Track 7 Tech Vectors to Take Advantage of Technological Acceleration

May 9, 2001 by Max More

In this update and expansion of his essay, “Taking Advantage of Technological Acceleration,” Max More reveals how businesses can keep up with accelerating technologies in seven primary vectors or metatrends.

Originally published on www.manyworlds.com August 1, 2003. Published on KurzweilAI.net April 7, 2004.

Puzzling Over Progress

Suppose you had recently returned to civilization after three years cast away on some mythical undiscovered island. Having sold your story to a studio with their eye on Tom Hanks, you had time and money to sit back and read about the developments of the last three or so years. If you started with a business focus, you would quickly get a downbeat feeling as you read headlines and stories peppered with phrases like “downsizing”, “recession”, “deflation fears”, “bankruptcies at record highs”, “after the bubble burst”, “postponing retirement”. Reading all this gloomy news, you might yearn for a return to your unknown island.

Switch tracks.

Suppose that you ignored the business and economic headlines spanning your absence from 2000 to 2003. After struggling to erect a simple shelter from what you remembered from Robinson Crusoe, you’re more interested in how technologies have advanced in those few years. You ask for a big pile of information on what’s been happening in technology over the last few years. Oh, and throw in any new projections of what’s just ahead.

You chuckle at the pictures of Roomba, the saucer-shaped carpet-sweeping robot from iRobot. Someone has finally made some kind of useful household robot. Honda’s Asimo is still clumsy, but is a surprising jump ahead, and you see too many new versions of Sony’s Aibo robopet to keep track of them all. Turning on the tablet PC someone lent you (nice toy!), you can’t help jotting down some of the advanced gadgets you’re reading about. High resolution yet small digital cameras, cellphones with built-in music players or cameras, the Segway Human Transporter, and large plasma displays for remarkable prices. Wait, you must get one of these GPS devices too!

Being reasonably technophilic, you feel a sense of exuberance rise within you as you skim through all the technological developments crammed into the last few years. LEDs replacing bulbs in traffic lights, ticketless air travel with what look like ATMs for check-in, autonomous computing, protein-folding supercomputers, terahertz transistors, computing devices made from carbon nanotubes, glassy metals, laser tweezers for grabbing individual molecules, and “sensemaking” software sniffing out patterns of terrorist activity.

There’s even an explosive growth in the number of extra-solar planets known. During those three years, their numbers have swollen from single figures to well over a hundred. Work in stem cells and therapeutic cloning seems to have zipped ahead, despite political opposition. These people have goats making silk; these folks have turned stem cells into multiple cell types; and Medtronic is hooking up pacemakers to the Internet and handing out high-tech wands for patients to transmit internal data to their doctor.

Take a small historical step back and these innovations seem like magic. As Arthur C. Clarke put it: “Any sufficiently advanced technology is indistinguishable from magic.” Here’s a corollary ‘law’: “Any sufficiently distinguished technology is magical.” Modern magic consists of action-at-a-distance and environments and non-human voices that respond to you in a unique way.

Those super-muscular mice, genetically engineered to overproduce IGF-1 are fascinating, not to mention the researchers who connected multiple brain cells with silicon chips. The new diseases are scary, but you can’t believe that bioscientists decoded the SARS virus genome in just six days. But then, other groups are seriously talking about being on the brink of one-day personal genome sequences for humans.

With all this technological rush, business must have been great! But wait what’s this? Deflation fears? Massive downsizing? Bankruptcies at record highs?

Anyone who missed the last three years only to catch up with events all at once might easily be baffled by this curious combination of economic malaise and business gloom alongside charging technological progress. A broader understanding would resolve this perplexity. You might note that despite all the sorry talk in business, productivity has remained far above its trend of the previous two decades. In 2002, the annual rate of output per worker grew a healthy 4.8%. That compares with a 1.3% productivity growth rate in the 1980s to early 1990s.

Clearly some businesses are finally figuring out how to reconfigure their processes, organizational structures, and incentive systems to make use of the expensive IT systems they were buying. At the same time, clusters of these technologies are adapting to the people who use them. When this co-evolution and adaptation of technologies and practices is complete, the technology becomes invisible (as John Seely Brown and others have put it).

While business has suffered the collapse of the overhang of irrational exuberance, it’s clear that technological advance is not only continuing ever faster, but some companies are using it craftily to push ahead. Companies as diverse as Wal-Mart, Dell Computer, Cemex, General Motors, Cisco Systems, DuPont, and Harrah’s are putting powerful technologies together with reshaped business processes and architectures to power their generation of value.

