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Archive for January 19th, 2008

Marty Secada (linkedinmarty at yahoo dot com)

Managing Director Broad and Wall Advisors (4,800+) Alternative Investments

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What is the future of social and business networks?

What is the future of social and business networking?

It seems that social networks are popping out of the woodwork at a faster pace than ever. New specialty business networks occur weekly and on Facebook alone, groups are created daily, the successful ones offering tremendous value to members. I just came across this article comparing Linkedin to Facebook and admiring Facebook for its richer environment. http://www.businessweek.com/technology/content/aug2007/tc2007085_238273.htm .

Many of Linkedins biggest users have complained about its lack of customer service and barren platform. Many of Linkedins power users take Linkedin to the next level for business development purposes. Is Facebook the future? Has Linkedin been left in the dust? How can Linkedin catch up? What are obstacles to a social or business community minded individual from making their own community and competing with Linkedin and Facebook, or just floating their own alternative without profit motive. Is there a substantial cost to building a state of the art social network or is it just a rush for large membership numbers.

Please share your views, we’d like to know and if you are on Facebook, feel free to connect with the many linkedin users there as well.

posted 5 months ago in Business Development, Web Development | Closed | Flag question as…

Answers (43)

Kristian Melhuus Brandser is a 2nd-degree contact

Kristian Melhuus Brandser

Computer professional & entrepreneur

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Hi,

We at Community Reborn (a company developing specialized community software for the entertainment industry) believe we will see a shift towards fewer larger “general” communities, like Facebook for college-socializing and LinkedIn for business-networking, complemented by a lot of smaller specialized communities with specific content and functionality. E.g. a “fly-fishing community” with custom fly-fishing-bait-design-application

We have created a common platform for such specialized communities. This gives economies of scale on development of common community functionality and opens the possiblity for user and content collaboration between communities.

Facebooks problem in my opinion is that is tries to do both things at the same time.. By being a generalist community it will not have appeal to specilist community users and vica verca.

Links:

posted 5 months ago | Flag answer as…

Stephen Bailey is a 2nd-degree contact

Stephen Bailey

Senior Executive Outsourcing Industry

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Stephen Bailey suggests this expert on this topic:

Hello Marty,
Thomas Power is one of, if not the world’s foremost experts in online communities. He is easy to find on Ecademy (as he founded it) and easy to contact (just google him and his telephone number will appear).

Thanks
Stephen

posted 5 months ago | Flag answer as…

Matt Genovese is a 2nd-degree contact

Matt Genovese

Social network builder in Austin, Texas; Hardware verification engineer, software consultant.

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Best Answers in: Using LinkedIn (7)see more, Professional Networking (1), Mergers and Acquisitions (1), Government Policy (1), Staffing and Recruiting (1), Viral Marketing (1), Business Development (1), Public Relations (1), Planning (1), Starting Up (1), Wireless (1) see less

Hi Marty,

I understand where you’re coming from. I think LinkedIn does a tremendous job at allowing people to network on a global scale, keep up to date with their contacts, and tap into a large online knowledge base of users.

However, where it falls short is in facilitating local networking. For example, I recently started a LinkedIn group and associated website just for high-tech professionals in my home town of Austin, Texas. The goal is to network within our own geographic region, which has its own benefits (for instance, the ability to physically meet with the people you interact with online, and to discuss issues relevant to our locality and professional “scene”.) In my mind, that type of networking is very beneficial and much more tangible, but yet outside the more global scope of LinkedIn as it stands today. I think of the regional LI group as an extension of LinkedIn, and in turn LI would do well to enable bootstrap such initiatives.

Cheers,

Matt

Links:

posted 5 months ago | Flag answer as…

Alastair Bathgate is a 2nd-degree contact

Alastair Bathgate

Managing Director at Blue Prism Limited

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I envisage a future where everyone has their own website (currently mostly blogs but this will probably be only one feature of a personal website).
Social and business networking sites will then become little more than URL exchanges. Except they will still be able to offer added value in connecting groups, events etc. The key difference though is that they need to become non proprietary. Many poeple have already complained about the walled gardens of networking sites and asked for the sites to be opened up. The easy way to achieve this is to separate the personal information layer from the connecting layer. I wonder which networking site will be first to recognise that this is an opportunity not a threat?

Links:

posted 5 months ago | Flag answer as…

Ravi Shekhar Pandey is a 2nd-degree contact

Ravi Shekhar Pandey

Manager, Syndicated Research, Springboard Research

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While there are many aspects to the answer you are seeking, I will just focus on one — the ablity of the social / business networks to foster a more dynamic and vibrant culture of knowledge sharing and innovation. My understanding is that these networks will play a key role in all future innovations that the world will see. For instance, imagine this situation — Company A had just launched a new Computer which is being discussed threadbare by millions of its prospective customers from around the world on a networking site that brings together people with deep interest in computers — a million customers discussing a new product — that means a million new ideas for that company.

posted 5 months ago | Flag answer as…

Matthew Gallagher is a 2nd-degree contact

Matthew Gallagher

Vice President, Interactive Creative Director

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LinkedIn is an interesting community, but it is also a closed community. One of the issues with any of these social networks is its detachment from the other networks that a member may be participating within. For example, one of the technology boards I frequent had a post asking “How web 2.0 are you?”; meaning what networks do you participate in.

I am active in over a dozen sites (linkedin, flickr, delicious, twitter, etc.) and member of nearly twice that many. Some are business experiments, while others are social experiments.

Facebook has an open API as well as some competitions that are inviting programmers to develop applications for Facebook; integrating it with the way that people use the whole web, not just social connections. Facebook, also has some legal troubles on the horizon. If they can weather them, their platform is far superior to competitors than MySpace.

Netvibes is another site that has gained in popularity because it allows the user the ability to customize their experience to how they wish to receive the data.

In my opinion, the success will hinge on not only expansion of the services offered, but the integration with the work-flow of the visitor.

posted 5 months ago | Flag answer as…

Matthew Zachary is a 2nd-degree contact

Matthew Zachary

Founder & Executive Director, I’m Too Young For This! + Advisor, Google Health

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The next two big things in social and business networking, in my belief, will first be the convergence of consumer health (with various disease verticals) followed by aggregates such as early startup SocialURL.com. User profiles will become mini-wikis with branches into all sectors and sociological components of “me-generation” metrics. Google Health, for which I am an advisor, along with other emerging enterprises such as Steve Case’s Revolution Health demonstrate a clear direction from where the next big thing is coming.


Matthew Zachary
11-Year Young Adult Survivor
Founder, Executive Director
i[2]y, I’m Too Young For This!
Advisor, Google Health
w: 877-735-4673 x701
f: 718-745-1928
e: MZachary@ImTooYoungForThis.org

I’m Too Young For This! is a global support community for young adults
affected by cancer who get busy living and rock on. We use music to make it
hip to be a survivor and talk about stupid cancer by providing ‘one-stop’
access to hard to find resources, peer support and social networks.

Got cancer? Under 40? Sucks, huh? Get busy living!

Website: http://ImTooYoungForThis.org
*TIME MAGAZINE TOP 50 WEBSITES, 2007*

Links:

posted 5 months ago | Flag answer as…

Bart Suichies is a 3rd-degree contact

Bart Suichies

New Media Strategist

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I believe that the next step in social media will be ‘distributed social networks’, where there’s not one site or platform that will become the winner, but instead all individuals will have their own networks with them at all times.

Users are going to decide which network they need (adhocracy) at the moment they need it. Current networks like facebook/linkedin will become obsolete or will just become open storage facilities for contacts. A standard identity protocol will arise (like openID) for authentication in any given network and XML / microformats / etc will do the rest.

Adhoc networks will be created on demand on any device (we’ll see a strong rise in mobile – contextual – social networks) that communicates an open/standard language. This will give users an unprecedented level of privacy, flexibility and value.

posted 5 months ago | Flag answer as…

Danny Small is your connection (1st-degree)

Danny Small

Motivational Change Consultancy – Business & Personal Support [danny@kelta-associates.co.uk]-LION

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Hi Marty,

The future is looking good for networks, it’s all about the people and their expectations, like most developments that we as humans have created we all get on board and as times go by, we make more demands and our needs and desires grow.

My son is using a social network, he is in touch with friends who he can “Actually meet” also virtual one. It depends on what you need to get out of the network, it is after all just a tool. For connecting, communication are just a way to find out how to make money.

If the people find that the ‘tool’ does not serve a purpose they just discard it and find something more usefull.

It is musch the same with a TV, it started of small and has developed along the way but as it changes so do the things around it and we get comparisons and variables and alternatives.

I think the future is what you have said “Its in the distance” and we will Change as the Future changes! or we also will get left behind.

I have not been involved with LinkedIn as long as some people but I’m learning fast – How long have you been onboard and do you think there is a period where it just does not satify expectatons.

Good question.

Danny

posted 5 months ago | Flag answer as…

Michael Stephen Ruiz is a 2nd-degree contact

Michael Stephen Ruiz

Entrepreneurial, Bottom-line Visionary with Multiple Talents & Resources in High Technology & Security, CIPP

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Marty, that is an excellent, timely question. This is the answer:

http://www.cio.com/documents/webcasts/socialtext/wiki_workplace/

Mass collaboration inside/outside the present corporate structure to create, develop and facilitate products/services to the 80M 13-29 year old individuals who are the “masses” at this point in time.

The development of wikis is unsatisfactory to me at this point. No security protocols, no flexibility, and no dynamic liquidity. I want to move, shape, and absorb my resources in real-time instead of directing them in a two dimensional manner. The company who creates the new advanced wiki will surely create a paradigm-shifting event.

Michael Stephen Ruiz also suggests this expert on this topic:

posted 5 months ago | Flag answer as…

Diane Danielson is a 3rd-degree contact

Diane Danielson

CEO, downtownwomensclub.com

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Best Answers in: Mentoring (2)

Very good question. My experience with social networking for business is that it’s more “task-oriented” and less “social” than the name connotates. Hence, I LOVE this Answers feature (I use the Harvard Start-ups yahoo group similarly) and that is really my main use of LinkedIn, other than running a LinkedIn group for my business. Per an earlier answer, I found that I have a better relationships with my “blogging buddies” rather than individuals on any “social network.” But, often those relationships have involved introductory phonecalls or face-to-face meetings. However, I confess to being an “older Gen X’er” so many of my peer and boomer contacts (even if they are bloggers) are less likely to be on social networks, and still prefer the phone.

posted 5 months ago | Flag answer as…

Ido Goldberg is a 2nd-degree contact

Ido Goldberg

IT Manager at Kidaro

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Hi ,
I think that is a very interesting question,
And I’ll try to keep my answer simple because it’s a very philosophical question.
I think that WEB 2.0 and the future WEB 3.0 (and their vision) made us , the internet users and companies, realize that networking .. both social and business are the main reason we actually use the internet, I mean .. of the “old days” before 2000 we logon on the get some information on a service , product and so forth info we wanted in a certain time i.e. tickets for movies, train hours …
On WEB2 and 3 its been clear that the internet has grown in to communities its not only a big shopping mall or information counter, its where people do a lot of socialing and business in an infinite ocean of information and the smart thing to do is realize what do YOU want and when so we can provide useful information,
I think that the real answer is how the internet will allow us to overcome the cyber way on doing things on WEB 4 maybe :).
But for now networking is the real way we do business therefore there is a great future in it.

posted 5 months ago | Flag answer as…

Eric Mariacher is your connection (1st-degree)

Eric Mariacher

Embedded Software Manager ▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄▀▄ [eric.mariacher@gmail.com] LION/MyLink500.com

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The future is ning where you can create your own social network.

Links:

posted 5 months ago | Flag answer as…

Mike Myatt is a 2nd-degree contact

Mike Myatt

Managing Director at N2growth, America’s Top CEO Coach and author of Leadership Matters…The CEO Survival Manual

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Hi Marty:

I hope all is well…Okay, here’s my take…I believe social networking will follow the same macro and micro economic trends consistent with new technology/market genres. The first movers will be pushed to adapt and evolve by the fast followers, and frothy capital markets interest will fuel high-velocity growth until the reality of business sets in…

This vertical will go through a harsh consolidation phase where flawed business models will be weeded out and the overall vertical will be strengthened as the strongest brands survive and prosper. Sites like LinkedIn cannot rest on their laurels. They must begin to pay attention to member needs and use a focus on member centricity to drive innovation. Your question addressed the cost side of developing social media and that is part of the problem is that there is not a significant cost barrier to entry. A simple mash-up social community can be launched in a matter of weeks. The challenge is in creating value, attracting and retaining members. Even a large member base can erode or churn if they don’t perceive a commitment on the part of principal owners to continue to add value.

Links:

posted 5 months ago | Flag answer as…

Zygmunt Lozinski is a 3rd-degree contact

Zygmunt Lozinski

Telecom Industry Technical Leader at IBM

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I believe social networks are here to stay, but that we will see changes in how they are used and how they are designed.

Three trends:

1. Social networks will become platforms, with open APIs which allow new services. The value of these networks will then be driven by the new applications and services they support. We have seen this already in Facebook, and Second Life, and MySpace has also announced it will create open APIs.

