Synthetic Data: Benefits, Limitations, and Its Role in Visual Intelligence

What Synthetic Data Brings to the Table

Traditional data collection often involves significant cost, time and logistical hurdles—especially when one needs labeled images of rare or extreme scenarios (for example damaged infrastructure in low-visibility conditions). Synthetic data offers an alternative: large volumes of data can be generated in controlled ways, with precise labels and repeatable conditions. For instance, NVIDIA has documented workflows that use its Omniverse and Cosmos platforms to generate synthetic training imagery for perception-AI and robotics applications. 

In the medical space, synthetic datasets have been used to circumvent privacy constraints: a recent article pointed out how artificially generated data can help when real patient records are limited due to regulation or scarcity.  In a research interview at MIT, the scientist Kalyan Veeramachaneni estimated that more than sixty percent of data used for certain AI applications in 2024 might be synthetic or at least synthetic-augmented. 

From a strategic viewpoint, major players have begun treating synthetic-data generation as a core infrastructure component. For example, at the CES 2025 conference the CEO of NVIDIA highlighted synthetic data generation as one of three key problems the company is addressing. 

Big Moments in the Real World

A striking moment occurred when NVIDIA acquired the synthetic-data start-up Gretel AI for over USD 300 million, signalling that synthetic data had moved from research niche to strategic business asset. 

Another important development happened in June 2025 when NVIDIA and the search-AI firm Perplexity AI announced a partnership with more than a dozen European and Middle-Eastern model-makers. That deal included generating synthetic data in regional languages like French, German, Spanish, Swedish and Polish for training reasoning models. 

Most recently, the chief data officer at Goldman Sachs publicly stated that “we’ve already run out of data” for AI training and that synthetic data would become necessary to continue model development — though he also warned of the risks of relying on it exclusively. 

These milestones demonstrate that synthetic data is not just a technical curiosity—it is increasingly central to industrial scale AI and vision systems.

Where the Limitations Lie

Despite its advantages, synthetic data is not a panacea. One of the core challenges is the “reality gap” — the difference between simulated environments and the messiness of the real world. Many generative pipelines create ideal images: good lighting, clear object boundaries, minimal sensor noise. But when a model trained in that environment meets rain, fog, dirt on the lens, or unexpected object types, performance can degrade.

For example, researchers found that when adding over sixty percent of synthetic images to a real image dataset, the test-training accuracy gap narrowed to about 1-2 percent. But the study also noted that using purely synthetic data without real images still under-performed compared to training on real images. 

Bias is another concern. Synthetic data inherits the assumptions of the generative model or environment. If the simulation assumes one kind of object appearance, lighting or demographic, those assumptions propagate into the dataset. As MIT’s Veeramachaneni put it in his interview: “You can build synthetic data if you have a little bit of real data—but you still need to validate it carefully.” 

Finally, governance and validation are often neglected. Synthetic datasets may lack the clear provenance of real-world datasets, making auditing harder. An article in Nature emphasised that more focus is needed on validating synthetic data, especially in regulated fields like healthcare. 

Best Practice Integration

In practical applications, synthetic data should be treated as a supplement rather than a substitute for real-world data. A sound approach is to seed the pipeline with real data, use synthetic data to expand coverage (especially for rare edge cases), then validate model performance on withheld real-world samples. Domain adaptation techniques (for example style transfer, domain-randomisation) help reduce the reality gap.

For vision systems deployed in challenging environments — e.g., surveillance cameras in low light, aerial imagery in conflict zones, or autonomous vehicle perception in heavy weather — synthetic data is particularly valuable for generating scenarios that are hard or dangerous to capture. One commercial case for autonomous vehicles described how synthetic data enabled training on “fallen objects, live animals on the highway, dense fog” that are rare in real datasets. 

Documentation and governance are also critical. Teams should track the assumptions used in simulation, the distribution of synthetic scenarios, and maintain audit trails of model performance drift over time.

Beor’s Perspective

At Beor we treat synthetic data as a tool for model resilience, not a replacement for real-world intelligence. In our visual-intelligence pipelines we use synthetic data to generate difficult, rare or dangerous scenarios (for example sensor anomalies, degraded imagery, occlusions) which real-world collection might not capture efficiently. But the foundation remains real imagery — the full-context, ambient-noise, real-world conditions that matter for operational accuracy.

Our workflow includes validation stages where models trained on mixed real and synthetic data are benchmarked on pure real-world hold-out sets. We monitor for drift, domain-shift impact, over-confidence from synthetic artifact learning, and bias amplification. The goal is not only model performance but operational reliability and interpretability.

Conclusion

Synthetic data is a significant evolution in how we build training datasets for vision and intelligence systems. It unlocks scale, adaptability and privacy-compliance in ways that real-world data collection cannot always match. At the same time, its effectiveness depends on thoughtful integration with authentic data, rigorous validation, and awareness of its limitations.

Used strategically, synthetic data becomes a valuable complement to real-world information — helping us see what we might otherwise miss — rather than an artificial replacement for what we already know.