Clearly, technological and business advance does not move along in tandem. Most companies are botching their IT installations due to ignoring the cultural and psychological dimensions. Others are using new IT to run faster but in the wrong direction, following a faulty strategy map. The most extreme technocentric prognosticators ignore such factors in their projections of imminent runaway change. Chips may be a thousand times smaller and faster, but the practical effect on our activities is much less. As economist Brian Arthur has argued in detail, history repeatedly shows a long lag time between technological invention and productive use.

We face problems even if we’ve figured out how to work with new technologies. Our PCs outrun yesterday’s supercomputers, yet we spend precious time deleting spam, hunting down viruses, reinstalling drivers, and calling tech support. (IBM recognizes these problems as an opportunity to address with their “autonomic computing” program.) Drug companies use remarkable bioinformatics tools to discover drug targets and winnowing possibilities, along with new collaboration and information management tools. Yet, they must still work through an expensive and arduous testing and approval process. New technologies enable but don’t guarantee comparable advances in practice or business results.

Deciphering the Drivers, or, How to Ride a Rocket with Three Engines

The technocentric, pure-IT-driven futurists who point to a trillionfold growth in computing power per dollar over the last century, need to mind this gap. Yet their charts of exponential or even double-exponential trends look promising when you consider that each doubling of power per dollar is bigger than the last—and bigger and bigger and bigger. Eight doublings amounts to a lowly 256. 16 doublings yields 5,536. 32 doublings (half the squares on the chessboard) comes to a mighty 4.295 billion. But at the end of the chessboard, with 64 doublings, we have 18,446,744,073,709,551,616 (18.447 quintillion).

Initial doublings may not change much (whether we’re measuring in instructions per second or simply in dollars), but doubling of extremely large numbers gets interesting. As the core technologies get faster, tinier, smarter, and more adaptive, the impact on business and life in general, expands. Gene sequencers that speed up the process from four years to two years are good. But machines that could sequence your personal DNA sequence in a day at a reasonable price will thoroughly alter the economics of the pharmaceutical industry and the structure of healthcare. We are reaching tipping points with fuel cells and microturbines, wireless bandwidth speed, reliability, and security, inexpensive RFID tags for every individual product, and with practically real-time customer and market-sensing and responding systems.

The rocket fuel of information technology—smaller, faster, cheaper, ubiquitous—is far from the only factor powering technological acceleration. If we peer with a wide field of vision, we can see a second potent driver of surprising, disruptive, game-changing developments. Not only are advances in individual fields standing on the ever-broader and taller shoulders of their predecessors, they are sending out shoots in diverse directions while running into other streams of technological development. The result: intersection, combination, blending, cross-fertilization and mutation, deflection, penetration, coalescence and fusion.

This intersecting of fields occurs in fits and spasms—unpredictable and astonishing. Yet, we remain far from E.O. Wilson’s scientific dream of “consilience”—the deep, systematic, unified perspective on far-flung fields of study. Even the cross-fertilizations we are seeing are producing remarkable offspring. Cognitive psychology is feeding into behavioral finance and economics; nanofibers are being woven into jeans, imbuing them with unprecedented stain resistance; MEMS (microelectromechanical systems) and radio frequency ID tags are colliding with supply chain optimization, global positioning, customer analytics, and collaboration tools to enable novel products, services and solutions, along with reinvented business processes and strategies.

A third driver of technological acceleration—systematization—overlaps with and draws on the other two. When a field of knowledge becomes systematized, it sets off a rapid period of progress. At the grandest scale, we should thank Roger Bacon for systematizing science itself into an empirical, disciplined method of investigation. We may now be seeing the beginnings of the systematization of innovation—the perfect complement to and extension of the scientific method. (If this remark sounds mysterious, you will find plenty of hints in the Innovation Methods topics at ManyWorlds.com.)

Descending from the abstract heights of Science and Innovation, we can see the marks of systematization on individual disciplines. Chemistry crawled along until the 1870s, when the periodic table took shape. Over the following decades researchers and entrepreneurs were able to vastly increase their discovery, synthesis, and output of new chemical products. In turning the search for knowledge from haphazard to honed, systematization brings analytical rigor to experimentation. Biotechnology appears to have reached a similar state of systematization due to genomics and bioinformatics.

Biotechnology provides a good example of all three of these drivers at work. The very term “bioinformatics” embodies the first two drivers. IT companies from IBM to Oracle, are pushing hard into the biotech space. An array of other technologies, all add momentum—from expert systems that interpret cardiac rhythms, to brain implants that control tremors, to high-resolution body scanners that reveal incipient cancer clusters. Within biotechnology, genomics has much promise. But to deliver on that promise, the second and third drivers need to be in alignment. That means matching genomics with the systematic forms of other “omics”—proteomics, glycomics, transcriptomics, metabolomics, and even bibliomics—mining the published scientific literature for unexpected connections between these fields.

