We had the chance to meet with George Varghese, the Chief Strategy Officer of 0G Labs and a significant investor in the project, in person at Bitcoin Amsterdam, where they had intended to make their impending announcement as a cohesive team. Varghese gave us a thorough explanation of the fundamental problems with decentralized AI, the current bottlenecks in the industry, and the approach 0G is taking to address them. He gave us a rare glimpse into 0G’s next significant step toward AI-scale blockchain infrastructure during our conversation, as well as an exclusive early look at a significant announcement the team is getting ready to make. Here is a thorough analysis of our discussion.
0G positions itself at the intersection of AI and blockchain infrastructure. From a
strategic standpoint, what is the single biggest bottleneck you believe must be
solved for decentralized AI to become a reality?
The lack of transparent data provenance is now the largest obstacle to AI in general, including decentralized solutions. We frequently observe hallucinations, prejudice, and other “abnormal” behavior since today’s AI systems are black boxes because we don’t know what data they were trained on.The majority of AI users are not experts in the field, so they may not always be able to identify the issue, but they may definitely sense that something is “off.”Verifiable data is the cornerstone of decentralized AI, according to 0G, since you cannot construct reliable intelligence on unverifiable inputs.
In practical terms, this refers to data that is cryptographically anchored on a chain, allowing for constant verification of its authenticity and provenance. Verifiability by itself, however, is insufficient; infrastructure needs to be redesigned for workloads of the AI scale. We have developed a new modular architecture that can manage the throughput and compute that decentralized AI demands because existing blockchains were not created for the data intensity of machine learning.
We’re entering a world where AI agents may need censorship-resistant settlement. Do you see Bitcoin’s base layer or its emerging L2s playing a role in the long-term architecture of decentralized AI?
Bitcoin proved the power of decentralized consensus, but its architecture isn’t optimized for the demands of AI infrastructure. Purpose-built environments provide the kind of throughput and programmability that AI systems require. Of course, it is theoretically possible to use Bitcoin as an AI settlement layer. Ordinals have demonstrated that if there is a will, there is a means, yet this is illogical from an economic and business standpoint.
Building atop smart contract chains ideally EVM-compatible is the logical way to take use of Ethereum’s network effects, liquidity, and vibrant L2 ecosystem. In this manner, you may develop something that is close to the center of the larger crypto ecosystem while still being quick and affordable enough to tackle workloads unique to AI. In this manner, you can produce products that are not only practical but also simple to include into already-existing protocols and Dapps.
Bitcoin proved that a decentralized network can coordinate billions in economic value without a central operator. What parallels do you see between Bitcoin’s coordination breakthroughs and the type of decentralized coordination needed for AI compute markets?
Bitcoin’s economic coordination, not merely its programming, was its true genius. It demonstrated that a global market for a limited resource that is fully maintained by independent players can be established with the correct incentive design. When given an incentive, people will work together to complete a task for the benefit of all. In essence, 0G applies the same idea to AI data and computation. Our objective is to establish an intelligence economy that can maintain itself, meaning that when demand for computers or data increases, so do the incentives for providing them.
The incentive alignment of these systems is exquisite. Like Bitcoin has done for digital currency, it is what enables a decentralized market for AI resources to operate without a central operator.
What is the future GTM of 0G and what is coming next that you would like to tell you about?
Maintaining momentum and creating innovative products that people will genuinely want to use are key components of our approach. We have been lucky to launch our mainnet with a lot of support from the start, not just from users but also from builders and partners. They have been important in getting us from zero to one, but we must now move past discussing the underlying technology to showcasing the real-world benefits that 0G-based apps can offer. Our short-term goals are to scale out all of our products, make AI Alignment Node NFT licenses transferable, deploy 0G Compute on the mainnet, and encourage enterprise use of 0G’s high-speed decentralized storage tailored for AI workloads.
Making AI agents a reality is something we are quite excited about as we move into 2026. In terms of implementing agentic frameworks and improving their interoperability, the industry has made significant strides in the past two years. However, I believe it is reasonable to state that there are still relatively few onchain users that have really made use of an AI agent at this time. As a result, although technology is developing, adoption still needs to catch up. App developers from all chains will be able to readily access storage and compute thanks to 0G Labs, allowing them to demonstrate the tangible power of AI.
The ability of agents to automate jobs, aggregate alpha, and streamline access to intricate procedures is accessible to anyone, including crypto OGs, legacy financial institutions, and complete novices. If RWAs reached adulthood in 2025, then onchain AI agents will emerge and become essential in 2026.