Science & Technology
Trends that will shape the subjects, methods, talent and institutions of scientific research and technological innovation.
Economy & Society
Trends that will shape the big picture for organizations and enterprises.
A Brief History of Research Parks
Science Parks & Science Cities
Incubators and University Research Parks
What's Next?
The New Global Map of Science
real estate-driven
suburban/outlying virgin land
attract corporate branch plants
1950s-1980s
1980s-today
2005-2025
For many, this model is working. For others it isn't.
driven by technology transfer
in orbit/shadow of research universities
focus is on new firm formation & growth
Forecasting activities:
3 expert panels (IASP, AURP, IEDC)
Online panel of outside experts in architecture, real estate development, urban design, innovation studies, economic geography, urban policy and venture capital
IFTF technology forecasts for UK government and DoD
As the global economy moves from crisis to slow recovery, public investments will set the agenda and drive growth. When recovery does come, the focus will be on building more resilient economic models.

Signals
NIH: $10b in stimulus
for biomedical research
WSJ Survey: Corporate R&D spending
holding steady (but for how long?)
Developing economies continue to
make large investments 
in research infrastructure
Implications
How will research organizations absorb a one-time injection of funds?
More pressure on tech transfer
Empty pipelines as companies cut back, but big science fails to deliver?
New hubs in the developing  make rapid gains
Tenants want more flexibility
From Free Markets to Stimulus Capitalism
Ecological Economics Comes of Age
The Group Economy
Growth of carbon markets
Signals
Personal carbon
tracking
Commercialization
of offsets
As governments and industries begin to address global warming, the measurement of the economic value of ecological processes will play an increasingly important role. In addition to their direct economic impacts, this will drive investment and innovation in ubiquitous sensing, simulation and modelling, and information visualization.
Implications
New growth industries
from carbon neutral to carbon negative as the standard
third-party monitoring and certificationreuse, renovation and recycling
higher density for higher efficiency
New tools for cooperation will drive down the cost of forming groups around any shared interest, identity or activity. As the cost of forming groups approaches zero, ad hoc structures will transform work, companies, and organizations. New models for wealth creation will emerge at the intersection of the social web and grassroots movements. Existing organizations will be transformed through the adoption of these tools and practices, becoming  less hierarchical, more agile and more collaborative.
Signals
Companies adopt
social software
Obama campaign
online organizing
Knowledge
commons
Implications
need for new metrics of networks and groups (the in-between stuff)
ad hoc infrastructure and space
new kinds of tenants
new physical demands from tenants - more collaboration spaces, less "warehoused" workers
3000+ incubators worldwide
Research Triangle Park
Sophia Antipolis
Ubiquitous Computing
Signals
Implications
The spread of digital sensing, computation and communications into every corner of the man-made and natural environments will create vast new datasets for scientific research in fields from public health to civil engineering to marine biology. These same tools will also turn everyday activities like travel, shopping, talking, etc. into data-generating activities. This information will create value for retailers, manufacturers, and consumers. 
Sensing everywhere,
top-down and bottom-up
New user interfaces: computing fades
into the background
Everything connected,
all the time
New industries: technology, applications and making sense of the data
Transformation of existing industries: logistics, retailing, etc.
Need for parks to become "living labs" for these technologies
From Artificial to Hybrid Intelligence
For decades, computer science has sought to create simulated (or even real) analogues of the human mind, capable of complex reasoning and natural language communication with people. That vision is shifting, as social networks and more limited forms of machine intelligence combine in hybrid structures that collaboratively filter and extract meaning from data about our environments and ourselves. As the social web develops, more and more of our friends will be “bots” and intelligent software agents, doing useful things on our behalf.
SIGNALS
IMPLICATIONS
Tools for finding expert knowledge in large organizations and social networks
People training computers and algorithms
"Reality mining"
Dramatically different kinds of organizations
Will require new kinds of workspaces
Need for places that are easily "mine-able" (what is the 21st century version of Kevin Lynch's "imageability"?)
Biology By Design: Nature as Source and Code
SIGNALS
IMPLICATIONS
Syn-dustry:
Intentional Biology
Biomimicry
Biomolecular
nanotechnology
Bioscience is transdisciplinary and translational, requires new adjacenies and layouts on every scale
Biotech industry's structural flaws (Pisano, HBS)
Safety!
New architectural, organizational metaphors
From synthetic genomics (which seeks to design micro-organisms that perform useful functions) to stem cell therapy (which seeks to harness the body’s own ability to heal itself), biology will become a more important source of scientific and technological breakthroughs. Key drivers include global ecological challenges, the health needs of a richer, aging global population and advances in informatics that help decipher the code of life. Biological concepts about how to organize systems and structures will also inspire designs for everything from buildings to organizations to algorithms. Yet the biotech industry is structural unsound – without change it won’t be able to fully realize the commercial potential of these new technologies.
The New Scientists
Young scientists, and scientists from emerging economies, will transform how scientists work, how they build they careers, and the sources of knowledge they draw upon and develop in their work. As options outside academia grow, publishing becomes more open, collaborative and real-time, and entrepreneurship gains more legitimacy, the means by which scientists create professional reputation will be transformed. These new scientists will be both transdisciplinary and ultra-specialized, drawing on various disciplines to answer complex, focused questions. The role of amateurs will expand, as both independent researchers and partners of professional science.
SIGNALS
IMPLICATIONS
Student entrepreneurship
JoVE: YouTube for science
Amateur renaissance
New kinds of careers means new needs
Very different ideas about how work, social and play lives are integrated/separated
Learning in everything
How can parks connect to and support new pools of talent?