It was still possible in the eighteenth century for the most highly educated person to know most things about everything. Today’s post-postmodern Renaissance Man could not begin to master all the branches of mathematics, physics, biochemistry, the social sciences, complexity studies, the engineering disciplines, materials science, the neurosciences, management methodologies, micro- and nanofabrication, genetics, and so on and on without end. Our understanding of the principles governing the multiple levels of our world has grown so vast in expanse, that top-rank geniuses in one field may be ignorant pretenders in others. This efflorescence of distributed human knowledge frustrates the would-be know-it-all. More importantly, it confuses executive decision-makers faced with a sense of vast but invisible threats both within and beyond their industry. The very notion of an “industry” with definable boundaries is fast losing its coherence amidst the tech whirlwind. Leading companies like Microsoft understand this well, fitting within their strategy, everything from software to gaming to toys to media—without having to be a conglomerate.

But all this buzzing, booming, productive confusion reflects the success of human intelligence. It also represents a proliferation of business opportunities for value creation and capture by companies with superior foresight. Amazon.com has pulled ahead by smart use of collaborative filtering; Cisco has transformed its employee recruitment and training procedures with e-learning and human capital management techniques; energy companies have glued 3D seismic imaging systems, together with drills, engineered to move sideways, terminated by sensor-laden drill-heads. Many companies are trying to give shape to the convergence of Web services, service-oriented architecture, loosely-coupled processes, collaborative product development. Add to the mix, decentralized “open innovation” research, analytics and data mining, and alternate-scenario simulations, and you have a recipe for sensing and anticipating market shifts, reconfiguring the value network and developing innovative solutions to customer demands.

Extinction or Dominance?

Companies that don’t push hard to keep up and to see ahead will perish ever faster as value zones move away, leaving them gasping for revenues from mysteriously vanishing customers. If the dotcom bust was akin to the K-T boundary asteroid strike that wiped out most of the species on our planet some 65 million years ago, then surging, interacting technological waves are the economic equivalent of the Cambrian Explosion. These waves wash away poorly adapted business species with more nimble and more intelligent organizations.

The forces of acceleration, intersection, and systematization of technologies make it harder to predict exact futures. This creates the imperative for your organization to systematically scan technologies and other driving forces outside “your domain”. At ManyWorlds, we find the richly interlaced knowledge database at manyworlds.com, a powerful tool contributing to that end. In all our work with leading companies, we’ve yet to see a comprehensive and maximally effective set of processes in place to anticipate, sense, and respond to the opportunities, seductions and threats of plausible futures. What would such an organization look like?

“Nonreactive companies deal with changing consumer needs and technologies by doing what they have always done . . .”

Prescient-Proactive Enterprises

The main goal of this paper is to convey a sense of the depth, power, and direction of technological change. I shall use the three driving forces of acceleration, intersection, and systematization to bring into focus seven major technological vectors of change. Each of these vectors brings with it opportunities for future-ready organizations to pull away from the pack. This section briefly creates a context for the seven vectors or metatrends (trends of trends) described below. You can learn more by exploring topic areas at manyworlds.com, especially the topics Organizational Futures, Business Technology Futures, and Advanced Forecasting and Planning.

How can you enhance your ability to “learn from the future”? Executives may view the unknown future with a less romantic attitude than that embodied in futurist F.M. Esfandiary’s “nostalgia for the future”. Some peer ahead in fear, others with greed, and a few armed with leading practices. The most advanced leaders are, what I call, Prescient-Proactive Enterprises (PPEs). PPEs will achieve and sustain their leadership due to two core qualities: A refined foresight capability that senses existing, emerging and foreseeable technological logics; plus an executive ability to act quickly and decisively to take advantage of insights gained, and patterns recognized.

At the bottom of the scale of future-readiness, we find the Nonreactive Enterprise. In such a company, no one has responsibility for scanning for these extinction-level events. Forecasting and simulations are unheard of in strategic plans. And leaders overconfidently believe that they grasp the future intuitively, or that it will be insignificant, or that they can adapt instantly without advance warning. Nonreactive companies deal with changing consumer needs and technologies by doing what they have always done, because “it’s always worked for us”. The true nonreactive organization will perish quickly in all but the slowest-moving (and continually shrinking) areas of the market.

“Reactive enterprises know that being stationary amounts to a death wish.”

“Adaptive enterprises make an art out of quickly recognizing change and responding flexibly.”

“The Prescient-Proactive Enterprise (PPE) builds on the adaptive enterprise, adding capabilities, processes, and future-focus to see further ahead as well as in acting on that extended vision.”

