Key Takeaways

• Jacob Steeves' background, experience at Google, and role in founding Bittensor.
• Explanation of Bittensor: An open-source protocol applying Bitcoin-like mining incentives to AI computation.
• Insights into Chinese developer engagement and the competitive landscape.
• Potential collaboration between Bittensor and tech giants like OpenAI.
• Bittensor's focus on incentive computing rather than just aggregating AI models.
• Impact of the upcoming TAO halving event on the ecosystem.
• Bittensor's revenue streams and the future vision for the network.

Introduction

At the forefront of the convergence of blockchain technology and artificial intelligence lies the realm of decentralized AI, a captivating concept that has garnered the attention of the global tech community. Bittensor (TAO), as an open-source protocol, presents a unique approach by applying a Bitcoin-like "mining incentive" mechanism to AI computations. This protocol organizes various subnets—including inference and training—on a blockchain, allowing suppliers to compete and earn rewards based on their contributions. According to CoinGecko data, the Bittensor token TAO appeared on exchanges in March 2023. As of this writing, the coin is priced at $423, with a market capitalization of approximately $4 billion, ranking 42nd among cryptocurrencies. Recently, TAO Synergies Inc., a TAO treasury company, announced the closing of an $11 million private placement, featuring investors including TAO strategic advisor James Altucher and DCG, the parent company of Grayscale. In this dialogue with Jacob Robert Steeves, the pioneering founder of Bittensor, we delve into his technological vision, the journey from Google to entrepreneurship, and how Bittensor aims to break the barriers of traditional AI through "incentive computing."

From Google to Decentralized AI: How Bittensor Applies Mining to AI

ChainCatcher: In recent months we have noticed growing interest in Bittensor (TAO) in the United States, and rapid momentum in Asian communities. We hope this conversation will shed more light on Bittensor and your thinking on the future of "decentralized AI." Let's start with your background. Many readers know that you worked as a software engineer at Google. Why did you leave Google to start your own business? What was the biggest impact of that experience on you? Jacob: I studied mathematics and computer science at Simon Fraser University in Vancouver, Canada. After graduation, I worked for a DARPA contractor on brain-computer interface chips. My mentor (who was also the founder of the company)—an early Bitcoin supporter—introduced me to concepts such as "thermodynamic computation,” which helped me truly understand Bitcoin. Since 2015, I have been deeply involved in both Bitcoin and AI. These two areas naturally fit together because the core of AI is the study of feedback loops (backpropagation, genetic algorithms, reinforcement learning, etc.), while Bitcoin is the first programmable economic feedback loop. I later joined Google as a machine learning engineer and continued developing Bittensor as a hobby until 2018 when I decided to dedicate my full time to Bittensor. The mainnet was launched in 2021. During my time at Google, I witnessed the release of the paper “Attention Is All You Need” (Transformer), which drove the exponential development of large models like GPT. I also learned a lot of distributed machine learning practices from frontline teams, such as parameter servers, model parallelism, and data parallelism—these experiences were crucial in building Bittensor's computing architecture. ChainCatcher: Before we continue, can you give us a brief introduction to Bittensor? Jacob: Sure. Bittensor is an open protocol that applies a Bitcoin-like mining mechanism to AI: we use programmable economic incentives to organize distributed computing power, models, data, and applications into a fair market. Bittensor is a blockchain with a native TAO token, running approximately 128 subnets, which cooperate and compete around various tasks such as inference, training, reinforcement learning, code agents, storage, and prediction/trading signals. AI is essentially a computational problem; Bitcoin has proven that “incentives + competition” can effectively coordinate distributed resources. We are simply transferring this primitive set to intelligent production. From a user perspective, developers can initiate or join subnets, contribute models and computing power, and continuously earn incentives based on their effectiveness. Requesters can purchase services such as inference, computing, AutoML, and prediction signals through the network. In short, Bittensor transforms the “miner—reward—consensus” paradigm into “useful AI supply—market rewards—network consensus.”

Reaching Chinese Developers: The Most Competitive Land and a New Source of Supply