2. The opening up of the data that underpins the social network. In effect enabling people to create data-mashups.

3. The changes in usage patterns. The question here is do we get convergence on a small number of massive platforms, or divergence onto multiple platforms. If the individual social networks allow cross-network linking, there is no reason for convergence on a single platform. (There are over 700 phone networks world wide but you can talk from anyone to any other.)

posted 5 months ago | Flag answer as…

Mark Wayman is a 2nd-degree contact

Mark Wayman

Co-founder – Social Gears (www.socialgears.com)

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Hello Marty,

A recent Bear Stearns report has Facebook valued at an astonishing $4.5 billion to $7 billion. MySpace, Facebook and LinkedIn and many more have proven social networks to be as a viable business model and also educated their respective user bases on the concept.

As to the future, I have to agree with Kristian that the new opportunities exist for more focused communities that include the core “general” functionality and extend it with features for their specific niche.

We are currently working on exactly this for the beauty industry around the http://www.salons.com domain name. Compared to a few years ago the startup cost for this venture is substantially less. The real challenge for us is awareness and creating the “spark” that keeps our customers coming back.

Cheers, Mark.

Links:

posted 5 months ago | Flag answer as…

Michael Cayley is a 3rd-degree contact

Michael Cayley

Actualizer: strategy, brands, new media

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In the future all businesses will employ social network platforms to help them leverage these networks in the development of corporate social capital. Social capital is the aggregate of benefits of an individual’s (and a corp is a kind of individual) social networks. For corps this means employing platforms that empower/integrate constituencies such as investors/analyst/media, suppliers (think WalMart), employees and above all customers (particularly as volunteer product development, marketing & sales forces). Each individual within the social network can now be theoretically as powerful as NBC.

Corporate valuation of many companies is already far more attributable to corporate social capital than the traditional notion of brand. Take Google, Amazon, YouTube and MySpace as a few examples. As corporate social capital becomes more apparent as an authentic source of corporate valuation, in the same way that brand has since the late 1980’s (when the Barbarians were at the Gate), more companies will invest in the development of their social networks (first through tech platforms, then through initiatives that mobilize the constituents enabled by the platforms).

Attention Getting Bold Prediction: Within the next 10 years corporate social capital, an authentic, tangible asset, will account for more corporate valuation than brand (intangible, conceived to be manipulative) in more than 50% of companies.

Once the motive for one of mankind’s most efficient forms of organisation is firmly established (i.e., social capital as a source of valuation for corporations) lots of cool things are going to start to happen. Companies will reinvest in employee loyalty in new and exciting ways, corps may find that entering markets with low social capital (no democracy, little transparency, corruption) and being a positive force of change may be a source of high corporate valuation … McLuhan’s tribal beat and global villiage (frightening notions according to him) are becoming reality as traditional broadcast media is replaced by a totally interactive, highbandwidth format that we are only beginning to discover and do not understand.

posted 5 months ago | Flag answer as…

Marty,

a few months ago I posted some thoughts under the title: “Corporations, networks … what next?” Please use the link below:

Links:

posted 5 months ago | Flag answer as…

Carrie Bedingfield is a 3rd-degree contact

Carrie Bedingfield

Owner of B2B Marketing and Internal Comms agency, Onefish Twofish

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Hi Marty – it’s a really great question.

I think the big issue is around revenue streams. Will social/business networks remain free, in the most part, or will they start to generate their primary income from subscriptions and services. I can’t be the only person who feels that it’s now becoming quite expensive to do simple things on LinkedIn! The business model seems to be ‘get them hooked while it’s free, then start to add in the charges’. I’m a member of a couple of other networks which have moved from free to far-from-free too quickly for my liking.

I’m not sure what the price elasticity of online networks is – at what point do profits start to fall as prices increase? At what point is loyalty significantly eroded?

My prediction is that one major player in each field (social and business) will continue to offer a free, fully functional service and really start to monopolise the market. They will make their money on alternative services and clever diversification which permeates other online and offline industries. This is how the search engine market evolved – perhaps a good indicator of what’s to come for online networking?

Carrie Bedingfield
http://www.onefishtwofish.co.uk

posted 5 months ago | Flag answer as…

Laban Johnson is a 2nd-degree contact

Laban Johnson

Founder, the Laban Johnson Group -“Improving the Quality of Life”

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I’d say the future of social and business networking is what we make of it!

posted 5 months ago | Flag answer as…

Sandra Voss is a 2nd-degree contact

Sandra Voss

Realtor at Michael Saunders & Co and Owner, Sandra Voss, Realtor

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I believe social and business networks are going to continue to grow and meld. I think every salesperson knows the value of referred leads. How else will we grow them except through our personal contact, social and business networking? However, very few salespeople know how to generate a consistent supply of referred leads….and it seems to me that the IT sector is taking the lead. In my business — real estate, as well as other service industries, relationships are the cornerstone of a successful business. The methods employed to generate leads, however, are often non-relational – activities like cold calling, door knocking, direct-mail, advertising etc. It is amazing how many folks are out there still stuck in that rut ONLY doing those things. I think if you are in a service business, building relationships through giving excellent personal service + increasing you social and business network is the number one way to increase business. But having a system of balancing these is a must to have in place. For me – I have to intentionally generate those referrals. A continuous stream of referrals doesn’t just happen. It is created and cultivated….including social and business networking.

Sandra Voss also suggests these experts on this topic:

posted 5 months ago | Flag answer as…

Sinnary Sam [LION] is a 2nd-degree contact

Sinnary Sam [LION]

Founder & CEO

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Marty, thank you for asking this question. The answers give me a wealth of information that I can now research as well. I am still not sure as to what the true benefits of Linked In will be for me. I have found some past co-workers & contacts. Other than that, I would like something that will work with my organization so that my members are profiled and can be filtered as such, but still be connected to the entire network as well.

Sinnary Sam
http://www.FOREnetworking.com

Links:

posted 5 months ago | Flag answer as…

John Inman Ed.M. PHR is a 2nd-degree contact

John Inman Ed.M. PHR

{LION} Expert in Human & Organizational Development {jinman@wetherhaven.com} {MyLink500.com}

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Best Answers in: Career Development (1)

I seem to be invited to an ever increasing number of obsure networking sites. My advice is to focus. Unless one has endless amounts of time, I do not see how to keep up on too many sites. I am on at least 6 main sites but only am really active on LinkedIn. My profiles are complete on each that I join. And I am not sure the question is can LnkedIn catch up to facebook. I do have a facebook account but it does not feel streamlined for business use to me. Maybe it is just me. In business I do not want to have too much cute stuff out there. My LinkedIn profile is my calling card, my online resume, my vita. I think there is a risk to make yourself look too cute for at least the current main stream.

This certainly could change within a hand full of years but by then, linkedin will still have innovated and met the needs of a changing market, at least the core business community.

Just thinking outloud. Great questions.

John

posted 5 months ago | Flag answer as…

Peter Nguyen is a 2nd-degree contact

Peter Nguyen

Editor in Chief, CareerKnowledge.net (omnidigitalbrain@yahoo.com)

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Einstein said our age is characterized by “profusion of means and confusion of ends.”

I think it applies to new technologies and applications, including social networking sites.

The key question is, Why do you need to connect to other people? What is the message you carry, or what is the value you offer?

Technology cannot save people or make any person successful. Only clarity of purpose and constancy of aim.

posted 5 months ago | Flag answer as…

Hans Sluijter is a 3rd-degree contact

Hans Sluijter

Vice President at ABN AMRO- Business Manager Services Western Europe

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Hans Sluijter suggests this expert on this topic:

posted 5 months ago | Flag answer as…

Glenn Dhooghe is a 2nd-degree contact

Glenn Dhooghe

CTO at Emmis Belgium Broadcasting. Expert in PC hardware and PC-audio solutions.

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Hi Marty,

I’ve been giving this topic some thought too, and it was the root for many interesting discussions. Thank you for bringing it up here!

There are many insightful and viable answers here already.

I like Zygmunt Lozinski’s answer, and would like to add my 5 cents to his answer:

API’s will allow integration with other resources. As informatics are finding their way into every place (media stations at lifestyle locations: bars, hotels, restaurants, shops; home; car; …), I believe integration with these other resources will be a huge step forward. You can track people that have the same interests as you – people you cold have met in real life, but just didn’t bump in to.
In these locations, it could also be possible to use your network. It could very well once happen that your PDA warns you of a contact that is nearby, so you could finally meet that guy whose interesting blogs you’ve been reading for weeks – because he’s sitting 10 feet from you, sipping from his cocktail.
As Matthew Zachary said, connections with health administration could allow you to contact people with similar medical conditions. You are no longer facing situations alone!

Stores could remember you and your profile, so when you pass by the store, it could display advertisements that are specifically suited to your unique taste – based on previous purchases, or with which lifestyle groups you’re affiliated.

Cross-networking will eliminate the need to keep track of 20+ sites. If you like the more localized layout of another community – use that with the crosslinked databases. If you’re looking for worldwide contacts, come here. The benefits will be more emphasized, and the rough edges flattened.

Clarification added 5 months ago:

I have completed some realizations in this sector. If anyone is interested in starting business ventures, or exchanging ideas, feel free to contact me!

posted 5 months ago | Flag answer as…

Paul Pajo is a 2nd-degree contact

Paul Pajo

Regional Sales Manager for Emerging Markets at Asia Payment Technology Corporation

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It will be integrated into secure-online payment gateways as well as with SMS. I think that’s the “last mile” for social media optimization

posted 5 months ago | Flag answer as…

David Burta is a 3rd-degree contact

David Burta

Owner, ProVent Associate

see all my answers

One core component to the future of social and business networks is the addition of CONTENT and interaction around that content. An emerging example of that is a public learning forum where anyone can place learning materials and anyone can take advantage of it called LatitudeU which can be found at http://www.latitudeu.com .

Links:

posted 5 months ago | Flag answer as…

Marc Rapp is a 2nd-degree contact

Marc Rapp

Creative/New Business Development at Renaissance Creative

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The future, in my opinion is; a desktop widget with a video avatar, message system from a drop down menu of contacts, public notifications, updates, etc. All existing networks that I belong too can be easily accessed through an icon-driven navigation. I can also drag+drop links, files and text documents onto other user’s avatar’s and have the information sent. I may choose to visit the ‘hub’ ( main website ) for more information and functionality throughout the day. However, I should not have too. ‘Getting online’ will become a secondary step to ‘connecting’ as re-skinnable widgets become our main working environment. Mainly because they offer companies an opportunity to brand themselves and customize the windows we navigate the web in.

This of course, will replace the browser and it’s a triple interface experience . Skitch is on it’s way to something similar.

In the immediate, I would suggest that we bare a few things in mind, LinkedIn is not a social network. It is a business network. Start there.
Freelancing services.
Invoicing.
Transactions.
Company profiles.
Portfolios.
Audio/Video resumes.
Plaxo style contacts and connections.
Ziki style feeds and updates.

Social networks are capable of becoming the new homepage for users.
Treat it that way. One interface to rule them all.

Just a few thoughts.

Clarification added 5 months ago:

Also, let us not forget that we live and breath in this environment. There is a great deal of education needed on the consumer/user side. The more layered these systems become, the more likely they will be ignored by prospects. Some social networks are dangerously close to loosing their context in spite of their content.

posted 5 months ago | Flag answer as…

Robert Hahn is a 3rd-degree contact

Robert Hahn

VP, Marketing at OnBoard

see all my answers

Interesting question.

Since I’m implementing a social network for my enterprise as I write this, I’m somewhat biased in the direction of private social networks. For a variety of reasons, corporations simply cannot use a fully public social network for its internal network — which, I argue, is far more important than a bunch of people out there on the web, for day to day productivity.

The next big thing, I hope, is a common set of data standards that will allow all social networking sites/tools to share data with each other. Single point of data entry is an absolute requirement if this social networking thing is going to expand.

So for example, we will have some 7,000 members within the Coldwell Banker Commercial network who are busy doing deals with each other, networking within the company, etc. If we could interface directly with LinkedIn or FaceBook or whatever, from a single data-entry source, that would elevate the entire industry space to the next level. Without data sharing, we’ll all be stuck in our individual silos. That’s just a fact.

Think of something like Trillian that aggregates multiple IM services, but works in the social networking arena. That is what this industry space needs.

-rsh

posted 5 months ago | Flag answer as…

Ofer Vilenko is a 2nd-degree contact

Ofer Vilenko

Acquisitions Manager for a Manhattan investments firm

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Hi Marty,

I think Matt Genivese was right on the money. The need is out there and someone has got to satisfy it, whether it will be linkedIn or another service I really can’t tell but speaking from a business/professional point of view people would like to have both their global and local networks to work with. A simple matter of convemience

posted 5 months ago | Flag answer as…

Alex Kent is a 2nd-degree contact

Alex Kent

Corporate and Investment Real Estate Strategic Planning and Transaction Management at JULIEN J. STUDLEY

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eHarmony-like psych interviews combined with Google Desktop search helps people to find business partners, new hires, and even customers that share their values and mindset.

Who’s in the best position to make this happen? Google, of course.

posted 5 months ago | Flag answer as…

Andy Lopata is a 2nd-degree contact

Andy Lopata

Business Networking Strategist

see all my answers

At the moment, ‘social networking’ is a catch all term that covers a multiplicity of approaches. That is why LinkedIn and Facebook get mentioned in the same sentence, despite performing completely different functions. A host of ‘business network dwellers’ are going over to investigate how to make the most of Facebook and finding it a strange environmet, because it’s designed for social interaction rather than referral generation and profile building.

That’s not to say that you can’t do business on Facebook; but it does take time to work out how to leverage it most effectively.

