Anthropic · OpenAI · Nvidia · Apple · Google · TechCrunch AI
Trajectory raises $45 million to make AI model tuning accessible to nontechnical experts
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Whether developed by OpenAI or Anthropic or built on open source alternatives, current AI models share a critical limitation: They don’t learn from their mistakes without further training.
Key facts
- The 18-person company announced that it raised $45 million in a Series A round at a $300 million valuation, led by Sequoia, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital
- Trajectory’s approach to continual learning shares some similarities with recursive self-improvement (RSI), the idea that AI systems can improve their own capabilities
- Trajectory co-founder Ronak Malde compared foundational models to a smart PhD student who’s always starting a new job
- With Trajectory, lawyers, doctors, and other specialists can improve the models near automatically themselves, Malde said
Summary
Businesses adapting these models for specialized fields, such as law and finance, typically get around this by fine-tuning general models. Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, wants to automate that process, allowing AI models to learn from feedback and continuously improve at specialized tasks. The 18-person company announced that it raised $45 million in a Series A round at a $300 million valuation, led by Sequoia, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital Ventures. Trajectory co-founder Ronak Malde compared foundational models to a smart PhD student who’s always starting a new job.