This paper investigates whether Bittensor can be considered the Bitcoin of decentralized Artificial Intelligence by directly comparing its tokenomics, decentralization properties, consensus mechanism, and incentive structure against those of Bitcoin. Leveraging on-chain data from all 64 active Bittensor subnets, we first document considerable concentration in both stake and rewards. We further show that rewards are overwhelmingly driven by stake, highlighting a clear misalignment between quality and compensation. As a remedy, we put forward a series of two-pronged protocol-level interventions. For incentive realignment, our proposed solutions include performance-weighted emission split, composite scoring, and a trust-bonus multiplier. As for mitigating security vulnerability due to stake concentration, we propose and empirically validate stake cap at the 88th percentile, which elevates the median coalition size required for a 51-percent attack and remains robust across daily, weekly, and monthly snapshots.

POKT Network's decentralized Remote Procedure Call (RPC) infrastructure, surpassing 740 billion requests since launching on MainNet in 2020, is well-positioned to extend into providing AI inference services with minimal design or implementation modifications. This litepaper illustrates how the network's open-source and permissionless design aligns incentives among model researchers, hardware operators, API providers and users whom we term model Sources, Suppliers, Gateways and Applications respectively. Through its Relay Mining algorithm, POKT creates a transparent marketplace where costs and earnings directly reflect cryptographically verified usage. This decentralized framework offers large model AI researchers a new avenue to disseminate their work and generate revenue without the complexities of maintaining infrastructure or building end-user products. Supply scales naturally with demand, as evidenced in recent years and the protocol's free market dynamics. POKT Gateways facilitate network growth, evolution, adoption, and quality by acting as application-facing load balancers, providing value-added features without managing LLM nodes directly. This vertically decoupled network, battle tested over several years, is set up to accelerate the adoption, operation, innovation and financialization of open-source models. It is the first mature permissionless network whose quality of service competes with centralized entities set up to provide application grade inference.

SingularityNET CEO Ben Goertzel teased some of the company's new plans. In detail, Geortzel shared that SingularityNET is aiming to launch projects much smarter than ChatGPT on its network. Furthermore, Goertzel believes that an AI revolution in the blockchain space would transform peoples' perceptions towards crypto. However, Goertzel also shared that it will take a while to launch these projects. "It's going to take a little while, but we know how to do it. We're working on it," he said during an interview with Crypto Influencer Ben "BitBoy Crypto" Armstrong.

If it stays this way, we can see a monopoly in the AI field in the long run. As a result, it would cause unfair pricing a lack of transparency, and we will probably have no say on how things work. This is where decentralized artificial intelligence comes into the picture. Decentralized artificial intelligence refers to the model that enables the isolation of data processing without the disadvantage of aggregate knowledge sharing. In other words, it allows you to process the data independently.

When a decentralized computing model, like blockchain, is combined with artificial intelligence, the best of both worlds can be leveraged for a scale of resources. Decentralized Artificial intelligence is a model that allows for the isolation of processing without the downside of aggregate knowledge sharing. By virtue, it enables the user to process information independently, among varying computing apparatuses or devices. In doing so, one can achieve different results and then analyze the knowledge, creating new solutions to a problem which a centralized AI system would not be able to. Decentralized AI has incredible potential across businesses, science, and collective people.

AI today is fairly centralized and is limited to the ownership of a single entity, such as Facebook or Google. This presents a unique set of challenges that don't actually further additional advancements for the betterment of society. More importantly, there's no collaboration when things are centralized. The future of artificial intelligence (AI) will be determined by how much weight we put into collaboration. Fundamentally, collaboration relies on a group of people (or machines) who share their individual knowledge to solve a problem.

AI technology is transforming the world right now, in remarkable and practical ways. And these are still early days for AI -- experts foresee that the coming years and decades will bring dramatic new AI advances such as Artificial General Intelligence, human-like robots walking the streets, and emergent Internet-scale intelligence. We are currently seeing an increasing centralization of AI R&D and deployment in a small set of large tech companies and governments. We believe that both the present and future of AI and humanity will result in a better future if a greater element of decentralized and widespread participatory control is introduced into the picture.

Last week, I presented a session at the "AI With The Best" conference about one of my favorite topics, decentralized artificial intelligence(AI). The "AI With the Best" conference is notorious for bringing together a rate mix of AI researchers and practitioners as part of the same audience so, as a speaker, you have to have the right balance between deep AI research and practical topics. In the case of my talk, I tried to summarize some of the ideas I've been exploring in the decentralized AI space. The complete slide deck is available below or here. The remaining of this post summarizes some of the most relevant ideas included in my presentation.

The emerging field of decentralized artificial intelligence(AI) is becoming one of the most exciting technology trends of the last few months. A lot has been written about the potential value of the intersection of artificial intelligence(AI) and blockchain technologies and we, this year, we have even entire conferences dedicated to the subject of decentralized AI. However, I feel that a lot of the hype behind decentralized AI fails to highlight some of the key value propositions of the new technology movement that can make it one of the most foundational technology trends of this decade. If you believe in the idea that AI is going to become an increasingly influential factor in our daily lives, I believe decentralized AI will be an essential element to guide the impact that machine intelligence will have in future generations. Let's look at some of the economic dynamics behind decentralized AI to try to clarify our point.

Every new year brings new expectations and hopes for technology markets. In the case of machine learning, the space is moving so fast that is hard to differentiate hype from reality. Many of the ground-breaking advancements in machine learning or artificial intelligence(AI) research are simply unpractical to apply in real world solutions and many of the basic artifacts needed to streamline the adoption of machine learning technologies in real world scenarios are still missing. At Invector Labs, we spend a lot of time building large scale machine learning solutions. As a result, we are in constant exposure to different machine learning technology stacks as well as the latest research from academic institutions.