Inherent, an AI laboratory based in London and established by former Google DeepMind employees, has announced that its AI agent has surpassed significantly larger models from competitors Anthropic and OpenAI, all while being considerably smaller in size.
Among the many startups launched by former Google DeepMind members, Inherent has not garnered as much attention as its better-funded peers. However, the London-based team is beginning to unveil its innovative work. Shortly after revealing itself from stealth mode and securing a $50 million seed funding round, the startup reported that its latest AI agent, named Faraday, was able to independently replicate the findings of published scientific studies without prior guidance, outperforming larger and more prominent models.
Though it may appear to be a simple demonstration, this task of replicating research is actually a fundamental exercise for human scientists, as co-founder and chief scientist Edward Hughes pointed out. "Many PhD students actually start by doing this," he noted, emphasizing the practical relevance of the achievement.
Hughes shared with TechCrunch that their focus was not merely on the victory over other AI systems but rather on the methodology behind their success. The standout aspect of Faraday lies in its operational framework: while models like Anthropic’s Claude Opus 4.8 and OpenAI’s GPT-5.5 are built on vast systems, Faraday operates on a relatively smaller model known as Qwen 3.6, which only consists of 27 billion parameters. This parameter count broadly reflects a model's size and associated training expenses. They set the bar for success even higher than mere accuracy; they aimed for Faraday to exhibit "research taste," an intuitive sense for valuable experiments and effective study designs.
Instilling a sense of taste in an AI is complex, and this is where the concept of reinforcement learning becomes valuable. This approach rewards the AI for successful outcomes instead of issuing a strict set of rules to follow. Rather than concentrating solely on the methodology of scientific research, Inherent employs this reinforcement technique, believing it will better equip its agents for their ultimate objective: to contribute meaningfully across diverse scientific domains.
“We’re continually guided by the vision of creating an AI scientist agent infused with taste,” Hughes remarked. This vision has influenced Inherent’s development strategy, including opting to use OpenAI's GPT-5.5 Codex instead of creating their coding tools from scratch, mirroring how actual scientists leverage existing technology rather than reinventing the wheel.
The company aims to develop agents that provide honest feedback rather than tell users only what they wish to hear. Hughes envisions a collaboration where an ideal teammate returns with new results after conducting experiments, fostering a culture of inquiry and exploration.
Moreover, Inherent's internal culture reflects this collaborative spirit, with its team of twelve working together in-person at their office in King’s Cross—a once-neglected area revitalized by Google DeepMind’s influence and transformed into a leading hub for AI innovation. "We believe that London is the place to be," Hughes stated.
While Hughes is optimistic about London’s concentration of AI expertise, he has also advocated for the end of "garden leave," the common practice in the U.K. of prohibiting departing employees from joining or starting competing firms for an extended period post-resignation. This practice creates a disadvantage for U.K. startups aiming to recruit talent readily available in the U.S. startup ecosystem. “This is a personal view rather than a company perspective, but I faced challenges due to garden leave,” he shared.
Despite obstacles, Hughes and his co-founders—two other DeepMind veterans and a fourth partner—successfully launched Inherent. The startup has plans to expand its team to between 20 and 25 members by year-end. With aspirations in developing world models, alongside staffing adjustments at DeepMind, Inherent's growth could present an attractive option for former DeepMind employees contemplating new opportunities.