I think that social media will begin to seperate into distinctive camps:

1 – Truly ‘social’ networks, like Facebook, Bebo and Friendster. The prime users of these sites will be a younger demographic using them to keep in touch with friends, arrange parties and share photos and videos.

2 – ‘Social Business’ networks, like Ecademy and LinkedIn. Although Ecademy has a social element, it is still designed for and populated by business people, predominantly small business owners. Earlier responses to this question mentioned the need for locally-based social networks and referral generation, we are currently in the process of launching a new ‘social business network’ at http://www.wordofmousenetwork.com. The model will be much more locally based, bringing together businesses for referral-generation in the way BNI do offline.

3 – ‘Private Internal Social Networks’ – as IBM already run with the ‘Blue Pages’, other large organisations will slowly recognise the need to find an effective way to share expertise across a large, global workforce. Social Networks will provide the best solution but the need to get over both security and efficiency fears will be the key to the speed of this development.

4 – Brand Networks. Bigger brands are starting to recognise the need to not only engage with their consumers but involve them. The Guardian Newspaper has just launched a social network in the UK and other British brands are looking at the media. In both the UK and US (and, I am sure, elsewhere), politicians have launched their own social networks.

I am sure that there are a number of developments for Social Networking, including niche networks and consumer oriented networks. It’s a question of when people will start to look at their use in distinct platforms rather than meshing everything together.

Links:

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Gregg Butler is a 3rd-degree contact

Gregg Butler

Vice President at n-tara, inc.

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Perhaps you will find this study interesting reading, Marty. I did. It is generously made available to all of us by Heidi Browning, a senior executive at Fox Interactive. http://www.myspace.com/neverendingfriending

posted 5 months ago | Flag answer as…

Johan Vermij is a 2nd-degree contact

Johan Vermij

Networked Virtual Environments & Innovative Projects

see all my answers

Best Answers in: Professional Networking (1)see more, Business Development (1) see less

The future of the social and business networks is integration.

The web is used in 3 basic areas of life:
1. Private
2. Social
3. Professional

Each of these areas provide different needs, as well as a large overlap. The same functionality but preferably in separate streams.

More and more the web is used as an environment for sharing and non-localised access, moving applications and files (like documents, video’s and images) from your desktop to the web.

Most web 2.0 sites now focus either on social or on professional networking. None take into account that nowadays people may have different ‘identities’ on the web. The wen 2.0 killer app should have a Single Point of Entry to the web and should be able to deal with multiple identities.

Alongside with your real You, it will offer the option to add various a.k.a. profiles. From your FriendFactory addressbook you can select who (individually or groupwise) can see which profile.

Aside from managing your individual contacts, your friends need to be categorised. Your basic networks are:
1. Family
2. School Friends
3. Professional Contacts
These can be subdivided into primary school, secondary school, college etc. as well as collegues and clients on the professional networks. For each of these networks you will be able to set permissions as to who can see which part of you.

Aside from the basic layout of your network, it’s time to get in touch with them. Import your email adresses from IE, Thubderbird, Hotmail, Gmail etc. and invite them to join your network.

The ultimate web 2.0 integration site will also have room for sharing media, documents, feeds and tags.

(needs a bit more thought, but the best killer app design I could come up with in 5 minutes)

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Brent Williams is a 2nd-degree contact

Brent Williams

Chief Technology Officer at Anakam

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Best Answers in: Enterprise Software (1)see more, Information Security (1) see less

I am seeing more and more concern over the security and vulnerabilities of identities within social networking services. As these services expand and overlap, more and more hackers are finding ways to exploit these services for their personal gain, and these exploitations tend to come through the the falsification of credentials or identity. We are seeing greater and greater interested in mass-scale, low-cost authentication solutions that can counter these vulnerabilities and dramatically improve the confidence you have in the fact that you are doing business with people whom you intend to interact.

Links:

posted 5 months ago | Flag answer as…

Seref Turkmenoglu, CMA is a 2nd-degree contact

Seref Turkmenoglu, CMA

Finance Professional (oil & gas)

see all my answers

It seems to me that Linkedin with its direct focus on business is much more viable than Facebook. Facebook is richer in features but its broad target crowd and effort to cover all from dating to business type of business model is not working for me.

posted 5 months ago | Flag answer as…

Vikram. Singh2 is a 3rd-degree contact

Vikram. Singh2

Commercial Director – Business Jets at KLM Royal Dutch Airlines

see all my answers

Last year we created 2 business communities and 1 lifestyle community in the form of Club Africa, Club China and FB Golf club. Its been exciting to see the response and challenging to implement the learnings. As Diane and several others mentioned below the web-meeting point is just the start. These clubs/networks and communities need to be supported and nurtured by off-line platforms.
So i can see in the future that there will be some melding of these communities not only online but also off-line.
The guys who can pull that off, will infact take it to the next level!

Links:

posted 5 months ago | Flag answer as…

Scott Steimle is a 2nd-degree contact

Scott Steimle

Manager, Lotus Flower Trading, LLC

see all my answers

Best Answers in: Databases (1)see more, Information Storage (1), Software Development (1) see less

Networking is all about building opportunities. Even the most altruistic of us will receive opportunities in response to generosity, whether prompted or not. In line with other responses, I see two main categories of networks — personal and professional. Just like dating and career sites, members of these social communities will seek to be matched with or referred to people, services or businesses they desire relations with. Social networking sites that facilitate this will take the lead.

Links:

Clarification added 5 months ago:

From a technological standpoint, using a rule-based reasoner that matches RDF-based profiles is one approach.

posted 5 months ago | Flag answer as…

Rory Murray is your connection (1st-degree)

Rory Murray

Consultant specialising in Strategic Transformation for Telecoms and other complex environments

see all my answers

It really depends what features best suit the purposes of the individual in achieving their objectives…….

Some people like the personal interaction provided by sites like Facebook and Ecademy (especially important for freelancers, who may be sat at home working without the social interaction of an office environment).

Others like the database aspects of LinkedIn – the ability to build a searchable network of verifiable individuals, in order to find the knowledge, skills and experience you need to further your business, but without the same level of distracting chit-chat. Xing, on the other hand, seems to have found a balance between the two and is succeeding for these reasons.

Other networks are springing up to service a niche market and there are many who are jumping on the bandwagon to try and make money, with no obvious unique features that will make them attractive to a large enough audience and are likely to collapse relatively quickly as a result.

The real power is in combining the most valuable attributes of these platforms to create a more 3-D approach – I use Ecademy, LinkedIn, Xing and Facebook together and manage my contacts using Plaxo. This gives me a much richer perspective on my contacts and allows me to build “trusted relationships” with real people (who I may never be fortunate enough to meet in person) but we are able to get to know each-other to an extent that means we can create referrals and recommendations for each-other.

I have written about this concept, which I called Return on Relationships “ROR” and there’s a link to my blog below, if you’re interested in reading further.

Links:

posted 5 months ago | Flag answer as…

Saurabh Oberoi is a 2nd-degree contact

Saurabh Oberoi

Sales & Marketing – North India

see all my answers

Best Answers in: Business Development (1)see more, Lead Generation (1) see less

The future for any networking lies that at the end person looks for the benefit that site has for him/her. The benefit could be in terms of knowledge or money etc.

As long as the networking site addresses this and is free of cost, it will be successful supported with a good revenue model.

posted 5 months ago | Flag answer as…

Brian Ehrlich is a 2nd-degree contact

Brian Ehrlich

Co-Founder, Honeydo.com

see all my answers

Some great responses have been posted to a very timely question.

In my opinion the next evolution will be a blending of both social and business networks. Communities in which we increasingly rely on our social connections to accomplish tasks of commerce. Our society inherently trusts opinions from fellow consumers, much more than “expert” advice. We’re taking the neighborly advice and expanding it exponentially across the nation and globe. Brands for all sizes and types of businesses will begin to live and die on the support from these communities. The viral power that these networks represent can only increase.

The evolution will eventually lead, as others here have stated, to “my profile” becoming my portal to the web and that profile will follow us in the coming years everywhere via the mobile platforms that are being created.

These are some very interesting times in the evolution of communication!

posted 5 months ago | Flag answer as…

Sadiq Baig is a 2nd-degree contact

Sadiq Baig

Marketing; International Trade; Virtual Assistance; Representative Services

see all my answers

Best Answers in: Customer Service (1)see more, Foreign Investment (1), Computers and Software (1) see less

Though business networking is not a new phenomenon, online networking itself is yet emerging and it has rightly been called as a ‘social technology’; it can work wonder in various realms including business.

Technically, it depends on a good websites with right functionalities, search-engines friendliness and keen and knowledgeable users.

Practically, though almost all the available social forums provide for various activities, including business, yet a business-field focused networking forums, i.e. world-wide importers of a certain product, consulting services in certain activities, could serve business people more directly within a wide classifications.

Generally, networking as a social technology is capital-intensive where expertise and concentrated hard work also matter. Hence it is prone to be controlled by money which can buy almost anything in a materialistic environment of our human society.

Thus, it has yet to be seen whether it will help forming or breaking cartels and vested interest or it can be used for wider benefit through developing genuine relationships between people-to-people and producer-to-consumers if technical curbs are not affected by the power that be in various realms always making room for the middle-persons.

It is irony of fate or what; never in the world any specialist such as a scientist, has ruled over a country, though many statesmen did. Perhaps this way nature provides for those who are endowed with specialties but are best in making use of others capabilities.

Anyway, networking seem to have far-reaching consequences for human society, the question is how to harness it for the optimum benefit so as to make it ‘totally war-free’ or free from unnecessary wars, arms buildup and other conflicts.

Kindest regards. Sadiq

Clarification added 5 months ago:

Recently, we in Pakistan witnessed something unusual – Our dictator president General Musharraf was forced NOT to impose emergency despite he had made a decision based on counsel of his cronies. In a meeting (networking?), his top crony let the word out which reached to media and within minutes almost all notable world leaders got alarmed. This made Dr. Condi to call Musharraf at 2 a.m and next morning he announced, “No Emergency”.
It means networking and media are hands in gloves and get fast results when blended together, but beware it is double-edge sword!

Clarification added 5 months ago:

Now issues raised in the question:

Face book: has it been taken to court? See link at my profile there.

LinkedIn: I think the management is mindful of its future and does necessary R&D.

Obstacles to social/business …:Chiefly it seems money, businesses/groups must solicit and finance innovative projects.

Existing social networks still have many free users; these deserve good ROI. Ways and means should provide for financing operational costs through ad.

My experience shows that members of a large network can rarely interact amongst them.

Suggestion: Instead of a very big network, an Umbrella Network should have cluster of networks within enabling members of any cluster network to interact with others freely and ‘evolved relationships’ must replace the referrals so as to make transactions – RoR. Yes, it will take time but once any two or more people know about each other fully well through interaction asking questions tantamount to sort of ‘due diligence, transactions will be self-facilitated and follow.

Here Ecademy’s launch in other regions/countries can be replicated as applicable.

posted 5 months ago | Flag answer as…

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Web 2.0

Web 2.0

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November 2005

Does “Web 2.0” mean anything? Till recently I thought it didn’t, but the truth turns out to be more complicated. Originally, yes, it was meaningless. Now it seems to have acquired a meaning. And yet those who dislike the term are probably right, because if it means what I think it does, we don’t need it.

I first heard the phrase “Web 2.0” in the name of the Web 2.0 conference in 2004. At the time it was supposed to mean using “the web as a platform,” which I took to refer to web-based applications. [1]

So I was surprised at a conference this summer when Tim O’Reilly led a session intended to figure out a definition of “Web 2.0.” Didn’t it already mean using the web as a platform? And if it didn’t already mean something, why did we need the phrase at all?

Origins

Tim says the phrase “Web 2.0” first arose in “a brainstorming session between O’Reilly and Medialive International.” What is Medialive International? “Producers of technology tradeshows and conferences,” according to their site. So presumably that’s what this brainstorming session was about. O’Reilly wanted to organize a conference about the web, and they were wondering what to call it.

I don’t think there was any deliberate plan to suggest there was a new version of the web. They just wanted to make the point that the web mattered again. It was a kind of semantic deficit spending: they knew new things were coming, and the “2.0” referred to whatever those might turn out to be.

And they were right. New things were coming. But the new version number led to some awkwardness in the short term. In the process of developing the pitch for the first conference, someone must have decided they’d better take a stab at explaining what that “2.0” referred to. Whatever it meant, “the web as a platform” was at least not too constricting.

The story about “Web 2.0” meaning the web as a platform didn’t live much past the first conference. By the second conference, what “Web 2.0” seemed to mean was something about democracy. At least, it did when people wrote about it online. The conference itself didn’t seem very grassroots. It cost $2800, so the only people who could afford to go were VCs and people from big companies.

And yet, oddly enough, Ryan Singel’s article about the conference in Wired News spoke of “throngs of geeks.” When a friend of mine asked Ryan about this, it was news to him. He said he’d originally written something like “throngs of VCs and biz dev guys” but had later shortened it just to “throngs,” and that this must have in turn been expanded by the editors into “throngs of geeks.” After all, a Web 2.0 conference would presumably be full of geeks, right?

Well, no. There were about 7. Even Tim O’Reilly was wearing a suit, a sight so alien I couldn’t parse it at first. I saw him walk by and said to one of the O’Reilly people “that guy looks just like Tim.”

“Oh, that’s Tim. He bought a suit.” I ran after him, and sure enough, it was. He explained that he’d just bought it in Thailand.

