A Year at the Cusp: 2025

As the year comes to a close, we’ve taken a moment to reflect on the past 12 months. Not just on individual announcements or milestones, but on where the systems we’ve been building are now running and the impact they’re starting to enable.

The CuspAI team in 2025

The problem is the size of the space

The central challenge in materials discovery is the scale of the space it operates in. Even when restricted to chemically plausible compounds, conservative estimates place relevant chemical space well beyond 1⁰⁶⁰ candidates. If each possible material were a point, chemical space would be like a universe. Even scanning a vanishingly small fraction of it would take longer than the lifetime of any lab.

Progress depends on learning where not to look, and how to move efficiently toward the regions that matter.

From the outset, our goal at CuspAI has been to build a system that can navigate this space continuously and coherently. Not as a one-off optimisation, but as a persistent learning system that can be deployed, integrated, and run over time.

Over the past year, we completed the first full iteration of that system and began operating it end to end with partners. Targets are specified in terms of function and constraint. Candidate materials are generated and evaluated using physics-based simulation. Experiments are selected based on their expected contribution to reducing uncertainty. Results are fed back into the system and influence future decisions.

From research to deployment

Materials discovery sits at the intersection of simulation and experiment. Large parts of the search happen in silico, where simulations are used to explore design spaces and rule out implausible candidates early. The challenge is ensuring that these simulations remain tightly aligned with experimental reality, so that each design–build–test cycle meaningfully updates the system’s internal models.

This has shaped how the system behaves in deployment. Simulations are used aggressively, but always in service of deciding which experiments to run and why. Experimental results are used to recalibrate models, refine decision policies, and improve how uncertainty is handled across the system.

Over time, this leads to more selective searches and faster convergence on materials that survive real constraints. The system improves because the decision logic governing the loop becomes better informed with continued use, with several orders of magnitude speed ups being accomplished already.

We demonstrated this behaviour through SkyVault, where carbon capture materials moved from generative design through synthesis and experimental validation in six months. More important than the timeline was what it showed about the system’s ability to operate continuously and improve with experience.

The impact is now

In 2025, we deployed the system across several industrial environments where materials performance sets hard limits on progress. These deployments are long-lived by design: the system is embedded within partner workflows and continues to learn as programmes evolve.

In mobility, we started work with **Hyundai Motor Group on next-generation materials for future vehicle platforms, where durability, manufacturability, and lifetime are tightly coupled. In water treatment, we deployed with [Kemira](https://www.kemira.com/news-and-stories/newsroom/releases/kemira-and-cuspai-forge-strategic-partnership-to-pioneer-ai-driven-materials-innovation/) to develop materials capable of selectively removing PFAS under real operating conditions. In catalysis, we began deployments with Topsoe**, where incremental improvements in materials efficiency translate directly into large-scale industrial and energy-system impacts.

We also deepened our deployment with Meta, including joint work with the Georgia Institute of Technology, to expand the space of candidate materials for carbon capture and to release datasets that support broader research in the field.

Alongside these efforts, we significantly expanded deployments in semiconductors and advanced compute. As device scaling slows, progress increasingly depends on materials choices across logic, memory, interconnects, packaging, power delivery, and thermal management. These problems are characterised by narrow feasibility windows and strong coupling between physical performance and manufacturing constraints.

In this setting, systems that accumulate knowledge about which materials are both performant and compatible with real processes develop a structural advantage. Learning carries forward across programmes, rather than resetting each time. We’ll share more of this work in 2026.

We were also grateful for public recognition from Jensen Huang, who mentioned CuspAI as one of the AI companies he’s excited about when speaking alongside UK Prime Minister Keir Starmer.

Scaling

In September, we closed a $100M+ Series A, led by Temasek and NEA with strong backing from NVIDIA, alongside our Seed investors and strategic angels. Their support reflects a shared belief that deployed learning systems for the physical world will define the next phase of AI.

Our team has grown across Cambridge, Amsterdam, Berlin, and Tokyo, and we welcomed Professor Aron Walsh as Chief Scientific Officer. We were also joined by advisors including Lord John Browne, former CEO of BP, and Martin van den Brink, former CTO and President of ASML, alongside existing advisors Geoffrey Hinton, Yann LeCun, Kristin Persson, and Verity Harding.