Institutional Transformation
Experiments with new organizational forms and incentive structures will shake the foundations of century-old scientific institutions. Scientific publishing is already under full-scale attack, its economics and social conventions completely rewritten by cheaper, faster, more democratic online alternatives.  Privately funded research centers, like Canada’s Perimeter Institute, and scientific challenges like the X-Prize can create new incentives for innovation. The sheer complexity of the scientific challenges of the 21st century will require massive new global partnerships that effortlessly span political and organizational boundaries.
SIGNALS
IMPLICATIONS
Truly global collaborations
Open access online:
The end of science publishing?
Private benefactors
for big science
Parks need appeal as sites where new networks convene
Attracting future research institutions
What would a network that directly threatens parks look like?
Need for deeper, larger, more diverse local investor networks
More innovation from startups
From protecting IP to encouraging openness
Models and Places for R&D
If science in the 20th century was a pyramid, with the United States, the United Kingdom, Germany, Russia, and Japan at the apex, science in the 21st century will be more like a network, with multiple, linked centers of excellence. Successful countries and sub-national regions will pursue strategies to blend targeted investments in basic science with local industrial or cultural resources, to create unique and hard-to-reproduce centers of global excellence. Meanwhile, the shift from brain drain to brain circulation; the rising capability of moderate Islamic states to support scientific communities; and the growth of new "South-South" collaborative networks mean that these centers of global excellence can develop in a wider range of countries than in the 20th century. 
Trends that will shape technology commercialization and the production of knowledge.
SIGNALS
IMPLICATIONS
Rise of science and technology
on the Indian Ocean Rim
Rising science stars
New global circulation of talent, knowledge and investment
Strategic shift from zero-sum competition to niches in global R&D "supply" chains
Global suppliers of research space?
Universities: From Ivory Tower
to Economic Engine
The long-term shift of basic science and technology research from corporations to universities will expand (perhaps dramatically), driven by increased public investment in basic science and corporate focus on rapid, incremental innovation. Increased expectations about universities’ ability to spur economic growth will aside in the US from public stimulus spending, and elsewhere in effort to jumpstart new university-centered science and technology clusters. However, not all will make this transition successfully. Innovation in technology transfer will be the key difference between universities that rise to this challenge and those that do not.
SIGNALS
IMPLICATIONS
Texas A&M to reward faculty
entrepreneurship in tenure review
Fixing tech transfer:
The Cal Tech model?
From university research parks
to innovation zones
Need for better technology transfer mechanisms, especially as stimulus funding boosts basic research
Parks moving onto campus?
Need more tools to make university talent and resources available to outsiders

