Tag Archives: ecosystems

America Needs to Innovate Its Innovation Ecosystem

America Needs to Innovate Its Innovation Ecosystem

GUEST POST from Greg Satell

The world today just seems to move faster and faster all the time. From artificial intelligence and self-driving cars to gene editing and blockchain, it seems like every time you turn around, there’s some newfangled thing that promises to transform our lives and disrupt our businesses.

Yet a paper published by a team of researchers in Harvard Business Review argues that things aren’t as they appear. They point out that total factor productivity growth has been depressed since 1970 and that recent innovations, despite all the hype surrounding them, haven’t produced nearly the impact of those earlier in the 20th century.

The truth is that the digital revolution has been a big disappointment and, more broadly, technology and globalization have failed us. However, the answer won’t be found in snazzier gadgets or some fabulous “Golden Era” of innovation of years long past. Rather we need to continually innovate how we innovate to solve problems that are relevant to our future.

The Productivity Paradox, Then and Now

In the 1970s and 80s, business investment in computer technology was increasing by more than 20% per year. Strangely though, productivity growth had decreased during the same period. Economists found this turn of events so bizarre that they called it the “productivity paradox” to underline their confusion.

Yet by the late 1990s, increased computing power combined with the Internet to create a new productivity boom. Many economists hailed the digital age as a “new economy” of increasing returns, in which the old rules no longer applied and a small initial advantage, a first mover advantage, would lead to market dominance. The mystery of the productivity paradox, it seemed, had been solved. We just needed to wait for technology to hit critical mass.

Yet by 2004 productivity growth fell once again and has not recovered since. Today, more than a decade later, we’re in the midst of a second productivity paradox, just as mysterious as the first one. New technologies like mobile computing and artificial intelligence are there for everyone to see, but they have done little, if anything, to boost productivity.

Considering the rhetoric of many of the techno-enthusiasts, this is fairly shocking. Compare the meager eight years of elevated productivity that digital technology produced with the 50-year boom in productivity created in the wake of electricity and internal combustion and it’s clear that the digital economy, for all the hype, hasn’t achieved as much as many would like to think.

Are Corporations to Blame?

One explanation that the researchers give for the low productivity growth is that large firms are cutting back on investment in science. They explain that since the 1980s, a “combination shareholder pressure, heightened competition, and public failures led firms to cut back investments in science” and point to the decline of Bell Labs and Xerox PARC as key examples.

Yet a broader analysis tells a different story. Yes, while Bell Labs and Xerox PARC still exist, they are but a shadow of their former selves, but others, such as IBM Research, have expanded their efforts. Microsoft Research, established in 1991, does cutting edge science. Google runs a highly innovative science program that partners with researchers in the academic world.

So anecdotally speaking, the idea that corporations haven’t been investing in science seems off base. However, the numbers tell an even stronger story. Data from the National Science Foundation shows that corporate research has increased from roughly 40% of total investment in the 1950s and 60s to more than 60% today. Overall R&D spending has risen over time.

Also, even where corporations have cut back, new initiatives often emerge. Consider DuPont Experimental Station which, in an earlier era, gave birth to innovations such as nylon, teflon and neoprene. In recent years, DuPont has cut back on its own research but the facility, which still employs 2000 researchers, is also home to the Delaware Incubation Space, which incubates new entrepreneurial businesses.

The Rise of Physical Technologies

One theory about the productivity paradox is that investment in digital technology, while significant, is simply not big enough to move the needle. Even today, at the height of the digital revolution, information and communication technologies only make up about 6% of GDP in advanced economies.

The truth is that we still live in a world largely made up of atoms, not bits and we continue to spend most of our money on what we live in, ride in, eat and wear. If we expect to improve productivity growth significantly, we will have to do it in the physical world. Fortunately, there are two technologies that have the potential to seriously move the needle.

