99% Of Data Will Be Consumed By AI Agents, Supported With Blockchain

While many are excited about the potential of AI, it does come with a large environmental impact, data hygiene issues, and other concerns. AI is powered by resource-intensive servers which consume huge amounts of energy. Data storage centers (server farms) account for more than 1% of global energy. 

These giant server farms back up everything from government records to social media profiles to corporate data, and AI greatly exacerbates this rise. A single query from OpenAI's ChatGPT uses ten times more electricity than a Google query. 

Researchers estimate that the computational power cost required to train AI models doubles every six months. Data centers further require a huge amount of water for cooling, burning another precious resource. 

In the near future, online content could be more than 99% AI-generated, rendering the internet unrecognizable. Plus, AI agents will also be playing an increasing role in human activities to execute tasks. 

Introducing AI Agents

An AI agent independently fulfills duties on behalf of users. This can include booking flight tickets or hiring an employee. The human sets the goals, while the AI agent acts autonomously to execute the task in the most efficient manner. 

To do this, they could access your email, calendar, contacts, messages, etc, as well as accessing a multitude of third-party apps to execute transactions. This will involve many RPC and API calls, requiring significant resources and testing. However, it could vastly improve the human experience having benefits in terms of customer service, cost reduction, and improved efficiency. 

These AI agents will work from foundational LLMs, such as ChatGPT. At present, AI agents are still very much developmental and there are ethical considerations to account for, such as data privacy and resource usage. There are a multitude of AI agent types to be developed including simple reflex, model-based reflex, goal-based agents, utility-based agents, learning agents, hierarchical agents, etc. 

Such AI agents could drastically reduce the time spent on prompt engineering; a once-exciting field could be rendered redundant in a surprisingly short space of time, depending on other related innovations. They will have applications in multiple industries, including healthcare, insurance, gaming, education, etc. 

Data Suggests That Agents Are The Future of AI 

According to Gartner, 33% of AI interaction could be done through agents as soon as 2028, less than four years time. The first wave of such agents will likely be focused on chat agents instead of more sophisticated, multidimensional models. AI agents can act as specialized self-learning employees dedicated to a distinct function. 

AI chat agents turn out higher-quality content and reduce review times in comparison to typical AI-powered chatbots. Google, Microsoft, and Amazon have all admitted to building AI agents and even dedicated power stations to support them. 

In the realm of financial services, an AI agent could detect financial fraud as it is happening, alerting the appropriate authorities and instigating security procedures. Within marketing, they could monitor budgets and proactively identify opportunities for expansion. In HR, candidates could be interviewed and hired/rejected based on performance, with little overhead. 

Currently, most organizations are not prepared to leverage AI agents. But this is largely because the infrastructure is not there yet. Startups could be very active in the next few years pushing forward AI agent technology; it could prove to be a lucrative field as the next major technological development on the planet.

Supporting AI Agent Development

The sheer volume of data is growing relentlessly, and non-AI systems can’t keep up. It's only natural to assume that AI will be responsible for the majority of data processing. Yet in order for AI agents to make purchasing decisions on behalf of users, an essential function, the data needs to be transactional and trusted. These are words long associated with blockchain, a far more efficient architecture than the traditional server model. 

This is especially true when the disparate networks begin to work synergistically, with full cross-chain interoperability. It's a known fact that the combined output of individual components working together generates an output far greater than each working in isolation. It could also help reduce the growing environmental burden associated with continual AI development. 

Blockchain is the perfect infrastructure to assist AI agents in executing economic transactions for users, instead of simply interpreting data. However, the blockchain architecture itself needs to be re-engineered until it can accurately parse data and feed it to these economic AI agents. Until then, we are unlikely to see AI agents really take off, as they have no working architecture. 

In sum, AI agents are perfectly suited to blockchain, but only when on-chain data is made more readily available for consumption.