The world of artificial intelligence (AI) is expanding at an unprecedented rate, which can be overwhelming to comprehend. Even experts in AI find it challenging to keep up with the game-changing innovations developed every week. The pace of advancement is beyond what our human brains can fathom. While we are accustomed to linear growth, AI's growth is exponential. It takes only five years of development to reach a milestone, but it doubles its improvement in a year. We are actively participating in pushing technology forward at an incredibly fast pace that we can hardly gauge.

Most people don't understand how AI works—how it is developed, coded, trained, and "thinks"—which significantly hinders building effective AI systems. This creates multiple complications when trying to develop AI models that are more advanced than what we have today. The development process for AI should involve companies competing to create increasingly capable tools. While AI has made some progress, many companies have invested heavily in the software and hardware necessary for these innovations to succeed.

The use of cloud infrastructure is finite, and with exponential development, it becomes increasingly difficult for a single company to bear the burden. Even with Google investing $10 billion in OpenAI's products, it is clear that this won't be enough in a few years. Exponential growth cannot follow the traditional model, and if AI is to continue growing at this rate, it needs a different type of infrastructure to handle it. A decentralized AI model is emerging, and it is not new but has been supercharged with the emergence of blockchain. The parallel growth of AI and blockchain is not a coincidence, and the implications and opportunities are enormous.

The fusion of decentralization and AI is inevitable and will undoubtedly accelerate the progress of AI. Several thought leaders in the field, including Stability AI's former CEO, Emad Mostaque, have also recognized this fact. Mostaque has stated that centralized AI cannot be defeated with more centralized AI. A report by TenSquared Capital highlights the pivotal role of blockchain capabilities in AI's growth. Without a doubt, there are compelling reasons why decentralized AI will outperform centralized AI.

1. Scalability

The biggest constraint for AI in the coming years will undoubtedly be scalability. As AI continues to evolve and develop new capabilities, it demands an insatiable amount of training data and model learning/development. In particular, running a Large Language Model (LLM) such as ChatGPT requires a staggering amount of hardware resources. However, as advanced as ChatGPT is, it is only a fraction of the size of the models that will be developed and trained shortly.

The more data we generate and collect, the more sophisticated AI models will become. And as AI models become more efficient and versatile, their demand will only increase. Therefore, any AI scaling effort must be decentralized to support this growth.

While decentralized processing has existed for decades, AI presents unique challenges because it requires both hardware and software to scale. The collaboration required for an AI model is far more complex than just processing data. Parallel computing must be accurately parsed out and then reintegrated into the model. Additionally, typical devices such as PCs, laptops, and phones are not equipped to run complex algorithms, which makes specific hardware ideal.

Currently, HyperCycle is the leading AI machine builder that creates AI machines for users to purchase and operate, acting as nodes in a decentralized network and sharing computational tasks under the guidance of a coordinating architecture that integrates the processed data. The modular format of this approach is perfectly suited for indefinite scalability. Moreover, the cost for clients who use the system for their AI needs will depend on the supply and demand, following the natural market model. Thus, as demand goes up, prices go up, incentivizing more nodes to be purchased and set up by users.

2. Security

It's important to note that once an AI processing infrastructure becomes decentralized, it is exposed to major security risks that can have catastrophic consequences if not addressed properly. However, with the help of blockchain technology, we can develop the necessary tools and processes to keep AI models safe and secure from bad actors. To achieve this, we can encrypt the models using various techniques, with Zero Knowledge ZK methodology being the most notable. This allows data to remain on-chain and encrypted, while still enabling interaction with other nodes through Multi-Party Computation (MPC). Decentralized AI architecture must have this critical component of secure and private encryption to enable countless use cases, from processing sensitive data such as company private or personal health records to developing proprietary models.

3. Transparency

It is imperative to ensure that all decentralized AI models are not only secure, but also transparent for certain use cases that serve the public good. In order to achieve maximum trustworthiness, full transparency must be maintained, allowing anyone to scrutinize the model's thought process, decision-making and the weights/biases used. On-chain information provides the perfect solution for transparency, as it can be posted and viewed in a completely visible manner. Moreover, the use of blockchain technology ensures that the data remains immutable, thus protecting both the data and the community that relies on certain AI use cases to be completely clear and transparent.

4. Democratization of AI

Decentralized AI uses blockchain technology to transform opaque AI systems into transparent networks, enabling trustless applications to use AI without depending on a few trusted entities. This democratization of AI development and applications is critical as AI systems progress, facilitating innovation across industries without the centralization of control that centralized AI systems impose.

5. Reduced Influence of Centralized Entities

Today, most of the AI technology is centralized in black boxes owned by a few influential organizations. This concentration of control can hinder the democratizing potential of AI, giving an unfair advantage to a handful of unchecked entities in areas such as society, finance, and creativity. However, decentralized AI can mitigate this issue by distributing control over AI technology. This prevents any single entity from imposing a monopoly on the technology and ensures that there is no single set of incentives, constraints, or goals.

6. Reduced Bias and Collective Intelligence

Decentralized AI networks utilize a variety of data inputs to minimize bias, resulting in more equitable and impartial AI-driven decisions. Furthermore, models in a decentralized network continually learn from one another, improving the overall intelligence of the network and allowing it to self-improve over time.

7. Transparency and Verifiability

Decentralized AI networks have the potential to allow open testing benchmarks and guardrails. This can provide transparency into how foundation models operate without requiring trust in a specific provider. The importance of this transparency cannot be overstated when evaluating the benefits of decentralization in AI, particularly in large foundation models like GPT-4. Understanding the inner workings of such models is impractical, but transparency can help us assess their strengths more effectively.

8. Addressing the Gap in AI Development:

The dominant position held by major AI providers leads to a significant disadvantage for their competitors. However, an efficient decentralized network model could establish an environment where multiple parties work together to enhance model quality, making knowledge more attainable and sharing benefits more accessible.

Closing Thoughts

As AI models become increasingly complex and ubiquitous in our daily lives, decentralized AI is the only way forward for AI development, training, and deployment of models. The decentralized nature is perfectly suited to AI's exponential growth, and platforms like HyperCycle are already developing the necessary hardware and software to deploy AI that is scalable, secure, and transparent when necessary. The combination of AI and blockchain is truly unparalleled, and will enable our AI journey to continue growing stronger as we move into the future.