Combinatorial innovation and technological progress in the very long run

Source: Matt Clancy/Substack, Nov 2021

Strange Dynamics of Combinatorial Innovation

In Weitzman (1998), innovation is a process where two pre-existing ideas or technologies are combined and, if you pour in sufficient R&D resources and get lucky, a new idea or technology is the result. Weitzman’s own example is Edison’s hunt for a suitable material to serve as the filament in the light bulb. Edison combined thousands of different materials with the rest of his lightbulb apparatus before hitting upon a combination that worked. But the lightbulb isn’t special: essentially any idea or technology can also be understood as a novel configuration of pre-existing parts.

An important point is that once you successfully combine two components, the resulting new idea becomes a component you can combine with others. To stretch Weitzman’s lightbulb example, once the lightbulb had been invented, new inventions that use lightbulbs as a technological component could be invented: things like desk lamps, spotlights, headlights, and so on.

That turns out to have a startling implication:

combinatorial processes grow slowly until they explode.

For Weitzman, innovation is a purposeful pairing of two components, but for Koppl, Devereaux, Herriot, and Kauffman, this is modeled as a random evolutionary process, where there is some probability any pair of components results in a new component, a lower probability that triple-combinations result in a new component, a still lower probability that quadruple-combinations result in a new component, and so on.

They show this simple process generates the same slow-then-fast growth of technology.

If random tinkering is allowed to happen, with or without a profit motive, then you can get a phase-change in the technological trajectory of a society once the set of combinatorial possibilities grows sufficiently large.

The reason technological progress does not accelerate in all times and places is because in addition to ideas, Weitzman assumes innovation requires R&D effort. In the beginning, we will usually have enough resources to fully fund the investigation of all possible new ideas.

So long as that’s true, the number of ideas is the main constraint on the rate of technological progress and we’ll see accelerating technological progress. But in the long run, the number of possible ideas explodes and growth becomes constrained by the resources we have available to devote to R&D, not by the supply of possible ideas.

Why might technological progress be exponential?

Jones (2021) maps out an alternative plausible scenario. So far we have assumed some ideas are “useful” and others are not, and progress is basically about increasing the number of useful ideas. But this is a bit dissatisfying. Ideas vary in how useful they are, not just if they’re useful or not. For example, as a source of light, the candle was certainly a useful invention. So was the light bulb. But it seems weird to say that the main value of a light bulb was that we now had two useful sources of light. Instead, the main value is that light bulbs are a better source of light than candles.

Instead, let’s think of an economy that is composed of lots and lots of distinct activities. Technological progress is about improving the productivity in each of these activities: getting better at supplying light, making food, providing childcare, making movies, etc. As before, we’re going to assume technological progress is combinatorial. But we’re now going to make a different assumption about the utility of different combinations. Instead of just assuming some proportion of ideas are useful and some are not, we’re going to assume all ideas vary in their productivity. Specifically, as an illustrative example, let’s assume the productivity of combinations is distributed according to a normal distribution centered at zero.

Productivity of new combinations

This assumption has a few attractive properties. First off, the normal distribution is a pretty common distribution. It’s what you get, for example, if you have a process where we take the average of lots of different random things, each of which might follow some other distribution. If technology is about combining lots of things and harnessing their interactions, then some kind of average over lots of random variables seems like not a bad assumption.

Second, this model naturally builds in the assumption that innovation gets progressively harder, because there are lots of new combinations with productivity a bit better than zero (“low hanging fruit”), but as these get discovered the share of combinations with better productivity get progressively less common. That seems sensible too.

the point of Jones’ paper is to show these processes balance each other out.

Under a range of common probability distributions (such as the standard normal, but also including others), finding a new technology that’s more productive than the current best gets explosively harder over time.

However, the range of options we have also grows explosively, and the two offset each other such that we end up with constant exponential technological progress. Which is a pretty close approximation to what we’ve observed over the last 100 years!

Jones has a different notion of R&D in mind.

In Jones’ model, we still need to spend real R&D resources to build new technologies. But it’s sort of a two-stage process, where we costlessly sort through the vast space of possibilities and then proceed to actually conduct R&D only on promising ideas.

As a mathematician, Poincaré is aware of the fact that the space of possible combinations is astronomical. Mathematical creation is about choosing the right combination of mathematical ideas from the set of possible combinations.

