How knowledge shapes regional innovation
Building successful regional innovation ecosystems is not primarily a question of investment. It is a question of how knowledge accumulates, recombines, and moves.
This was the starting point for a recent lecture I gave at Alliance Manchester Business School, which led to a discussion connecting directly to Manchester’s ongoing efforts to strengthen innovation and entrepreneurship. These efforts are ambitious and necessary. But their success depends on whether investment aligns with the mechanisms that govern the growth and diffusion of knowledge, a topic I explore in my latest book The Infinite Alphabet.
Getting the structure right
A useful way to see this is through contrast. In the lecture I discussed the case of Yachay, a $1 billion attempt to build a “city of knowledge” in the highlands of Ecuador. Yachay was conceived as a top-down effort to create an innovation ecosystem largely through infrastructure and planning. It serves as a cautionary tale. By contrast, Zhongguancun, an innovation district in the northwest of Beijing, grew out of an already dense knowledge environment and was later accelerated through the introduction of “guiding funds”.
The difference is not simply one of scale or governance. It is structural. Yachay attempted to create knowledge where little existed. Zhongguancun scaled knowledge that was already there. Innovation ecosystems are not built from scratch. They grow out of existing concentrations of capabilities, skills, and institutions. Investment can accelerate these processes, but it cannot substitute for them.
This is also where the role of scholar-entrepreneurs becomes critical. Knowledge is not only stored in books or databases. It is embodied in people. The individuals who develop ideas are often the ones best positioned to translate them into products, firms, and new industries. When that link is broken, knowledge transfer becomes slower, noisier, and less effective. When it is preserved, the process of innovation becomes more fluid and more scalable.
So what does this imply for Manchester?
One of the recurring themes in the discussion after my lecture was the importance of physical infrastructure in the city’s recent success. Over the past three decades, Manchester has invested significantly in its built environment, and this has played an important role in its transformation. The question now is how to balance continued investment in infrastructure with investment in human capital and knowledge creation.
If I were to advise Manchester's economic development strategy, one approach I would consider is the creation of an endowment-like structure for innovation funding. Instead of deploying capital in a purely expenditure-based model, part of the capital could be preserved and invested, with its returns used to fund programmes that attract and retain top talent. A portion of the underlying capital could still be allocated to infrastructure, such as lab space or offices for start-ups, but the core idea would be to sustain investment in people over the long term.
This approach mirrors, in part, what we observe in many US university cities, where endowments support both physical assets and the continuous attraction of academic talent. The key point is that talent is often a binding constraint. Bringing 30 or 40 leading researchers to a city can have effects that are disproportionate to the cost, but only if those individuals are given the stability and resources they need to build teams, train students, and interact with industry.
Significance of AI
Finally, the Q&A turned to AI. As AI systems become more capable, the relative importance of what they cannot easily replicate increases. AI can process information, generate text, and even assist in discovery, but it does not automatically provide the social and professional networks required to mobilise ideas into action. It does not create trust nor the relationships that give life meaning.
This is where institutions like business schools continue to matter. Over the years, I have seen many former students go on to build companies, raise funding, and develop careers that rely heavily on the networks they formed during their studies. Business schools are not just places where knowledge is transmitted. They are environments where relationships are formed, ideas are tested, and ventures begin to take shape.
If AI leads to firms that require fewer employees, the adjustment will not simply be a reduction in work. It will require a reorganisation of economic activity. Fewer employees per firm implies the need for more firms. And more firms implies more entrepreneurship.
Innovation ecosystems, in that sense, are not just about producing knowledge. They are about creating the conditions in which knowledge can be mobilised, scaled, and sustained over time.
