Feature
As artificial intelligence (AI) models continue to grow in complexity and capability, concerns are mounting about whether they are running out of quality training data.
The rapid advancement of AI — particularly large language models (LLMs) and generative AI systems — requires vast amounts of data to improve performance and accuracy. However, with much of the internet’s publicly available data already scraped and used, the question arises: is there truly a shortage of new training data, or are alternative solutions, such as synthetic data, bridging the gap?
The Data Diet of Modern AI
As AI models continue to evolve, their need for vast amounts of training data is growing at an unprecedented rate. Large-scale models such as OpenAI’s GPT, Google’s Gemini and Anthropic’s Claude have demonstrated remarkable capabilities in generating human-like text, understanding context and even reasoning. However, these improvements come at a cost — each new iteration demands exponentially more data to refine accuracy, reduce biases and expand knowledge across diverse domains.
AI models learn by identifying patterns across massive datasets, improving their accuracy and contextual understanding with each iteration. However, as high-quality, diverse data sources diminish, securing fresh training material is becoming increasingly difficult.
Some experts argue that while data abundance isn't the issue, quality and usability remain significant challenges. Ryan Doser, SEM/PPC specialist at Empathy First Media, explained, "Of course, there’s no shortage of data for AI models to scrape, but the quality of that data presents the biggest challenge in training these models going forward. I do believe there is a shortage of high-quality training data since the internet has been heavily mined." He also noted that AI companies are increasingly turning to partnerships with media firms to obtain legally compliant, high-quality datasets.
While public datasets have fueled many AI advancements, their limitations have also led to notable failures that underscore the need for better data strategies. One of the most infamous cases was Microsoft's chatbot, Tay, which was trained on publicly available social media data. Within hours of deployment, Tay began generating offensive and racist content, reflecting the biases and toxicity prevalent in online conversations. This incident showed the risks of AI models learning from unfiltered public sources, where misinformation and harmful content abound.
Similarly, Google's AI health initiative faced scrutiny when its model, trained on publicly available medical literature, provided inaccurate and potentially harmful recommendations for treating illnesses. In a December 2024 report on health-related searches, 70% of Google's AI Overviews responses were considered risky by medical experts. Google itself uses the disclaimer: "Your AI-generated responses may include inaccurate or offensive information."
The challenge in such cases lies in the quality and reliability of public data, which often lacks proper verification and can introduce errors that lead to serious consequences when applied in real-world scenarios.
These incidents stress the growing need for high-quality, curated datasets that can ensure AI models produce reliable, ethical and unbiased outcomes. The reliance on public data without rigorous oversight can lead to reputational damage, regulatory scrutiny and ethical dilemmas, prompting businesses to explore alternative data sources such as proprietary and synthetic data to mitigate these risks.
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Is There Really a Shortage of Training Data?
The idea that AI models are running out of training data has sparked considerable debate among experts. With the rapid expansion of AI capabilities, much of the internet’s publicly available data — including web pages, books, research papers and social media content — has already been scraped and incorporated into existing models. While this raises concerns about a potential shortage, the reality is more nuanced. The availability of training data depends on factors such as the quality of existing sources, evolving data collection strategies and the ethical and regulatory issues surrounding data use.
AI models primarily rely on three main sources for training data:
- Internet Scraping: Involves collecting vast amounts of content from websites and digital platforms. While it remains widely used, it faces growing scrutiny over consent, copyright and data quality.
- Public Datasets: Structured and vetted information offered by governments, research institutions and nonprofits. One limitation, however, is that these datasets often lack the scale and diversity required to train advanced AI models.
- Proprietary Data: High-quality data obtained through partnerships, licensing agreements and internal collections that often includes relevant insights tailored to specific domains. Yet it comes with challenges related to cost, privacy regulations and legal compliance.
Despite the perceived saturation of traditional data sources, some may argue that AI is not facing a critical shortage but rather a shift in how data is acquired and used. New data sources, such as real-time data streams from IoT devices, transaction records and user interactions, continue to provide fresh input. Additionally, techniques like transfer learning allow models to use existing datasets more efficiently, reducing the need for constantly expanding raw data pools.
However, growing regulatory hurdles present significant challenges to data collection efforts. Laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) impose stringent requirements on data usage, limiting what AI developers can collect and store. Ethical concerns about data ownership, consent and bias have also led to increased scrutiny from both regulators and the public. Businesses must now deal with a complex environment of compliance while ensuring their data collection practices are transparent and ethical.
While concerns about data scarcity persist, some experts argue that AI developers need to focus more on better data utilization strategies rather than seeking an endless influx of new data.