Long-run Growth and AI

But what if there are limits to this process? Human minds may have some unknown process of organizing combinations, to efficiently sort through them. But there are quite a lot of possible combinations. What if, eventually, it becomes impossible for human minds to efficiently sort through these possibilities? In that case, it would seem that technological progress must slow, possibly a lot.

This is essentially the kind of model developed in Agrawal, McHale, and Oettl (2018). In their model, an individual researcher (or team of researchers) has access to a fraction of all human knowledge, whether because it’s in their head or they can quickly locate the knowledge (for example with a search engine). As a general principle, they assume the more knowledge you have access to, the better it is for innovation.

assume research teams combine ideas they have access to in order to develop new technologies. And initially, the more possible combinations there are, the more valuable discoveries a research team can make.

But unlike in Jones (2021), Agrawal and coauthors build a model where the ability to comb through the set of possible ideas weakens as the set gets progressively larger. 

Eventually, we end up in a position like Weitzman’s original model, where the set of possibilities is larger than can ever be explored, and so adding more potential combinations no longer matters. Except, in this case, this occurs due to a shortage of cognitive resources, rather than a shortage of economic resources that are necessary for conducting R&D.

As we suspected, they show that as we lose the ability to sort through the space of possible ideas, technological progress slows (though never stops in their particular model).

But if the problem here is we eventually run out of cognitive resources, then what if we can augment our resources with artificial intelligence?

Agrawal and coauthors are skeptical this problem can be overcome with artificial intelligence, at least in the long run.

They argue convincingly that no matter how good an AI might be, there is always a number of components where it becomes implausible for a super intelligence to search through all possible combinations efficiently.

If that’s true, then in the long run any acceleration in technological progress driven by the combinatorial explosion must eventually stop when our cognitive resources lose the ability to keep up with it.

To illustrate the probable difficulty of searching through all possible combinations of ideas, let’s think about a big number: 10⁸⁰. That’s about how many atoms there are in the observable universe. That would seem like a difficult number of atoms for an artificial intelligence to efficiently search over. Yet if we have just 266 ideas, then the number of possible combinations is about equal to 10⁸⁰, i.e., the number of atoms in the universe!undefined

Dynamics of Technological Progress

two kinds of process may work to stymie continued explosive growth.

Innovation might get harder. If the productivity of future inventions are like draws from some thin-tailed distribution (possibly a normal distribution), then finding better ways of doing things gets so hard so fast that this difficulty offsets the explosive force of combinatorial growth.

Exploring possible combinations might take resources. These resources might be cognitive or actual economic resources. But either way, while the space of ideas can grow combinatorially, the set of resources available for exploration probably can’t (at least, it hasn’t for a long while).

To start, it seems to me that it must take resources to explore the space of possible ideas, whether those resources are cognitive or economic.

It may be that, we are still in an era where human minds can efficiently organize and tag ideas in the combinatorial space so that we can search it efficiently (or maybe science provides a map of the combinatorial terrain).

the ultimate rate of technological progress depends on how rapidly we can increase our resources for exploring the space of ideas.

(If we need more cognitive resources, then that means resources in the form of artifical intelligence and better computers). And our ability to increase our resources with new ideas is a question that falls squarely in the domain of Jones (2021): how productive are new ideas?

suppose the productivity of new ideas follows a fat-tailed distribution.

That’s a world where extremely productive technologies – the kind that would be weird outliers in a thin-tailed world – are not that uncommon to discover.

Well, in that world, Jones (2021) shows that the growth rate of the economy will be faster than exponential, at least so long as it can efficiently search all possible combinations of ideas.

And faster than exponential growth in resources is precisely what we would need to keep exploring the growing combinatorial space.

Slowed canonical progress in large fields of science

Source: PNAS, Oct 2021

The size of scientific fields may impede the rise of new ideas.

Examining 1.8 billion citations among 90 million papers across 241 subjects, we find a deluge of papers does not lead to turnover of central ideas in a field, but rather to ossification of canon. Scholars in fields where many papers are published annually face difficulty getting published, read, and cited unless their work references already widely cited articles.

New papers containing potentially important contributions cannot garner field-wide attention through gradual processes of diffusion.

These findings suggest fundamental progress may be stymied if quantitative growth of scientific endeavors—in number of scientists, institutes, and papers—is not balanced by structures fostering disruptive scholarship and focusing attention on novel ideas.

a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance.