The first is synthetic biology, driven largely by advances in gene editing such as CRISPR, which have dramatically lowered costs while improving accuracy. In fact, over the last decade efficiency in gene sequencing has far outpaced Moore’s Law. These advances have the potential to drive important productivity gains in healthcare, agriculture and, to a lesser extent, manufacturing.

The second nascent technology is a revolution in materials science. Traditionally a slow-moving field, over the past decade improved simulation techniques and machine learning have improved the efficiencies of materials discovery dramatically, which may have a tremendous impact in manufacturing, construction and renewable energy.

Yet none of these gains are assured. To finally break free of the productivity paradox, we need to look to the future, not the past.

Collaboration is the New Competitive Advantage

In 1900, General Electric established the first corporate research facility in Schenectady, New York. Later came similar facilities at leading firms such as Kodak, AT&T and IBM. At the time, these were some of the premier scientific institutions in the world, but they would not remain so.

In the 1920s new academic institutions, such as the Institute for Advanced Study, as well as the increasing quality of American universities, became an important driver of innovation. Later, in the 1940s, 50s and 60s, federal government agencies, such as DARPA, NIH and the national labs became hotbeds of research. More recently, the Silicon Valley model of venture funded entrepreneurship has risen to prominence.

Each of these did not replace, but added to what came before. As noted above, we still have excellent corporate research programs, academic labs and public scientific institutions as well as an entrepreneurial investment ecosystem that is the envy of the world. Yet none of these will be sufficient for the challenges ahead.

The model that seems to be taking hold now is that of consortia, such as JCESR in energy storage, Partnership on AI for cognitive technologies and the Manufacturing USA Institutes, that bring together diverse stakeholders to drive advancement in key areas. Perhaps most conspicuously, unprecedented collaboration sparked by the Covid-19 crisis has allowed us to develop therapies and vaccines faster than previously thought possible.

Most of all, we need to come to terms with the fact that the answers to the challenges of the future will not be found in the past. The truth is that we need to continually innovate how we innovate if we expect to ever return to an era of renewed productivity growth.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Four Innovation Ecosystem Building Blocks

Four Innovation Ecosystem Building Blocks

GUEST POST from Greg Satell

It’s hard to find anyone who wouldn’t agree that Microsoft’s 2001 antitrust case was a disaster for the company. Not only did it lose the case, but it wasted time, money and—perhaps most importantly—focus on its existing businesses, which could have been far better deployed on new technologies like search and mobile.

Today, Microsoft is a much different organization. Rather than considering open source software a cancer, it now says it loves Linux. Its cloud business is growing like wildfire and it is partnering widely to develop new quantum computers. What was previously a rapacious monopolist, is now an enthusiastic collaborator.

That’s no accident. Today, we need to compete in an ecosystem-driven world in which nobody, not even a firm as big and powerful as Microsoft, can go it alone. Power no longer comes from the top of value chains, but emanates from the center of networks. That means that strategy needs to shift from dominating industries to building collaborative ecosystems.

1. Connect to Startups

In its heyday, Microsoft enthusiastically followed Michael Porter’s five forces model. It saw threats coming not only from direct competitors, but also suppliers, customers, substitute products and new market entrants. Startups, in particular, were targeted for either acquisition or destruction if they were seen as posing a potential threat.

Today, however, Microsoft actively supports startups. Take, for example, its quantum development effort, in which it is partnering with more than a dozen entrepreneurial companies. These firms also get free access to Microsoft technologies, such as its Azure cloud platform and go-to-market resources and advice, through its Microsoft for Startups program.

Another approach that many firms take is corporate VC programs which actively invest in promising new companies. Unlike a typical investor, corporations bring a wealth of market and technical expertise, can help with things like distribution, supply chain management and marketing acumen. Corporations, for their part, get far more insight into new technologies than they could as an operating company.

Scott Lenet, President of Touchdown Ventures, which operates venture funds for corporations, told me that, “Startups thrive on new ideas and big firms know how to scale and improve those ideas. We’ve seen some of our investments really blossom based on that kind of partnership.”

