Synthetic Users in More Industries
Synthetic Users in the AI Industry
By Sherry Jones (September 2025)
Synthetic users are AI-generated personas designed to mimic real human behavior, built on large language models trained on massive human data. These digital agents simulate thoughts, needs, and experiences of specific customer segments, revolutionizing how companies test products and understand users.
Companies use synthetic users throughout product development - from early design feedback to post-deployment testing - reducing costs, accelerating insights, and exposing edge cases without involving real people. This comprehensive guide explores key enterprise applications across the U.S. market.
Product Testing and UX Research Revolution
Synthetic users are transforming product testing by enabling teams to generate hundreds of AI-driven personas for usability testing before any live users interact with products. YC-backed startup Synthetic Society deploys swarms of synthetic users that proactively catch bugs, UX flaws, and edge cases in software before release.
These synthetic UX tests offer major speed advantages - generating hundreds of participants can replace weeks of recruiting. Need enterprise IT managers in their 40s? Done in hours, not weeks. The Nielsen Norman Group found synthetic users excel at catching obvious design flaws but caution they provide shallow feedback compared to real users.
01
Generate Personas
Create hundreds of AI-driven user profiles matching target demographics and behaviors
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Run Simulations
Deploy synthetic users to interact with applications and identify usability issues
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Analyze Results
Review feedback and fix obvious problems before real user testing
04
Validate with Humans
Conduct targeted real user research on remaining critical design decisions
Marketing and Customer Intelligence
Beyond UX testing, synthetic personas are revolutionizing marketing through AI-generated customer models that predict responses to new offerings. A 2025 Bain report highlights how companies simulate synthetic customer reactions to features, pricing, and campaigns before launch, creating data-driven personas for segmentation.
Campaign Testing
Test messaging and value propositions across diverse synthetic customer segments before expensive real-world deployment
Price Optimization
One major telecom used synthetic customers to test service plans, with AI predictions increasingly aligning with real outcomes
Support Training
Train sales and call-center staff using role-play dialogues with synthetic customer agents representing various scenarios
LivePerson introduces "synthetic AI customers" as always-on mystery shoppers that probe chatbots with thousands of conversation paths, identifying compliance lapses and hallucinations before real customers encounter them.
Synthetic Users in UX Research
The application of synthetic users extends powerfully into UX research, offering a novel approach to understanding user behavior and product interaction. The Synthetic Society video demonstration provides a compelling example, showcasing how AI-driven virtual participants can simulate diverse user journeys.
This demonstration highlights how synthetic users can be deployed to conduct comprehensive UX research, identifying potential usability issues, evaluating design effectiveness, and predicting user sentiment before a product even reaches real-world users. By simulating thousands of interactions across various demographic and psychographic profiles, teams can gain rapid insights, iterate on designs more efficiently, and uncover critical pain points that might otherwise be missed, all within a controlled and scalable environment.
AI Model Training at Scale
Computer Vision Breakthroughs
NVIDIA's simulation platforms generate millions of annotated images for autonomous vehicles and robotics. These synthetic datasets include rare scenarios like poor weather or obstacle-strewn roads that are difficult to capture naturally, dramatically accelerating training while improving safety.
MIT research found models trained on fully synthetic video datasets sometimes outperformed those trained on limited real videos in action-recognition tasks, sidestepping copyright and privacy issues.
Robotics Training
Virtual warehouses and environments produce labeled sensor data for navigation systems, enabling safe testing of edge cases
NLP Enhancement
AI-generated dialogues train conversational systems, especially in sensitive domains like medical or legal consultations
Fraud Detection
Banks generate synthetic fraudulent transactions to harden algorithms against emerging attack patterns before they occur
AI Model Training, AI Agents, and the Future of Robotics
NVIDIA explores the concept of synthetic users, which are AI-powered agents designed to simulate human behavior and interactions with applications and systems. It delves into how these digital counterparts can be leveraged to conduct comprehensive testing, identify usability issues, and gather valuable feedback throughout the development lifecycle, accelerating product iteration and improving user experience without relying on real human testers.
