3 things corporates can learn from PhD students

In this guest blog, Conception X’s CEO Riam Kanso shares her thoughts on what corporates can learn from PhDs.

Deloitte Ventures worked with Conception X – a deep tech venture programme that helps PhD students from across the UK turn their cutting-edge university research into viable start-ups. We shared our industry experience, whilst gaining access to deep tech innovation that could support our clients.

Fail up to success

Universities employ PhD students to set up experiments that initially do not work, refine them and then fail up to eventual success. Scientists that receive leading PhD degrees are world-class iterators. Universities design laboratories to support constant, incremental developments, and manage failure. Here, not only is failure tolerated, it is expected.

Increasingly, leading companies adopt similar structures and strategies. Large technology corporations mimic and compete with universities by investing in large R&D operations and research labs. Today, breakthrough papers in advanced subjects come not only from universities, but also from large technology companies. Just like PhD founders, successful companies are eager to experiment with failure to recognise market shifts and innovate at speed.

“I have not failed 1,000 times. I have successfully discovered 1,000 ways not to make a lightbulb,” Thomas Edison allegedly said.

We are witnessing new mindsets in the worlds of university research, start-ups, and venture capital. Sandboxes of ideas, possibilities, and failure. Places where people are expected to fail continuously on their way to success, where industry incumbents are both competitors and collaborators.

Companies that aren’t set up to manage failure are not set up to survive.

Deep tech research is key

It takes on average 5-12 years for lab innovations to become products and reach markets. The more complex the product, the longer the time horizon. By exploring the evolution of emerging technologies ahead of time and anticipating trends, PhD work can offer a glimpse into the products and markets of the future.

For instance, Conception X startup Humanloop sees the future of work as a series of collaborations between humans and automated systems. The team has developed a tool that makes machine learning more intuitive, allowing a larger pool of people to carry out tasks that could previously only be done by a narrow group of specialists. To do that, the team set out not only to understand machine intelligence, but also human intelligence, and the intersections and interactions between the two.

Automation makes the entire canon of human knowledge available instantaneously, from anywhere. Yet, rather than compete with automated systems, successful companies learn how to design tasks around technology-augmented workforces.

Competitive collaborations are the way forward

PhDs move both scientific and technical fields forward, while also striving for individual achievements and recognition. Academic departments around the world, therefore, exist in states of both collaboration and competition.

This is a theme of the machine intelligence epoch. Where do businesses collaborate, and where do they compete? Where they compete, it makes sense to keep data private, for it is part of one’s competitive advantage. Where they do not compete, they do better if they collaborate and train machine intelligence based on shared data.

This holds true across several industries. Very few companies and individuals in the world compete on energy costs, for instance. Rather, the positioning is inherently collaborative. Everyone wants to reduce energy costs. While Google’s acquisition of DeepMind continues to confound some UK financial analysts, the companies have been relatively public about DeepMind’s value to Google. DeepMind was founded by PhD students from University College London. Already, its machine intelligence systems increased data centre efficiency and reduced energy costs, generating an ROI far and above capital invested. These savings represent a material competitive advantage for Google in a field where data centre use is exploding, and energy consumption directly impacts cost, revenue, competitiveness, and profitability. Therefore, this is a proprietary development for them.

Conception X’s team Kapacity.io has built a business around the principle of competitive collaboration. They’re working on optimising energy efficiency for buildings and heat pumps. Distributing heat is a hard problem, but doing so on a low-carbon basis is also expensive.

Kapacity.io empowers real estate owners to collaboratively share energy consumption data from their buildings, helping them better manage costs and reduce carbon emissions thanks to the startup’s optimisation and machine learning technology. Despite competing in other areas, owners have a shared interest in reducing energy costs, so collaboration is the way forward.

This blog is one in a series of pieces Deloitte Ventures is publishing on our start-up ecosystem. If you’re curious to know more or discuss our work with Conception X, please reach out to helloventures@deloitte.co.uk.


Disclaimer: The views and opinions expressed in this piece are the author’s own and are not on behalf of Deloitte

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