A rung above those blind business organisms we find the largest cohort—the Reactive Enterprise. Reactive enterprises know that being stationary amounts to a death wish. Reactive enterprises can be highly sophisticated, making use of Five Forces and Balanced Scorecard strategy frameworks, reengineering their processes to meet changing competitive conditions, and eagerly adopting best practices to catch up with the recognized leaders of the day.

At the third and penultimate level of organizational evolution dwells the Adaptive Enterprise. This has much in common with the “real-time enterprise” (RTE), making use of many of the latter’s IT-powered sense-and-respond processes, combined with improvisational strategy and rapid decision-making. Yet, this is still driven by a sense of the present: operating in <I>real-time</I> but not in future-time. Adaptive enterprises make an art out of quickly recognizing change and responding flexibly.

The Prescient-Proactive Enterprise (PPE) builds on the adaptive enterprise, adding capabilities, processes, and future-focus to see further ahead as well as by acting on that extended vision. A true PPE must have both of these elements—Prescience: a sense of existing and emerging and foreseeable technological logics, and Proactivity: an executive ability to act quickly and decisively, taking advantage of insights gained, and patterns recognized.

Prescience is not like a state of enlightenment to be attained once and held forever, but more like a future-oriented kaizen—a continuous process of improving the organization’s understanding of possible futures. Many methods exist to try out (see the paper, “Grasping the Future”), and different combinations will suit different companies according to their current capabilities, leadership style and organizational culture. Some of them will be heavily based on using information technology, as in the case of “sensemaking” software, simulations, modeling, or what Michael Schrage calls “hyperinnovation”—the ability to virtually test vast numbers of possibilities in a short time.

Most revolve around human cognition and relationships along with a highly developed ability to synthesize and interpret diverse information. These approaches include: Scanning widely for unexpected developments; open or collaborative innovation for R&D, idea futures markets, and using other networks and alliances to draw on the foresight of others. Additionally, on the cognitive side, being able to think outside your assumptions, frames and biases. All of these approaches can feed into and enrich scenario thinking practices. This can help prevent scenario thinking from degenerating into rationalizing assumptions about the most likely future.

Proactivity includes a host of elements from product innovation, business model innovation, and business process innovation and enhancement, to all those leadership-driven, culturally-mediated, and decision-enabled processes that translate ideas into action. Individuals and teams must be responsible and accountable for creating and maintaining the alliances and doing the scanning and other prescience processes, as well as for devising and implementing innovative business models, architectures, processes and products and services that embody the flow of foresight. Strategists and technologists need to collaborate to select, invent and deploy technologies of all kinds that assist these processes.

Tying together prescience and proaction are continuous adaptive learning processes, an adaptive-proactive culture, advanced decision-making capabilities, and the best human capital development practices. Obviously, building the capabilities of prescience and proactivity will be highly challenging, but cannot be pursued further here. You will find a rich source of information on many of these practices and capabilities in ManyWorlds.com’s topics of Organizational Futures, Business Futures and Technology, Advanced Decision-Making, Technological Innovation, R&D, Advanced Forecasting and Planning, and Innovation Processes.

Having created the context, the purpose of the remainder of this paper is to draw out of the background noise, some signals from the future. This means using the frame of the three drivers—acceleration, intersection and systematization—to bring into focus, major waves or vectors of technology-driven or mediated change. The aim is not to produce an impossibly complete list or perfect categorization, but to stimulate your own ongoing observation, making changes more salient in your mind and in your organization.

Technology Vectors 

This section of the paper will present a structured perspective to make sense of the panoply of technological changes. The previously identified drivers—Acceleration, Intersection and Systematization—helped to filter the apparently formless range of changes. I ended up with seven primary vectors or metatrends. Each of these consists of technological trends, often themselves complex in nature. The vectors lie at a level of abstraction intended to assist each person to make sense out of all the disconnected pieces of information we absorb daily.

At this level, the vectors themselves should remain useful tools for many years, even as their components continue to develop and shift. Yet, even these high-level trends of trends will eventually cease to act as an effective map. This point will be obvious to many readers, but bears emphasizing, given the human mind’s penchant for clinging rigidly to any structure in the face of confusing reality.

It should be equally obvious, given the limited length of this paper, that I make no pretense at anything close to a complete list of important existing and emerging technologies within the vectors. These seven vectors should stimulate each of us to recognize additional and new technologies and trends by priming our minds. So long as we are aware of the nature of the vectors as cognitive frames, we can use them to filter and structure incoming information without distortion.

