Like any healthy system with a significant output, AI models and agents have to eat and learn. The global surge in AI investment is generating astonishing figures, including a projected
The world’s largest labs, such as OpenAI, Anthropic, Google DeepMind, and others, have hit a “data wall” as nearly all public internet content has already been consumed for pre-training. Moreover, regulatory frameworks such as the
Naturally, people found a solution that will satisfy both sides. Hence, the surge in the synthetic-data market, so-called “ghost datasets” generated by AI rather than humans. According to the
Synthetic Datasets: Ghosts Of The AI Future?
Despite my tendency to be overly dramatic, synthetic databases do evoke mixed feelings in me. They are not just taken out of thin air, but do they represent true statistics? Are they reliable? Will they produce more glitches than the AI model can overcome after training? These are all legitimate questions, and I will do my best to get you some answers.
Basically, synthetic datasets are divided into three main categories: fully synthetic data, partially synthetic data, and hybrid datasets. The fully synthetic ones are produced entirely by generative models. With partially synthetic datasets, authentic data points are augmented or replaced with simulated ones. Hybrid datasets integrate synthetic data with anonymized real-world samples, with the selection guided by application context and privacy requirements. The distinction is important, as we’ll shortly dive into the methods of how they are sourced.
How “Ghost Datasets” Are Created & Why They Matter
Broadly, ghost datasets are produced through three major mechanisms:
Self-Training LLM Loops. Sounds fancy, but the core is quite simple to understand. An AI model generates additional examples based on its existing training distribution. It extends its knowledge by ‘hallucinating’ new text that resembles earlier human-authored data.
Bootstrapped Multi-Agent Systems. Here, multiple models, or checkpoints of the same model, generate question–answer pairs or reasoning chains for each other. It’s a bit like having two students quiz each other to prepare for an exam. They’re not copying a teacher’s material, but by generating and discussing new questions, they expand the set of examples the next model can learn from.
Style-Transfer Synthesis. This method uses models to reproduce the style of original datasets while altering signature features to avoid direct copying or licensing issues. Style-transfer techniques in NLP are widely researched, and synthetic paraphrasing/rewriting is commonly used for data augmentation. In a nutshell, style-transfer synthesis means teaching an AI to rewrite real text in a similar style but with different wording, so it looks and sounds like the original without actually copying it.
Synthetic datasets are undeniably attractive: they scale cheaply, avoid many privacy issues, and allow training on domains where real data are scarce. However, they carry a structural weakness. When models learn from data generated by earlier versions of themselves, they risk falling into a feedback loop of self-reference.
This phenomenon is often called model collapse or AI inbreeding. Set aside the gross human associations, it’s still quite disturbing in its own way. Over time, models trained predominantly on artificial data tend to drift toward homogenized, low-variance output. Errors accumulate, linguistic diversity shrinks, and the model becomes increasingly detached from real-world patterns.
Synthetic data is an essential tool. That said, it requires careful balancing. Without it, the industry cannot scale. With too much of it, the industry risks teaching machines to learn only from their own shadows. Thus, it’s high time to discuss who’s behind the risks associated with the overuse of synthetic databases.
Synthetic Economy But Very Real Greenback Giants
The synthetic-data economy is rapidly evolving into a monetised, shadow-infrastructure beneath the broader AI ecosystem. According to several
Within this growing shadow economy, there are several ghosts we can nonetheless identify:
Synthetic-data farms: enterprises that operate GPU clusters or other high-throughput infrastructure to generate large volumes of synthetic datasets for AI training and testing. These are the guys that feed the beast.
Data laundries: firms that process, modify, or “wash” unlicensed or scraped datasets, transforming them into materials that can be resold as synthetic or pseudo-synthetic data. These are the guys that slap the “abidas” label on the pair of sneakers that vaguely resemble the legit ‘adidas’ you were hoping for. You get the picture.
Dataset brokers: intermediaries who resell these processed or pseudo-synthetic datasets, often via private networks or closed marketplaces. In other words, folks who are ready to make a buck off of any person on the market. No prejudice.
AI insurers/data provenance services: organisations that specialise in validating the origin, licensing, and traceability of datasets used in enterprise AI systems. Again, people who found a niche in the growing market took their knowledge and filled it.
All these players show how “ghost data” has become a traded asset class, generating new value flows in the data economy. What drives the wheel into motion? Scarcity of real labelled data, privacy/regulation constraints, and the fast growth of AI model training demands. For example, the financial services regulator Financial Conduct Authority (FCA)
Corporations Are No Dummies. Ghosts Likely To Linger
Despite the known risks, corporations are not going to abandon ghost data anytime soon. Not because of inherent evil, or any other trumped-up reason like that. The reasons are pragmatic rather than ideological. Synthetic datasets offer what companies have always craved: control, speed, and legal safety.
By generating their own training material, firms bypass many of the copyright and privacy entanglements that come with real-world data. Artificially created records are free from personal identifiers, making them easier to use under tightening data protection laws.
Control is equally important. Synthetic data allows companies to design “clean” and balanced training sets that eliminate human annotation errors, missing labels, and language patterns deemed toxic. This makes scaling far smoother and more predictable, particularly in sensitive domains like finance or healthcare. And for models at the frontier, i.e., systems approaching GPT-5 scale, the sheer quantity of required data makes synthetic generation almost inevitable. Training a model of that magnitude demands vast resources. On the other hand, creating them synthetically is faster, cheaper, and more sustainable than licensing comparable volumes of real data. Like eating tiger shrimp and oysters just for nourishment, when we all know chicken and beef will do the trick.
This shift is already visible in corporate language. The term AI-clean data is quietly replacing real data in internal documentation and marketing. The irony is striking: reality itself is increasingly seen as noise. It’s messy, biased, and unstructured, while artificial data, once a workaround, is becoming the standard. It is troubling to hear, but unfortunately true.
Ghost data, then, is not a temporary solution but a structural evolution in how information is produced, valued, and trusted. It allows the AI industry to scale beyond the limits of human-made text and images, even as it deepens the paradox of teaching machines to learn from their own reflections.
The challenge ahead is not whether to use synthetic data, but how to ensure that in this mirror world of our own making, the signal doesn’t fade into the echo.