The race for the next AI killer app, or unicorn is well underway. Up until now, this incredible surge in AI-driven innovation has led to the creation of thousands of startups in this field. And we’re only getting started.
These businesses all have the same objective: to leverage Large Language Models (LLMs) and similar AI technologies to create innovative products and services. “Powered by AI” is now the hot new buzzword of the moment.
But here’s the reality. The success of these AI tools and businesses depends on one key commodity: data.
The LLMs are only as good as the quality and quantity of the data that fuels them. You can think of this technology as a car engine that runs better and longer on the right, high-quality type of fuel. And just like a car needs to be continuously refuelled to continue driving, AI continuously needs new and fresh data.
Why? High-quality data is the lifeblood of AI, especially for LLMs that rely on accuracy and context to stay reliable, competitive, and ethical.
Why a High-Quality Data Solution is the Missing Ingredient for AI
Right now, there is a huge market opportunity when it comes to the availability of high-quality, structured data.
This is largely because scalable solutions to meet this need are non-existent. And it’s more than a technical problem. It’s a foundational issue that could impede the evolution of AI-powered LLMs – and the industries that are rapidly forming around them.
Those who do fill this market opportunity first, stand to win big. In fields like real estate, accurate data about properties and their surroundings is key for creating high-quality Real World Assets (RWAs). Similarly, in Corporate Social Responsibility (CSR), diverse data sources are required to validate claims about carbon offsetting or social responsibility, making these data ecosystems crucial for compliance and emerging regulations.
To realize the full potential of AI and LLMs, a solution that can provide continuous, reliable, and diverse data is needed.
The Importance of Decentralization and Fairness
Addressing the lack of the availability of data will require establishing a fair and transparent ecosystem for data exchange, one that encourages a community of individual and institutional players to share their data.
Decentralization plays a crucial role here, allowing for a fairer distribution of resources and rewards. This approach would allow the creation of data-sharing environments where infrastructure and resources are collectively managed and utilized, based on the privacy needs of each partner.
Simply put, the use of on-chain reward mechanisms has incredible potential to incentivize stakeholders like businesses to share their data, leading to a more democratic data ecosystem.
The Current Market Landscape Lacks a Unifier
In the current landscape, startups and established tech giants are grappling with data acquisition challenges. Recent trends have shown major companies seeking collaborative approaches to data sourcing.
For example, Apple approached New York Times to purchase their data directly, signaling a shift towards more cooperative data management models. To do so, businesses and data enthusiasts alike need a unified platform that allows them to search and share the data they need to compete, on their terms.
Whether it is through private data-sharing environments, or connecting with data experts in their field on a public marketplace, the more options they have to control how they share their data, the more likely it is for them to collaborate.
Legal and Ethical Considerations
The need for a structured data solution is also driven by legal and ethical considerations. High-profile cases, like the New York Times lawsuit against OpenAI for using data without proper compensation, were a stark reminder of the blurred lines between data ownership and usage rights in the era of AI. Establishing clear guidelines and systems for data management in a unified platform is crucial to navigating these challenges.
At the same time, LLMs can only be useful and ethical if their output is based on a rich set of data from multiple sources, where they weigh all variables of that data point. AI technology that is based on poor, or biased data can not only lead to bad outcomes – it can be outright harmful.
Economic and Innovation Impacts
Beyond ethics, rewards, and accessibility, a need for easy access to high-quality, structured, and diverse data can have eye-grabbing effects on the bottom line.
From a business perspective, efficient data management can lead to significant cost savings. Studies by leading consultancy firms like Deloitte and McKinsey have shown that effective data merging and procurement from third parties can reduce costs by up to 20%.
On top of this, the innovation potential unlocked by a robust data ecosystem is immense. Data from various domains can interlink, leading to unexpected insights and breakthroughs.
Simply put, to remain competitive, modern businesses in all industries need cost- and time-efficient ways to share, and access new data.
Thousands of AI Startups: Who is Taking Charge?
The need for a structured, standardized data solution is ever-growing, with a dozen new startups created daily leveraging AI. The demand for high-quality data will only grow as AI continues to evolve.
Blockchain and AI are a promising combination. A data use case leveraging the former and powering the latter could solve the issues that are slowly emerging while the AI era is taking off. A decentralized, fair, and transparent system for data exchange benefits AI development and is also essential for addressing the ethical, legal, and economic aspects of AI deployment. The future of AI innovation depends on how effectively we can meet this critical challenge.
Some startups have already started to innovate new products to address this market opportunity and upcoming use case of blockchain. One of them is Nuklai, a layer 1 blockchain hosting a data ecosystem, marketplace, and infrastructure. According to their website, one of the main objectives of the AllianceBlock-affiliated startup is to “power up AI with high-quality” data that this data ecosystem will generate. Who becomes the main blockchain-based data fuel source for the AI economy, however, remains to be seen.