The 2005 Web 2.0 conference reminded me of Internet trade shows during the Bubble, full of prowling VCs looking for the next hot startup. There was that same odd atmosphere created by a large number of people determined not to miss out. Miss out on what? They didn’t know. Whatever was going to happen—whatever Web 2.0 turned out to be.

I wouldn’t quite call it “Bubble 2.0” just because VCs are eager to invest again. The Internet is a genuinely big deal. The bust was as much an overreaction as the boom. It’s to be expected that once we started to pull out of the bust, there would be a lot of growth in this area, just as there was in the industries that spiked the sharpest before the Depression.

The reason this won’t turn into a second Bubble is that the IPO market is gone. Venture investors are driven by exit strategies. The reason they were funding all those laughable startups during the late 90s was that they hoped to sell them to gullible retail investors; they hoped to be laughing all the way to the bank. Now that route is closed. Now the default exit strategy is to get bought, and acquirers are less prone to irrational exuberance than IPO investors. The closest you’ll get to Bubble valuations is Rupert Murdoch paying $580 million for Myspace. That’s only off by a factor of 10 or so.

1. Ajax

Does “Web 2.0” mean anything more than the name of a conference yet? I don’t like to admit it, but it’s starting to. When people say “Web 2.0” now, I have some idea what they mean. And the fact that I both despise the phrase and understand it is the surest proof that it has started to mean something.

One ingredient of its meaning is certainly Ajax, which I can still only just bear to use without scare quotes. Basically, what “Ajax” means is “Javascript now works.” And that in turn means that web-based applications can now be made to work much more like desktop ones.

As you read this, a whole new generation of software is being written to take advantage of Ajax. There hasn’t been such a wave of new applications since microcomputers first appeared. Even Microsoft sees it, but it’s too late for them to do anything more than leak “internal” documents designed to give the impression they’re on top of this new trend.

In fact the new generation of software is being written way too fast for Microsoft even to channel it, let alone write their own in house. Their only hope now is to buy all the best Ajax startups before Google does. And even that’s going to be hard, because Google has as big a head start in buying microstartups as it did in search a few years ago. After all, Google Maps, the canonical Ajax application, was the result of a startup they bought.

So ironically the original description of the Web 2.0 conference turned out to be partially right: web-based applications are a big component of Web 2.0. But I’m convinced they got this right by accident. The Ajax boom didn’t start till early 2005, when Google Maps appeared and the term “Ajax” was coined.

2. Democracy

The second big element of Web 2.0 is democracy. We now have several examples to prove that amateurs can surpass professionals, when they have the right kind of system to channel their efforts. Wikipedia may be the most famous. Experts have given Wikipedia middling reviews, but they miss the critical point: it’s good enough. And it’s free, which means people actually read it. On the web, articles you have to pay for might as well not exist. Even if you were willing to pay to read them yourself, you can’t link to them. They’re not part of the conversation.

Another place democracy seems to win is in deciding what counts as news. I never look at any news site now except Reddit. [2] I know if something major happens, or someone writes a particularly interesting article, it will show up there. Why bother checking the front page of any specific paper or magazine? Reddit’s like an RSS feed for the whole web, with a filter for quality. Similar sites include Digg, a technology news site that’s rapidly approaching Slashdot in popularity, and del.icio.us, the collaborative bookmarking network that set off the “tagging” movement. And whereas Wikipedia’s main appeal is that it’s good enough and free, these sites suggest that voters do a significantly better job than human editors.

The most dramatic example of Web 2.0 democracy is not in the selection of ideas, but their production. I’ve noticed for a while that the stuff I read on individual people’s sites is as good as or better than the stuff I read in newspapers and magazines. And now I have independent evidence: the top links on Reddit are generally links to individual people’s sites rather than to magazine articles or news stories.

My experience of writing for magazines suggests an explanation. Editors. They control the topics you can write about, and they can generally rewrite whatever you produce. The result is to damp extremes. Editing yields 95th percentile writing—95% of articles are improved by it, but 5% are dragged down. 5% of the time you get “throngs of geeks.”

On the web, people can publish whatever they want. Nearly all of it falls short of the editor-damped writing in print publications. But the pool of writers is very, very large. If it’s large enough, the lack of damping means the best writing online should surpass the best in print. [3] And now that the web has evolved mechanisms for selecting good stuff, the web wins net. Selection beats damping, for the same reason market economies beat centrally planned ones.

Even the startups are different this time around. They are to the startups of the Bubble what bloggers are to the print media. During the Bubble, a startup meant a company headed by an MBA that was blowing through several million dollars of VC money to “get big fast” in the most literal sense. Now it means a smaller, younger, more technical group that just decided to make something great. They’ll decide later if they want to raise VC-scale funding, and if they take it, they’ll take it on their terms.

3. Don’t Maltreat Users

I think everyone would agree that democracy and Ajax are elements of “Web 2.0.” I also see a third: not to maltreat users. During the Bubble a lot of popular sites were quite high-handed with users. And not just in obvious ways, like making them register, or subjecting them to annoying ads. The very design of the average site in the late 90s was an abuse. Many of the most popular sites were loaded with obtrusive branding that made them slow to load and sent the user the message: this is our site, not yours. (There’s a physical analog in the Intel and Microsoft stickers that come on some laptops.)

I think the root of the problem was that sites felt they were giving something away for free, and till recently a company giving anything away for free could be pretty high-handed about it. Sometimes it reached the point of economic sadism: site owners assumed that the more pain they caused the user, the more benefit it must be to them. The most dramatic remnant of this model may be at salon.com, where you can read the beginning of a story, but to get the rest you have sit through a movie.

At Y Combinator we advise all the startups we fund never to lord it over users. Never make users register, unless you need to in order to store something for them. If you do make users register, never make them wait for a confirmation link in an email; in fact, don’t even ask for their email address unless you need it for some reason. Don’t ask them any unnecessary questions. Never send them email unless they explicitly ask for it. Never frame pages you link to, or open them in new windows. If you have a free version and a pay version, don’t make the free version too restricted. And if you find yourself asking “should we allow users to do x?” just answer “yes” whenever you’re unsure. Err on the side of generosity.

In How to Start a Startup I advised startups never to let anyone fly under them, meaning never to let any other company offer a cheaper, easier solution. Another way to fly low is to give users more power. Let users do what they want. If you don’t and a competitor does, you’re in trouble.

iTunes is Web 2.0ish in this sense. Finally you can buy individual songs instead of having to buy whole albums. The recording industry hated the idea and resisted it as long as possible. But it was obvious what users wanted, so Apple flew under the labels. [4] Though really it might be better to describe iTunes as Web 1.5. Web 2.0 applied to music would probably mean individual bands giving away DRMless songs for free.

The ultimate way to be nice to users is to give them something for free that competitors charge for. During the 90s a lot of people probably thought we’d have some working system for micropayments by now. In fact things have gone in the other direction. The most successful sites are the ones that figure out new ways to give stuff away for free. Craigslist has largely destroyed the classified ad sites of the 90s, and OkCupid looks likely to do the same to the previous generation of dating sites.

Serving web pages is very, very cheap. If you can make even a fraction of a cent per page view, you can make a profit. And technology for targeting ads continues to improve. I wouldn’t be surprised if ten years from now eBay had been supplanted by an ad-supported freeBay (or, more likely, gBay).

Odd as it might sound, we tell startups that they should try to make as little money as possible. If you can figure out a way to turn a billion dollar industry into a fifty million dollar industry, so much the better, if all fifty million go to you. Though indeed, making things cheaper often turns out to generate more money in the end, just as automating things often turns out to generate more jobs.

The ultimate target is Microsoft. What a bang that balloon is going to make when someone pops it by offering a free web-based alternative to MS Office. [5] Who will? Google? They seem to be taking their time. I suspect the pin will be wielded by a couple of 20 year old hackers who are too naive to be intimidated by the idea. (How hard can it be?)

The Common Thread

Ajax, democracy, and not dissing users. What do they all have in common? I didn’t realize they had anything in common till recently, which is one of the reasons I disliked the term “Web 2.0” so much. It seemed that it was being used as a label for whatever happened to be new—that it didn’t predict anything.

But there is a common thread. Web 2.0 means using the web the way it’s meant to be used. The “trends” we’re seeing now are simply the inherent nature of the web emerging from under the broken models that got imposed on it during the Bubble.

I realized this when I read an interview with Joe Kraus, the co-founder of Excite. [6]

Excite really never got the business model right at all. We fell into the classic problem of how when a new medium comes out it adopts the practices, the content, the business models of the old medium—which fails, and then the more appropriate models get figured out.

It may have seemed as if not much was happening during the years after the Bubble burst. But in retrospect, something was happening: the web was finding its natural angle of repose. The democracy component, for example—that’s not an innovation, in the sense of something someone made happen. That’s what the web naturally tends to produce.

Ditto for the idea of delivering desktop-like applications over the web. That idea is almost as old as the web. But the first time around it was co-opted by Sun, and we got Java applets. Java has since been remade into a generic replacement for C++, but in 1996 the story about Java was that it represented a new model of software. Instead of desktop applications, you’d run Java “applets” delivered from a server.

This plan collapsed under its own weight. Microsoft helped kill it, but it would have died anyway. There was no uptake among hackers. When you find PR firms promoting something as the next development platform, you can be sure it’s not. If it were, you wouldn’t need PR firms to tell you, because hackers would already be writing stuff on top of it, the way sites like Busmonster used Google Maps as a platform before Google even meant it to be one.

The proof that Ajax is the next hot platform is that thousands of hackers have spontaneously started building things on top of it. Mikey likes it.

There’s another thing all three components of Web 2.0 have in common. Here’s a clue. Suppose you approached investors with the following idea for a Web 2.0 startup:

Sites like del.icio.us and flickr allow users to “tag” content with descriptive tokens. But there is also huge source of implicit tags that they ignore: the text within web links. Moreover, these links represent a social network connecting the individuals and organizations who created the pages, and by using graph theory we can compute from this network an estimate of the reputation of each member. We plan to mine the web for these implicit tags, and use them together with the reputation hierarchy they embody to enhance web searches.

How long do you think it would take them on average to realize that it was a description of Google?

Google was a pioneer in all three components of Web 2.0: their core business sounds crushingly hip when described in Web 2.0 terms, “Don’t maltreat users” is a subset of “Don’t be evil,” and of course Google set off the whole Ajax boom with Google Maps.

Web 2.0 means using the web as it was meant to be used, and Google does. That’s their secret. They’re sailing with the wind, instead of sitting becalmed praying for a business model, like the print media, or trying to tack upwind by suing their customers, like Microsoft and the record labels. [7]

Google doesn’t try to force things to happen their way. They try to figure out what’s going to happen, and arrange to be standing there when it does. That’s the way to approach technology—and as business includes an ever larger technological component, the right way to do business.

The fact that Google is a “Web 2.0” company shows that, while meaningful, the term is also rather bogus. It’s like the word “allopathic.” It just means doing things right, and it’s a bad sign when you have a special word for that.

Notes

[1] From the conference site, June 2004: “While the first wave of the Web was closely tied to the browser, the second wave extends applications across the web and enables a new generation of services and business opportunities.” To the extent this means anything, it seems to be about web-based applications.

[2] Disclosure: Reddit was funded by Y Combinator. But although I started using it out of loyalty to the home team, I’ve become a genuine addict. While we’re at it, I’m also an investor in !MSFT, having sold all my shares earlier this year.

[3] I’m not against editing. I spend more time editing than writing, and I have a group of picky friends who proofread almost everything I write. What I dislike is editing done after the fact by someone else.

[4] Obvious is an understatement. Users had been climbing in through the window for years before Apple finally moved the door.

[5] Hint: the way to create a web-based alternative to Office may not be to write every component yourself, but to establish a protocol for web-based apps to share a virtual home directory spread across multiple servers. Or it may be to write it all yourself.

[6] In Jessica Livingston’s Founders at Work.

[7] Microsoft didn’t sue their customers directly, but they seem to have done all they could to help SCO sue them.

Thanks to Trevor Blackwell, Sarah Harlin, Jessica Livingston, Peter Norvig, Aaron Swartz, and Jeff Weiner for reading drafts of this, and to the guys at O’Reilly and Adaptive Path for answering my questions.

Comment on this essay.

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Why Startups Condense in America

Want to start a startup? Apply for funding by October 11. March 24: Free one-day startup school at Stanford.

May 2006

(This essay is derived from a keynote at Xtech.)

Startups happen in clusters. There are a lot of them in Silicon Valley and Boston, and few in Chicago or Miami. A country that wants startups will probably also have to reproduce whatever makes these clusters form.

I’ve claimed that the recipe is a great university near a town smart people like. If you set up those conditions within the US, startups will form as inevitably as water droplets condense on a cold piece of metal. But when I consider what it would take to reproduce Silicon Valley in another country, it’s clear the US is a particularly humid environment. Startups condense more easily here.

It is by no means a lost cause to try to create a silicon valley in another country. There’s room not merely to equal Silicon Valley, but to surpass it. But if you want to do that, you have to understand the advantages startups get from being in America.

1. The US Allows Immigration.

For example, I doubt it would be possible to reproduce Silicon Valley in Japan, because one of Silicon Valley’s most distinctive features is immigration. Half the people there speak with accents. And the Japanese don’t like immigration. When they think about how to make a Japanese silicon valley, I suspect they unconsciously frame it as how to make one consisting only of Japanese people. This way of framing the question probably guarantees failure.