ChainCatcher: Is this your first visit to China? Why did you choose to come to China at this time to give lectures? Jacob: This is the first time. I currently live in Peru and have not done overseas tours before. I came to China specifically to talk about Bittensor. First of all, Bittensor applies Bitcoin mining to AI, and China is one of the fastest growing countries in the world in the field of artificial intelligence, and possibly the most powerful. When Bitcoin mining was legal, Chinese computing power exceeded 50%, and it still produces 90% of the chips in the world today. I deeply respect China’s technological strength in network construction and hope that more Chinese developers can participate in the construction of the Bittensor network to help us expand the scale of the network. Bittensor is a decentralized, permissionless, and transparent open network in which any region can participate fairly. This is a meaningful hedge against the highly centralized AI infrastructure of today. We have proven feasibility in some directions: through subnets, we introduce GPU resources and model services into the market, competing with centralized solutions in terms of price and efficiency. The goal of coming to China is to put these paths into a larger developer ecosystem. ChainCatcher: What are the key messages you hope to convey to Asian developers and investors on this trip? Are there any Chinese projects or communities that impressed you? Jacob: Yes, there are. We often hear a saying in Bittensor: once Chinese miners enter a subnet, the competition becomes so intense that many of those originally in it choose to withdraw—this is entirely expected because the intensity of competition in China is truly amazing. Starting from the way universities are organized and trained, you are among the most competitive groups in the world, so I think China and Bittensor are a natural fit. On this visit, I would like to convey the message that this is a completely new and fair economic platform where Chinese engineers, builders, and miners can make truly productive contributions—contributions that are public, transparent, and fair. Regarding specific projects, one of the largest subnets on Bittensor, Affine, is being built by Chinese developers, and it is becoming one of the most competitive mechanisms on the entire network. I hope to encourage more teams like this team to join because the level of engineers here is extremely high, almost unparalleled. ChainCatcher: What do you think of the unique positions of China, Hong Kong, and Singapore in Web3 and AI? Jacob: Currently, companies in China, Singapore, and East Asia are leading the trend of open-source artificial intelligence. Top open-source models such as DeepSeek come from Chinese teams. Hong Kong and Singapore have greater flexibility in terms of compliance and capital, which facilitates industrialization and cross-border collaboration. Overall, Asia is pushing the “open mode + engineering implementation” to the forefront, which is a necessary combination for decentralized AI. In addition, major Chinese universities such as Peking University and Tsinghua University are also making significant contributions to academic and knowledge advancement. ChainCatcher: You mentioned that Bittensor has approximately 128 subnet projects. Can you talk about the distribution of resources or engineers? Jacob: The top three subnet projects (Subnet Ecosystem Projects) were created by Chinese teams, and I think this is very significant. Bittensor is an anonymous platform, but what we can confirm is that a significant number of Asian teams and computing power are connected. For example, Lium is a leading subnet that provides GPU resources. It creates an unauthorized market where anyone can contribute GPU computing power and access GPU resources through the network. Many Chinese miners have contributed these chips (we can see from the IP addresses of these devices that they are actually located in Asia), and we are bringing these resources to the global market. ChainCatcher: Is there any contact with investment institutions currently? There should be many investment funds or investment companies interested in Bittensor. Jacob: Yes, we frequently receive contacts from investors who want to participate and purchase TAO. However, I am not the person directly responsible for these matters, I am just an engineer. The Bittensor network is open, and the market is liquid. Therefore, we recommend that everyone participate directly in the TAO secondary market because we believe that this is the fairest way, where everyone can enter this market in the same way. In fact, investment companies often contact us, but we prefer that everyone participate fairly in the market. ChainCatcher: Is there a possibility of cooperation between Bittensor and traditional network giants (such as OpenAI, Alibaba, and Baidu) in the future? Jacob: Yes, it is possible, but it depends on whether the concepts are aligned. Some centralized laboratories in the United States are unlikely to have much interest because they are more inclined to collect and control, while we emphasize openness and permissionlessness. Conversely, more open teams such as DeepSeek, Kimi, and Moonshot can connect resources to Bittensor, launch subnets on the network and monetize them, and they can also consume network supplies. I think it’s just a matter of time: either cooperate or adopt our approach to decentralized training. If we can truly achieve decentralized training with Moonshot, we welcome it.

The Foundation in Bittensor: Using Crypto-Economic Incentives to Conduct AI Research

ChainCatcher: You recently mentioned on X that Crypto + AI is a superficial statement. The really important thing is incentive computing. Many people understand Bittensor as an “AI model aggregator,” but you seem more focused on it being an “incentive network.” Can you explain to our readers: What is the biggest difference between Bittensor and traditional aggregation platforms? What exactly has “decentralization” changed? Jacob: Understanding Bittensor as an “AI model aggregator” is incorrect. The essence of Bittensor is to embed “programmable incentives” into the AI learning process: whoever provides more useful inference, training, or tools gets more rewards, which is completely different from “piling models together.” Over the past 15 years, AI breakthroughs have come from the adaptive learning of feedback/rewards (such as BP and RL). What we are doing is programming money and incentives directly into this mechanism, and using market signals to continuously optimize supply and quality. The meaning of “decentralization” lies in permissionless entry and resistance to single points, meaning that any individual/team can launch a subnet and participate in the competition. Good supply is amplified through incentives, and bad supply is naturally eliminated. At the same time, distributed resources and flexible routing allow services to be more resilient to single points of failure. But our goal is not “decentralization for the sake of decentralization,” but to allow incentives to scale useful computing—this is the fundamental difference between Bittensor and traditional aggregation platforms. However, the so-called Crypto + AI is just applying cryptocurrencies to AI, or applying AI to cryptocurrencies. This kind of thinking does not touch the core of what we are doing. What we are actually doing is using crypto-economic incentives to conduct AI research. ChainCatcher: AWS experienced a large-scale outage a few days ago, and many AI services were suspended. How do you interpret this? Jacob: I think this event proved a value of decentralization—that it can provide resilience to single points of failure. Bittensor was not shut down because we rely on decentralized resource allocation, and that is what sets us apart. However, this event also proved that many supposedly decentralized ecosystems are not completely decentralized, because there were indeed some projects that were unable to recover after the outage. Bittensor has not made decentralization its core goal. Of course, we have used censorship-resistant mechanisms in the core of the technology, but that is not the fundamental driving force of Bittensor.

Economy and Vision: TAO Halving, Protocol Revenue Sources, Prediction Markets, and Five-Year Goals

ChainCatcher: 2025 is the first halving cycle for TAO. What impact do you think this halving will have on the behavior of developers and validators in the ecosystem? Jacob: Actually, I think the only impact of halving on Bittensor is that supply will become tighter. But this will not affect the network's basic incentive mechanism. The network still has huge economic incentives to encourage developers to build on the platform. ChainCatcher: Where does Bittensor's protocol layer revenue mainly come from? Jacob: It mainly comes from selling inference, selling compute, selling AutoML, and selling signals to prediction markets. Chain