As artificial intelligence becomes ubiquitous, a critical flaw persists: traditional neural networks are too power-hungry, computationally expensive, and opaque for practical use in edge devices or regulated industries. Their “black box” nature makes it nearly impossible to explain decisions — a crucial issue in sectors like finance, healthcare, and pharmaceuticals, where transparency is non-negotiable. Literal Labs was founded to tackle these intertwined challenges head-on, aiming to deliver radically more efficient and inherently explainable AI.
In an exclusive conversation with TFN, Noel Hurley, CEO of Literal Labs, explains, “Traditional neural networks are too computationally expensive to run at the edge, meaning it is often not possible to effectively implement AI in use cases where connectivity or compute power is limited. Neural networks are also power-hungry and therefore expensive, making them unfeasible for many battery-based applications.”
“Neural networks also lack inherent explainability, which is crucial in highly regulated fields, such as finance, healthcare, or pharma. Literal Labs’ AI models are logic-based, meaning they are naturally explainable,” he adds.
From academic roots to commercial ambition
Literal Labs was spun out of Newcastle University in 2023 by Professors Alex Yakovlev and Rishad Shafik, both world leaders in logic-based AI, with support from Cambridge Future Tech. In collaboration with the University of Agder’s Centre for AI Research, their research focused on developing machine learning using data binarisation, propositional logic, and Tsetlin Machines.
“Literal Labs is founded on years of our research on advanced compression methods, data representation, and highly modular, parallel implementations of the Tsetlin machine. We believe that this novel logic-based approach will fundamentally transform the next generation of machine learning applications where low energy consumption and explainability are crucial,” said Hurley.
Noel Hurley, who joined as CEO in 2023 after more than two decades at Arm, where he led the CPU division responsible for over 70% of revenue, brings deep commercial expertise to the team. The company doubled its headcount from 6 to 12 in 2024, including the appointment of Leon Fedden, formerly AI Deep Learning platform lead at AstraZeneca, as CTO.
The company features 17% female full-time employees and a board that is 28% female.
Behind Literal Labs: a logic-based solution
Literal Labs is pioneering a fundamentally different approach. Their models use propositional logic and data binarisation, inspired by mathematician Mikhail Tsetlin, to deliver orders of magnitude faster, more energy-efficient, and inherently explainable AI.
Recent MLPerf benchmarking shows startups’ technology achieves 54x faster inference and 52x less energy consumption than equivalent neural networks, and up to 250x faster performance than XGBoost for machine learning applications. “Our logic-based AI offers a new solution for those who want and need high-performing AI that is faster, more energy efficient, and more explainable than what’s currently available via neural networks,” says Hurley.
To advance its technology further, Literal Labs closed a £4.6 million pre-seed round, led by Northern Gritstone, with co-lead Mercuri and participation from Sure Valley Ventures, Cambridge Future Tech, and several angel investors. The new capital will fund the launch of Literal Labs’ first commercial product later in 2025, enable further team growth, and support engagement with early customers, particularly those in EdgeAI, regulated markets, and battery-sensitive sectors.
“Literal Labs is Northern Gritstone’s first investment linked to Newcastle University, renowned for its technology-related research. We are delighted to support Noel Hurley and the Literal Labs team at a time when innovation can truly benefit from greater efficiency in AI,” says Duncan Johnson, CEO of Northern Gritstone.
Esha Vatsa, Partner at Mercuri, adds: “We are excited to back Literal Labs as it redefines AI with a radically efficient alternative to existing neural networks. The team, combining deep research expertise and proven industry leadership, is uniquely positioned to commercialise this innovation”.
What’s next for Literal Labs? Rewriting the rules of what AI could achieve
The company’s vision is clear: “AI that improves life for all and treads lightly on our environment.” As Hurley puts it, “This funding comes at a time when we’re ready to significantly speed up our product development and will enable us to bring our first product to market later this year.”
Without direct rivals in logic-based AI, Literal Labs seeks to establish itself as the leader in the Edge AI market within the next three to five years, expanding its technology into areas where other AI models struggle to function.
Literal Labs is transforming the standards for what AI can accomplish, its potential applications, and its reliability.