The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas.

Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory.

When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work.

These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

Prediction

we predict that when the number of papers published each year grows very large, the rapid flow of new papers can force scholarly attention to already well-cited papers and limit attention for less-established papers—even those with novel, useful, and potentially transformative ideas. Rather than causing faster turnover of field paradigms, a deluge of new publications entrenches top-cited papers, precluding new work from rising into the most-cited, commonly known canon of the field.

These arguments, supported by our empirical analysis, suggest that the scientific enterprise’s focus on quantity may obstruct fundamental progress. This detrimental effect will intensify as the annual mass of publications in each field continues to grow—which is almost inevitable given the entrenched, interlocking structures motivating publication quantity. Policy measures restructuring the scientific production value chain may be required to allow mass attention to concentrate on promising, novel ideas.

We predict that when fields are large, the dynamics change. The most-cited papers become entrenched, garnering disproportionate shares of future citations. New papers cannot rise into canon by amassing citations through processes of preferential attachment. Newly published papers rarely disrupt established scholarship.

when many papers are published within a short period of time, scholars are forced to resort to heuristics to make continued sense of the field. Rather than encountering and considering intriguing new ideas each on their own merits, cognitively overloaded reviewers and readers process new work only in relationship to existing exemplars (16⇓–18).

A novel idea that does not fit within extant schemas will be less likely to be published, read, or cited. Faced with this dynamic, authors are pushed to frame their work firmly in relationship to well-known papers, which serve as “intellectual badges” (19) identifying how the new work is to be understood, and discouraged from working on too-novel ideas that cannot be easily related to existing canon.

The probabilities of a breakthrough novel idea being produced, published, and widely read all decline, and indeed, the publication of each new paper adds disproportionately to the citations for the already most-cited papers.

Example

when the field of Electrical and Electronic Engineering published ∼10,000 papers a year, the top 0.1% most-cited papers collected 1.5% and the top 1% most-cited collected 8.6% of total citations.

When the field grew to 50,000 published papers a year, the top 0.1% captured 3.5% of citations, and the top 1% captured 11.9%.

When the field was larger still with 100,000 published papers per year, the top 0.1% received 5.7% of citations within the field and the top 1% received 16.7%. The bottom 50% least-cited papers in contrast decreased in share as the field grew larger, dropping from garnering 43.7% of citations at 10,000 papers to slightly above 20% at both 50,000 and 100,000 papers per year.

Discussion

These findings suggest troubling implications for the current direction of science. If too many papers are published in short order, new ideas cannot be carefully considered against old, and processes of cumulative advantage cannot work to select valuable innovations.

The more-is-better, quantity metric-driven nature of today’s scientific enterprise may ironically retard fundamental progress in the largest scientific fields. Proliferation of journals and the blurring of journal hierarchies due to online article-level access can exacerbate this problem.

Reducing quantity may be impossible. Proscribing the number of annual publications, shuttering journals, closing research institutions, and reducing the number of scientists are hard-to-swallow policy prescriptions.

Even if a scientist wholeheartedly agreed with the implications of our study, curtailing their output would be impractical given the damage to their career prospects and those of their colleagues and students, for example. Limiting article quantity without altering other incentives risks deterring the publication of novel, important new ideas in favor of low-risk, canon-centric work.

Still, some changes in how scholarship is conducted, disseminated, consumed, and rewarded may help accelerate fundamental progress in large fields of science. A clearer hierarchy of journals with the most-prestigious, highly attended outlets devoting pages to less canonically rooted work could foster disruptive scholarship and focus attention on novel ideas.

Reward and promotion systems, especially at the most prestigious institutions, that eschew quantity measures and value fewer, deeper, more novel contributions could reduce the deluge of papers competing for a field’s attention while inspiring less canon-centric, more innovative work.

A widely adopted measure of novelty vis a vis the canon could provide a helpful guide for evaluations of papers, grant applications, and scholars. Revamped graduate training could push future researchers to better appreciate the uncomfortable novelty of ideas less rooted in established canon. These measures, while not easy to implement across large fields, may help push scholarship off the local attractor of existing canon and toward more novel frontiers.