2. Form Ties to the Academic World

When Sun Microsystems co-founder Bill Joy said, “no matter who you are, most of the smartest people work for someone else,” he was explicitly referring to Bill Gates’s assertion that Microsoft was an “IQ monopolist.” Joy’s position was that “It’s better to create an ecology that gets all the world’s smartest people toiling in your garden for your goals. If you rely solely on your own employees, you’ll never solve all your customers’ needs.”

Make no mistake. Innovation is never a single event. It is a process of discovery, engineering and transformation and those three things almost never happen in the same place or at the same time. That’s why the most innovative companies work hard to build links to the best minds in the academic world.

Today Microsoft has an extensive academic program that extends grants to graduate students and faculty members that are pursuing research that is of interest to the company. Google takes it even a step further, inviting dozens of the world’s top minds to work alongside its scientists and engineers for a sabbatical year.

Microsoft and Google are, of course, firms with enormous resources. However, just about any business can, for example, support the work of a young graduate student or postdoc at a local university. For even a senior researcher to collaborate with your staff is rarely prohibitively expensive. Researchers care far more about genuine support of their work than the size of your investment.

3. Leverage Domain-Specific Consortia

By the mid-1980’s, the American semiconductor industry seemed like it was doomed. Tp respond to what it saw as a national security threat, the American government created SEMATECH in 1986. It was a consortium of government agencies, research institutions and private firms focused on making the industry more competitive. By the mid 1990’s, the US was once again dominating semiconductors.

Any significantly complex technology takes years—and often decades—to develop before it becomes mature enough to engineer into a marketable product. So there is great potential in collaborating, even with competitive firms, in the pre-competitive phase to figure out the basic principles of a nascent technology.

For example, Boeing and Airbus are arch-rivals in aviation, much like DowDupont and BASF are in chemicals. Yet all of these companies, along with many others, collaborate at places like the Composites Institute (IACMI). They do this not out of any altruism, of course, but self-interest, because it is at places like the Composites Institute that they can collaborate with academic scientists, National Labs and startups working in the space.

As technology becomes more complex, domain specific consortia are becoming essential to any ecosystem strategy. The Composites Institute is just one node in the network of Manufacturing Institutes set up under the Obama Administration to support this type of collaboration. In areas ranging from advanced fabrics and biofabrication to additive manufacturing and wide-gap semiconductors, firms large and small are working with scientists to uncover new principles.

And the Manufacturing Institutes are just the start. The Internet of Things Consortium is helping bring computation to the physical world, while the Partnership on AI focuses on artificial intelligence and the Joint Center for Energy Storage Research is helping to develop advanced battery technology. All are open to the largest multinationals and the smallest startups.

4. Move From Hierarchies to Networks

Back in the 90s, when Microsoft still dominated the tech world, markets were still based on linear value chains dominated by one or two industry giants. Yet as I explain in Cascades, we are quickly moving from a world of hierarchies, to one dominated by networks and ecosystems. That changes how we need to develop and grow.

In a hierarchy-driven world, the optimal strategy was to build walls and moats to protect yourself against would-be invaders, which is why Microsoft fought tooth and nail to protect its operating system monopoly. Today, however, industry lines have blurred and technology moves too fast to be able to build effective barriers against disruption.

That’s why today “Microsoft loves Linux”, why it developed an academic program to collaborate with scientists at universities and why it often partners with startups instead of always trying to crush them. The technology being developed today is simply too complex for anyone to go it alone, which is why the only viable strategy is to actively connect to ecosystems of talent, technology and information.

Power today no longer sits at the top of hierarchies, but emanates from the center of ecosystems and you move to the center by widening and deepening connections. Closing yourself by erecting barriers will not protect you. In fact, it is an almost sure-fire way to hasten your demise.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Innovation Ecosystems and Information Rheology

Innovation Ecosystems and Information Rheology

GUEST POST from Arlen Meyers

Rheology is the study of flow. There are many ways to measure flow. For example, Volume Flow is defined as the volume quantity that flows through a given section at a considered time interval. The most common volume units are:m3/s, m3/h, l/h, l/min, GPM (gallons per minute), Nm3/h (normal cubic meter per hour), SCFH (normal cubic foot per hour), among others.