Jacob W. Anderson, president of Beyond Ordinary Software Solutions, suggested that the idea of a data shortage may be misleading. He pointed out that the focus should be on improving training methodologies rather than constantly seeking more data. AI models, he explained, are already ingesting what can be considered the entirety of human knowledge, and instead of more data, the industry should focus on refining how this existing information is used. "The notion that we need more training data is akin to the transactional observation that I need more food simply because my plate is empty," he said.
Where Is New Data Coming From?
As AI models exhaust traditional data sources, businesses are turning to proprietary and enterprise datasets to maintain model performance and relevance.
According to Dr. Iain Brown, head of data science at SAS Northern Europe, "Proprietary and enterprise datasets are critical to advancing AI capabilities, offering high-quality, domain-specific data that is often unavailable in public datasets. These datasets provide organizations with a competitive advantage, enabling the development of tailored AI solutions for unique enterprise challenges." However, he warned, compliance and evolving privacy regulations remain a critical concern.
Many businesses are taking advantage of their vast repositories of customer interactions, transactional records and operational data to train AI models specific to their needs. Partnerships and licensing agreements between AI companies and enterprises are becoming increasingly common, allowing access to high-quality, domain-specific data that is otherwise unavailable to the public. Industries such as healthcare, finance and retail are particularly rich in proprietary data, providing AI developers with specialized datasets that enhance their models’ capabilities while maintaining a competitive edge. However, securing and using this data comes with challenges related to privacy, security and regulatory compliance.
Another critical avenue for new training data is user-generated content and real-time data collection. AI developers are tapping into a constant influx of fresh data from social media platforms, online forums, customer service interactions and product reviews. However, the use of such data has not been without controversy. OpenAI’s reliance on Reddit’s vast repository of community discussions, for instance, sparked significant debate over issues of consent, data ownership and fair use. The platform’s moderators and users raised concerns about whether their freely shared content should be used for commercial AI applications without explicit permission or compensation, highlighting the broader ethical and legal complexities surrounding the use of user-generated data.
Real-time data from sources like IoT devices, mobile apps and connected wearables provide valuable insights that inform about current trends and behaviors. Brands are also deploying AI systems that continuously learn from user interactions, refining their models based on evolving customer needs and preferences.
In addition to enterprise and user-driven data, AI developers are increasingly exploring alternative sources such as domain-specific datasets and government initiatives. Various industries are generating structured datasets through research collaborations, academic studies and open data projects supported by governments and nonprofit organizations. Government initiatives, in particular, offer access to valuable public data across sectors such as transportation, healthcare and environmental monitoring. Datasets from regulatory agencies, census reports and scientific research institutions provide authoritative and reliable data that can enhance AI applications in policy-making, infrastructure planning and social services.
What Happens When the Data Runs Dry?
As AI models become increasingly sophisticated, the potential scarcity of high-quality training data poses significant challenges that could hinder their growth and effectiveness.
One of the most pressing risks of model stagnation is diminishing returns. AI models, particularly large-scale ones like GPT and Gemini, thrive on diverse and voluminous data to improve accuracy, expand contextual understanding and enhance reasoning capabilities. However, as developers exhaust readily available sources, the incremental improvements gained from additional data begin to plateau. Without new, high-quality inputs, AI models risk becoming stagnant — failing to provide meaningful enhancements and struggling to keep up with evolving user expectations.
Beyond performance limitations, AI developers face mounting ethical and legal implications tied to data scarcity. Privacy concerns, intellectual property rights and data ownership disputes are becoming increasingly prominent as companies seek new data sources. Stricter data privacy regulations impose limitations on how personal and proprietary data can be collected, stored and used for training AI systems. Unauthorized data scraping or reliance on questionable sources can lead to significant legal liabilities and reputational damage. As a result, businesses must prioritize transparency and compliance.
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Navigating evolving data privacy regulations requires businesses to adopt a proactive approach to compliance and transparency. Anderson suggested, "Always get permission, be transparent and find a way to share the data with the public. Stewardship is critical to complying with regulations and satisfying the public's expected baseline of ethical use." He suggested that clear data governance policies, regular audits and adherence to privacy-by-design principles can help businesses align with regulatory requirements and maintain public trust.
A shortage of high-quality data also exacerbates potential biases and generalization problems. AI models trained on limited or biased datasets may develop skewed perspectives, reinforcing harmful stereotypes or failing to accurately represent diverse populations. This can lead to serious consequences, especially in sectors such as healthcare, hiring and law enforcement, where biased outputs can perpetuate inequalities or result in flawed decision-making.
Related Article: AI Regulation in the US: A State-by-State Guide
Synthetic Data: A Solution or Complication?
As concerns about the availability of high-quality training data grow, synthetic data has emerged as a promising alternative to fill the gaps. Synthetic data is artificially generated information that mimics the characteristics of real-world data without being sourced directly from human interactions or natural environments. It is created using algorithms, simulations and generative AI models to produce data that reflects patterns and distributions seen in actual datasets. While synthetic data offers a potential solution to data scarcity, its adoption comes with both advantages and challenges that must be carefully considered.