Declining US Productivity Reduces US GDP

Source: Marginal Revolution, Oct 2021

In Launching the Innovation Renaissance I said that “If total factor productivity had continued to grow at its 1957 to 1973 rate then we today would be living in the world of 2076 rather than in the world of 2014.” Sadly, the future is continuing to recede.

Consider the graph below. If growth had continued at the rate expected by the CBO in 2005 then we today would be living in the world of 2037 rather than in the world of 2021. (n.b. I am eyeballing.)

By the way, don’t bother blaming this on the forecasters. The forecast was reasonable, the reality is below expectation.

Breakthrough innovations and where to find them

Source: ScienceDirect, Jan 2022

Breakthrough or radical innovations are generally regarded as ruptures along specific technological trajectories, possibly leading to shifts or transformations in the prevailing technological paradigm (Dosi, 1982). Thus, they play a crucial role in the “creative destruction” process that characterizes the long-run dynamics of technological evolution (Ahuja and Lampert, 2001).

In contrast, the literature refers to continuous or incremental innovations when the outcome of the innovation process is an improvement of the existing technology (Garcia and Calantone, 2002). While incremental innovations occur, more or less continuously, breakthrough innovations are sporadic. As Fleming puts it, in the technology landscape, “almost all inventions are useless; a few are of moderate value; and only a very, very few are breakthroughs” (Verbatim).

The identification of breakthrough innovations is mostly based on the technical merits of an innovation and most commonly carried out using patent citations. Dahlin and Behrens (2005) and Verhoeven et al. (2016) looked at the pattern of patents’ backward citations to spot breakthrough innovations based on their ex-ante characteristics. The intuition is that breakthrough patents differ in how they source and recombine existing knowledge compared with previous patents in the same field.

Other studies classify inventions as breakthroughs using forward citations, as they will be cited in many follow-up patents (Ahuja and Lampert, 2001; Fleming, 2001).

a dataset comprising 138,467 patents filed between 1976 and 2013, with 17,176 patents meeting our criteria for breakthrough innovations ((Capponi et al., 2021). Relevant robustness checks confirm the validity of our method and the quality of the dataset.

The sources of breakthrough innovations

From an evolutionary perspective, breakthrough innovations emerge from a process of recombinant search and selection: technical novelty is associated with the ability to find new recombinations of prior knowledge which are then evaluated in the selection phase.

universities and more in general public institutions can potentially create a productive research environment for single inventors, to the extent that they offer a combination of formal support and intellectual freedom.

Finally, it is interesting to notice that organizations’ experience over the past 10 years is negatively associated with the probability of a patent to be a breakthrough.

This evidence supports the incumbent curse argument whereby incumbents would be less likely than new entrants to come up with breakthrough innovations.

Conclusion

introduced a method to identify breakthrough innovations on a large scale starting from award-winning innovations.

The output of our procedure is a dataset comprising 138,467 patents filed over 37 years, of which 17,176 are classified as breakthroughs. We then exploit this dataset to investigate the sources of breakthrough innovations depending on the type of assignee and the number of inventors.

Our proposed method emphasizes the link between patent data and innovations with demonstrated technical and commercial success. Through external validation of patent value, we seek to advance research in this field by addressing the often missing connection between measures of patent value and the actual use and commercial success of such patents. It is also worth stressing that future contributions in this direction could replicate, adapt or extend our approach taking as inputs other prize schemes or different evidence of innovations’ economic and technical performance

MIT Media Lab

Source: Fast Company, Aug 2021
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if we were at our best, we’d be working across, among, and between art, science, engineering, and design. It’s always been a fascination of mine. So why the Media Lab? I can’t imagine a better place in the world to explore and be immersed in this trans-disciplinary thinking.

Four Ps.

It’s about the people.

That’s the magic, the genius, that means our entire community. Everyone does bring a sparkle, something unique we can all learn from.

It’s going to be about parity . . .

we can focus on all types of amazing things. But of those technologies and experiences we’re inventing, what will have the largest impact for society?

Then we get to play.

This is a playful place. And it needs to be. Play brings out some of the best in us in terms of taking risks, trying this, trying that. It’s not linear thinking. You have to give yourself time to think and be creative.

… the future state is “possibilities.”

This is a lab of possibilities. We have to know what we want to be in the future. And what problems we want to solve as well.

This [other idea] is bubbling up: Policy, maybe?

As [we develop] technologies and experiences for humanity, we have to think about, What are the future of governances around them? One example is our digital currency initiative. It’s amazing, but if you are going to have impact, it’s like you have to develop policy with tech.

