Synthetic Users

Humanity is the training data

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Are departments dead? Is software being eaten? Yes. In part. A little glimpse into the future (actually it's the present). First off, check these notes from YC. 'Hidden' vertical AIs will dominate 2025 . $300bio+ companies are being built and I also believe single person departments are being engineered at this very moment. I'll break down the notes and share some personal thoughts as well. We have a clear outline/ a clear vision for the future with AI: Vertical AI agents are highly specialized tools in various industries, as opposed to larger platforms, these vertical agents will instead maintain their distinct roles. Need examples? → MTic, for example, has developed an AI that automates QA testing and is gaining rapid traction → Cap.AI has created a top-tier chatbot that supports developers → Salient, whose AI manages voice calls for auto loan collections, already partnered with major banks. YC mentions that these tasks are usually applied to mundane tasks everyone dreads. These "butter passing jobs," are simple and repetitive… this is where AI can make a significant impact. (Small side note, I’d argue it’s not only for Mundane tasks… also for strategic tasks or important decision-making: → I created a customGPT for decision making accessible with a rapid shortcut and through audio → Want the first steps of a full go-to-market strategy? Use M1-Project → Want customer insights at scale? Check out Synthetic Users  → Want a pricing strategy? Turns out AI is really good at pricing https://lnkd.in/deAyystc) End side note) When pitching AI internally or when trying to sell your solution, target the top executives. Why? The decision-makers who stand to gain from automation and aren't threatened by it. It's crucial to frame AI as a tool for enhancement, not replacement, to avoid resistance. What's transformative about vertical AI is that it goes beyond just upgrading software; it fundamentally changes how companies allocate their labor and resources. SMEs that traditionally invested heavily in human resources are finding that AI can perform many functions faster, cheaper, and more effectively. I’m seeing this daily also. Augmentation and Automation like never before. This shift isn't just a trend—it's a leap forward, with AI increasingly capable of taking over complete functions and departments. Check out this piece by Jacco van der Kooij https://lnkd.in/dkyVpNJM The AI revolution, as projected by these Y Combinator insights, promises a scale of impact potentially surpassing the SaaS boom of the 2000s. Start learning. Keep tinkering. I’m sharing my step by step learning journey + a road to automation in a series. Just leave a short comment and I'll send the link over.

Portfolio company Synthetic Users, which uses AI to create accurate synthetic interviews, recently hosted Jimmy Wales, co-founder of Wikipedia, the Free Encyclopedia - one of the key training sources for large language models (LLMs). In this session, Jimmy shared his perspective on the current state of AI and explored various topics related to synthetic research. This is a must-watch for anyone with an interest in AI: https://lnkd.in/gBUPnkRH #UrbanInnovation #AI #Innovation Kwame Ferreira Hugo Alves

Mastering AI tools isn’t just optional—it will be vital for UX researchers in 2025. Are you ready? Join John Whalen, PhD for a 1-hour LinkedIn Live to gain the critical skills you need to stay ahead: ✨ How to use AI moderation effectively ✨ Unlocking the potential of synthetic users ✨ Taking full advantage of AI-powered repositories ... and much more! RSVP now to save your spot for our next AI + UX Coffee Talk 👉 https://bit.ly/3VbI7AM #UXResearch #AI4UX #AIReady #LinkedInLive