What are these seven vectors? In short, they are:

  • The Ubiquitous Intelligence Vector
  • The Complexity Vector
  • The Infobiotech Vector
  • The Dematerialization Vector
  • The Infosphere Management Vector
  • The Materials Vector
  • The Relationship Vector

This section is intended not as a detailed account of hundreds of technologies, but as a pointer to them and as a rough map of their interactions. So, for each of the vectors, you will find a short explanation followed by a list of the technologies and technology-infused trends. To dig up more information on any of the components of the vectors, I have suggested the most relevant topics on ManyWorlds.com. Alternatively, simply go to the search options, select All for content type as well as All for fields to search, then type in the search word and see what turns up. Finally, you will find almost a hundred relevant pieces of content related to this paper. Just choose the lowest level of relevance to see all of them, or set it to 3 or higher to reduce the number of items. (By doing this, you will be tapping into vector #5—The Infosphere Management Vector.)

You will find considerable overlapping, as one technology or trend often has major effects on more than one vector. As programmers used to be fond of saying: That’s a feature, not a bug.

Ubiquitous Intelligence

“Every breath you take, every step you make, I’ll be watching you.” The words of that tune by The Police sound ominous, but sum up the trend toward ubiquitous intelligence. Here you will find everything from today’s instant messaging systems and the XML standard to the far extreme of a world where every thing is a thing that thinks, senses, and responds to humans, a world where “matter becomes code”. This most outlying point will make more sense after combining the component of Ubiquitous Intelligence with those of the Materials Vector.

Component Technologies and Trends

  • Ubiquitous computing—computational power in just about every physical object in the environment
  • Pervasive computing—a trillion new Internet-ready devices to be added to the network in the next 10 years. Already there are 7.5 billion controller chips compared to 150 million CPUs
  • Omnipresent cameras and other recorders (not just for people, for detailed environmental study and management, traffic flow control, etc.)
  • Embedded/invisible computing—a term some prefer to express much the same as “ubiquitous” and “pervasive” computing, with an emphasis on the technology fading out of sight
  • Embedded sensors & actuators—RFID (radio frequency ID tags)
  • Information supply chain—wirelessly traceable products and shipments. Products that “phone home” for maintenance even before a breakdown occurs
  • Affective computing—computers and computerized objects able to recognize your facial expressions, gestures, or mood
  • Next-generation interfaces—several new interfaces that depart completely from the files-and-folders metaphor are being developed
  • Instinctual computing—related to affective computing, but referring to products that use feedback to respond instantly to your physical movements or changing moods. The Segway Human Transporter is an existing example
  • Adaptive objects—less instant than instinctual computing, and an extension of existing user-modifiability but taken to a new level. For example: automobiles with downloadable driving styles and computer-controlled feel (“drive-by-wire”) and changeable outer shells
  • Location-aware products and devices; Logistical location tracking; global position monitoring
  • Customized experiences based on public databases and information you share (or cannot control)—a plausible illustration: the knowledgeable and personalized shops in Minority Report
  • Single sign-on networks
  • Universal mobile identity profiles that range across all modes of communication
  • Biometrics—machine recognition of fingerprints, voice, retinal blood vessel patterns, etc
  • Object-oriented, reusable software components
  • XML, RDF, messaging, open architectures and other standards facilitating the borderless transfer of information
  • Web services & service-oriented architecture (SOA)
  • Agent-based monitoring software for security
  • Software agents, using either expert systems-type rules (even for commonsense reasoning as in the case of Cog), or neural network-based learning/observer agents
  • Precise positional sound control as well as sound cancellation
  • Analytics, data mining
  • Autonomic computing—self-maintaining and self-healing computer networks, as championed by IBM
  • Processors that continually reconfigure themselves through interconnections and software, to become the application you currently need
  • Distributed or grid computing
  • Peer-to-peer networking—including mesh networks, ultra-wideband (UWB) transmission, ad hoc network architectures, and smart antennas.
  • Distributed information architectures
  • Microsensors and nanosensors—tiny chemical detection devices such as Caltech’s artificial nose and, eventually, an augmented immune system
  • Robots—moving beyond industrial applications, ranging from software bots to microbots and nanobots, to home bots like Roomba
  • Augmented reality—visual overlay of context-relevant information presented by means of heads-up display (HUD), retinal laser “painter” or, eventually, direct input to the optic nerve

Related ManyWorlds.com topics:

  • Artificial Intelligence
  • Business Technology Futures
  • Emerging IT Architectures
  • Enabling Technologies
  • Information Technology
  • Internet Directions

Complexity Vector

Whereas the Ubiquitous Intelligence vector primarily involves tech adapting to us, the Complexity vector involves us using improved understanding of complexity to affect the environment. However, there will be increasing crossover of the two vectors as technologies and people co-evolve and co-adapt. The two vectors touch most closely in the area of instinctual computing. The Complexity vector revolves around understanding both negative (governing, suppressing, balancing) and positive (reinforcing) feedback loops, and knowing how and when to intervene to produce desired results—whether that means preventing gridlock or a heart attack. These feedback loops and complex systems include both natural and designed (or cultivated), the latter usually being fast feedback loops. Using feedback to understand and manage complexity will give us many new abilities but, as Michael Malone put it in “Feedback Universe”, we “will be asked to “surrender to machines reacting to the world far quicker than we are”.