We’re identifying, What are the tough challenges here? We’ll play in that space. If we get it right, we can hopefully have the most impact for society.

[I have] an AI platform engine curating petabytes of climate data I get every day from satellites . . . but what if you are immersed in that data, interacting with it? What if it’s education, but it’s tangible?

Innovation Cycles

Source: Zero Hedge, Jul 2021

Market Power

To the economist Schumpeter, technological innovations boosted economic growth and improved living standards.

However, these disruptors can also have a tendency to lead to monopolies. Especially during a cycle’s upswing, the strongest players realize wide margins, establish moats, and fend off rivals. Typically, these cycles begin when the innovations become of general use.

Of course, this can be seen today—never has the world been so closely connected. Information is more centralized than it has ever been, with Big Tech dominating global search traffic, social networks, and advertising.

Like the Big Tech behemoths of today, the rail industry had the power to control prices and push out competitors during the 19th century.

At the peak, listed shares of rail companies on the New York Stock Exchange made up 60% of total stock market capitalization.

Imagination @ Work

Source: HBR, Jun 2021

how companies reinvent themselves to achieve success. And he has found that an essential ingredient in that process is imagination. It’s something we cultivate in children but rarely practice deliberately in the business world.

Our quant research says that while the performance of large corporations may be attractive, that the growth potential tends to decline over time. So what we call the vitality, which is the potential for future growth declines by three percentage points for every doubling in the scale or size.

So essentially, the stereotype which is true on average, is that large corporations are great at exploiting yesterday’s products of innovation, but not necessarily renewing that legacy. 

in the past that large companies were primarily focused on exploiting yesterday’s successful recipe because it was a reasonable assumption that the advantage in that recipe would persist over time, that’s no longer a reasonable assumption in most businesses.

So, large corporations, if they are to possess need to be constantly reinventing themselves, and therefore, we do have to ask ourselves, what is this innate human tendency, and how can we practice it somewhat systematically?

a structure around imagination, which is something that’s so organic and hard to contain. It starts with generation. Where do ideas come from?

it all starts with surprise. And the surprise comes in different flavors. It’s an anomaly, something that doesn’t fit with what we expected. It’s an accident. We were trying to do something and then something else happened. Or it’s an analogy, you say, “This is a bit like that other thing, which is a surprise, a conceptual surprise.”

seeking surprise is part of this first stage that we call the seduction, the reason to reimagine.

hen we see these anomalies, of course, the anomaly doesn’t tell you what to do, it doesn’t tell you what to think, it’s just a trigger.

So we have to reflect and comprehend what might this mean. And that’s a constructive act in the sense that the reality is not inevitable, it’s a signal of possibility, but we have to supply the imagination as to what this could mean.

quite hard for companies to entertain thoughts that go against their current business model. And it’s easy to understand why because you’re stepping into the unfamiliar, you’re stepping into the risky, you’re questioning the basis for your success. Sometimes there are taboos about doing that.

One of the antidotes to that, and a very important theme throughout the book is the importance of plays.

play is de-risked accelerated learning.

We suggest 12 managerial games in the book that can be used to playfully create awareness that our mental models are just models and playfully entertain alternatives and to try on different modes of thinking and to break this linkage between what you think and what you do, because thinking is free, executing on thinking is expensive but we need to dealing with the two.

6-Step Framework

one of the stages in harnessing imagination is step two in our six-step framework, which is working the idea. So it’s very important to work a mental model. You see the anomaly, you think about what it might mean, and you evolve it, and you apply constraints and you release constraints, and you recombine the elements.

stage three, which we call the collision, where you re collide the evolved idea, the idea for new business with reality. There, again, the idea gets tested in the real world. And that’s a subtle process because on the face of it, what’s going on is you’re testing an idea for validity. And you are doing that, but also you’re generating new surprises. There are things about reality that you discover that are then fodder to reimagine further. So that’s physical and mental.

the social stage, where the idea has to leap from one mind to another. And that often involves something that you can point to, which is a prototype or an experiment in the real world.

