Google Simula generates synthetic training data — and on some tests, it actually beats real data. Here's the honest comparison.
The synthetic vs real data question used to have one answer: real wins.
Google Simula changes that.
This post is the honest comparison.
What Simula's benchmarks actually show.
Where synthetic wins.
Where real still wins.
Implications for AI training in your industry.
The Quick Verdict
Synthetic (Simula) wins when:
- Real data is locked up (private, expensive, illegal).
- Coverage matters (synthetic can be more complete).
- Cost matters (synthetic is dramatically cheaper).
- Speed matters (synthetic is faster to generate).
Real data wins when:
- The teacher model isn't strong enough.
- Subtle real-world nuance matters.
- Stakes are very high (medical/legal/regulatory).
For most use cases, hybrid wins.
What The Numbers Actually Show
Google ran experiments.
Math benchmark (GSMAT)
Compared low-complexity vs high-complexity Simula data.
64,000 data points each.
Result: High-complexity gave 10% accuracy gain.
10% is massive in AI terms.
Legal benchmark
Important caveat: the teacher model was only 57% accurate.
When weak teacher labelled high-complexity data:
Result: Performance dropped.
Lesson: Synthetic complexity only helps when the teacher model is strong enough.
Coverage benchmark
In multiple tests, Simula data sets covered MORE of a topic than real reference data sets.
Lesson: Real-world data shows up randomly based on what people happen to write.
Simula covers on purpose.
Synthetic can be more complete than real.
Where Synthetic Specifically Wins
Three specific advantages.
1 — Privacy
Synthetic data isn't real data.
No privacy concerns.
For medical, legal, financial AI — this matters.
2 — Coverage control
You design the coverage.
Real data is what people happened to produce.
Synthetic is what you specifically need.
3 — Cost
Generating synthetic data is cheaper than acquiring real data.
Especially for specialist fields.
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Where Real Data Specifically Wins
Be honest.
1 — When you have it cheaply
If you already have real data and it's not locked up, use it.
Real data has natural nuance synthetic can miss.
2 — When the teacher model is weak
Simula's gains require a strong labelling model.
If you don't have that, synthetic might hurt.
3 — For very long-tail edge cases
Real data captures truly novel situations synthetic might miss.
For safety-critical AI, real edge cases matter.
Honest Quality Comparison
Per use case.
General-purpose chatbot training
- Real wins: Has natural conversational patterns.
- Synthetic helps: For diversity coverage.
- Best: Hybrid (real + synthetic).
Specialist scam detection
- Real loses: Privacy/legal blocks access.
- Synthetic wins: Allows training at all.
- Best: Synthetic (proven by Google's deployment).
Medical diagnosis support
- Real wins: Clinical nuance.
- Synthetic helps: Privacy + coverage.
- Best: Real + synthetic, with expert validation.
Legal research assistant
- Real wins: Case law specifics.
- Synthetic helps: Edge cases + privacy.
- Best: Hybrid with legal expert review.
Customer service training
- Real wins: Real customer language.
- Synthetic wins: Privacy + scaling.
- Best: Hybrid.
For most cases, hybrid wins.
The Hybrid Approach In Practice
Google's own deployment is hybrid:
- Synthetic for cases where real data is locked.
- Real for cases where it's available.
- Validation across both.
This is the model others will follow.
Cost Comparison
For training a specialist AI model.
Real data acquisition:
- Years of partnerships.
- Privacy negotiations.
- Cleaning + annotation.
- £100,000s minimum.
Synthetic data generation (Simula-style):
- Days of generation.
- No privacy negotiations.
- Built-in quality filter.
- £1,000s.
For most specialist AI projects, synthetic dramatically lowers cost.
What This Means For The AI Industry
Three implications.
1 — Specialist AI floods the market
Industries previously locked out of AI now get tools.
Legal, medical, financial all benefit.
2 — Privacy-friendly AI gains share
Marketing advantage for "trained on synthetic data".
3 — Smaller players compete
Used to be that only big tech could train good models.
Synthetic levels the playing field.
What Solo Operators Should Care About
Practical implications.
1 — Better specialist tools coming to your industry
Whatever you do, expect new AI tools trained on synthetic data.
Be early adopter.
2 — Privacy positioning matters
If you market AI tools, "synthetic-trained" is a privacy selling point.
3 — Mechanism design pattern applies broadly
Apply Simula's thinking to your own AI workflows:
- Map full domain.
- Cover edge cases.
- Critic step.
I apply this in Hermes Agent Swarm workflows.
Common Misconceptions About Synthetic Data
1. "Synthetic data is fake therefore worse."
Wrong.
Tests show synthetic can match or beat real data.
Quality depends on generation process.
2. "Synthetic data has no real-world value."
Wrong.
Models trained on synthetic data work in the real world (Android scam detection proves this).
3. "Synthetic data is just random nonsense."
Wrong.
Mechanism design produces structured, logical data.
4. "Real data always wins."
Sometimes.
But not always — coverage often beats quantity.
Where Synthetic Data Could Go Wrong
Be honest.
1 — Bias inheritance
Synthetic data inherits the biases of the generating model.
If generator is biased, output is biased.
2 — Generative collapse
Synthetic-on-synthetic-on-synthetic could compound issues.
Best practice: anchor synthetic generation to high-quality teachers.
3 — Distribution mismatch
If synthetic differs from real-world distribution, models may fail in production.
Validation in real conditions matters.
These are real concerns the field is working through.
Strategic Implications For Your Business
If your business handles:
- Sensitive customer data.
- Regulated industry data.
- Confidential client info.
Then specialist AI trained on synthetic data could:
- Replace tools you've avoided due to privacy.
- Enable workflows you couldn't automate.
- Open new product opportunities.
Watch for synthetic-trained AI launches in your industry.
Be first to adopt.
How To Spot Quality Synthetic Data Tools
Three signals.
1 — Mentions specific generation approach
"Trained on synthetic data using mechanism design" > vague "AI trained on data".
2 — Has critic/filter step
Quality synthetic generation always includes filtering.
3 — Publishes coverage benchmarks
Honest tools share what their training covers.
Black-box "trust us" tools are riskier.
Predictions
Where I think this goes.
1 — Synthetic becomes default for specialist AI
In 2-3 years, most new specialist AI will be synthetic-trained.
2 — Hybrid becomes standard for general AI
Real + synthetic = best results.
3 — Open source benefits
Same techniques applied open source.
The closed vs open race continues.
4 — Cost of AI development drops
Synthetic generation is cheaper.
More AI tools possible.
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FAQ — Google Simula vs Real Data
Can synthetic data really beat real data?
In some tests yes — particularly for coverage.
Is synthetic-trained AI as accurate?
For most tasks, yes.
For some specific tasks where teacher model isn't strong, no.
Should I prefer synthetic or real?
Depends on the task.
For privacy-sensitive: synthetic.
For nuance-heavy: real (or hybrid).
Will synthetic data become the default?
For specialist AI, yes.
For general-purpose AI, hybrid.
Is synthetic data biased?
It inherits biases of the generating model.
Manage carefully.
Can I trust AI trained on synthetic data?
Same scrutiny as any AI.
Test on real-world tasks.
Will my industry get specialist synthetic-trained AI?
Likely yes — within 2-3 years.
Related Reading
- Google Simula Overview — what Simula does.
- Google Simula Mechanism Design — technical detail.
- Kimi 2.6 Benchmark — open source AI option.
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Google Simula vs real training data isn't either/or — for most AI applications, hybrid wins, but synthetic-only options unlock entire specialist AI fields that were previously blocked.