Component Technologies and Trends

  • Complex adaptive system (cas) modeling to gain understanding and the ability to intervene
  • Simulations—of everything from competitive threats in business to the movements of objects in a physics class to biological organs and whole organisms. Also used to generate and test vast numbers of hypotheses (what Michael Schrage calls “hyperinnovation”)
  • Modeling of complex phenomena at a tactical level—already in early use in business for supply chain optimization
  • Autonomic computing, adaptive systems—as in the case of instinctual computing, many of the same technologies are relevant to Ubiquitous Intelligence, but emphasizing different aspects. Autonomic computing systems, according to an IBM viewpoint, are characterized by qualities such as self-knowledge, an inherent optimization ability using control theory and decision support, self-protection using pattern recognition, social network analysis, visualization, and biometrics; and they anticipate how to optimize availability of resources while staying hidden in the background
  • Distributed information architectures that are also adaptive
  • Control of decentralized multi-agent systems (swarms)—a good example being DARPA’s Mixed Initiative Control of Automa-Teams for controlling huge number of sensors and robots
  • Ad hoc peer-to-peer networking that dynamically encompasses new computational and communication resources nearby
  • Artificial neural networks—computing systems that generate outputs not predictable from the inputs
  • Genetic algorithms
  • Automated roads—beginning with simple automated collision avoidance, adding effective traffic flow management, and developing into networks for guiding vehicles to prevent accidents and optimize travel time
  • Closed-cycle feedback loops used for performance metrics and organizational alignment
  • Systematic change management tools that take into account how a change in one variable affects another—such as the Matrix of Change, and Altschuller’s TRIZ matrix

Related ManyWorlds.com topics:

  • Advanced Decision Making
  • Advanced Forecasting & Planning
  • Artificial Intelligence
  • Business Technology Futures
  • Complexity

Infobiotech Vector

The Infobiotech vector is actually the convergence of biology, information technology, micro- and nanotechnology, sensors, electrical engineering and neuroscience. This vector sees biological organisms (most prominently human beings) and “machines” converging.

The result is not the dull, conformist drones of “the Borg”, but heightened abilities for self-repair and augmentation.

Component Technologies and Trends

  • Neuromorphic engineering—basing circuit and control designs on the neural circuits of biological brains
  • Biological computing—including DNA computing; good for some massively parallel calculations
  • Rational drug design, guided evolution
  • Scanning devices—beyond MRI, and PET to include SQUIDs, which use quantum effects to detect minute changes in magnetic signatures, allowing better diagnosis, prevention and treatment. Hitachi’s magnetocardiogram could help accelerate diagnosis of arrhythmia and ischemic heart disease, or enable obstetricians to monitor the hearts of fetuses in the womb
  • Expert systems for diagnosis
  • Biometric tracking—using some of the technologies of pervasive computing
  • Gene therapy and transgenesis
  • Tissue engineering—including a panoply of new methods such as therapeutic cloning, stem cells, distraction angiogenesis, telomerase, and artificial chromosomes
  • DNA and RNA chips
  • Personal genome sequencing—the front runners being nanopore sequencing and a combination of DNA polymerase and specialized optics
  • Proteomics, glycomics, transcriptomics
  • Intelligent drug delivery and tailored medications—such as implantable silicon chips that release drugs at precise rates and intervals, in just the right place. These technologies will come together with measurement of the body’s response and data transmission abilities to enable detailed, real-time biological activity monitoring
  • Artificial pancreas—these use a membrane studded with nanopores
  • Protein folding computation—IBM’s Blue Gene aims to achieve a quadrillion operations per second in order to decode the folding sequences of proteins, to produce more effective and specific drugs
  • Polyvalent drugs
  • in silico biology—simulation of cellular activity or organs
  • Sensory implants—cochlear implants, retinal implants
  • Virtual surgery
  • Brain “pacemakers”
  • BioMEMS—a zoo of devices including cultivars and angiochips
  • Nanomedicine—also known as bionanotechnology, including NEMs (nanoelectromechanical systems)—for biodiagnostic sensing of activity between cells, and eventually medical nanobots able to wipe out any disease and carry out repairs on a cellular level
  • Multiplexing—using nano-level bar codes that tag tiny samples of cells, enabling researchers to more accurately track and measure complex biological interactions
  • Direct neural-computer interfaces, synthetic neurons