That’s how we communicate ideas because, of course, I can’t directly see the idea that you’re thinking about. It’s what philosophers call the intersubjectivity problem. We have to be sure that what I’m imagining is what you’re imagining. So that is mind to mind via reality.

codifications, that’s extremely down to earth, that’s about, what are the things that employees have to do to replicate the success? And you have to restrict that to the essential elements

Imagination for Career Development

making sure that you’re building a career that has multiple stages of experience. And it’s also partly about creating a sufficient stock of and facility with mental models.

What stops us from seeing the surprises and the inspirations which somebody will pick up, one of our competitors will likely pick up. What stops us from seeing that, it’s curiosity, it’s range of experience, a number of mental models that we can apply to any situation is practicing so-called counterfactual thinking, think about things that are not the case that could be the case. It’s a skill of listening and communicating and entertain the best case for ideas, early-stage ideas, which are still very fragile, and doing that in a team context.

Related Resource:

https://www.bcg.com/featured-insights/imagination-machine

 

 

Involution: 996 ICU

Source: FT, Jun 2021

Forced ranking is just one of many recent flashpoints that have generated scrutiny of workplace practices at China’s large tech companies. Allegations of overwork, abuse and injury have become the subject of heated nationwide debate. Last December, the deaths of two young staffers at one of Alibaba’s competitors, Pinduoduo, fuelled the fire. (One collapsed on the way home from work. The other died by suicide.)

Such working conditions compelled Chinese software developers to launch a campaign to raise international awareness of their working conditions, christened “996 ICU”. The name refers to a colloquial saying among tech workers that if you work 9am to 9pm, six days a week, as some managers demand, you’ll end up in intensive care. Over the past two years, 996 ICU has crowdsourced allegations of mistreatment from employees at more than 200 Chinese companies.

Even for the large number of engineers working under less brutal conditions, a pervasive sense of the drudgery of uncreative, repetitive tech work has led them to self-identify with manual and agricultural labourers. Many refer to themselves as “code peasants”, and the most common nickname for the tech giants as a group is “big factories”.

Netizens have latched on to a newly fashionable term: “involution” (nei juan). Anthropologist Clifford Geertz popularised the word in the 1960s in work describing unusual aspects of one rural economy in Java, Indonesia. Geertz posited that, over the course of centuries, ever-increasing amounts of labour had poured in but output remained constant. No innovation had occurred. Geertz called this involution.

In 2018 and 2019, the term became popular in China thanks to the writing of Cao Fengze, a PhD student in China’s top school of engineering at Tsinghua University. Cao’s posts chronicled intense, zero-sum competition between classmates for admission to elite universities. He argued the result of so much competition for so few spaces is that students work harder and harder without gain.

“Every effort is nothing other than an attempt to push another snowflake over the edge,” Cao wrote, alluding to the proverb “every snowflake in an avalanche is responsible”. For him, involution is catastrophe.

Cao, whose hundreds of thousands of online followers call themselves the “Cao School”, believes the root of involution is the struggle over limited resources. The metaphor of resource competition is one of the most pervasive in Chinese society. Discussions about the country’s problems tend to culminate in the assertion, “there are too many people in China”

Yet for all the glorification of innovation, a lack of creative work is common for the vast majority of technical employees. “They don’t have the time to come up with ideas or control the direction of their work,” says one ByteDance employee. “They have so much mental load from the tasks they have in the narrow scope they’re given, that they don’t have the latitude to think about anything extra.”

According to a Tencent manager, squeezing employees’ time is often an intended outcome of the management system, rather than an inefficiency. “When we consider why some products succeed, it’s not necessarily because they have better technology,” he says. “It’s because they simply have more people labouring away.” Most developers, he says, are in fact completing repetitive tasks known as “create, read, update and delete” work, such as changing the minute details of a user interface over and over.

“The growth of China’s tech giants has not come from true innovation but from labour intensity. It’s very difficult to automate certain parts of the software sector,” says Xiang Biao, a professor of social anthropology at Oxford university.

“Forced ranking is not about efficiency or fair reward, but about control. This single method destroys all solidarity between peers. It generates obedience and fear towards the person above.”

Tim, the recent Alibaba hire who spent years competing to get there, describes the company’s values as “more like ‘chicken soup’”, slang for a meaningless story told merely to soothe the listener. Another term he uses in describing the relation of older managers to younger employees is “PUA”, short for pick-up artist. He and his colleagues mostly use it to describe managers who over-promise opportunities for advancement to the point of creating a dishonest or even abusive relationship. PUA is another neologism. In China, the linguistic space for complaining about your boss has been expanding lately.