A silicon valley has to be a mecca for the smart and the ambitious, and you can’t have a mecca if you don’t let people into it.

Of course, it’s not saying much that America is more open to immigration than Japan. Immigration policy is one area where a competitor could do better.

2. The US Is a Rich Country.

I could see India one day producing a rival to Silicon Valley. Obviously they have the right people: you can tell that by the number of Indians in the current Silicon Valley. The problem with India itself is that it’s still so poor.

In poor countries, things we take for granted are missing. A friend of mine visiting India sprained her ankle falling down the steps in a railway station. When she turned to see what had happened, she found the steps were all different heights. In industrialized countries we walk down steps our whole lives and never think about this, because there’s an infrastructure that prevents such a staircase from being built.

The US has never been so poor as some countries are now. There have never been swarms of beggars in the streets of American cities. So we have no data about what it takes to get from the swarms-of-beggars stage to the silicon-valley stage. Could you have both at once, or does there have to be some baseline prosperity before you get a silicon valley?

I suspect there is some speed limit to the evolution of an economy. Economies are made out of people, and attitudes can only change a certain amount per generation. [1]

3. The US Is Not (Yet) a Police State.

Another country I could see wanting to have a silicon valley is China. But I doubt they could do it yet either. China still seems to be a police state, and although present rulers seem enlightened compared to the last, even enlightened despotism can probably only get you part way toward being a great economic power.

It can get you factories for building things designed elsewhere. Can it get you the designers, though? Can imagination flourish where people can’t criticize the government? Imagination means having odd ideas, and it’s hard to have odd ideas about technology without also having odd ideas about politics. And in any case, many technical ideas do have political implications. So if you squash dissent, the back pressure will propagate into technical fields. [2]

Singapore would face a similar problem. Singapore seems very aware of the importance of encouraging startups. But while energetic government intervention may be able to make a port run efficiently, it can’t coax startups into existence. A state that bans chewing gum has a long way to go before it could create a San Francisco.

Do you need a San Francisco? Might there not be an alternate route to innovation that goes through obedience and cooperation instead of individualism? Possibly, but I’d bet not. Most imaginative people seem to share a certain prickly independence, whenever and wherever they lived. You see it in Diogenes telling Alexander to get out of his light and two thousand years later in Feynman breaking into safes at Los Alamos. [3] Imaginative people don’t want to follow or lead. They’re most productive when everyone gets to do what they want.

Ironically, of all rich countries the US has lost the most civil liberties recently. But I’m not too worried yet. I’m hoping once the present administration is out, the natural openness of American culture will reassert itself.

4. American Universities Are Better.

You need a great university to seed a silicon valley, and so far there are few outside the US. I asked a handful of American computer science professors which universities in Europe were most admired, and they all basically said “Cambridge” followed by a long pause while they tried to think of others. There don’t seem to be many universities elsewhere that compare with the best in America, at least in technology.

In some countries this is the result of a deliberate policy. The German and Dutch governments, perhaps from fear of elitism, try to ensure that all universities are roughly equal in quality. The downside is that none are especially good. The best professors are spread out, instead of being concentrated as they are in the US. This probably makes them less productive, because they don’t have good colleagues to inspire them. It also means no one university will be good enough to act as a mecca, attracting talent from abroad and causing startups to form around it.

The case of Germany is a strange one. The Germans invented the modern university, and up till the 1930s theirs were the best in the world. Now they have none that stand out. As I was mulling this over, I found myself thinking: “I can understand why German universities declined in the 1930s, after they excluded Jews. But surely they should have bounced back by now.” Then I realized: maybe not. There are few Jews left in Germany and most Jews I know would not want to move there. And if you took any great American university and removed the Jews, you’d have some pretty big gaps. So maybe it would be a lost cause trying to create a silicon valley in Germany, because you couldn’t establish the level of university you’d need as a seed. [4]

It’s natural for US universities to compete with one another because so many are private. To reproduce the quality of American universities you probably also have to reproduce this. If universities are controlled by the central government, log-rolling will pull them all toward the mean: the new Institute of X will end up at the university in the district of a powerful politician, instead of where it should be.

5. You Can Fire People in America.

I think one of the biggest obstacles to creating startups in Europe is the attitude toward employment. The famously rigid labor laws hurt every company, but startups especially, because startups have the least time to spare for bureaucratic hassles.

The difficulty of firing people is a particular problem for startups because they have no redundancy. Every person has to do their job well.

But the problem is more than just that some startup might have a problem firing someone they needed to. Across industries and countries, there’s a strong inverse correlation between performance and job security. Actors and directors are fired at the end of each film, so they have to deliver every time. Junior professors are fired by default after a few years unless the university chooses to grant them tenure. Professional athletes know they’ll be pulled if they play badly for just a couple games. At the other end of the scale (at least in the US) are auto workers, New York City schoolteachers, and civil servants, who are all nearly impossible to fire. The trend is so clear that you’d have to be willfully blind not to see it.

Performance isn’t everything, you say? Well, are auto workers, schoolteachers, and civil servants happier than actors, professors, and professional athletes?

European public opinion will apparently tolerate people being fired in industries where they really care about performance. Unfortunately the only industry they care enough about so far is soccer. But that is at least a precedent.

6. In America Work Is Less Identified with Employment.

The problem in more traditional places like Europe and Japan goes deeper than the employment laws. More dangerous is the attitude they reflect: that an employee is a kind of servant, whom the employer has a duty to protect. It used to be that way in America too. In 1970 you were still supposed to get a job with a big company, for whom ideally you’d work your whole career. In return the company would take care of you: they’d try not to fire you, cover your medical expenses, and support you in old age.

Gradually employment has been shedding such paternalistic overtones and becoming simply an economic exchange. But the importance of the new model is not just that it makes it easier for startups to grow. More important, I think, is that it it makes it easier for people to start startups.

Even in the US most kids graduating from college still think they’re supposed to get jobs, as if you couldn’t be productive without being someone’s employee. But the less you identify work with employment, the easier it becomes to start a startup. When you see your career as a series of different types of work, instead of a lifetime’s service to a single employer, there’s less risk in starting your own company, because you’re only replacing one segment instead of discarding the whole thing.

The old ideas are so powerful that even the most successful startup founders have had to struggle against them. A year after the founding of Apple, Steve Wozniak still hadn’t quit HP. He still planned to work there for life. And when Jobs found someone to give Apple serious venture funding, on the condition that Woz quit, he initially refused, arguing that he’d designed both the Apple I and the Apple II while working at HP, and there was no reason he couldn’t continue.

7. America Is Not Too Fussy.

If there are any laws regulating businesses, you can assume larval startups will break most of them, because they don’t know what the laws are and don’t have time to find out.

For example, many startups in America begin in places where it’s not really legal to run a business. Hewlett-Packard, Apple, and Google were all run out of garages. Many more startups, including ours, were initially run out of apartments. If the laws against such things were actually enforced, most startups wouldn’t happen.

That could be a problem in fussier countries. If Hewlett and Packard tried running an electronics company out of their garage in Switzerland, the old lady next door would report them to the municipal authorities.

But the worst problem in other countries is probably the effort required just to start a company. A friend of mine started a company in Germany in the early 90s, and was shocked to discover, among many other regulations, that you needed $20,000 in capital to incorporate. That’s one reason I’m not typing this on an Apfel laptop. Jobs and Wozniak couldn’t have come up with that kind of money in a company financed by selling a VW bus and an HP calculator. We couldn’t have started Viaweb either. [5]

Here’s a tip for governments that want to encourage startups: read the stories of existing startups, and then try to simulate what would have happened in your country. When you hit something that would have killed Apple, prune it off.

Startups are marginal. They’re started by the poor and the timid; they begin in marginal space and spare time; they’re started by people who are supposed to be doing something else; and though businesses, their founders often know nothing about business. Young startups are fragile. A society that trims its margins sharply will kill them all.

8. America Has a Large Domestic Market.

What sustains a startup in the beginning is the prospect of getting their initial product out. The successful ones therefore make the first version as simple as possible. In the US they usually begin by making something just for the local market.

This works in America, because the local market is 300 million people. It wouldn’t work so well in Sweden. In a small country, a startup has a harder task: they have to sell internationally from the start.

The EU was designed partly to simulate a single, large domestic market. The problem is that the inhabitants still speak many different languages. So a software startup in Sweden is still at a disadvantage relative to one in the US, because they have to deal with internationalization from the beginning. It’s significant that the most famous recent startup in Europe, Skype, worked on a problem that was intrinsically international.

However, for better or worse it looks as if Europe will in a few decades speak a single language. When I was a student in Italy in 1990, few Italians spoke English. Now all educated people seem to be expected to– and Europeans do not like to seem uneducated. This is presumably a taboo subject, but if present trends continue, French and German will eventually go the way of Irish and Luxembourgish: they’ll be spoken in homes and by eccentric nationalists.

9. America Has Venture Funding.

Startups are easier to start in America because funding is easier to get. There are now a few VC firms outside the US, but startup funding doesn’t only come from VC firms. A more important source, because it’s more personal and comes earlier in the process, is money from individual angel investors. Google might never have got to the point where they could raise millions from VC funds if they hadn’t first raised a hundred thousand from Andy Bechtolsheim. And he could help them because he was one of the founders of Sun. This pattern is repeated constantly in startup hubs. It’s this pattern that makes them startup hubs.

The good news is, all you have to do to get the process rolling is get those first few startups successfully launched. If they stick around after they get rich, startup founders will almost automatically fund and encourage new startups.

The bad news is that the cycle is slow. It probably takes five years, on average, before a startup founder can make angel investments. And while governments might be able to set up local VC funds by supplying the money themselves and recruiting people from existing firms to run them, only organic growth can produce angel investors.

Incidentally, America’s private universities are one reason there’s so much venture capital. A lot of the money in VC funds comes from their endowments. So another advantage of private universities is that a good chunk of the country’s wealth is managed by enlightened investors.

10. America Has Dynamic Typing for Careers.

Compared to other industrialized countries the US is disorganized about routing people into careers. For example, in America people often don’t decide to go to medical school till they’ve finished college. In Europe they generally decide in high school.

The European approach reflects the old idea that each person has a single, definite occupation– which is not far from the idea that each person has a natural “station” in life. If this were true, the most efficient plan would be to discover each person’s station as early as possible, so they could receive the training appropriate to it.

In the US things are more haphazard. But that turns out to be an advantage as an economy gets more liquid, just as dynamic typing turns out to work better than static for ill-defined problems. This is particularly true with startups. “Startup founder” is not the sort of career a high school student would choose. If you ask at that age, people will choose conservatively. They’ll choose well-understood occupations like engineer, or doctor, or lawyer.

Startups are the kind of thing people don’t plan, so you’re more likely to get them in a society where it’s ok to make career decisions on the fly.

For example, in theory the purpose of a PhD program is to train you to do research. But fortunately in the US this is another rule that isn’t very strictly enforced. In the US most people in CS PhD programs are there simply because they wanted to learn more. They haven’t decided what they’ll do afterward. So American grad schools spawn a lot of startups, because students don’t feel they’re failing if they don’t go into research.

Those worried about America’s “competitiveness” often suggest spending more on public schools. But perhaps America’s lousy public schools have a hidden advantage. Because they’re so bad, the kids adopt an attitude of waiting for college. I did; I knew I was learning so little that I wasn’t even learning what the choices were, let alone which to choose. This is demoralizing, but it does at least make you keep an open mind.

Certainly if I had to choose between bad high schools and good universities, like the US, and good high schools and bad universities, like most other industrialized countries, I’d take the US system. Better to make everyone feel like a late bloomer than a failed child prodigy.

Attitudes

There’s one item conspicuously missing from this list: American attitudes. Americans are said to be more entrepreneurial, and less afraid of risk. But America has no monopoly on this. Indians and Chinese seem plenty entrepreneurial, perhaps more than Americans.

Some say Europeans are less energetic, but I don’t believe it. I think the problem with Europe is not that they lack balls, but that they lack examples.

Even in the US, the most successful startup founders are often technical people who are quite timid, initially, about the idea of starting their own company. Few are the sort of backslapping extroverts one thinks of as typically American. They can usually only summon up the activation energy to start a startup when they meet people who’ve done it and realize they could too.

I think what holds back European hackers is simply that they don’t meet so many people who’ve done it. You see that variation even within the US. Stanford students are more entrepreneurial than Yale students, but not because of some difference in their characters; the Yale students just have fewer examples.

I admit there seem to be different attitudes toward ambition in Europe and the US. In the US it’s ok to be overtly ambitious, and in most of Europe it’s not. But this can’t be an intrinsically European quality; previous generations of Europeans were as ambitious as Americans. What happened? My hypothesis is that ambition was discredited by the terrible things ambitious people did in the first half of the twentieth century. Now swagger is out. (Even now the image of a very ambitious German presses a button or two, doesn’t it?)

It would be surprising if European attitudes weren’t affected by the disasters of the twentieth century. It takes a while to be optimistic after events like that. But ambition is human nature. Gradually it will re-emerge. [6]

How To Do Better

I don’t mean to suggest by this list that America is the perfect place for startups. It’s the best place so far, but the sample size is small, and “so far” is not very long. On historical time scales, what we have now is just a prototype.

