HESA

Higher Education Strategy Associates

Category Archives: Tech Ecosystems

December 08

Innovation Ecosystems: Promise and Opportunism

We sometimes think of innovation policy as being about generating better ideas through things like sponsored research.  And that’s certainly one part of it.  But if those ideas are generated in a vacuum, they go nowhere – making ideas spread faster is the second pillar of innovation policy (a third pillar – to the extent that innovation is about new product-generation – has to do with venture capital and regulatory environments, but we’ll leave those aside for now).

Yesterday, I discussed why the key to speeding up innovation was the density of the medium through which new ideas travel: basically, ideas about IT travel faster in Waterloo than in Tuktoyaktuk; ideas about marine biology travel faster in Halifax than in Prince Albert.  And the faster ideas travel and collide (or “have sex” in Matt Ridley’s phrase), the more innovation is produced, ceteris paribus.

Now, although they don’t quite use this terminology, the proponents of big universities and big cities alike find this logic pretty congenial.  You want density of knowledge industries?  Toronto/Montreal/Vancouver have that.  You want density of superstar researchers?  U of T, McGill, and UBC have that (especially if you throw in allied medical institutes).  That makes these places the natural spot to invest money for innovation, say the usual suspects.  All you need to do is invest in “urban innovation ecosystems” (whatever those are – I get the impression it’s largely a real estate play to bring scientists, entrepreneurs, and VCs into closer spatial proximity), and voila!  Innovation!

This is where sensible people need to get off the bus.

It’s absolutely true that innovation requires a certain ecosystem of researchers, and entrepreneurs, and money.  And on average productive ecosystems are likelier to occur in larger cities, and around more research-intensive universities.  But it’s not a slam dunk.  Silicon Valley was essentially an exurb of San Francisco when it started its journey to being a tech hub.  This is super-inconvenient to the “cool downtowns” argument by the Richard Floridas of this world; as Joel Kotkin has repeatedly pointed out, innovative companies and hubs are as likely (or likelier) to be located in the ‘burbs, as they are in funky urban spaces, mainly because it’s usually cheaper to live and rent space there.  Heck, Canada’s Silicon Valley was born in the heart of Ontario Mennonite country.

We actually don’t have a particularly good theory of how innovation clusters start or improve.  Richard Florida, for instance, waxes eloquent about trendy co-working spaces in Miami as a reason for its sudden emergence as a tech hub. American observers tend to attribute success to the state’s low tax rate, and presumably there are a host of other possible catalysts.  Who’s right?  Dunno.  But I’m willing to bet it’s not Florida.

We have plenty of examples of smaller communities hitting tech take-off without having a lot of creative amenities or “urban innovation strategies”. Somehow, despite the lack of population density, some small communities manage to get their ideas out in the world in ways that gets smart investors’ attention.  No one has a freaking clue how this happens: research on “why some cities grow faster than others” is methodologically no more evolved than research on “why some universities become more research intensive than others”, which is to say it’s all pretty suspect.  Equally, some big cities never get particularly good at innovation (Montreal, for instance, is living proof that cheap rent, lots of universities, and bountiful cultural amenities aren’t a guarantee of start-up/innovation success).

Moreover, the nature of the ecosystem is likely to differ somewhat in different fields of endeavor.  The kinds of relationships required to make IT projects work is quite different from the kinds that are required to make (for example) biotech work.  The former is quick and transactional, the latter requires considerably more patience, and hence is probably less apt to depend on chance meetings over triple espressos in a shared-work-environment incubator.  Raleigh-Durham and Geneva are both major biotech hubs that are neither large nor particularly hip (nor, in Raleigh’s case, particularly dense).

It’s good that governments are getting beyond the idea that one-dimensional policy instruments like “more money in granting councils” or “tax credits” are each unlikely on their own to kickstart innovation.  It’s good that we are starting to think in terms of complex inter-relations between actors (some, but not all of which involve spatial proximity), and using “ecosystem” metaphors.  Complexity is important. Complexity matters.

But to jump from “we need to think in terms of ecosystems” to “an innovation agenda is a cities agenda” is simply policy opportunism.   The real solutions are more complex. We can and should be smarter than this.

December 07

H > A > H

I am a big fan of the economist Paul Romer, who is most famous for putting knowledge and the generation thereof at the centre of  discussions on growth.  Recently, on (roughly) the 25th anniversary of the publication of his paper on Endogeneous Technological Change, he wrote a series of blog posts looking back on some of the issues related to this theory.  The most interesting of these was one called “Human Capital and Knowledge”.

The post is long-ish, and I recommend you read it all, but the upshot is this: human capital (H) is something stored within our neurons, which is perfectly excludable.  Knowledge (A) – that is, human capital codifed in some way, such as writing – is nonexcludable.  And people can use knowledge to generate more human capital (once I read a book or watch a video about how to use SQL, I too can use SQL).  In Romer’s words:

Speech. Printing. Digital communications. There is a lot of human history tied up in our successful efforts at scaling up the H -> A -> H round trip.