The discussion highlights the efficiency and scalability benefits of using synthetic users in various testing scenarios, from performance and load testing to uncovering edge cases in complex user journeys.
Waymo Driver: Training in Simulated Environments
Waymo's autonomous driving technology, the Waymo Driver, relies heavily on a sophisticated simulation platform for its continuous improvement and validation. This platform creates billions of miles of virtual driving every day, allowing the AI to encounter countless scenarios, including rare and challenging edge cases that would be impractical or unsafe to test in the real world.
Through this virtual world, machine learning models that power the Waymo Driver are trained and refined. This robust simulation environment allows for rapid iteration and testing of new algorithms, ensuring the system can safely and efficiently navigate complex road conditions before deployment. You can learn more about how Waymo trains its AI in simulation by watching this video: Waymo Simulation Training.
Bias Detection and Fairness Testing
Synthetic users are critical in identifying and mitigating biases within AI models. By simulating diverse demographic profiles and interaction patterns, companies can proactively expose discriminatory outcomes that might otherwise go unnoticed until post-deployment. This ensures AI systems are equitable and perform fairly across all user groups.
Leading organizations use synthetic data to stress-test models for fairness, comparing outcomes across simulated gender, racial, and socioeconomic groups. This approach allows for large-scale, systematic evaluations, pinpointing subtle biases in everything from loan application approvals to medical diagnostic tools.
Synthetic users play a crucial role in detecting AI bias. Researchers used GPT-3.5 to simulate thousands of resumes with controlled gender and ethnic names, revealing significant discrimination - identical resumes scored lower when attached to female or minority names, exposing a "silicon ceiling" in hiring tools.
Algorithmic Fairness
Detect and correct biases in algorithms, ensuring fair treatment for all synthetic user demographics.
Data Imbalance
Identify and address underrepresentation or overrepresentation of specific groups in training datasets.
Ethical AI
Develop AI systems that adhere to ethical guidelines and promote social good through rigorous testing.
Controlled Testing
Generate synthetic profiles with specific demographic characteristics to audit models for discrimination patterns
Compliance Monitoring
LivePerson's synthetic testers verify AI responses never leak customer data or break policy through continuous audit layers
Bias Correction
Platforms adjust synthetic agents to match real population distributions, correcting geographic and ideological skews

Expert Warning: Synthetic feedback can perpetuate biases if not carefully managed. Best practice treats synthetic results as hypotheses requiring validation with real data, not conclusive truths.
Industry Adoption Landscape
1K+
Stanford Agents
Researchers created over 1,000 synthetic agents matching real survey respondents for policy testing
25
Social Simulation
AI agents held believable daily interactions, including gossiping about mayoral elections
2025
Market Growth
YC-backed Synthetic Society and other startups commercialize automated UX testing platforms
Major U.S. companies are actively exploring synthetic user technology. NVIDIA provides simulation toolkits for autonomous vehicles, Meta creates virtual avatars for metaverse testing, and IBM uses synthetic data for fraud detection. Academic institutions like Stanford's Human-Centered AI Institute demonstrate synthetic populations for policy experimentation.
Management consultancies like Bain now advise corporate clients on integrating synthetic customer personas into innovation processes, while policy think-tanks consider new legal frameworks for synthetic societies.
Real-World Success Stories
1
Telecom Innovation
Major provider tested new service plans with synthetic customers, enabling faster go-to-market decisions as AI predictions aligned with real outcomes
2
Autonomous Driving
Tesla and Waymo use virtual simulation to pre-train systems before collecting real road footage, exploring extreme weather scenarios
3
Healthcare AI
Leading LLMs produce high-fidelity synthetic patient-physician dialogues for training medical AI systems while preserving privacy
4
Financial Services
Banks generate synthetic transaction data to test recommendation engines and detect emerging fraud patterns proactively
These implementations demonstrate synthetic users' practical value: reduced development costs, faster iteration cycles, and improved product quality. Companies report significant time savings in user research while maintaining product safety through comprehensive edge-case testing.