So let’s look at Silicon Valley the way you’d look at a product made by a competitor. What weaknesses could you exploit? How could you make something users would like better? The users in this case are those critical few thousand people you’d like to move to your silicon valley.

To start with, Silicon Valley is too far from San Francisco. Palo Alto, the original ground zero, is about thirty miles away, and the present center more like forty. So people who come to work in Silicon Valley face an unpleasant choice: either live in the boring sprawl of the valley proper, or live in San Francisco and endure an hour commute each way.

The best thing would be if the silicon valley were not merely closer to the interesting city, but interesting itself. And there is a lot of room for improvement here. Palo Alto is not so bad, but everything built since is the worst sort of strip development. You can measure how demoralizing it is by the number of people who will sacrifice two hours a day commuting rather than live there.

Another area in which you could easily surpass Silicon Valley is public transportation. There is a train running the length of it, and by American standards it’s not bad. Which is to say that to Japanese or Europeans it would seem like something out of the third world.

The kind of people you want to attract to your silicon valley like to get around by train, bicycle, and on foot. So if you want to beat America, design a town that puts cars last. It will be a while before any American city can bring itself to do that.

Capital Gains

There are also a couple things you could do to beat America at the national level. One would be to have lower capital gains taxes. It doesn’t seem critical to have the lowest income taxes, because to take advantage of those, people have to move. [7] But if capital gains rates vary, you move assets, not yourself, so changes are reflected at market speeds. The lower the rate, the cheaper it is to buy stock in growing companies as opposed to real estate, or bonds, or stocks bought for the dividends they pay.

So if you want to encourage startups you should have a low rate on capital gains. Politicians are caught between a rock and a hard place here, however: make the capital gains rate low and be accused of creating “tax breaks for the rich,” or make it high and starve growing companies of investment capital. As Galbraith said, politics is a matter of choosing between the unpalatable and the disastrous. A lot of governments experimented with the disastrous in the twentieth century; now the trend seems to be toward the merely unpalatable.

Oddly enough, the leaders now are European countries like Belgium, which has a capital gains tax rate of zero.

Immigration

The other place you could beat the US would be with smarter immigration policy. There are huge gains to be made here. Silicon valleys are made of people, remember.

Like a company whose software runs on Windows, those in the current Silicon Valley are all too aware of the shortcomings of the INS, but there’s little they can do about it. They’re hostages of the platform.

America’s immigration system has never been well run, and since 2001 there has been an additional admixture of paranoia. What fraction of the smart people who want to come to America can even get in? I doubt even half. Which means if you made a competing technology hub that let in all smart people, you’d immediately get more than half the world’s top talent, for free.

US immigration policy is particularly ill-suited to startups, because it reflects a model of work from the 1970s. It assumes good technical people have college degrees, and that work means working for a big company.

If you don’t have a college degree you can’t get an H1B visa, the type usually issued to programmers. But a test that excludes Steve Jobs, Bill Gates, and Michael Dell can’t be a good one. Plus you can’t get a visa for working on your own company, only for working as an employee of someone else’s. And if you want to apply for citizenship you daren’t work for a startup at all, because if your sponsor goes out of business, you have to start over.

American immigration policy keeps out most smart people, and channels the rest into unproductive jobs. It would be easy to do better. Imagine if, instead, you treated immigration like recruiting– if you made a conscious effort to seek out the smartest people and get them to come to your country.

A country that got immigration right would have a huge advantage. At this point you could become a mecca for smart people simply by having an immigration system that let them in.

A Good Vector

If you look at the kinds of things you have to do to create an environment where startups condense, none are great sacrifices. Great universities? Livable towns? Civil liberties? Flexible employment laws? Immigration policies that let in smart people? Tax laws that encourage growth? It’s not as if you have to risk destroying your country to get a silicon valley; these are all good things in their own right.

And then of course there’s the question, can you afford not to? I can imagine a future in which the default choice of ambitious young people is to start their own company rather than work for someone else’s. I’m not sure that will happen, but it’s where the trend points now. And if that is the future, places that don’t have startups will be a whole step behind, like those that missed the Industrial Revolution.

Notes

[1] On the verge of the Industrial Revolution, England was already the richest country in the world. As far as such things can be compared, per capita income in England in 1750 was higher than India’s in 1960.

Deane, Phyllis, The First Industrial Revolution, Cambridge University Press, 1965.

[2] This has already happened once in China, during the Ming Dynasty, when the country turned its back on industrialization at the command of the court. One of Europe’s advantages was that it had no government powerful enough to do that.

[3] Of course, Feynman and Diogenes were from adjacent traditions, but Confucius, though more polite, was no more willing to be told what to think.

[4] For similar reasons it might be a lost cause to try to establish a silicon valley in Israel. Instead of no Jews moving there, only Jews would move there, and I don’t think you could build a silicon valley out of just Jews any more than you could out of just Japanese.

(This is not a remark about the qualities of these groups, just their sizes. Japanese are only about 2% of the world population, and Jews about .2%.)

[5] According to the World Bank, the initial capital requirement for German companies is 47.6% of the per capita income. Doh.

World Bank, Doing Business in 2006, http://doingbusiness.org

[6] For most of the twentieth century, Europeans looked back on the summer of 1914 as if they’d been living in a dream world. It seems more accurate (or at least, as accurate) to call the years after 1914 a nightmare than to call those before a dream. A lot of the optimism Europeans consider distinctly American is simply what they too were feeling in 1914.

[7] The point where things start to go wrong seems to be about 50%. Above that people get serious about tax avoidance. The reason is that the payoff for avoiding tax grows hyperexponentially (x/1-x for 0 < x < 1). If your income tax rate is 10%, moving to Monaco would only give you 11% more income, which wouldn’t even cover the extra cost. If it’s 90%, you’d get ten times as much income. And at 98%, as it was briefly in Britain in the 70s, moving to Monaco would give you fifty times as much income. It seems quite likely that European governments of the 70s never drew this curve.

Thanks to Trevor Blackwell, Matthias Felleisen, Jessica Livingston, Robert Morris, Neil Rimer, Hugues Steinier, Brad Templeton, Fred Wilson, and Stephen Wolfram for reading drafts of this, and to Ed Dumbill for inviting me to speak.

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33 posts categorized “Global Brain and Global Mind”

November 21, 2007

Powerpoint Deck: Making Sense of the Semantic Web, and Twine

Now that I have been asked by several dozen people for the slides from my talk on “Making Sense of the Semantic Web,” I guess it’s time to put them online. So here they are, under the Creative Commons Attribution License (you can share it with attribution this site).You can download the Powerpoint file at the link below:

Download nova_spivack_semantic_web_talk.pptOr you can view it right here:

Enjoy! And I look forward to your thoughts and comments.

October 18, 2007

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The New York Times   

  

November 11, 2006

New York Times Article About the Emerging Semantic Web

A New York Times article came out today about the Semantic Web — in which I was quoted, speaking about my company Radar Networks. Here’s an excerpt:

Referred to as Web 3.0, the effort is in its infancy, and the very idea has given rise to skeptics who have called it an unobtainable vision. But the underlying technologies are rapidly gaining adherents, at big companies like I.B.M. and  Google as well as small ones. Their projects often center on simple, practical uses, from producing vacation recommendations to predicting the next hit song.

But in the future, more powerful systems could act as personal advisers in areas as diverse as financial planning, with an intelligent system mapping out a retirement plan for a couple, for instance, or educational consulting, with the Web helping a high school student identify the right college.

The projects aimed at creating Web 3.0 all take advantage of increasingly powerful computers that can quickly and completely scour the Web.

“I call it the World Wide Database,” said Nova Spivack, the founder of a start-up firm whose technology detects relationships between nuggets of information mining the World Wide Web. “We are going from a Web of connected documents to a Web of connected data.”

Web 2.0, which describes the ability to seamlessly connect applications (like geographical mapping) and services (like photo-sharing) over the Internet, has in recent months become the focus of dot-com-style hype in Silicon Valley. But commercial interest in Web 3.0 — or the “semantic Web,” for the idea of adding meaning — is only now emerging.

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What is the Semantic Web, Actually?

I’ve read several blog posts reacting to John Markoff’s article today. There seem to be some misconceptions in those posts about what the Semantic Web is and is not. Here I will try to  succinctly correct a few of the larger misconceptions I’ve run into:

  • The Semantic Web is not just a single Web. There won’t be one Semantic Web, there will be thousands or even millions of them, each in their own area. They will all be part of one Semantic Web in that they will use the same open-standard languages and their data will be universally accessible, but they won’t all be run by any single company. They will connect together over time, forming a tapestry. But nobody will own this or run this as a single service. It will be just as decentralized as the Web already is.
  • The Semantic Web is not separate from the existing Web. The Semantic Web won’t be a new Web apart from the Web we already have. It simply adds new metadata and data to the existing Web. It merges right into the existing HTML Web just like XML does, except this new metadata is in RDF (since RDF can in fact be expressed in XML).
  • The Semantic Web is not just about unstructured data. In fact, the Semantic Web is really about structured data: it provides a means (RDF) to turn any content or data into structured data that other software can make use of. This is really what RDF enables.
  • The Semantic Web does not require complex ontologies. Even without making use of OWL and more sophisticated ontologies, powerful data-sharing and data-integration can be enabled on the existing Web using even just RDF alone.
  • The Semantic Web does not only exist on Web pages. RDF works inside of applications and databases, not just on Web pages. Calling it a “Web” is a misnomer of sorts — it’s not just about the Web, it’s about all information, data and applications.
  • The Semantic Web is not only about AI, and doesn’t require it. There are huge benefits from the Semantic Web without ever using a single line of artificial intelligence code. While the next-generation of AI will certainly be enabled by richer semantics, AI is not the only benefit of RDF. Making data available in RDF makes it more accessible, integratable, and reusable — regardless of any AI. The long-term future of the Semantic Web is AI for sure — but to get immediate benefits from RDF no AI is necessary.
  • The Semantic Web is not only about mining, search engines and spidering. Application developers and content providers, and end-users, can benefit from using the Semantic Web (RDF) within their own services, regardless of whether they expose that RDF metadata to outside parties. RDF is useful without doing any data-mining — it can be baked right into content within authoring tools and created transparently when information is published. RDF makes content more manageable and frees developers and content providers from having to look at relational data models. It also gives end-users better ways to collect and manage content they find.
  • The Semantic Web is not just research. It’s already in use and starting to reach the market. The government uses it of course. But also so do companies like Adobe, and more recently Yahoo (Yahoo Food has started to use some Semantic Web technologies now). And one flavor of RSS is defined with RDF. Oracle has released native RDF support in their products. The list goes on…

Learning more:

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Between the Lines

February 14th, 2007

From semantic Web (3.0) to the WebOS (4.0)

Posted by Dan Farber @ 6:08 am Categories: General, Personal Technology, Web Technology, Social networking Tags:

Nova Spivack of Radar Networks maps out his view of the evolution of the Web over the next 25 years. Nova said he isn’t sure about exact dates or technologies on the top end of the map, but his view of ten-year blocks to fully evolve each phase is realistic. Nor should we get hung up on the naming convention–1.0, 2.0, etc. The idea that the next major deepening of the Internet as a platform will involve the semantic Web is reasonable, and was the subject of much discussion in November.Nova’s stealth-mode company is working on what he describes as a “Java-based framework for semantic web applications and services that has some similarities to Ruby on Rails, and also includes a lot of other technology such as our extremely fast and scaleable storage layer for semantic data tuples, powerful semantic query capabilities, and a range of algorithms for analyzing data and doing intelligent things for users.” The service that Radar Networks plans to introduce later this year “will enrich and facilitate more intelligent online relationships, community, content, collaboration and even commerce,” he said in a recent blog post. In addition, another stealth startup, MetaWeb, is building something to do with the semantic Web.Given that few predicted how Web 2.0 would come to be defined during the early stages of Web 1.0, the concept of Web 3.0 is still a bit fuzzy, and Web 4.0, the WebOS on Nova’s map, is really hazy. The WebOS implies that machine intelligence has reached a point that the Internet becomes the planetary computer, a massive web of highly intelligent interactions.

 
Source: Nova Spivack and Radar Networks 

Ray Kurzweil predicts that by 2029, the WebOS will be parallel to the human brain:

By 2029, sufficient computation to simulate the entire human brain, which I estimate at about 1016 (10 million billion) calculations per second (cps), will cost about a dollar. By that time, intelligent machines will combine the subtle and supple skills that humans now excel in (essentially our powers of pattern recognition) with ways in which machines are already superior, such as remembering trillions of facts accurately, searching quickly through vast databases, and downloading skills and knowledge.

But this will not be an alien invasion of intelligent machines. It will be an expression of our own civilization, as we have always used our technology to extend our physical and mental reach. We will merge with this technology by sending intelligent nanobots (blood-cell-sized computerized robots) into our brains through the capillaries to intimately interact with our biological neurons. If this scenario sounds very futuristic, I would point out that we already have blood-cell-sized devices that are performing sophisticated therapeutic functions in animals, such as curing Type I diabetes and identifying and destroying cancer cells.

I can’t predict whether Kurzweil is on target with his view of the future, but Web/Internet is gradually going to get a lot smarter, growing more similar to the human brain and collective intelligence that conceived it.

Dan Farber, vice-president of editorial at CNET Networks and editor in chief of ZDNet, has more than 20 years of experience as an editor and journalist covering technology. See his full profile and disclosure of his industry affiliations.

