The Real-time Web (RTW) is buzzing all over the place, but I’m amazed by the amount of different interpretations of it. The RTW – or better said: large scale activity streams – are indeed reshaping the entire business and technology landscape, but how? This will be the first post of a series of articles that will be centered around this topic.
Micro-blogging (status updates) are the most visible form of the RTW, but in parallel we already had things like: RSS feeds, social bookmarking and video favorites, etc. Combine them all together and you have your first lifestreaming services. It’s important to note that lifestreams are the first form of activity streams that have more meta-data than for example a Twitter stream. A lifestream can for example include tags about a certain video that was favorited.
Instead of using the buzz word RTW, I’d prefer using the term Activity Stream. Why? Because the real-time aspect is only one half of the story. The important part is the ‘activity’.
Because of several intersecting trends, we have now arrived at yet another paradigm shift that will radically change business and technology.
The Attention Economy
Twitter is very cool and all, but can it be monetized? Hell yeah!! It’s like in the 19th century when oil was discovered: what the hell do we do with all this black stinky stuff? Well, it burns, so what? Activity is the new oil and Twitter will be the new ExxonMobil. Now it’s time to invent the appliances that run off of it.
In books about Google, like John Battelle’s The Search, the big take away is that Google is uniquely positioned between supply and demand. Well, they are, but they’re still very much in the middle. Like in most of their products, Google misses out on the ‘social stuff’. Having a direct activity communication line with demand is what Google is missing. In the Twittersphere, the dynamics are such that the supply trickles up to the demand (the people) in a very targeted way, much more targeted then Google Search.
This is where the true Attention Economy takes shape. When people explicitly state things about their activities, they leave an attention trail. This trail can be used to actively push relevant services to people.
But there is a caveat, in order to start using this ‘attention trail’, we need two things:
- We need a lot more activity. Right now we only have activity going out that is highly explicit, like the favoring of a picture. But an attention trail should include much more activity events, like “How many seconds did you spend looking at this picture?”. Right now, most systems have separate mechanisms for measuring this implicit attention data (i.e. analytics), but one can imagine that this data will be highly desired to do internal content recommendation.
- In order to properly analyze the attention trail, machines need to be able to understand each individual event. For example: “What was on the picture?”. One way of accomplishing this is by having proper meta-data on activity events. Something I have been struggling with in my Kakuteru project and the very thing that prompted me to build my own aggregation framework.
The Semantic Web
Data mining – the extraction of structured data from text – has been around for a long time. Over the past years several web companies have started doing this by means of open APIs, most notably Zemanta and OpenCalais. But interestingly, someone at Zemanta told me that most of their API usage is for extracting facts from micro-content like Tweets (so I imagine that they will adjust their product accordingly). Unfortunately, many view extracting facts from micro-content as more challenging than large formal texts. In a way it is, because a Tweet for example has a lot of slang and abbreviations in it, but you can make things a lot simpler by mixing in nano-formats. You can for example imagine that a hash-tagged tweet like this is easy for a machine to parse:
“I’m boarding my #flight KL862 to Amsterdam”
So if you combine status updates with the extraction of structured information, you can already imagine an Agent or Twitter-bot providing some useful information in return. In this case for example, it can provide information about the weather in the destination city. Or it could direct-message you if people in your social graph are boarding the airplane as well. These Semi-Intelligent Agents can be considered the first step towards the reasoning stage of the web.
The Synaptic Web
This word came on my radar the first time I was looking at a stream integrated commenting plugin for Wordpress called JS-Kit Echo. I think the word accurately describes what’s happening: the neuronification of the Web (thanks @mcmurrak). Futurist Kevin Kelley has written about this phenomenon as well. In essence, the connected nodes (people and computers) are an emergent global organism. But very concretely, the word “Synaptic Web” captures all important properties: ubiquity, meaning and interconnectedness (social).
So what is this Synaptic Web, and how will it change everything? Well, that remains to be seen and it’s something I plan to elaborate on in many articles to come. But here are some appetizers for thought:
- Google’s Adwords were a very effective form of advertising because they were so targeted, but what if advertisements become so targeted that they are essentially offers/solutions provided to you? Will we still think of them as advertisements? How much are these ‘advertisers’ willing to pay for an advertisement that is 90% likely to trigger a full engagement with the consumer?