Technical Implementation Challenges
Data Quality Concerns
Synthetic feedback tends to be shallow or overly favorable compared to real user insights, requiring careful calibration and validation
Bias Amplification
AI models can perpetuate existing biases in training data, requiring active correction and diverse representation in synthetic populations
Computational Resources
Generating high-fidelity synthetic users at scale requires significant computing power and advanced AI coordination systems
Regulatory Compliance
Synthetic data platforms must comply with consumer protection and anti-discrimination laws, creating new legal considerations
Despite these challenges, industry experts recommend a tiered approach: use synthetic users for initial hypothesis generation and obvious issue detection, then validate critical decisions with real human participants. This "cheap bots first, humans later" strategy maximizes efficiency while maintaining quality.
Future Directions: Synthetic Societies
The most provocative application involves constructing entire artificial societies of AI agents to model complex social dynamics. Stanford's recent project scaled from 25 agents holding believable daily interactions to over 1,000 agents representing U.S. society for policy testing.
Policy Simulation
Governments could test law impacts on synthetic populations before implementation
AGI Training
Synthetic worlds could serve as training grounds for advanced AI systems
Social Research
Researchers could study pandemic responses and climate policies in controlled environments
"Generative AI agents are going to power a lot of future policymaking and science. We're essentially rehearsing our future with synthetic societies."
- Stanford University Researcher
This evolution raises significant questions about computational requirements, ethical responsibilities, and the rights of simulated populations as synthetic beings become increasingly sophisticated.
The Synthetic User Revolution
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AGI & Societies
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Policy Simulation
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Bias Detection & Fairness
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Model Training & Validation
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Marketing & Customer Intelligence
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Product Testing & UX Research
Synthetic users represent a fundamental shift in how companies approach product development, customer research, and AI training. From accelerating UX testing to enabling policy experimentation, these AI-generated personas offer unprecedented scale and speed while raising important questions about bias, authenticity, and ethics.
As the technology matures, the key lies in thoughtful implementation - leveraging synthetic users' strengths for rapid iteration and edge-case discovery while maintaining human validation for critical decisions. The future promises synthetic societies that could revolutionize everything from product design to governance, making this one of the most transformative applications of AI in the modern enterprise.
References
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[16] Zewe, A. (2022, November 3). In machine learning, synthetic data can offer real performance improvements. MIT News. https://news.mit.edu/2022/synthetic-data-ai-improvements-1103
[17] [21] [22] [27] RoX818 (2025, February 15). Training AI With Synthetic Data: Game Changer? https://aicompetence.org/training-ai-with-synthetic-data-game-change/
[19] [20] Haider, S. A., Prabha, S., Gomez-Cabello, C. A., Borna, S., Genovese, A., Trabilsy, M., Collaco, B. G., Wood, N. G., Bagaria, S., Tao, C., & Forget, A. J. (2025, July 10). Synthetic Patient-Physician Conversations Simulated by Large Language Models: A Multi-Dimensional Evaluation. PubMed. 25(14): 4305. https://pubmed.ncbi.nlm.nih.gov/40732431/
[23] [2405.04412] Armstrong, L., Liu, A., MacNeil, S., & Metaxa, D. (2024, November 15). The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in Hiring. https://ar5iv.labs.arxiv.org/html/2405.04412
[24] [25] (n.d.). How we deal with bias. Synthetic Users. https://www.syntheticusers.com/science-posts/how-we-deal-with-bias
[28] [29] [30] Miller, K. (2025, January 21). AI Agents Simulate 1,052 Individuals’ Personalities with Impressive Accuracy. Stanford HAI. https://hai.stanford.edu/news/ai-agents-simulate-1052-individuals-personalities-with-impressive-accuracy