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July 08, 2004

A Physics of Ideas: Measuring The Physical Properties of Memes

by Nova Spivack
http://www.mindingtheplanet.net

Original: July 8, 2004
Revised: February 5, 2005(Permission to reprint or share this article is granted, with a citation to this Web Page: http://www.mindingtheplanet.net)

This paper provides an overview of a new approach to measuring the physical properties of ideas as they move in real-time through information spaces and populations such as the Internet. It has applications to information retrieval and search, information filtering, personalization, ad targeting, knowledge discovery and text-mining, knowledge management, user-interface design, market research, trend analysis, intelligence gathering, machine learning, organizational behavior and social and cultural studies.

Introduction

In this article I propose the beginning of what might be called a physics of ideas. My approach is based on applying basic concepts from classical physics to the measurement of ideas — or what are often called memes — as they move through information spaces over time.

Ideas are perhaps the single most powerful hidden forces shaping our lives and our world. Human events are really just the results of the complex interactions of myriad ideas across time, space and human minds. To the extent that we can measure ideas as they form and interact, we can gain a deeper understanding of the underlying dynamics of our organizations, markets, communities, nations, and even of ourselves. But the problem is, we are still remarkably primitive when it comes to measuring ideas. We simply don’t have the tools yet and so this layer of our world still remains hidden from us.

However, it is becoming increasingly urgent that we develop these tools. With the evolution of computers and the Internet ideas have recently become more influential and powerful than ever before in human history. Not only are they easier to create and consume, but they can now move around the world and interact more quickly, widely and freely. The result of this evolutionary leap is that our information is increasingly out of control and difficult to cope with, resulting in the growing problem of information overload.

There are many approaches to combating information overload, most of which are still quite primitive and place too much burden on humans.  In order to truly solve information overload, I believe that what is ultimately needed is a new physics of ideas — a new micro-level science that will enable us to empirically detect, measure and track ideas as they develop, interact and change over time and space in real-time, in the real-world.

In the past various thinkers have proposed methods for applying concepts from epidemiology and population biology to the study of how memes spread and evolve across human societies. We might label those past attempts as “macro-memetics” because they are chiefly focused on gaining a macroscopic understanding of how ideas move and evolve. In contrast, the science of ideas that I am proposing in this paper is focused on the micro-scale dynamics of ideas within particular individuals or groups, or within discrete information spaces such as computer desktops and online services and so we might label this new physics of ideas as a form of “micro-memetics.”

To begin developing the physics of ideas I believe that we should start by mapping existing methods in classical physics to the realm of ideas. If we can treat ideas as ideal particles in a Newtonian universe then it becomes possible to directly map the wealth of techniques that physicists have developed for analyzing the dynamics of particle systems to the dynamics of idea systems as they operate within and between individuals and groups.

The key to my approach is to empirically measure the meme momentum of each meme that is active in the world. Using these meme momenta we can then compute the document momentum of any document that contain those memes. The momentum of a meme is a measure of the force of that meme within a given space, time period, and set of human minds (a “context”). The momentum of a document is the force of that document within a given context.

Once we are able to measure meme momenta and document momenta we can then filter and compare individual memes or collections of memes, as well as documents or collections of documents, according to their relative importance or “timeliness” in any context.

Using these techniques we can empirically detect the early signs of soon-to-be-important topics, trends or issues; we can measure ideas or documents to determine how important they are at any given time for any given audience; we can track and graph ideas and documents as their relative importances change over time in various contexts; we can even begin to chart the impact that the dynamics of various ideas have on real-world events. These capabilities can be utilized in next-generation systems for knowledge discovery, search and information retrieval, knowledge management, intelligence gathering and analysis, social and cultural research, and many other purposes.

The rest of this paper describes how we might attempt to do this, some applications of these techniques, and a number of further questions for research.


Background
Before I go into the details of my proposal, a little background may be relevant. In 1993 I worked as an analyst at Individual, Inc. Individual’s business was to provide filtered strategic business intelligence to the top decision-makers of major corporations. In that job I was part of a sophisticated information filter. Individual used artificial intelligence to automatically collect news and other content from thousands of sources in real-time. Their system then filtered this  information into news feeds tailored to the strategic interests of their customers.

It was a two-phase system. First the computers sorted incoming content into topic-oriented buckets. Next these buckets of potentially interesting articles were routed to a team of human analysts with expertise in the relevant topic areas. The analysts would go through the articles in the buckets to prioritize them, remove duplicates or items that had come through in previous articles as well as items that did not belong, and add in any items that should be included. Finally the analysts would place the most strategically relevant articles from these various buckets into newsfeeds for each customer. Thus the humans were a very important part of the algorithm — they provided the intuition, knowledge, prioritization and trend detection capabilities of the system. This combination of machine and human filtering resulted in very high-quality strategic newsfeeds for their customers.

As one of Individual’s analysts, what this meant in practical terms was that every night from about 8 PM until 1 AM I had to personally read through around 1600 news articles. My beat was emerging technology, software, broadband, online-services, multimedia and satellite applications. It was a challenge to merely read through, let alone make sense of, such a volume of information every night. Furthermore, not only did I have to figure out what was important and how to prioritize it for each of the approximately 20 global corporations that I filtered for, but I also had to remember if I had ever seen and published anything about a given subject before in the previous year. By trial and error I gradually evolved a solution to this problem and this in turn led me to formulate the ideas that are the foundation of this paper.

The human brain is incredibly adept at recognizing patterns — and in particular we are tuned to detect subtle changes in size, mass and velocity. Many examples of this can be found in nature — for example in frogs. Frogs have interesting visual systems. They are tuned to focus on things that move. They are most sensitive to size and velocity, but they also notice changes in velocity. Things that are small and that don’t move are not of particular interest to them. Things that move in erratic ways are most interesting. But human brains are far more sophisticated — we don’t merely detect the size and velocity of things, we track changes in momentum. Momentum relates the “mass” or “size” of things to the way in which they change or move over time. What is important about momentum is that a low-mass thing moving quickly can have just as large a momentum as a large-mass thing moving slowly. In other words, we can detect small but “hot” emerging trends as well as large but gradual trends. We are extremely sensitive to momentum.

What I realized at Individual back in 1993 was that the way I figured out what articles to prioritize was not so different from how a frog finds flies to eat — but more sophisticated. I realized that I filter information according to the momenta of ideas — how the various memes in the articles I was reading were growing and moving through space and time in the culture I lived in and the communities I was interested in.

Human brains are highly sophisticated momentum detectors — our brains are constantly filtering billions of inputs and patterns in real-time and computing their momenta in order to differentiate signal from noise and to attenuate to what is most important at any given time. Furthermore as trends in the world emerge, grow, peak and fade away, so do their momenta, and we are able to very sensitively detect these changes in momentum in real-time, adjusting our priorities and attention accordingly. There is nothing magical about this process: it can be modeled mathematically,  and therefore there is good reason to believe that computers can eventually be made to do this as well.

Memes

The Physics of Ideas is the science of micro-memetics — a science of the micro-level dynamics of individual memes. It is therefore necessary to define what we mean by the term “meme” (pronounced “meem”)? — basically, a meme is any replicable idea. More formally, a decent definition of a meme is:

“/meem/ [coined on analogy with `gene’ by Richard Dawkins] n. An idea considered as a {replicator}, esp. with the connotation that memes parasitize people into propagating them much as viruses do. Used esp. in the phrase `meme complex’ denoting a group of mutually supporting memes that form an organized belief system, such as a religion. This lexicon is an (epidemiological) vector of the `hacker subculture’ meme complex; each entry might be considered a meme. However, `meme’ is often misused to mean `meme complex’. Use of the term connotes acceptance of the idea that in humans (and presumably other tool- and language-using sophonts) cultural evolution by selection of adaptive ideas has superseded biological evolution by selection of hereditary traits. Hackers find this idea congenial for tolerably obvious reasons.” (Definition from: The Hacker’s Dictionary)

Memes are essential to the way the human brain processes ideas and how it decides what is important. We are basically “meme processors” — we are “life-support systems for memes” to put it another way. To use a computer analogy, our physical bodies are like the hardware and operating system, and our minds — the dynamical activity and state of this hardware — are like the software applications and content running on the hardware. Our minds could be viewed as systems of interacting memes — complex systems of ideas that interact within us, and across our relationships.

Memes are capable of spreading across human social relationships via human interactions, and via human usage of static storage vehicles such as printed media, audio or video, and digital storage media — they are highly “communicable.” (And soon, as I have proposed in other articles, with the coming Semantic Web memes will be able to spread and interact without needing humans at all — machines will be able to process them on their own).
The Media is the Mirror

Before we can measure the physical properties of memes, we need a way to identify the memes we are interested in analyzing. We can identify memes by analyzing textual media such as document collections, wire services, and the Web.

The memes within text appear to be dormant — they are frozen digital representations. They do not move or reproduce on their own — they need help from humans (for the moment). But by inference, static textual representations of memes provide a mirror of the actual “active memes” that are taking place in the minds of the people who author and consume that media. What this indicates is that by analyzing textual media we are not merely looking at the memetic properties of text, we are looking at the memetic properties of people’s minds and of organizations, societies and cultures. In a sense, by selectively choosing the right media we can make a virtual focus group — we can see what people in this group are thinking.

The media is a mirror of our minds and cultures. By analyzing suitably selected information sources (for example, “all news articles from USA newspapers”) we can effectively focus on a reflection of the memes that are actually present within the minds of humans in a particular place, time, industry, community, demographic, etc. The more we know about the information sources, the more we can infer about the memes we find, and thus the memes taking place within the minds of the people who interact with those information sources.

The simplest approach to identifying memes in textual media is to simply pre-specify a list of memes we are interested in and to then search for any matching strings. For example we might be interested in measuring memes related to a particular trend, such as “Java technology,” so we could compile a list of terms related to Java and then use search techniques to locate all instances of those terms. We can then measure their properties.

A more sophisticated approach than specifying interesting memes in advance is to discover them empirically by analyzing text to see what’s there. To do this we might automatically identify nouns or noun-phrases and then measure their dynamics to see whether they are interesting enough to warrant further analysis. There are many existing computational liguistics techniques for isolating parts of speech and linguistic expressions.

Each of these nouns or phrases is a potential meme (we may consider them to all be actual memes or we may filter for only those memes that exhibit dynamics in space and time that meet our threshold for what constitutes “interesting” or “memelike” behavior. Another, more brute-force approach, would be to simply analyze every noun and phrase in a document or corpus for any that exhibit “memelike” dynamics in order to discover memes empirically instead of specifying them and then gathering their stats.

We can use various standard methods from text-mining and natural language processing to do a smarter job of identifying memes (for example, we can use stemming to consolidate various forms of the same word, we can use translation to consolidate expressions of the same meme in different languages, and we can use conceptual clustering and even ontologies to consolidate different memes that are equivalent to the same underlying meme). But for now, we can start by identifying memes in a simple way — the same way we might identify “topics” or “keywords” in a document. Once we can do this we can then measure the physical properties of those memes as they move through time and various spaces of interest.

(Note: We don’t necessarily have to analyze every document in a corpus to gather valid statistics for memes within it. We can use random sampling techniques for arbitrary degrees of accuracy if we wish to optimize for faster results and less computation. Instead of analyzing every occurance of each meme, we can analyze a statisically valid sample of the corpus.)

The Physics of Ideas

I suggest that the physics of ideas will be quite similar, if not equivalent to, the physics of the natural world. Everything in the universe emerges from the same underlying laws, even memes. The intellectual processes taking place within our own minds, as well as across our relationships and social organizations are similar to the dynamics of particle systems, fluid flows, gasses, and galaxies. We should therefore be able to map existing physical knowledge to the memescape, the dimension of memes.

Here are a set of basic measurements of the physical properties of memes and documents:

Absolute meme mass = how “large” the meme is. There are various ways to come up with a measure of mass for memes and I don’t claim to have come up with the only, or even the best, way to do so. This is still a subject for further investigation. However, to begin, one approach at least is to interpret the mass as the total number of times a meme is mentioned in the corpus since the beginning of time to the present. However, it has been pointed out that this interpretation will cause the mass to increase over time. Still, it may be a useful interpretation, and in this paper I will use it provisionally. Another and perhaps better possibility, is to quantify the relative importance of particular memes in advance (for example by having analysts rate the terms that are most important to them) and to use these values as the mass of those memes.  Note: When computing meme mass, we can choose to count repeat mentions or ignore them — doing so has slightly different effects on the algorithm. We can also, if we wish, get more fancy and look at clusters of memes (via semantic network indexing or entity extraction, for example) that relate to the same concepts in order to compute “concept-cluster momenta” but that is not required.

Absolute meme velocity = how fast the meme is moving in the corpus in the present time interval = The rate of occurrances (or “mentions”) of the meme per unit time (minutes, hours, days, etc.) in a given time interval.

Absolute meme momentum = the force or importance of the meme in the corpus = the meme’s absolute mass x the meme’s absolute velocity

Relative meme mass = the mass of a meme within a subset of documents or data in the corpus representing some set of interests. (Note: we call a subset of mutually co-relevant documents a “reference frame” or a “context.”) such as a set of interests, a particular period in time, etc. (rather than in the entire corpus).

Relative meme velocity = the velocity of a meme within a reference frame.

Relative meme momentum = the relative meme mass X the relative meme velocity.

On the basis of these we can then compute derivatives such as:

Absolute meme acceleration = how the absolute meme velocity is changing in the entire corpus = The change in absolute velocity per unit time of the meme in the corpus.