🚀 Supercharging Survey Design with Synthetic Data: A Step-by-Step Guide for Market Researchers Synthetic data might sound futuristic, but for market researchers, it’s quickly becoming a game-changer. If you’ve ever struggled with: 👉 Drop-off rates 👉 Confusing question logic 👉 Hidden biases ...then synthetic data could be the tool you didn’t know you needed. It helps stress-test surveys, refine design, and save time—all while delivering cleaner, more actionable insights. 🤔 What Is Synthetic Data in Market Research? Synthetic data doesn’t replace the insights from real respondents. Instead, it creates simulated profiles that mirror your target audience’s diversity and behaviours. Think of it as a virtual rehearsal for your survey—catching errors before they impact your live research. 🔍 How Synthetic Data Improves Survey Design Here’s how synthetic data can help: 💡 Catch Drop-Off Points: Simulate how respondents interact with your survey to identify where fatigue or confusion might occur. 💡 Test Complex Logic: Have branching pathways? Synthetic data ensures your “If yes, skip to Question 8” flow works without errors. 💡 Uncover Question Bias: Test responses from diverse synthetic profiles to spot leading or confusing questions. 💡 Validate Accessibility: Ensure clarity for non-native speakers or audiences with varying literacy levels. 💡 Simulate Niche Audiences: Target rare profiles (e.g., high-net-worth individuals or early adopters) to refine your design. ✨ Why Use Synthetic Data? Here’s what it brings to the table: ✅ Saves Time and Money: Refine surveys before launch to avoid costly re-fielding. ✅ Improves Data Quality: Catch issues that could impact accuracy. ✅ Supports Innovation: Explore new markets or niche audiences risk-free. ✅ Boosts Inclusivity: Test for accessibility to ensure everyone can respond effortlessly. 🌟 Real-World Use Cases Researchers are already leveraging synthetic data to: 🔹 Test Employee Feedback Surveys: Ensure sensitive topics are worded neutrally and presented effectively. 🔹 Refine Multilingual Surveys: Simulate responses across languages and cultures for global audiences. 🔹 Pre-Test Product Feedback: Predict how customer segments might engage with your survey. 🔹 Reach Niche Audiences: Create profiles for rare demographics to tailor questions. 💬 Why Synthetic Data Should Be in Your Toolkit Synthetic data isn’t replacing the real world—it’s about working smarter. It helps you: ✔️ Catch errors ✔️ Minimise risks ✔️ Deliver sharper insights For forward-thinking researchers, it’s an essential tool to elevate your work. What’s your take on synthetic data? Have you used it to improve your surveys? #MarketResearch #SurveyDesign #SyntheticData #Innovation #DataQuality #MRX #PunkMRX

I know you shouldn't always be too euphoric about #genai and use superlatives, but this study and its results really have the potential to change #socialscience #research. In their preprint study, Joon Sung Park, Carolyn Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Morris, Robb Willer, Percy Liang, and Michael Bernstein developed a novel #generative #agent architecture that simulates the attitudes and behaviors of humans. How did they do that (see figure below)? 1) Recruitment of 1,052 individuals from the U.S. (based on age, census division, education, ethnicity, gender, income, neighborhood, political ideology, and sexual identity) 2) Participants complete a two-hour audio interview with an #AI interviewer, followed by surveys and experiments 3) Creation of generative agents for each participant using their interview data 4) Generative agents and participants complete the same surveys and experiments (humans retook the surveys and experiments again two weeks later) 5) Comparison of agent responses to human participants' original responses (adjusting for how consistently each participant successfully replicates their own responses two weeks later) The Results: -Replication of participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later -Comparable performance in predicting personality traits and outcomes in experimental replications The Implications: -(Bottom-up) Simulations involving individual or multiple agents across a variety of different domains and questions, e.g., a) the impact of new government policies on economic behavior b) the impact of social media interventions on political polarization c) factors that influence whether institutions foster or erode prosocial behavior when social groups grow Study link in the first comment. Google DeepMind Stanford University Stefano Puntoni Oguz A. Acar Dhruv Grewal Abhijit Guha Gunter Hermann Marko Sarstedt

UX researchers are not paid to talk to people. They're paid to understand user needs. Despite the hate he gets, Hugo Alves, Co-Founder of Synthetic Users, is building a product to help with this. Many founders claim to have thick skin. In Hugo's case, this is constantly on display. People have called him stupid, a wanker, and everything in between. Why? - because he is creating human-like AI participants for user research… In this week's edition of Hiring Humans, we discuss: - The role of UX researchers: they are paid for the outcome, understanding user needs, not for how they get there (talking to humans is an option, but not a requirement). - Real and synthetic humans share the same flaw: a desire to please. He's built his product from the ground up to avoid this flaw. - What he believes flags a great candidate: side projects. Whether it's tinkering with a new technology or simply trying to solve a problem in their personal life. Curiosity is key. Full post in comments. ______________________________ 🤝 If you'd like AI tips for hiring or how to run your startup, I send a weekly 30-second ( + longer form post) newsletter with lessons learned from conversations with founders and early-stage teams. 📩 Subscribe in the comments.

Happy to help!

Since Hugo started spilling the beans, we need to start preparing for the demo. Can you help us out? What should be the test? Who do you want to learn about and what do you want to learn? Throw some hard challenges at us, please!