Riam Kanso is the founder and CEO of Conception X, a company that helps PhD students go to market.
Tell us a bit about the idea and history behind Conception X
Our mission is to identify talented PhD students and transform them into venture scientists. We define this as researchers who take cutting-edge science from the lab and translate it into scalable tech products and services. We work at a very early stage, helping to bridge the gap between academia and commercial innovation.
I experienced these challenges firsthand. When I did my PhD at Oxford in 2009, I saw how long it took for research to leave the lab. By the time something was published, it was often just information that researchers moved on from. With Conception X, we focus on identifying scientific discoveries that few people know about and figuring out how they can create real-world value.
Our approach is unique in Europe. Instead of having PhD students treat their research as purely academic, we help them repurpose their PhDs to build startups. The UK and Europe provide a great environment for this because PhD students typically have three years of funding and access to some of the most advanced labs in the world. That time can be used not just to write a thesis that few people will read, but to create something with real impact.
Up to this point, Conception X has operated as a not-for-profit. We do not take equity, and our primary goal is to accelerate the pace at which scientific breakthroughs emerge from research institutions to benefit society and the environment. Since most PhD students already have stipends and salaries, our role is to help them secure funding from other sources to bring their research to market.
A weekly dispatch featuring exclusive interviews with deep tech founders & a roundup of the most important deep tech news.
Tell us about how you find and recruit the right PhD candidates?
In the past seven years, Conception X has grown significantly from our start at University College London. We now recruit from over 60 universities, and 450 teams have gone through our program. When students start, they don’t yet have companies, but many incorporate during the program. So far, this has resulted in the creation of 150 new deep tech startups. We recently completed recruitment for our latest cohort, which is 25% larger than last year. This year, we have 125 teams, and we expect around 50 new startups to emerge from this group.
Recruitment is a major focus for us. We actively go on-site, hold information sessions, and speak with PhD students about their ideas to assess whether they are a good fit for the program. We also do a lot of education around deep tech commercialization. Attitudes in academia have shifted. Universities are under increasing pressure to adapt. Traditionally, PhDs were expected to focus solely on research and publishing papers. But with funding becoming more limited and academic job opportunities shrinking, professors are more open to alternative paths for their students. Universities also see a greater win when a PhD student launches a deep tech company.
The sooner we find PhD students, the more their startup journey becomes intertwined with their research. The great thing about this approach is that if the startup does not work out, nothing is lost. They still get their PhD, and at worst, they leave having reframed their research through a more value-based perspective.
How do you evaluate which research projects have real commercial potential?
We have a structured process. We interview every applicant and assess them based on four main criteria. The first is the founder or founding team. How promising are they, even if they have no prior entrepreneurial experience. We look for leadership qualities, whether that comes from leading a research project or even being the captain of a sports team. What matters is that they have experience in people-facing roles and can take initiative.
The second factor is the scientific pedigree. We recruit from leading research labs, and we ensure that all projects are grounded in serious science. Third, we assess how innovative the technology is and whether there is a real market for it. And finally, we consider how big the idea can be. What is the potential to scale?
How does Conception X help PhD students develop the skills they need to lead a startup?
There are a few important things to consider. One reason we focus on PhD students is that they are still at a stage in their careers where they are highly coachable. They have developed strong research skills and leadership within their labs, but they are still adaptable enough to learn new ways of thinking and communicating. We can help with skills that are essential for founders.
We don’t believe that every technical founder needs a business co-founder to take charge. Some of our top teams that have raised Series A rounds are led by scientist-founders. The landscape is shifting. Scientists today do not have to “dumb down” their work as much as they did ten years ago to secure funding. Communication is still key, but investors now have a better understanding of deep tech.
Ultimately, it is easier for a quantum physicist to learn business skills than for a businessperson to suddenly gain deep technical expertise. Entrepreneurship is not for everyone, and we are not targeting all PhD students. We focus on those who are not just doing research for the sake of publishing but who have the potential to take their discoveries and build something impactful.