And this is absolutely right.  The way we turn a patterns of thought in one person’s head into thoughts in many people’s heads is the single most important question in growth and innovation, which in turn is the single most important question in human development.  It’s the whole ballgame.

It also happens to be what higher education is about.  The teaching function of universities is partially about getting certain facts to go H > A > H (that is, subject matter mastery), and partially about getting certain modes of thought to go H > A > H (that is, ways of pattern-seeking, sense-making, meta-cognition, call it what you will). The entire fight about MOOCs, for instance, is a question of whether they are a more efficient method of making H > A > H happen than traditional lectures (to which I think the emerging answer is they are competitive if the H you are talking about is “fact-based”, and not so much if you are looking at the meta-cognitive stuff.  But generally, “getting better” at H > A > H in this way is about getting more efficient at the transfer of knowledge and skills, which means we can do more of it for the same price, which means that economy-wide we will have a more educated and productive society.

But with a slight amendment it’s also about the research function of universities.  Imagine now that we are not talking H > A > H, but rather H > A > H1.  That is, I have a certain thought pattern, I put it into symbols of some sort (words, equations, musical notation, whatever) and when it is absorbed by others, it generates new ideas (H1). This is a little bit different than what we were talking about before.  The first is about whether we can pass information or modes of thought quickly and efficiently; this one is about whether we can generate new ideas faster.

I find it helpful to think of new ideas as waves: they emanate outwards from the source and lose in intensity as they move further from the source.  But the speed of a wave is not constant: it depends on the density of the medium through which the ideas move (sound travels faster through solids than water, and faster through water than air, for instance).

And this is the central truth of innovation policy: for H > A > H1 to work, there has to be a certain density of receptor capacity for the initial “A”.  A welder who makes a big leap forward in marine welding will see his or her ideas spread more quickly if she is in Saint John or Esquimault than if she is in Regina.  To borrow Matt Ridley’s metaphor of innovation being about “ideas having sex”, ideas will multiply more if they have more potential mates.

This is how tech clusters work: they create denser mediums through which idea-waves can pass; hence, they speed up the propagation of new ideas, and hence, under the right circumstances, they speed up the propagation of new products as well.

This has major consequences for innovation policy and the funding of research in universities.  I’ll explain that tomorrow.

April 24

Canadian Innovation, Seen from Abroad

So, I came across this quite remarkable little document yesterday – it’s a report prepared by MIT-Skoltech on the universities around the world who contribute the most to their local innovation systems.

(What is Skoltech, you ask?  Well, it’s a university located in a nascent science and tech hub, just outside Moscow, in a place called Skolkovo, and is the pet project of the Medvedev wing of the Kremlin.  Anchoring this tech hub is the new Skolkovo University of Science and Technology, or Skoltech.  To emphasize its difference from the rest of Russian Academia, the institution’s hired an American, Edward Crawley, from MIT as its first President. In the stuffy world of Russian Academia, this was a Big Freaking Deal.  MIT quickly signed up for a long-term partnership deal to develop Skoltech; hence, MIT-Skoltech).

Anyways, since the whole point of the Skolkovo project is to create a self-sustaining economic cluster which isn’t totally penetrated by the usual gang of oligarchs and kleptocrats, the role universities can play in developing technology-based ecosystems is much on the minds of campus leaders.  And so they hired a consultant to interview some of the world’s leading thinkers on innovation, higher education, and tech clusters, and asked them to name, i) the universities around the world which have the most highly-regarded tech ecosystems, and ii) the universities that do the most to develop tech ecosystems in challenging circumstances.

Here’s what they had to say about Canadian universities…

Nothing.  Absolutely nothing.

Maybe it’s not a surprise that none of our universities would make the top ten in the world; though Technion, ETH Zurich, and the National University of Singapore all cracked the top ten’s inevitable Anglo-American cartel.  But the identities of the schools that made it into the “doing most with the least” category ahead of any Canadian university should raise eyebrows: Sophia Antopolis (France), Aalto University (Finland), and KAIST (Korea).  Oh, and the University of Auckland.  At innovation, we rank below hobbits.

Of course, this is just expert opinions; it’s not in any sense “factual”.  Maybe if one were to delve into some metrics (the paper actually has a very useful section on measuring innovation at universities, though it does not use them in its comparisons), one would find that places like Waterloo and UBC “deserve” a place at the top table.  But experts usually aren’t that far off the mark.  And even if they are, the fact remains: people who matter in this field don’t think of Canadian universities and “top innovators” in the same sentence.

Universities with a reputation for innovation and entrepreneurship attract investment and top-class industrial partners.  If that’s not the image we’re projecting, we should be asking ourselves some pretty tough questions.