In 2019, the highly renowned Peking University revealed that, of all the graduates that year who had signed university-approved employment agreements, 17 per cent went into China Communist party work or other parts of government, up from 11 per cent four years prior. In total, counting those who had gone into state-administered institutions and state-owned enterprises, three-quarters of graduates had gone “into the system”. In the same period, the proportion of graduates going into private businesses such as tech had been halved.

More Science Leads to More Innovation

Source: Matt Clancy/Substack, Mar 2021

if you want more technological innovation, more science helps.

lots of inventors (not all!) say in surveys that science is an important input to their inventions and about a quarter of patents in 2018 directly cite scientific papers.

when you increase or decrease scientific output in a field, that tends to respectively increase or decrease the use of science in related technological domains.

Tabakovic and Wollman exploit this idiosyncracy in US funding mechanisms to identify universities that receive unexpected research dollar windfalls. Specifically, they look at how university support for research changes when teams outperform or underperform preseason expectations as measured by votes in the NCAA top 25 AP poll.

As the left-most panel in the figure below indicates, universities that outperform expectations (receive more votes in the poll at the end of the season than at the beginning) get more institutional funding for research in the following year.

Meanwhile, the other two panels serve as a bit of a sanity check; they indicate football performance has no impact on the receipt of federal grants or other forms of funding, which is what we would expect.

What happens when researchers get more money?

Tabakovic and Wollman estimate that a 10% increase in funding is associated with 3% more publications in the following year. And it turns out the funded science also leads to patents: they find about $2.6mn in funding for university research is associated with about one more patent.

Lastly, they can use the revenue the university earns by licensing out these patents to get a rough estimate on whether the patents are valuable enough for a private sector firm to pay for access to them. And they are – a 10% increase in funding is associated with at least a 10% increase in licensing revenues.

Azoulay et al. (2019) want to know how NIH funding for basic biological research eventually leads to private biomedical innovation.

In general, an extra $10mn for a given disease-science area is associated with 2.6 more related patents. That’s $3.8mn per patent, which is not too different from what Tabakovic and Wollman find.

Azoulay and co-authors exploit idiosyncrasies in funding to get plausibly random variation in which grants are funded and which are not.

Using this more complicated approach they find basically the same thing – a random extra $10mn results in about 2.3 more related patents. But in this case, we can more confidently assert the relationship is causal: (biological) science leads to (biomedical) innovation down the road.

Innovation is Temporal & Local

Source: Discourse Magazine, Feb 2021

Innovation is the “main event” of the modern age. It’s the reason why after millennia of comparative stagnation, the last several hundred years featured sudden, dramatic improvements in technology and therefore living standards: from steam engines to search engines, from vaccines to vaping.

It’s also a strangely localized and temporary phenomenon. At any one time, there is usually one part of the world where innovation flourishes best, attracting talent from all over: California in 1960, the U.S. East Coast in 1920, Britain in 1800, Holland in 1650, Renaissance Italy in 1500, Song China in 1000, Abbasid Arabia in 800, ancient Greece in 500 B.C., the Ganges Valley before that. These were places that were relatively wealthy, free and open to trade at the time.

British inventor James Watt’s 1775 diagram of his first working steam engine. Image Credit: Wikimedia Commons

Today, the most innovative part of the world is probably China. The days when China was a smart copier, catching up with the West by emulating its products and processes, are over. China is leapfrogging into the future.

Chinese consumers are wholly mobile in their use of the internet, floating free of fixed computers. In cities at least they no longer use cash, or even credit cards: Mobile payments are universal. Digital money, controlled by Tencent and Alibaba, is evolving fast. You mostly no longer find menus in restaurants or cash registers in shops. QR codes are used to order and pay for everything. The cost of mobile data has plummeted there faster than anybody could have imagined. In five years, the price of a gigabyte of data plunged from 240 renminbi to just one.

Firms like WeChat started out as social media companies but are now providing everything consumers want: mobile wallets, apps for ordering taxis or meals, means of paying utility bills and much more. Things that require five different apps in the West can be done in China on one. Companies like Ant Financial are reinventing financial services, with 600 million users managing not just their money but their insurance and other financial services, all through a single app.

Paying with a smartphone in a Shanghai café. Image Credit: Asia-Pacific Images Studio/Getty Images.