In January, we will open our London Kings Cross office.

20x in 21 Months

Both the size of the CuspAI team, and the value of our contracts, have grown twentyfold in just 21 months. Our team is operating at a pace that would typically require a company several times larger. That is a direct consequence of being AI-native from the start, building learning and decision systems into the core of how work gets done.

As those systems continue to learn in deployment, the pace only further accelerates.

In 2026, we’ll continue to extend the reach of this materials intelligence layer across domains and geographies. We’ll also share more of our work in semiconductors, where the interaction between deployed learning systems and manufacturing reality is becoming increasingly central.

Our focus for the year ahead remains unchanged. Deliver.

— Dr Chad Edwards & Prof Max Welling, Co-founders, CuspAI

Here’s a summary of CuspAI’s 2025 milestones:

Partnerships:

• At the AI in Science Summit, CuspAI announced a collaboration with Topsoe and the Technical University of Denmark for creating more efficient and sustainable catalysts.
• CuspAI announced a strategic partnership with Hyundai Motor Group, working to enhance the efficiency, durability, and stability of next-generation materials for future smart mobility solutions.
• CuspAI partnered with **Kemira** to target the removal of PFAS (forever chemicals) from water.
• As part of our carbon capture partnership with Meta, we collaborated with them and the Georgia Institute of Technology on a research paper and dataset to increase the number of atomic structures for potential carbon capture materials by orders of magnitude.

Global footprint and talent:

• We hired preeminent materials scientist and Imperial College London Professor Aron Walsh as our Chief Scientific Officer.
• We’re opening our first site in London.
• This adds to our global footprint including Amsterdam, Cambridge, Tokyo and Berlin.
• We’ve welcomed new leaders onto our advisory board:

1. Lord John Browne, former BP CEO
2. Martin van den Brink, ex-CTO and president of ASML

• They join existing members, the Nobel Laureate Geoffrey Hinton; Meta’s outgoing Chief AI Scientist Yann LeCun; Berkeley Distinguished Professor Kristin Persson; and former Google DeepMind ethics lead Verity Harding
• We continue to hire — you can see our open roles here and follow updates from the team on our LinkedIn page.

Capital:

• We announced a $100 million+ Series A funding round, co-led by US fund New Enterprise Associates (NEA) and Temasek, with participation from NVentures (NVIDIA’s venture capital arm), Samsung Ventures, Hyundai Motor Group, and returning investors.
• This follows our $30 million Seed round in 2024 led by Hoxton Ventures, with significant participation from Basis Set Ventures and Lightspeed Venture Partners.

Recognition and Awards:

• CuspAI was one of the frontier AI companies named in the UK government’s recently published AI for Science strategy and we’re among the first to get significant access to the Isambard AI supercomputer.
• The World Economic Forum (WEF) selected CuspAI for its Technology Pioneers program
• TechCrunch Disruptors60 list
• Sifted ranked CuspAI No 1 on their AI 100 list of rising European AI stars
• Founders Forum named us an FF Rising Star
• We won Business of the Year at the Business Weekly awards in Cambridge
• Norrsken included CuspAI in their annual Impact/100, projecting our logo in New York’s Times Square
• Accel featured CuspAI on their list of top 100 AI and Cloud companies in its Globalscape report

Media highlights:

• Fortune’s Jeremy Kahn wrote about the incredible AI/ML talent joining CuspAI
• Chad was interviewed by Robert Peston and Steph McGovern on The Rest is Money podcast
• Chad was interviewed on the Times Tech podcast
• Our fundraise was covered by outlets including Fortune, Bloomberg TV, Axios Pro Rata and more
• Chad was interviewed by Maddyness about co-founding CuspAI
• The Economist wrote about Cusp in their deep dive on AI for materials science
• Handelsblatt published an OpEd by Chief Strategy Officer Markus Hoffman on the physical AI opportunity
• Max was interviewed by De (Financiële) Telegraaf
• UK AI minister Kanishka Narayan called out Cusp’s “world-changing work” when he launched the AI for science strategy, and UK science minister Sir Patrick Vallance gave CuspAI a shout out on The Rest is Money
• *Check out our CuspAI Newsroom page.*