Information rheology is the study of how information passes from the sender to the receiver, the resistance to that flow, and how to address it.

The concept applies also to how products and services flow from one place in the world to another.

A reverse innovation is any innovation that is adopted first in the developing world. To be clear: What makes an innovation a reverse innovation has nothing to do with where the innovators are, and it has nothing to do with where the companies are. It has only to do with where the customers are.

Historically, reverse innovation has been a rare phenomenon. In fact, the logic for innovations flowing downhill, from the rich world to the developing world, is natural and intuitive. After all, it is the richest customers in the richest countries that will always demand the newest technologies. In due time, the costs of new technologies come down, and incomes in the developing world rise. As a result, innovations trickle down. Right?

Be careful. The intuitive assumption that poor countries are engaged in a process of gradually catching up with the rich world has become toxic. It is a strategic blind spot that has the potential to sink an increasingly common aspiration: to generate high growth in the emerging economies. The assumption can even inflict long-term damage in home markets. That is because surprisingly often, reverse innovations defy gravity and flow uphill to the rich world. As a result, a defeat in a developing country half a world away can lead directly to a stinging blow in your own back yard.

How information flows, like on cell phones during riots and protests, is not an exact science. Percolation theory illuminates the behavior of many kinds of networks, from cell phone transmissions to the COVID R number.

Most discussions of innovation ecosystem creation and growth focus on the anatomy i.e. the components necessary to be successful. For example, one author describes the 5P’s of human capital:pillars, patrons, pioneers, professionals and partners.

However, equally as important is the physiology of clusters -how the cluster elements work together .One of the key determinants of an innovative organization or cluster is information rheology. There are three basic elements to the equation.

The first has to do with the number of nodes in the network, both internally and externally. Network theory tells us that the more nodes, the more value. Having one fax machine in the world added nothing. It took a lot to unleash the value , as the development of social media has exemplified.

The second has to do with how the nodes are connected. Some are robust and some are not. The connections between the nodes are called edges.

Finally, and most importantly, the two previous parts are not nearly as important as the velocity, relationships, acceleration and lack of resistance to the flow of information from one node to the next. We usually refer to this as a cluster or innovation district being “user friendly” and is typified by the free and rapid flow of information from one place to the next. Malcolm Gladwell described facilitators in the process as mavens, experts and connectors.

There are many causes of poor information flow, but, fundamentally, they come down to :

  1. 1. The sender does not communicate effectively or in a an appropriate way
  2. The receiver is unaware that the message was sent or does not understand it
  3. The systems for transmitting information and verifying receipt are inadequate
  4. Third party interference muddles the message

Poor information flow in sickcare results in dropped handoffs and referral leaks which are the primary causes of medical errors and waste. The lack of data interoperability is one of the root causes.

There are several kinds of intermediaries that facilitate information flow in an ecosystem.

  • Architects engage in strict agenda-setting and coordination activities
  • Gatekeepers support the knowledge extraction and dissemination of the information
  • Conductors take care of information acquisition, transmission, and task sharing
  • Developers create concrete assets for the network based on knowledge mobility
  • Auctioneers set the agenda and joint vision for the innovation network
  • Leaders motivate and foster the voluntary collaboration and identifying roles of network members
  • Promoter support ecosystem members to work towards the same goal
  • Facilitator bring together quite different, even competing, parties to work together

Whether it is making clinical handoffs better or improving the flow of information in an ecosystem or cluster, the obstacles are substantial and the systems for preventing information flow blockage need to constantly be maintained, which can be costly and time consuming.

If you want to accelerate regional innovation clusters and communities, don’t concentrate so much on connecting the senders and receivers. Focus on removing the barriers to the flow of information and how to push and pull it through the pipes.

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