What does Conception X process of helping PhD students develop entrepreneurial skills look like?
Everyone starts on the same track, and it functions like a mini deep tech MBA. We teach core concepts like TRLs, how to assess where their technology stands, how to find and speak to customers, and how to iterate using their existing scientific mindset of testing and experimentation.
But we apply these principles in the context of a startup. So we are teaching them how to prototype early, engage with potential users, and present their product. They also learn how to build teams, understand equity and intellectual property, avoid common mistakes, and navigate regulatory landscapes. We cover the differences between selling to businesses versus consumers, as well as broader commercialization strategies.
Once they have mastered the basics, we identify who is serious about launching a startup. At that stage, they apply to the startup track, committing to incorporating their company by the end of the year. By then, their intellectual property (IP) situation is usually worked out—not always seamlessly, but never as a major blocker. Their university is aware of their plans, and from that point, we select the most promising teams for more tailored support.
This is when the program becomes highly specialized. Our selected startups receive one-on-one coaching, industry mentorship, as well as legal and fundraising guidance. With a strong network of partnerships in place, we ensure that founders receive the targeted support they need to scale successfully.
How do you help PhD students navigate intellectual property challenges when spinning out startups?
Most of our participants are still from the UK, but this year, 40% of our cohort comes from Europe. We have teams from Ireland, Italy, and Portugal, along with a few from other universities that join organically.
The landscape for IP and spinouts in Europe is evolving, but change takes time. Many universities were originally set up for a licensing model, particularly for biotech, where research is patented and then licensed to a company with a separate CEO. The idea of PhD students launching spinouts in a startup-like model, especially when no patents are involved, is still relatively new compared to the long history of university research.
In the UK, there has been a significant push in recent years to make spinout rules more attractive for founders. However, policies vary widely between universities. Some offer fair terms, while others claim 50% equity in a startup, making it uninvestable. A unique aspect of the UK system is that PhD students exist in a grey area. Some universities classify them as students, while others treat them as staff, similar to the European model. Where they are considered students, they typically own their IP. This creates a significant opportunity.
What shifts do you see among PhD students and the deep tech ecosystem?
There have been significant shifts, particularly in how investors and the broader community view PhD-led startups. Seven years ago, there was still skepticism—people would ask, “Where is the business co-founder?” That is changing. PhD-led startups are becoming more mainstream, and VCs are starting to take an interest much earlier in their journeys.
Another major shift is that PhD students themselves are more aware of their entrepreneurial options. In 2018, when I gave information sessions and asked who was interested in starting a company, only one or two hands would go up. Now, half the room raises their hand. Part of this is generational—they are more open to entrepreneurship and alternative career paths.
Universities are also launching more initiatives to support spinouts. However, the broader landscape is evolving in unpredictable ways, especially with AI. The way research is conducted and shared is changing. A PhD student typically spends their first year reviewing literature and understanding what has already been done. With AI and open research, that process is becoming much faster. While AI will not replace the entire research process, it dramatically accelerates information gathering and summarization.
Also, the idea that deep tech companies take seven years to exit is now outdated. Everything is accelerating in ways we do not fully understand yet. The question is whether we are optimists about this change—will these innovations lead to breakthroughs in energy, climate solutions, and other fields? Things are moving incredibly fast, and in five years, the landscape could look very different. It has already changed significantly since we started Conception X, and I expect the pace of change to increase.
How do you define deep tech?
That is always a topic of conversation. Deep tech refers to scientific discoveries and technologies that form the foundation of radically new products and services. It is technology based on a meaningful scientific breakthrough.
One of the reasons deep tech is hard to define is that it is constantly evolving. What was considered deep tech 10 years ago might now be an off-the-shelf product. The key factor is that deep tech involves an element of research and development (R&D) before it can be commercialized. If a technology still requires time and refinement before it is ready for the market, it is likely deep tech.