personal use of AI for writing, brainstorming, and automating tasks like coding messages into a CRM system.

The average user may want to adopt decentralized AI for complete visibility, verifiability, and transparency in AI models.

Michael emphasizes the importance of knowing the context in which AI models operate, especially for sensitive applications.

The interview concludes with a discussion of the practical applications of ZeroG's technology and its potential impact on the future of AI.

Transcripts

[Music]

hello everybody Welcome to our AI radio

room today we have Michael from zerog G

Labs joining us how you hey everyone

doing pretty well despite landing at 2:

a.m and you know being jetl I feel

pretty energized just being here so

thanks for wor yeah absolutely

absolutely so um maybe just to start you

want to tell us a little bit about your

project uh your journey what inspire you

to um start your Venture in the space

absolutely so um zuro G is a modu AI

chain the first module AI chain our

first product is uh ultra

performant um programmable data

availability and decentralized storage

Network and essentially the the future

that we saw was that right now a lot of

data availability networks have a

throughput of like Best in Class

anywhere from 1 to 10 megabytes per

second but if you truly want to bring

things like onchain training um to the

blockchain we need tens of gigabytes per

second and so we're many orders of

magnitudes of and so we really from a

first principes perspective thought

through how do we architect A system

that gets us to that throughput so that

really there's no barriers between

centralized and decentralized systems

from a performance and cost perspective

besides the physical limitations like

speed of light and so on so that's in a

nutshell what we stand for um our

mission is to make AI a public good and

how I got to this journey is I started

uh you know in deep Tech I um was an

engineer and a technical product manager

at Microsoft and sap labs and then moved

over more to the business side I worked

for ban company for a couple years where

I consulted Fortune 500 companies in

technology and finance afterwards moved

to the east coast and uh worked for

Bridgewater Associates and the portfolio

Construction Division uh quite fun so

about 60 bill the trades on a daily

basis so not a not a small amount quite

quite a bit there and then finally

decided to go back to graduate school at

uh Stanford and built my first web 2

company there and at that point I was

quite fed up with uh just wellbeing in

the workplace and so built a whole

company around that scaled out to about

650 people 100 million in contract at AR

raised about 125 million for it um so

became a top YC and unicorn company and

then late 2022 my classmate uh Thomas

who then became my co-founder later

basically said hey I invested in this

company called conflux um M and fun two

of the co-founders want to start

something more global scale they're the

best uh engineers and computer

scientists I've ever backed like would

you like to get together and he knew

that I wanted to do something in web 3

ever since investing uh into Bitcoin in

2013 and in lots of icos in 2016 and so

I was like yes let's get together let's

check this out um six month of

co-founder dating later I was came to

the same conclusion like Ming and fun

are the best computer scientists I've

ever worked with and uh basically said

we have to start something and so

started the company uh almost a year ago

at this point and um basically said well

what should we build and so we thought

about well where do we have domain

expertise in what's a major unlock for

the space and what are we passionate

about and so after speaking to quite a

few people in the space we basically

came to the conclusion that I just

mentioned around building a module AI

chain because we wanted to see that

future actually happening where we have

open transparent uh artificial

intelligent that's there for human good

yep and efficient efficiency is key

efficiency absolutely I mean in order

for that to happen you need the data

infrastructure to be in place otherwise

like what's the point um when we started

storing a gigabyte on ethereum like $60

million and of course it's cheaper if

you do it on an L2 and so on but um

doesn't solve the problem yeah okay

that's a good start uh so now we'll get

into like the quickfire questions about

like building an AI sure okay so just

quick Snappy short

answers uh you know like Instagram Tik

Tok kind of

style so takeways exactly right so the

first one is could you tell us more

about the project you're currently

working on and what makes it unique in

the AI have three yeah uh three things

uh performance programmability kind of

AI focus and so that whole modularity

piece is in there um you want me to go

deeper or sure sure uh performance I

mentioned already instead of 1 to 10

megabytes is tens of gigabytes and in

fact infinitely scalable once we release

