A group of AI researchers who previously worked at Google DeepMind, Apple, OpenAI, and Meta Superintelligence Labs announced on Wednesday they’re launching a new startup called Trajectory, which aims to help companies 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. The other cofounders of Trajectory include Arjun Karanam, a former AI researcher at Apple who worked on the Vision Pro, and Michael Elabd, who previously worked in Google DeepMind’s robotics division.
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. With Trajectory, Malde and his team of 11 researchers and engineers hope to apply a similar technique for improving AI-powered tools outside the coding space.
“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. Karanam says part of what Trajectory’s platform offers is helping optimize 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. For Decagon, a customer that builds AI customer support agents, Trajectory logs when its AI falls short—say, a customer trying to make a return gets their query bounced to a human—and uses those instances to post-train a new model as often as every week. Trajectory claims these post-trained models beat the frontier labs’ models on narrow tasks that matter most for a company’s product.
Corporate executives are eager to use AI for many different kinds of tasks, but to do that today they often need to hire teams of “forward deployed engineers,” or consultants and technical employees embedded inside a company who help build out AI products. Companies like OpenAI, Anthropic, and Palantir have rushed to fill that need. Elabd says Trajectory’s goal is to build a product that can improve on its own so that companies don’t need in-house engineers to continuously troubleshoot their AI stack. The startup says it has customers in a variety of fields already, including the enterprise sales startup Clay and the legal AI startup Harvey. While it currently works primarily with AI-native companies, Trajectory eventually plans to market its platform to the Fortune 500.
Critics could argue that Trajectory has not yet built true continual learning, at least not in the traditional sense. After all, the startup’s models only update once a week at this time, and they remain static between upgrades.
Elabd argues that Trajectory is just getting started. He claims the AI industry is moving towards a new paradigm where AI learns from experience—much like what’s already happening in the AI coding space. Elabd says Trajectory’s eventual goal is to build a platform that can update a company’s AI model every single day, or perhaps even more frequently.
“Every day may not be enough. It could be every hour, it could be every interaction,” says Elabd. “Maybe every company doesn’t need just one AI, you could train an AI to learn for every person at every company.”
This is an edition of Maxwell Zeff’s Model Behavior newsletter. Read previous newsletters here.
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