Three Big Ideas #57
Bot-strapping businesses, proactive policymaking, and scoring science spillovers
Welcome to our fortnightly Three Big Ideas roundup, in which we serve up a curated selection of ideas (and our takes on them) in entrepreneurship, innovation, science and technology, handpicked by the team.
🤖 Philip Salter, Founder
That vast tracts of our lives are now conducted in concert with and through digital technology is taken for granted by anyone growing up today. For the next generation, the same will be true of artificial intelligence. But, for those of us living through this paradigm shift, the uncertainty can be disconcerting.
Anthropic recently published findings from a large-scale qualitative study of more than 80,000 Claude users across 159 countries. As you might expect, the headline results are mixed: 28% cite economic empowerment as a benefit, while 18% fear displacement.
I will focus on the findings most relevant to entrepreneurship and the UK, though there are broader insights worth digging into.
Globally, 8.7% of users identify AI’s primary promise as helping them build and scale businesses. This is especially pronounced in Africa, South and Central Asia, the Middle East and Latin America, where AI is viewed as a capital bypass mechanism — in other words, a way to start businesses without traditional funding, hiring or infrastructure.
Independent workers and entrepreneurs appear to be the clearest economic beneficiaries. Nearly half report tangible gains from AI, compared with just 14% of institutional employees (47% vs 14%). Those running side projects benefit most, with 58% reporting economic gains.
While the report does not drill down specifically into the UK, it does reveal that Western European sentiment is slightly less positive than the global average (65% vs 67%). Concerns in the region centre on surveillance, privacy and governance. In developed economies like the UK, “life management” resonates more strongly than entrepreneurship, with users more likely to see AI as a tool for managing already complex lives.
This last point may come to matter a lot.
Nobody truly knows how quickly AI will come to dominate, but based on recent performance, those at the most bullish end shouldn’t be discounted out of hand. This will unlock economic growth, but also disrupt much of the status quo. Those with the skills and mindset to build with AI will flourish, as will the countries in which they live.
💊 Eamonn Ives, Research Director
In our latest UK AI Fieldbook interview, Murat Tunaboylu explained how artificial intelligence holds extraordinary promise for novel drug discovery.
One question I was particularly keen to get Murat’s thoughts on concerned whether or not our regulatory system was ready to handle a potential coming tidal wave of innovation. If AI does radically accelerate drug discovery, regulators could be inundated with approvals. In this world, innovation might be happening in one sense, but, in a more meaningful one, the benefits will lie dormant, waiting until a regulator gives groundbreaking drugs the green light.
As it happens, Murat was optimistic that we will avoid a backlog. But it nonetheless got me wondering about how we might be able to tweak regulatory approval systems to future-proof against sclerosis — in healthcare, and in other areas too.
One thing we discussed was the Medicines and Healthcare products Regulatory Agency’s pioneering approach to regulating the ‘processes’ involved in developing drugs, rather than individual drugs themselves. A not too far-fetched analogy here might be that if you asked a half-decent chef to cook you a meal from scratch, you’d probably trust them to make something edible simply by relying on tried and tested culinary techniques, rather than needing to forensically inspect whatever dish they eventually plate up for you.
An obvious riposte to this approach to regulation is that it could result in lower safeguards for things we consider extremely important. There may well be reasons why in some industries we very much do want to closely monitor final products and ensure they’re safe for their intended uses — be that drugs, or food, or anything else.
Perhaps we should therefore look to apply it in areas where there’s little to lose from a loosening of standards. In healthcare, this may be allowing companies working on hitherto ‘untreatable’ diseases to offer trials to patients who currently have no alternative. In logistics, it might be giving a longer leash to autonomous vehicles or drones operating in remote areas far from any human population centres. In education, it may be giving AI-powered teaching assistants to pupils for whom conventional teaching is unsuitable.
As more and more startups harness AI to advance innovation, an increasingly binding constraint on the good it could do will be our regulatory state. Now is the time to start thinking about how we can ensure it facilitates rather than frustrates modern miracles.
🧪 Mann Virdee, Head of Science and Technology
Some scientists are deeply committed to the idea of research as a good in and of itself — that is, simply to advance our understanding of the cosmos, even when its utility in our daily lives seems limited. For others, there is a strong societal dimension — they want their research to have a strong impact, such as in tackling climate change or in improving prosperity.
But that is a false dichotomy. Research can have spillover effects in all kinds of ways that may not initially be apparent.
So, how can we measure the spillovers from science into commercialised technologies? It’s an important question because quantifying spillovers helps us to understand the social returns from science and it’s useful for those designing policy.
That’s at the heart of a new discussion paper from the Centre for Economic Performance. The challenge is that the value scientific research generates in downstream technologies is diffused through chains of follow-on research.
The authors of this discussion paper propose a new measure called Science Rank, which uses combined patent and paper citations to assign a share of the private value of patented inventions to the scientific literature they directly (or indirectly) rely on.
The authors show that Science Rank outperforms other measures, such as patent-to-paper citation counts, in identifying influential scientific research. That’s because traditional metrics only count direct links from patents to papers. This is a bit like judging a tree by looking only at the trunk. It ignores the roots, the vast network of follow-on research that eventually leads to a breakthrough.
Science Rank is more effective at identifying the technological influence of foundational research than traditional metrics. For example, under conventional citation counts, nearly half of Nobel Prize-winning papers appear to have zero impact on technology. In contrast, Science Rank recognises the value of nearly all these prestigious papers, placing them in the top half of its distribution — with over 60% reaching the top 5%
The research also shows that the US remains the undisputed powerhouse not only in generating spillovers but also in keeping the commercial value domestic. The findings provide a more mixed picture for the UK; while we punch above our weight in generating global spillover value, a huge share of that value leaks out to foreign firms — as shown by the geography of beneficiaries.