As for discovery and invention, China is just as innovative, plunging into artificial intelligence, gene editing, and nuclear and solar energy with a gusto that the West can only dream of. The pace of development is breathtaking: 7,000 miles of new freeways a year over the past decade; train lines and metro networks that would take decades in the West appearing in a year or two; data networks bigger, faster and more comprehensive than anywhere else. This infrastructure spending is not innovation in itself, but it surely helps it happen.

What explains this speed and breadth of innovation fury? Partly, work. Chinese entrepreneurs and their employees are dedicated to the 9–9–6 week: 9 a.m. to 9 p.m., six days a week. That was what Americans were like too when they changed the world (Thomas Edison demanded inhuman hours from his employees); and Germans when they were among the most innovative people; and Britons in the 19th century; and the Dutch and Italians before that. Willingness to put in the hours, to experiment and play, to try new things, to take risks—these characteristics for some reason are found in young, newly prosperous societies and no longer in old, tired ones.

What Does This Mean?

China has been here before, of course. During the Song dynasty (960-1279) China experienced an unparalleled flourishing of science, technology and economic growth. Woodblock printing presses served a literate elite; compasses steered traders across the seas; craftsmen turned out porcelain and silk of unrivaled quality; windmills pumped water for rice irrigation; pound locks helped barges travel up rivers; gunpowder deterred barbarian invasions; metal workers forged new alloys; paper money came into circulation. Mathematics, cartography and astronomy all flourished. The population grew, but the food supply grew faster with new rice varieties and new methods of cultivation. Cities sprang up all over the empire.

Paper currency from the Song Dynasty. Image Credit: Wikimedia Commons

The Song emperors had hit upon a formula that worked. What was it? The secret sauce was freedom. To an extent unknown under previous Chinese emperors, Song-era merchants were free to do what they wanted. The government gradually withdrew from direct involvement in the economy, leaving administration and economic decisions to the local gentry, most of whom were closely involved in trade. Internal tariffs were largely absent, so long-distance trade flourished, along with the building of canals. Corruption was suppressed, taxes were fair and peasants were able to act as consumers. It was no paradise by today’s standards, but it was a highly inventive time.

This innovation fever came to a shuddering halt at the fall of the Song dynasty. Mongol rulers, followed by the first Ming emperors, reimposed central planning to an almost farcical degree. Merchants were told what they could and could not sell and where they could and could not go. They were forced to report their inventories regularly to mandarins. Overseas trade was restricted and eventually banned. More and more power was drawn to the center. Experiment and enterprise became impossible. China sank slowly into militarized poverty.

How then can one explain the flourishing of innovation in China today? It is after all a communist country run by an unelected nomenklatura of apparatchiks, to borrow some Russian words for the mandarinate. The answer lies beneath the surface. So long as they are not trying to invent democracy, or a new political party, innovators are surprisingly free to try anything. A Chinese entrepreneur faces almost none of the delays and restrictions that a Western one does. He is not required to get permits, licences and go-aheads from multiple beadles and bureaucrats of the state. He just gets on with it: hires new researchers, builds a new production line, sets up a new company. The speed with which business decisions are taken amazes Western visitors.

The deal that Deng Xiaoping did, and his successors mostly honored, was that in exchange for a monopoly on political power, the party would leave enterprise alone. Turns out that’s all enterprise needs: a hint of freedom and off it goes. Three decades of starvation, re-education and repression under Mao Zedong had not extinguished the spirit of the Chinese innovator.

But the party may now be over. Not because the West is sick of seeing its manufacturers undercut, its intellectual property undermined and its appetite for free trade sated, but because Xi Jinping, president for life, is a different type of ruler from those who went before him. The Ming are back. You can see it in the increasingly dictatorial powers and arbitrary decisions of his henchmen. The goose that lays the golden eggs is being throttled.

The West may be slowly forgetting to allow this to happen through bureaucratic strangulation, but China will surely stifle it through political authoritarianism.

Xi wants to (in his own words) “consolidate the shared ideological foundation underpinning the concerted efforts of the entire party and all the Chinese people,” which basically means telling people what to think. In an authoritarian system it will be all too easy for incumbent businesses, even those that started out as plucky outsiders, to raise barriers to entry against innovation. China’s spell at the top of the leader board for innovation will come to an end. Maybe not this decade, but soon.