Unlike real-world data collection, which can be time-consuming and expensive due to privacy regulations and manual processing, synthetic data can be generated in large volumes quickly and at a lower cost. This allows AI developers to access diverse datasets without the logistical challenges of acquiring sensitive or proprietary information. Additionally, synthetic data can be tailored to specific use cases, ensuring that AI models are trained with highly relevant and targeted inputs. Synthetic data can also reduce biases in AI training, making it especially valuable in regulated industries such as healthcare and finance.
As businesses increasingly turn to synthetic data to address the challenges of data scarcity, it's essential to consider both its potential and limitations. Nathan Brunner, CEO at boterview, told VKTR that synthetic data is gaining traction as a viable solution to fill data gaps. "Algorithms are deployed to fabricate data to fill in the gaps that real-world data cannot fill." However, he remains skeptical about whether synthetic data can significantly enhance AI capabilities, as the value of synthetic data largely depends on how it is generated.
"If the data is generated from well-defined, rule-based systems like chess, it is beneficial because it can generate new scenarios and improve model performance," he explained. "However, if a model is trained on its own synthetically generated data, it typically leads to overfitting or bias." In addition, synthetic data often lacks the full diversity and complexity of real-world data, which can limit an AI model’s ability to generalize effectively.
Despite these challenges, Brunner believes synthetic data can be valuable. "Ideally, companies should find a way to produce reliable synthetic data that introduces new edge cases or underrepresented scenarios that the model may not have encountered."
The AI Feedback Loop Problem
A growing concern in the AI community is the increasing prevalence of AI-generated content online, often referred to as the Dead Internet Theory. This theory suggests that much of the content available today — and even more so in the future — is produced by AI rather than humans. As AI systems rely heavily on internet-sourced data for training, this raises significant challenges regarding data authenticity, diversity and originality. AI models that inadvertently train on AI-generated content risk entering a self-referential loop, amplifying biases, inaccuracies and a lack of genuine human insights.
To mitigate this issue, synthetic data is being explored as a means to supplement training without relying on potentially compromised web-sourced content. Properly curated synthetic datasets, designed with diversity and quality controls in mind, can help AI models maintain robustness and avoid the pitfalls of overfitting to low-quality, AI-generated data. However, overreliance on synthetic data also poses risks — if synthetic datasets are derived from AI-generated sources rather than real-world interactions, the same self-referential issues may persist.
One of the biggest concerns with the use of synthetic data is its potential inaccuracies and lack of real-world complexity. While it can replicate known patterns, it may struggle to capture the nuanced and unpredictable nature of real-world interactions. This can lead to models that perform well in controlled environments but fail when deployed in real-world applications.
Critics argue that relying too heavily on artificially generated data could obscure important ethical questions around transparency and accountability. Additionally, the use of synthetic data raises concerns about its alignment with real-world legal and regulatory standards. Can businesses ensure compliance if their AI models are trained on data that doesn’t reflect real users or environments? And if synthetic data is used to generate insights that influence policy decisions, how can its accuracy be verified?
Finding the Right Balance: Real vs. Synthetic
As synthetic data becomes an increasingly viable alternative to real-world data, AI developers are faced with a critical question: should AI models rely on synthetic data, and if so, to what extent?
While synthetic data offers potential benefits, most experts agree that it should be used as a supplement rather than a replacement for real-world datasets. "Synthetic data is particularly useful in generating rare or edge-case scenarios that are difficult to capture, such as medical conditions in specific demographics or hazardous situations for autonomous vehicles," said Brown. "However, its effectiveness depends on rigorous validation to ensure realism and alignment with real-world complexities." He also pointed out that synthetic data offers privacy benefits and scalability, but emphasized the necessity of proper governance frameworks to ensure that models remain unbiased and aligned with ethical standards.
Several industries have already embraced synthetic data to address specific challenges. In autonomous vehicle development, companies like Waymo use synthetic data to simulate driving scenarios that would be difficult or dangerous to capture in real life, such as extreme weather conditions or rare road hazards. Similarly, in healthcare, synthetic patient data is used to develop AI-driven diagnostic tools without compromising patient privacy.
However, despite these successes, experts caution against an overreliance on synthetic data. Christian Hed, CMO at Dstny, stressed the importance of critical thinking when using synthetic data alongside real-world inputs. "Synthetic data can help fill the gaps, but it should never fully replace real-world complexity."
Feeding the Future of AI
The question of whether AI models are running out of training data shows a shift in how the industry approaches data acquisition and use. While traditional data sources may be nearing their limits, the future may lie in combining real-world data with synthetic alternatives and innovative collection methods. Success will depend on balancing data demands with ethical and regulatory considerations while ensuring quality and reliability.