From Parks to Regional Knowledge Ecosystems
Sticky Know-how
Many kinds of knowledge can circulate in books, online, and be useful and accessible anywhere. But the kind that creates unique competitive advantage - cutting-edge scientific knowledge, new research tools, and technical practices – are kept alive in creative clusters. While this tacit knowledge may eventually diffuse, much of it will remain “sticky” and stay embedded in loosely networked groups operating in the same places. Doing things like tightly linking R&D and manufacturing speeds and amplifies the creation of sticky know-how - some of the most important product development happens within factories. Future industries, like the translational research paradigm emerging in the biomedical world, are likely to place a higher value on tacit knowledge.
SIGNALS
IMPLICATIONS
Super-specialization @ Snowpolis
Locating R&D
with manufacturing
Translational research
in biomedicine
Refining prototypes
for mass production
Learning from the
factory floor
Boutique park,  designed to be highly specialized clusters of tacit knowledge 
Attracting the right kinds
of manufacturing
Knowledge-based blue collar jobs
Creating mixed-use parks
Industrial ecology
Lightweight Innovation
As the global economy slows down, the pressure to innovate faster and cheaper will only increase. Over the next decade, we will see a rapid expansion of lightweight models for innovation, drawing on new ideas about organizing research and development, and new tools for collaboration. Just as web startups now move from idea to implementation without traditional incubation, more and more of new product and service development will happen outside of existing pipelines. As open innovation casts a wide net for ideas, it will merge with lightweight infrastructures that put the tools into everyday people's hands as well.
IMPLICATIONS
SIGNALS
New investment models and tools
Open innovation
Innovation prizes
The Social Life of Small Research Spaces
1980s-style business incubators will fade away as a technology-transfer strategy, but new many kinds of spaces for entrepreneurship and collaborative research will evolve to replace them. Pop-up labs, co-working hubs, mobile incubators and temporary research parks will provide flexible physical spaces while new business models and management strategies will provide organizational freedom. At every layer – the region, the cluster, the firm, the academic or corporate department, and even among user & consumer groups – there will be new solutions for creating smaller, more distributed networks of spaces for collaborative work. Social software will play a major role in stitching these scattered spaces together. One outcome is that research spaces will become more transparent to and engaging of the public in prototyping and testing new technologies, products and lifestyles.
SIGNALS
IMPLICATIONS
Desigining the physical environment isn't enough
Research places need to be "produced" like a show
Need to map networks & flows
Real investment in social network tools is needed
Parks follow communtiies, not the other way
New business models for franchising social places
Rise of co-working
Pop-up labs
Temporary parks
The Future of
Research Parks and
Innovation Regions
The complexity of future R&D tasks will be too great for any single organization. The need to tap regional and global knowledge pools, research infrastructure and talent are at odds with economic development strategies that focus on particular parcels of land, campuses or local jurisdictions. In the future, we will see an expansion of new governance and development structures around regions and their knowledge ecosystems that are broader than a single industry. Their goals will be to encourage knowledge creation at the cutting edge and develop the organizational, human and social capital to compete in the global economy. These networks will stretch far beyond the major regional institutions of today to include informal networks of entrepreneurs, investors, professionals and hackers and other communities of mentoring and learning. The strength of regional knowledge ecosystems is that they can adapt faster than national systems, which are dictated by federal politics, and they can scale up successful enterprises much more effectively that individual research parks or municipalities. 
SIGNALS
IMPLICATIONS
Need to inventory and map regional assets
Policies that allocate resources regionally
Engagement with non-traditional institutions and networks
Overcoming parochial interests

Incubation ecosystems
Regional cluster strategies
Future Scenarios:
Research Parks 2025
Science and Technology Parks 3.0
Incremental change adds up
SkySong: ASU Scottsdale Innovation Center 
AdamsMorganWorks:
A co-working district in Washington, DC
From Research Parks to Research Clouds
Disruptive transformation from outside
Future Fail!
Research parks in decline
UNLV Harry Reid Research Park
These scenarios were generated by identifying four trends from our research that may end up impacting research parks in many different possible ways, depending on how we react to them:
The future of universities
The future of sustainability
The future of science institutions and collaboration
The future of the bio-industrial complex

By combining plausible hypotheses about how these factors might play out in combination, IFTF developed three scenarios for the future of research parks set around the year 2025.
Research parks are still recognizable to us, with the familiar players involved, but they have upgraded to the next “version”.
Universities are more entrepreneurial
While expensive, the transition to sustainable development is managable
Parks are not leaders in new science networks, but are participating
Real, but incremental progress in fixing biotech's structural problems
Parks are challenged by clouds of small-scale spaces that pop up around univerisites. They pop-up overnight as needs change, and disappear when their usefulness has run out. Social software ties the whole think together, and enables new reputation and business models.
Some universities leverage this, many can't understand it
Clouds are very light footprint, while existing parks have high-energy legacy
Clouds are vitual communities that find places to land and convene
Clouds make end-runs about the biotech indsutry and its problems with open, dsitributed research models
What it might look like
What it might look like
What this might look like
In 2011, the number of research parks worldwide peaks and then begins to decline. There are many possible triggers acting alone or in combination - high energy costs, falling R&D productivity, or a protracted global recession. Yet existing parks cannot survive these challenges, and they have failed to adapt individually or band together in regional ecosystems. What remains of R&D is highly virtualized to keep up ROI.
Universities retreat from commerce and tech transfer
Virtual networks are hard to footprint, parks aren't. So who gets the blame and regulation?
Parks don't market themselves to new institutions
Without experiments and reform, the bio-industry can't reach ignition point

 
The Group Economy
Stimulus Capitalism
Ecological Economics
Ubiquitous Computing
The New Scientists
Biology
Hybrid Intelligence
Institutional Transformation
Lightweight Innovation
Sticky Know-How
The New Global Map of Science
Univeristies: From Ivory Tower to Economic Engine
The Social Life of Small Research Spaces
From Parks to Regions
EXTERNAL TRENDS
Mapping Key Areas of Uncertainty
STRATEGIC IMPLICATIONS OF THE SCENARIO FORECASTS

Assume that thse are plausible, but not the only possible futures. Take 10 minutes to record your insights on the worksheet. For each scenario, ask yourself:

What happens to your organization in this future? How did we get here?
If you knew today this would come true, what you do differently today to change it?