Related ManyWorlds.com topics:

  • Business Technology Futures
  • Healthcare, Pharmaceuticals & Biotech
  • Information Technology
  • Infotech-Biotech-Nanotech Convergence
  • Technological Innovation

Dematerialization Vector

The Dematerialization Vector draws together all those technologies that achieve Buckminster Fuller’s dream of “doing more with less”. It’s a sign of this trend that the weight of output in the USA has declined over the last couple of decades, despite steadily growing levels of income and wealth.

Component Technologies and Trends

  • On-demand computing and scalability—just-in-time or just-in-case capabilities
  • Digital cameras, camcorders, and theaters that never need to receive shipment of a film print
  • Rapid prototyping and simulations’
  • Nanotechnology—the emerging discipline of developing a manufacturing technology that can inexpensively fabricate structures with molecular precision. This enables the replacement of bulky materials with superstrong materials in far smaller quantities.
  • Nanoelectronics—nanoscale electronics with components that generate less heat and so enable a multitude of products to be smaller, lighter, and more powerful. At the most sophisticated level, these would be self-constructing electronic systems
  • 3D printing—making some physical goods at home, reducing distribution needs
  • More intelligent use of spectrum—digitally-coded radio signals, smart antennae, self-organizing repeater networks, repeating from device to device at low energy, and increasing in capacity as the number of devices increases—cooperation gain (also achievable through means such as space-time coding to generate digital “software-defined radios”
  • Lower carbon energy footprint from fuel cells, microturbines, and energy grids
  • Virtual worlds and interfaces
  • Bose-Einstein Condensates (BECs), atomtronics, spintronics—all contributing to making devices smaller, lighter, and less power-hungry

Related ManyWorlds.com topics:

  • Broadband
  • Business Technology Futures
  • Drivers of Change & Growth
  • Enabling Technologies
  • Information Technology
  • Technological Innovation

Infosphere Management Vector

We’ve known about the need to manage our information environment since long before we heard the term “information anxiety”. Infosphere management includes filtering out the stuff you don’t want, getting computerized assistance, and direct cognitive augmentation in functions such as remembering, recalling, analyzing, judging, and deciding.

Component Technologies and Trends

  • RSS and other standards for content management
  • Real-time learning—just-in-need packaging and delivery of knowledge, adaptive learning systems
  • Filtering of online content—the current war against spam will drive developments and an arms race
  • Information visualization
  • Haptic interfaces
  • Structured innovative thinking—see the Matrix of Change above
  • Sensemaking—software that extracts hidden relationships between remote bits of usually unstructured information to draw meaningful conclusions
  • Digital rights management—understood broadly to include much more than controlling media. This includes using digital “wrappers” and data federation to select who has access to which bits of information
  • Information classification software—to bring you information of current interest, prioritizing and contextualizing it for you
  • Structured rationality—technological assistance in avoiding typical reasoning and perception biases. For example, SRI International’s Structured Evidential Argumentation System
  • Real-time personalized analytics
  • Instinctual computing—here again, this time for its role in reducing the degree of attention needed to perform a task
  • The Semantic Web
  • Natural language processing
  • Affective computing
  • Embedded & network-resident personal assistants
  • Augmented reality/perception
  • Transmedia (or multiplatform, or enhanced storytelling) and multi-channel enablers—to manage and leverage differing versions of content across mediums

Related ManyWorlds.com topics:

  • Business Technology Futures
  • Emerging IT Architectures
  • Enabling Technologies
  • Information Technology
  • Internet Directions
  • IT-Enhanced Processes
  • Knowledge & Learning
  • Publishing & Media

Materials Vector

As companies such as DuPont will happily tell you, the age of innovation in materials is far from over. In fact, we have entered a golden age for new materials, enabled by a range of technologies, including some of those already mentioned.

