OpenAI researcher on why soft skills are the future of work | Karina Nguyen (Research at OpenAI, ex-Anthropic)

Lenny's Podcast: Product | Growth | Career

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OpenAI researcher on why soft skills are the future of work | Karina Nguyen (Research at OpenAI, ex-Anthropic)

OpenAI researcher Karina Nguyen on working at OpenAI and Anthropic, model development tactics, how synthetic data will fuel AI, and the future of work

Feb 09, 2025

106

Transcript

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Karina Nguyen leads research at OpenAI, where she’s been pivotal in developing groundbreaking products like Canvas, Tasks, and the o1 language model. Before OpenAI, Karina was at Anthropic, where she led post-training and evaluation work for Claude 3 models, created a document upload feature with 100,000 context windows, and contributed to numerous other innovations. With experience as an engineer at the New York Times and as a designer at Dropbox and Square, Karina has a rare firsthand perspective on the cutting edge of AI and large language models. In our conversation, we discuss:

How OpenAI builds product
What people misunderstand about AI model training
Differences between how OpenAI and Anthropic operate
The role of synthetic data in model development
How to build trust between users and AI models
Why she moved from engineering to research
Much more

Some takeaways:

AI’s rapidly improving capabilities mean that the cost of reasoning and intelligence is dropping. This opens up opportunities to integrate smarter, smaller models into products. Whether you’re building for health care, education, or another industry, think about how your product could benefit from these advancements.
While AI models will continue to improve, the real “moat” around your product lies in your ability to listen to users and iterate quickly. Don’t just build the best product; build the product that resonates most with your users. Create mechanisms for continuous feedback, and make rapid iteration a core part of your development cycle.
The most valuable future skills will be the soft skills that AI struggles with:
1. Creativity in generating novel solutions
2. Empathy and emotional intelligence
3. Leadership and people management
4. Effective prioritization
5. Active listening and user understanding
Model training requires careful attention to data quality and understanding complex interactions between datasets. Success comes from:
1. Balancing contradictory training data
2. Managing model behavior across diverse scenarios
3. Using synthetic data for rapid iteration
4. Building robust evaluation frameworks