- Demand for something specific is always temporary. When a big clustering of needs is ‘detected’ small nano-companies can be formed to fulfill these needs until they are gone.
- Disambiguation is something that has been key to innovations in semantic technologies and will be very important for the Synaptic Web. Are we talking about the same thing, place, person? Machines need to be able to understand these things and serious innovation is needed here.
- When machines are able to translate a concept like ‘NYC’ to a specific location with coordinates, they might become an important tool in quickly transferring concepts to each other. So instead of communicating in text, audio or video we could communicate in actual concepts. Of course this all depends on how we interface with the web.
Rethinking the Knowledge Economy
During the few ‘off hours’ I have, I love thinking about innovation and technological change in the grand scheme of things. In fact, a couple of weeks ago I did a weekend trip to New York on my own budget to attend the Singularity Summit 2009 – a conference of futurists. In my recent thinking I’ve stumbled upon a nagging little problem: The concept of the Knowledge Economy is very ambiguous and ill defined.
When I look up the definition of Knowledge Economy on Wikipedia, there is already a big alarm bell going off in my head when I read: “In a knowledge economy, knowledge is a product, in knowledge-based economy, knowledge is a tool.”. The words product and tool treat knowledge on par with physical goods, which in my mind is the reflection of a fundamental disconnect.
Knowledge
In the academic world there is no real consensus on the difference between information and knowledge, a definition that’s not at all trivial. Asking the question will most likely yield different answers depending on which ‘field of expertise’ you consult (management, philosophy, computer science), but there is a common way of explaining the concepts of data, information and knowledge on a continuum:
“Knowledge is the whole body of cognition and skills which individuals use
to solve problems. It includes both theories and practical, everyday rules
and instructions for action. Knowledge is based on data and information, but
unlike these, it is always bound to persons. It is constructed
by individuals, and represents their beliefs about causal relationships”
(Probst, Raub & Romhardt, 2000)
There is also a very popular view (by Nonaka & Takeuchi) in which knowledge is divided up into explicit knowledge and tacit knowledge. But this widely accepted view has now come into dispute because many claim the concept of explicit knowledge is no different than information.
No matter what stance you take, it’s clear that knowledge always has a certain context associated with it. On one end of the spectrum this could simply mean that certain chunks of knowledge are useless without other chunks of knowledge (still explicit). In other cases it might be that the knowledge is completely useless without a lifetime of experience or skills (tacit).
However, I do think it’s important to realize that while tacit knowledge is a lot less ‘portable’ than explicit knowledge, this could change very quickly with the advent of technology. With the existence of zero-cost communication, learning-enhancement software and artificial intelligence, tacit knowledge is also becoming more ‘portable’.
Many thanks go out to Jozua Loots who helped me with the question “What’s the difference between knowledge and information?”. All done through an awesome new (synaptic) Q&A service called Aardvark.
Informational v.s. Physical
To understand the fundamentals of the problem, we have to take a look at the difference between information and physical objects. Physical objects abide by different laws than information. A physical object can only exist in one place in one time and it deteriorates when used or touched. Information on the other hand, can exist in many places at any time and multiplies when touched.
Thanks to zero-cost communication, the replicating nature of information has showed itself over the last decade. There are now vast amounts of knowledge (and obsoledge) being generated every day, making many derivatives of information (content, knowledge) a commodity. Kevin Kelley explains this well in his essay ‘Better than Free’, where he compares the internet to a giant copy machine where the copies drop in value. Interestingly, when those copies become abundant, the value starts shifting to what’s scarce: the attention of people. This is where the concept of the Attention Economy starts.
A Non-Economy?
If information is so fundamentally different than material goods, you can start asking the question: Does the term ‘economy’ apply to knowledge at all? Let’s take a look at the wikipedia definition of the word ‘economy’:
“An economy is the ways in which people use their environment to meet their material needs. It is the realized economic system of a country or other area. It includes the production, exchange, distribution, and consumption of goods and services of that area.”
And here is the definition of an ‘economic system’:
“An economic system is the system of production, distribution and consumption of goods and services of an economy. Alternatively, it is the set of principles and techniques by which problems of economics are addressed, such as the economic problem of scarcity through allocation of finite productive resources.”