pH two of the main net end of the year

because we also figured out a way to

horizontally scale consensus layers so

just like flipping on a switch getting

another AWS server we can do the same

thing on the consensus side

programmability because it's based

around a decentralized storage Network

um users of the system can determine how

long to store the data where to store

the data what type of data type to use

how much security they want in the data

so that's uh quite a big differentiator

and then the AI Focus we this is our

first product and then we'll start

adding more and more features so that

eventually we can do go to things like

onchain model training as well many

technical barriers to be overcome there

but um sort of that's the key plan good

stuff all right next

one what Drew you to work at the

intersection of AI and web 3 and why do

you believe this combination has such

potential what what I saw that I was

quite worried about is that if there's a

future where all of the I models are

controlled by centralized entities

what's to stop those centralized

entities to a want to you know do

something nefarious and B if they mistra

these models that these models

themselves uh kind of run out of control

so why can't we create an environment

where we know everything about them uh

where the data came from how it was

labeled where the data is stored what

version of the model is what weights

they have how they're making decisions

and so blockchain environments were

ideal is an ideal situation for that and

I think especially important for

societal use cases

so traffic controls systems

Manufacturing Systems administrative

systems I would want to know um that my

model wasn't trained on how to build a

bomb for example yeah so like specific

tailored training almost yeah specific

tailored training um knowing exactly

having input from multiple community

members around how the governance should

work uh what are the economic incentives

for the model putting it in a situation

where it can't cheat like if a model

tells me hey I picked up all the trash

in this environment and here are my

centralized database records and then

actually none of that happened then

obviously the model is cheating or the

agent that's kind of running on that

model and so on a blockchain environment

something has to happen for there to be

a record and so that can't be faked or

changed lovely yeah three more uh in

your opinion is the current Buzz around

onchain AI overhyped or underhyped and

what do you see as the future of this

technology yes so I'd say in web 3 it

seems at the point where it's starting

to get a bit overhyped but then if you

took take it from a broader perspective

in web 2 not enough people are talking

about it so it really depends on the on

the context and so the future is that

for different type of use cases in my

mind we can use different types of

agents and so if

you're booking a restaurant booking a

hotel you can have a you know agent on

your Edge device that's maybe connected

to a blockchain somewhere but can make

decisions for you you know very small

locus of control not a lot of negative

impact that can happen from that but

then again societal use cases

fundamentally need to be on the

blockchain sure so in keeping with use

cases uh what have you found to be your

favorite ways to use AI just in your

everyday

life well I tried to um have a AI

basically agent I used Cloud 3 to write

a lot of answers to my uh one of my

interviews and um it did a decent job I

still have to scrap about 60% but so I

like to use it that way um and then I

also just for some brainstorming like

for example what what does a good

onboarding document look like now I can

spend 10 minutes come up with a bunch of

stuff or I can just spend 10 seconds get

an initial answer and then say like okay

maybe this is missing this is missing um

so that's another use case and then for

automations as well one of the projects

I want to work on is take a lot of the

telegram messages I have and automatic

Ally kind of code them and put them into

CRM system so it's easier to kind of

follow up and so on brilliant use cases

yeah finally final question why would

the average user want to adopt a

decentralized mod model over existing

Solutions uh web 2 nlms sort of thing

yeah if you want complete visibility um

it goes to this whole you know

verifiability provenance um

transparency if you want to know exactly

what into your model and you have

specific use cases that require you to

know that exactly let's say uh the llm

is toing your child for example and

you'd want to know that the context that

it's toing your child in is a very

benevolent one then I think truly open

ai decentralized ai is the solution

there I would agree that's that's that's

one good solution to be fair um okay so

thank you very much for joining us

Michael um big pleasure hopefully we see

you again soon maybe in Brussels

potentially we will be there good stuff

thank you very much awesome big pleasure