When pretending to use AI is easier than actually building AI
Recently, cases like Builder.ai — a UK-based startup backed by tech giants like Microsoft — reignited a crucial discussion about transparency in AI. The company, once valued at over $1 billion, promised to use artificial intelligence to create custom apps. In practice, however, it was revealed that hundreds of engineers in India were doing the work manually, behind the scenes. The “algorithm” was, in fact, human [1].
And this isn’t an isolated case. Amazon faced similar criticism with Amazon Fresh, their cashierless checkout system. Behind the seamless interface, over 1,000 people in India were manually monitoring and correcting transactions [2]. Even the name of the tool Amazon uses to outsource such tasks — Mechanical Turk — is an ironic nod to this hidden human labor.
The problem: AI that only works when humans are hidden behind it
These systems aren’t truly AI-powered — they’re powered by people pretending to be AI. They may work temporarily, but they don’t scale, introduce hidden costs, and most importantly, undermine user trust.
Worse, by relying on real-world data and manual labeling, these companies face several challenges:
- Scaling costs with human labor
- Bias from uncontrolled data collection
- Legal risks around privacy and data use
- Inconsistent and error-prone annotations
The solution: synthetic data and real AI with SynthVision
At SynthVision, we take the opposite path: we actually use AI, from start to finish. No humans labeling images, no real-world data collection. Everything is built with cutting-edge simulation technology, 3D modeling, and automatically generated images with pixel-perfect annotations.
Why is that better?
🧠 1. Zero human intervention
All data is generated through simulation. No data is collected from the real world, and no one labels anything by hand. Your model learns from high-fidelity synthetic data from day one.
🛡️ 2. No real data, no legal risks
By eliminating real data entirely, we avoid any conflicts with privacy laws like the GDPR or LGPD. You train models with full legal safety.
🎛️ 3. Total control over data
Want to change lighting conditions? Simulate a specific camera? Ensure demographic balance in your dataset? With synthetic data, it’s not just possible — it’s easy.
📈 4. True scalability
Need more data for a new model? Generate it in minutes. Need to recreate a precise scenario? Simulate it exactly. This is real, scalable AI.
🤖 5. Truly intelligent models
Instead of training models with noisy, biased, or inconsistently labeled data, synthetic data enables your AI to learn from clean, diverse, and perfectly annotated examples. The result? Higher performance and greater trust in production.
No more fake AI powered by invisible workers
Builder.ai and Amazon Go show that many “AI” products actually depend on hidden human labor. It might work short term, but it’s not sustainable, ethical, or scalable.
SynthVision offers a different path — where AI learns for real, powered by tailor-made synthetic data with no human labor and no real-world data collection. That’s how we help teams build smarter, safer, and more transparent models.
Curious to see what synthetic data can do for your AI project? Let’s talk. We’re ready to show you what real AI looks like.