Component Technologies and Trends

  • Polymers—polymer skin, polymer nanotechnology, and “plastic spintronics”
  • Foldable computer screens and “electronic ink”
  • Superstrong materials—including “glassy metals” that have amorphous rather than crystalline structures that makes them more resistance to corrosion and fracture, as well as stronger.
  • Nanotubes—starting with buckminsterfullerene, this field continues to expand rapidly, while driving down the price of nanotubes. As yields grow, prices will fall rapidly from the recently cost of around ten times that of gold. Nanotubes have a tensile strength 60 times that of steel, low weight, stability, flexibility, and good heat conductance, giving them a wide range of applications from microelectronics to batteries, flat-panel displays, memory chips, and wireless applications made possible by the discovery of nanotube field emission.
  • Quantum dot nanocrystals (QDCs)—multi-part clusters that monitor biological experiments great sensitivity. One application is “magnetodendrimers” that have been used to track stem cells after implantation in the brains of rats. These and other “nanotube” drugs could mechanically block viruses from receptors and respond to bacterial threats much faster
  • Fuel cells—which could take off when hydrogen storage is boosted by nanotechnology, getting it down to 6.5 percent by weight
  • Microturbines
  • Quantum modeling software
  • MEMS—microscopic sensors for motion, temperature, and just about everything else
  • NEMs (nanoelectromechanical systems)—have obvious implications for materials and reduced energy consumption
  • Laser tweezers and scissors—enabling researchers to hold single cells and organelles in grips of light while carefully altering them
  • Bose-Einstein condensates (BECs)—a group of atoms with the same quantum wavefunction, constituting a new state of matter, discovered in 1995
  • Atomtronics—a field expected to emerge from BECs and related developments. Atom chips could enable better interferometry, lithography, holography and encryption systems
  • Quantum computing
  • Biomanufacturing, biofuels
  • New data storage media, molecular scale memory
  • DNA computing
  • High speed future Internet

Related ManyWorlds.com topics:

  • Broadband
  • Enabling Technologies
  • Healthcare, Pharmaceuticals & Biotech
  • Information Technology
  • Infotech-Biotech-Nanotech Convergence
  • Internet Directions
  • Technological Innovation

Relationship Vector

The New Economy goes by many names, including the innovation economy and the knowledge economy. Information technology is also making it a Relationship Economy in several senses. Information technology does far more than make things smaller and faster. When IT is matched by psychological and cultural intelligence, it can enable entirely new forms of collaboration and cooperation. Millions of us have used infotech to build new, virtual communities, while teenagers are using it to make up their own language for instant messaging on tiny keyboards in their peer groups. Technology—primarily but not exclusively IT—is enabling real-time, personalized learning, novel forms of shared innovation processes, and improved people management in organizations.

Component Technologies and Trends 

  • Collaboration technologies—a large class of software tools and virtual spaces that help knowledge-sharing. This group holds together various types of software, each trying to distinguish itself, from product development management (PDM), partner relationship management (PRM), to employee relationship management (ERM) software
  • Online learning and self-paced tuition
  • Customer-relationship management
  • Collaborative supply-chain management and optimization
  • Business process management and automation—enabled in part by Web services
  • Real-time learning from customers—including what has been called “innomediation”, “co-creation” and the “spy, then innovate” method. All these bring in customers as innovators
  • Online gaming and virtual worlds
  • Standards enabling device-independent collaboration
  • Smart contracts—a type of secure digital rights management that enforces contracts using code with no need for legal authorities to intervene
  • Digital asset management (DAM)
  • Match-making services using collaborative filtering
  • Personal area networks (PANs), wearable/invisible computing devices
  • “Ad hoc community” and dynamic wireless networking
  • Human capital management and development technologies such as real-time deployment tools, succession-planning tools, workforce-development and workforce-planning tools
  • Real-time budgeting and resource allocation in organizations
  • KPI dashboards
  • Advanced human capital development tools that link into external data about demand, supply of talent, current price of talent, and forecasts of demand
  • Social network analysis
  • Augmented reality—for sharing virtual environments
  • Virtual reality technologies, avatars
  • Cognitive communities and knowledge networks

Related ManyWorlds.com topics:

  • Advanced Decision Making
  • Business Processes & Architectures
  • Business Technology Futures
  • Human Capital Development
  • Information Technology
  • Innovation Processes
  • Internet Directions
  • IT Enhanced Processes
  • Knowledge & Learning
  • Organizational Futures
  • Technological Innovation

To learn more about applying the concepts in this white paper please contact:

ManyWorlds Inc
510 Bering Dr, Suite 470
Houston, TX 77057

Tel: 832 242 3508
Fax: 832 242 3512

Visit us at www.manyworlds.com—the premier business strategy resource on the web—or email us at contact@manyworlds.com.

© 2003 Max More. Reprinted with permission.

Footnotes

Three Drivers:

  • Acceleration: Super-exponentially accelerating information technology; smaller, cheaper, ubiquitous
  • Intersection: Combination, blending, cross-fertilization and mutation, and fusion
  • Systematization: Shifting the knowledge search from haphazard to honed, bringing analytical rigor to experimentation and innovation