Both definitions are inherently bound to ‘goods’ and the fundamentals of finite production. The problem is that with information, there is infinite free replication. This explains why it is so hard to use traditional methods of economics to measure and understand value created by information. But fortunately there is hope in the definition of ‘economics’ itself (by Lionel Robbins in 1932):
“The science which studies human behaviour as a relationship between ends and scarce means which have alternative uses.”
Because even though the characteristics of information/knowledge are so different , the economic fundamentals of abundance and scarcity still apply.
Informational Drivers of Economic Growth
The influence of information on wealth creation is quite complicated. The biggest mistake people make is treating information (or it’s derivatives) as a physical good that can be traded. So if you can’t sell it, it has no value? Yet information plays a profound role in driving economic growth.
Hans Rosling, a Swedish econometrist has given several TED talks in which he showed how developing countries have been caching up with great speeds. Every booming developing country had it’s own drivers of growth, but one can imagine that a common driver would be the availability of ‘know how’. This ‘know how’ took the Europeans centuries to develop and apply, but for developing countries this was readily available and could be applied fairly quickly. This application of explicit knowledge set off the main driver of growth: change. And when you apply new knowledge to a country that needs to build things from scratch, you get a rapid rate of non-incremental change.
On the scale of an entire economy, it’s really the non-incremental change that matters. An example of normal incremental change could be aesthetics, the improvement of physical products. Another example would be a well-oiled service industry that services existing markets. All of these dwindle in comparison to the amount of value created by fundamental change. Real innovation will create and destroy new markets (e.g. telephony, online advertising, social networking, etc) whereas incremental change merely optimizes existing market dynamics.
The rapid shift from physical systems to more informational systems – informationization – goes hand in hand with non-incremental change. When a system becomes more informational and has less physical obstacles, changes can happen more quickly. And these changes are non-incremental, meaning that informational systems will have more paradigm shifts, are less predictable and have more volatility. Nassim Nicholas Taleb (NNT) explains this in a different way in his famous book The Black Swan, whereby he defines an Extremistan (informational-law world) and a Mediocrestan (physical-law world).
To summarize:
Note: A nice example of informationization can be found in the biography of Nikola Tesla, one of history’s greatest inventors. Tesla had a special brain condition where he could visualize and iterate his inventions in his mind using his photographic memory. This allowed him to innovate at a very rapid pace, because he did far less physical experiments in the innovation process.
Stimulating Non-Incremental Change
Some governments, like my own (the Netherlands), come up with special ten year action plans that try to create a vibrant ‘Knowledge Economy’. What should these action plans entail and how relevant is the knowledge aspect of things?
The first thing that needs to happen is the stimulation of informationization. This requires removing physical constraints in for example bureaucracy. Many governmental organizations still require you to handle paperwork with real paper or require you to unnecessarily interface with a person. Also, there needs to be a general reduction of the amount of bureaucracy. This could be done for example by reducing the amount of certifications required to start a certain (informational) business.
Education needs to stimulate independent thinking. They need to stimulate their students to do new things (sponsor adventurous travels?), but more importantly: they need to shift focus from ‘knowing’ to applying knowledge and using creativity.
Corporations need to be formed ever quickly, but more importantly, they need to be dissolved quickly too. Companies – that are getting smaller and leaner – must be able to fail early and often in order for real innovation to happen. This also means that you need a culture that can deal well with failure and makes sure that the people involved don’t have to deal with ‘face loss’. On the flip side people need to be rewarded when they are successful, this might mean having a more loose taxation system for the wealthy. One idea here could be to allow tax-free re-investments of earned capital to stimulate successful entrepreneurs to become angel investors.
Which brings me to entrepreneurship: you need a very vibrant investment climate that has VC’s and angel investors that invest in bold ideas. Many countries outside of the US cope with a sickening amount of risk averseness. When you present a prototype to a European investor, they don’t ask you when you will generate revenue, no, they will ask you: “when will you break even?”.
Summary
The economics of a world that is becoming ever more complex are not as simple as they have been. The concept of a “Knowledge Economy” is outdated and is not factoring the new dynamics of informationization. The real driver behind economic growth is non-incremental change, something that is catalyzed by informationization. In order to gain economically, governing bodies, companies and people need to stop resisting informationization and go with the flow.