Artificial Intelligence (AI) is experiencing unprecedented growth, transforming industries, reshaping the global economy, and is extensively integrated into everyday life. According to a report, the AI market is expected to grow from $371.71 billion in 2025 to $2407.02 billion in 2032 at a CAGR of 30.6%. AI models are not only increasing in number but also in sophistication, with significant improvements in benchmarks for language, reasoning, and multimodal tasks.
For instance, the rapid development of LLM models over the past few years has raised the demand for high-quality, unbiased, and diverse datasets. According to an updated ML Trends dashboard by EPOCH AI, the training dataset size for language models has been growing at a rate of 3.7 times per year since 2010. At this rate, estimates show that we will likely run out of AI training data anywhere between 2026 and 2032, depending on the rate at which LLM models are trained.
As AI becomes embedded in daily business operations and consumer experiences, its economic and societal impact will continue to accelerate. The competitive landscape is globalizing, driving the demand for high-quality data to develop superintelligent AI models.
The success of any AI model hinges on the quality of its training data. High-quality datasets are the foundation upon which AI systems learn, adapt, and make intelligent decisions. The benefits of robust training data include:
Poor data quality can have significant consequences. For example, IBM estimates that bad data costs the U.S. economy over $3.1 trillion annually, and Gartner predicts that up to 85% of AI projects may fail due to biased or low-quality data. Furthermore, 80% of AI project time is often spent on collecting, cleaning, and labeling data, underscoring the critical role of data in AI development.
Despite the abundance of digital information, the AI industry faces a looming shortage of high-quality training data. Key challenges include:
Data Scarcity: As AI models become more sophisticated, their appetite for vast, diverse, and well-labeled datasets grows. Researchers warn that the supply of high-quality textual data could be exhausted as early as 2026, potentially slowing the progress of large language models and other advanced AI systems.
Quality Over Quantity: Not all data is suitable for training. Much of the content available online is of low quality, biased, or irrelevant, making it unsuitable for building reliable AI models.
Legal and Ethical Constraints: Privacy regulations, copyright issues, and ethical considerations limit access to certain datasets, especially in sensitive industries such as healthcare and finance.
Rising Costs: As high-quality data becomes increasingly scarce, the cost of acquiring, curating, and labeling suitable datasets continues to rise.
Bias and Representation: Inadequate or unrepresentative data can introduce bias, leading to unfair or inaccurate AI outcomes.
OORT
OORT, the data cloud for decentralized AI, is pioneering the future of decentralized AI with an end-to-end solution that empowers both enterprises and individuals to collect, process, and monetize AI data, including images, audio, or video, to improve AI and machine learning models.
The platform aims to become the powerhouse for future AI development by feeding trusted data. The platform leverages a global network of over 300,000+ contributors, enabling the collection of region-specific and diverse datasets that reflect real-world scenarios.
For contributors, OORT offers an exciting reward and incentive system in $OORT tokens, designed to recognize and motivate individuals for their valuable input in data collection and processing. The tokens have real value and can be traded or staked.
The broader market supports the OORT model, as global demand for labeled, training-ready data is rising rapidly. According to Grand View Research, the data labeling and collection market was valued at $3.77 billion in 2024 and is projected to reach $17.10 billion by 2030.
Synthetic data generation is the process of creating artificial data that mimics real-world data in terms of features but does not contain any actual personal or sensitive information. Computers use special algorithms and models to study patterns in real data and then produce new, fake data that share the same statistical features. This fake data can be used to train AI systems, test software, or do research while protecting privacy and avoiding the risks of using real data. In simple terms, it’s like making realistic “pretend” data that helps AI learn without exposing real people’s information.
Open-source datasets are collections of data made freely available to the public. Researchers, developers, and businesses can use these datasets to train and test AI models without the cost or legal hurdles of acquiring private data.
By embracing these strategies, the AI community can mitigate the impact of data scarcity, ensuring continued innovation and the development of robust, fair, and high-performing AI systems. The future of AI depends not just on smarter algorithms but on the quality and availability of the data that fuels them.
The rapid growth of AI models and the expanding market highlight the immense potential of artificial intelligence across industries. However, this boom also brings a pressing need for high-quality training data, which remains a significant bottleneck. Addressing the shortage of reliable data requires innovative approaches such as community-driven platforms like OORT, synthetic data generation, and leveraging open-source datasets. By combining these strategies, the AI community can overcome current challenges, ensuring AI systems become more accurate, fair, and scalable. As AI continues to evolve, investing in diverse and quality data sources will be crucial to unlocking its full transformative power for businesses and society alike.