Former Google and Apple Researchers Launch a Startup to Build AI's Missing Feedback Loop

to Build AI's Missing Feedback Loop">

["A group of AI researchers who previously worked at Google DeepMind, Apple, OpenAI, and Meta Superintelligence Labs have announced the launch of a new startup called Trajectory. The company's mission is to help businesses regularly improve their AI products by training on real-world user interactions. Trajectory wants to build a platform for AI that can learn continuously, a capability that researchers have long held up as a major barrier to further AI progress.", 'OpenAI, Google, and Anthropic have found success training increasingly capable versions of AI models, especially for domains such as coding, math, and science.

However, these systems stop getting smarter after their training is done. While there have been some recent breakthroughs in continual learning, tech companies have generally struggled to make AI products that learn from their errors in real time. In December 2025 at NeurIPS, one of the largest annual AI research conferences, Turing award winner Richard Sutton argued that continual learning is essential for building superintelligent agents.', "Trajectory has raised a $15 million seed round at a $115 million post-money valuation, led by the venture capital firm Conviction, with participation from Bessemer Venture Partners, Radical VC, and BoxGroup.

Individual investors also participated in the round, including Google DeepMind's chief scientist, Jeff Dean, as well as the so-called 'godmother of AI,' Stanford professor and World Labs CEO Fei-Fei Li. Trajectory's CEO and cofounder Ronak Malde was previously an AI researcher at Windsurf, and he later became one of only a handful of employees who went to work at Google DeepMind when it hired the coding startup's top talent in a $2.4 billion deal last year.", "Malde tells WIRED that some leading AI coding products, such as Cursor, are already doing an early version of continual learning—using real data about how people interact with their products to do post-training and regularly ship model improvements. He argues this is a core reason why AI coding products have taken off so rapidly, and is part of the reason why major AI labs have rushed to develop vibe coding applications of their own.

'Even the most powerful AI today is still static. The AI model that you used yesterday is going to make the same mistakes today,' says Malde. 'A couple companies are starting to get to that world of continual learning.

What we are doing is building the platform for every single company to get to continual learning.'", "The challenge with applying this logic to other domains is that coding is easily verifiable—code either runs or it doesn't—but some industries have looser definitions of success. Trajectory's platform offers help optimizing an AI model to a business's specific needs. Rather than starting from an off-the-shelf model from OpenAI or Anthropic, Trajectory has customers begin with an open-source model that has been post-trained for a specific AI product the company has in mind.