Relative meme acceleration = the change in relative velocity of a meme.

Absolute meme impulse = the change in importance per unit time = the change in a meme’s absolute momentum.

Relative meme impulse = the change of a meme’s relative momentum.

Next, we use the above concepts to look at sets of memes, for example documents:

Absolute document momentum = the force or importance of a document in the entire corpus = the sum of the absolute momenta of each meme that occurs in the document.  (Note: we may choose to count or ignore repeat occurrances of an article in different locations or at different times — this has different effects).

Relative document momentum = the force or importance of a document within a reference frame = the sum of the relative meme momenta in the document. This is a more contextually sensitive measure of document momentum — it couples momentum more tightly with a context, such as a particular query or time interval, or demographic segment.  (Note: we may choose to count or ignore repeat occurrances of an article in different locations or at different times — this has different effects).

Hybrid document momentum = a measure of momentum that combines both relative and absolute measurements = either relative mass X absolute velocity or absolute mass X relative velocity.

How To Analyze a Corpus Using These Methods

We can then apply the above measurements to entire corpuses (collections of documents). This enables us to empirically rank the ideas occurring in the corpus in any interval of time. Furthermore it enables us to rank and prioritize documents in the corpus according to their momenta within any time interval — in other words, how representative they are of “important” or “timely” ideas within any time interval.

To do this, first we must create an index of stats for all memes we are interested in. We can use the above mentioned techniques for identifying memes to do this. For each meme we identify, we create a record in our index that lists the stats we find for it by source location and time. We then analyze our text sources and update the records in this table (for a historical analysis we do this all at once; for a real-time analysis we do it continuously on an ongoing basis or in batches). As new instances of memes are found we append the corresponding records in the index.

We can now use these statistics to plot memes and documents according to our measurements of meme and document mass and velocity. This enables us to segment the memes or documents according to the various possible configurations of these dimensions. Each of these configurations has a useful meaning, for example a document with low absolute mass, moderate or high relative mass, high absolute velocity and high relative velocity contains “newly emerging trends of interest to the current context” whereas a document with high absolute mass, low relative mass, high absolute velocity and low relative velocity contains “established large trends that are not very relevant to the current context.”

By looking at the impulse (the change in momentum) we can also chart the direction of these trends (increasing or decreasing). Memes that have high positive impulse are becoming more “important” than those with lower impulses. This enables us to determine whether memes are “heating up” or “cooling off” — a meme is heating up if it is important and timely and has positive impulse.

Thus documents that have high document momenta contain memes that have high meme momenta — in other words they are representative of whatever ideas happen to be most important now. Tomorrow, when the momenta of various memes may have changed, the same documents might have different document momenta.

These techniques provide a way to rank documents that is in some respects like Google’s algorithm, except that it works for all types of information — not just information that is highly interlinked with hotlinks or citations but even for flat text — and it is capable of arbitrary resolution in time and space. For example, Google is basically estimating document popularity — or effectively, endorsements implied by citations — for each query. Google determines the rank of a page in a set of results by estimating the community endorsement of that page as implied by the number of relevant pages that link to it. Using the proposed physics of ideas however we can accomplish the same thing in a different and possibly better way — we can now compute the ‘potential community value’ of a document — without actually requiring links in order to figure that out. Instead, we can determine the relative strength of the ideas (the memes) that are present in the document and compare them to the memes that are present in the community of documents that are relevant to the keywords in our query.

For example, we do a query for “space tourism” and get back 6,830,000 documents in Google. Next we compute the above stats for each of those documents. We then rank the documents returned by this query according to their relative document momenta. This has the effect of ranking the documents according to the strengths of memes that are particularly of interest to the community represented by the query results. Thus it enables us to rank the resulting documents for our “space tourism” query to favor those documents that contain the highest momentum memes relative to set of memes that matter to the community — in other words the documents that contain ideas that are most “timely for the community” would appear higher. So this is a way to figure out not just what is relevant but what is important or in other words timely at a given point in time to people with a given set of interests.

Example Applications

Using the above techniques we can use momentum to provide a more sensitive way to filter any collection of information objects for which we can gather stats representing mass and velocity. There are numerous useful applications of doing this. Below I describe some of them.

Filtering E-Mail

For example, one might filter their e-mail using meme and document momenta in order to automatically view messages, people and topics with high momentum, low momentum, growing or declining momentum, etc. One could also use these techniques to data-mine the articles in a news feed or corpus for those that contain the “hottest trends.” It could be used to automatically detect “emerging hot topics,” “people to watch,” “companies to watch,” “products or brands to watch” etc. When ever you send a message the system measures the memes in that message and updates a special meme-stats index called “my interests” which just has the meme-stats for memes in messages you send. All incoming e-mail messages you receive can then be ranked according to their document momenta with respect to the meme momenta in the “my interests” index. This e-mail filter is automatically adaptive — as you send messages it learns what your current interest priorities are and this is reflected in changing meme momenta, even as your interests shift over time. These updated momenta are then used to filter incoming mail. So your mail filter learns what is important to you as you work and adapts to focus on your current priorities and interests, without you having to teach it. It just learns and adapts to model your current interests as you work.

Media Analysis

Beyond just that, these techniques can be used to perform more precise media analysis — for example they can be applied to measure the success of an advertising or marketing campaign by correlating the campaign placements with changes in momentum of the memes for the brand or product in the media.

Predicting Changes to a Stock Price

We can also use these techniques to make predictions — for example, we can correlate meme-momenta for memes related to a company with technical properties of its financials and stock price and then make predictions about price changes by analyzing news articles to detect changing meme-momenta related to the company. We can also do pure statistical correlations between meme momentuma and stock momenta for example. The financial news media is like a mirror reflecting what is taking place in the markets — but investors also use this mirror to decide what to do in the markets. So by measuring what appears in this mirror we can predict what investors are likely to do next.

Prioritizing Search Results and Implicit Query Expansion

We can also use these techniques to prioritize Internet search results — or any search results for that matter. For example, a set of Web documents can be prioritized by their document momenta, such that those that represent the memes that are currently the hottest can score higher — in other words, documents that are currently more timely can score higher than those that are less timely, and documents that are more timely yet less relevant (on a keyword level) can be ranked higher than those that are less timely but more keyword-relevant.

For example, suppose you search for “Asian restaurant.” If the meme “Vietnamese food” is currently in vogue in the media, meaning that it has higher momentum currently, then documents about restaurants that contain “Asian” or “restaurant” and that contain “Vietnamese food” will score higher than those that only mention “Asian” or “restaurant’ and “Chinese food” (assuming that Chinese food currently has a lower momentum). But this could change later as trends change. In other words, although we searched for “Asian food” we ended up getting documents ranked not merely by the keywords “Asian food” but by what topics related to Asian food have highest momentum today. This is a form of “implicit query expansion” and “implicit filtering.” In other words the system can prioritize search results for you according to the present momenta or in other words, the timeliness, of memes that occur in them. So it can show you the documents that are likely to be most important to you NOW in light of current trends and events, versus just the documents that have the best keyword relevancy.

Market Research

To make things even more interesting, we can add additional arguments to our “Rank of item” function and our meme-stats table — for example, not just a measure of mentions but also a measure of “hits” — hits on a meme increase whenever a document containing the meme is viewed. We can also add another dimension to represent the spatial distribution of memes. This will enable us to track the vectors of memes through time and space. We can do this by associating each source (each publisher) with a geographic location. We then segment our meme-stats table by geography to break out the momentum of each meme in each geographic region. This enables us to do things like filter documents by “how important they are to people in New York.”

By adding further dimensions — such as demographic profiles gleaned for example from the reader-surveys of publishers we can also segment by demographics, so we can even filter documents by “how important they are in the last month to professional, Democratic party affiliated, college educated, women in New York City who earn a median household income of $100,000.”

By adding still one more dimension to measure “sentiment” for each mention of a meme (as a function of the positive or negative language occurring near it or better yet, about it), we can even start to rank memes according to the percent of members of a given population that support or oppose them.In other words, this system can be used to empirically measure what polls and focus groups do informally. The notion here is that by selecting media sources that are representative of the community you are interested in understanding, you can then view memes and meme data relative to that group. You can also do this in the other direction, simply look to discover what memes have interesting stats for the group your are interested in. Another use of this technology might be to analyze intellectual history by computing meme-stats from historical documents or past news articles.

We can also leverage the fact that meme dynamics can be corellated with those of other memes to determine dynamical dependencies amongst them. This enables us to determine that some memes postively or negatively reinforce others. It also enables us to discover sets of related memes — such that we can learn that stats on a given meme should be inherited by related “child memes” in an automatically or manually generated taxonomy of memes.

Measuring and Mapping Ideas in the Semantic Web

We could also reference metadata about the semantics of various memes we can even filter for various types of memes — such as “memes related to vehicles” or “memes representing people” or “memes representing products ,” etc. This enables us to start measuring ideas as they occur and interact on the emerging Semantic Web — but not just particular memes, even conceptual systems of memes that are interacting or somehow ontologically related. By linking with an ontology, for example, we can track the momentum of all memes related to “American cars” versus those for “German cars.” The ontology enables inferences that help us find all memes that represent types of cars and classify them by nationality of manufacture.

Intelligence Analysis

These techniques might even be used to detect signs of potential terrorism, and to “get inside the minds” of various people or groups of interest — simply analyze the meme-stats for memes in documents they create or view to automatically generate a profile of the main ideas currently occupying their minds. Next by tracking this over time you can start to plot trajectories and make predictions. Intelligent agents can then be trained to notice “interesting” patterns in these trajectories and alert analysts as needed.

Advertising Targeting

The same methods could be used to better target advertisements or recommendations to users. Knowing what memes are currently most important to a party enables better personalization and targeting. In this case a Web site could track what memes are hottest for a given user account — derived from what pages they view and what messages they write or respond to. This data could then be used to augment the users’ interest profile with more dimensions of detail about each interest — such as how timely it is to the user, what particular nuances are specifically interesting, what their sentiment is. This could result in less irrelevance and spam for users and better results for marketers.

Knowledge Discovery

Now what gets interesting is the above methods can be used on both directions. We can use them to ask questions about memes we are interested in and we can also use to empirically discover memes we should be interested in within any corpus. So for example we can just empirically compute meme momenta and document momenta in any collection of information and then filter for whatever dynamics we are interested in, for example, “hot new emerging trends to watch.”

A New Kind of Portal

Using these methods it is possible to build a new kind of portal that provides a window into the collective mind of the planet (or any community of interest). It would show what people within the desired segment think is important over time. We could watch an animation on it of how memes for “Jihad” have spread, or for how those for a technology like “Java” have spread versus those for “Microsoft .Net,” or how a particular war is currently viewed by the public in different states or different demographic segments. A user could “drill down” into any meme to see it’s stats, all articles where it was mentioned, and related items on the Web, and maybe even products etc.

Open Questions & Directions for Further Research

It is important to note that these simple physical concepts could be taken much further. For example, using the above approach we should be able to determine the “gravity of a meme” or of a document or any set of memes or documents. We can then start to model the shape of memetic manifolds — the shape of space-time for ideas. We can also start to look at systems of memes as fields. Perhaps there may even be applications of fluid dynamics, relativity theory, or even quantum mechanics to what is taking place in the memescape — but today we are just taking baby-steps, just as Newton and the early natural philosophers did long before us. We need to begin to simply have the ability to measure memes and their basic interactions before we can go on to higher levels of analysis. I leave it to the physicists among us to take this to the next level of formalism — would anyone like to try their hand at formalizing the above proposed equations for the physics of ideas, or perhaps proposing even better ones?

There are a number of open questions I am still thinking about that suggest opportunities to refine these techniques. In particular, should we normalize documents somehow so that large documents don’t have an unfair advantage over small documents (because large documents have more terms in them and thus have higher document momenta)?

Another question is whether or not we should rank documents first by relevance to query, and then within each “relevancy band” further rank by document momentum within that band? This has the effect of limiting the impact of momentum versus relevancy — which may be useful if relevancy is considered to be more important. For example the top 100 most relevant documents are ranked by relevancy and then within that set they are ranked by document momentum and displayed, next the second 100 most relevant documents are ranked by relevancy and then within that set they are ranked by document momentum and displayed, etc.

Another question is whether there is an ideal set of priorities for the various measurement dimensions above with which to rank documents for general searches. We can let users choose their own priorities of course, for example, by letting users set their priorities for various memetic dimensions, we can then tailor our ranking for their needs. Are they just looking for all documents that are relevant to a query, or are they really trying to find documents that are representative of the most timely issues relevant to a query? We might enable users to set their weights for the absolute and relative measurements of documents in order to view different rankings of search results. Better yet, we could simply provide them with natural language filters to apply, such as “Filter for documents that contain currently hot topics related to this query.” In other words they can set priorities for the above dimensions in order to favor one dimension over another — so they might decide that query relevance is most important, document mass is second and velocity is least important. This would translate to a constraint such that it would be more difficult for documents with low relevance to be ranked higher than documents with high relevancy just because they have higher momenta On the other hand, they might want to favor momenta — for example if they really want to find documents that mention the latest trends related to a query — in which case we would favor document mass and/or velocity above document relevancy in our ranking. I am still thinking about the best way to handle these tradeoffs. Letting the user set their priorities is one way — but it may be possible to do a good job of satisfying most people with a particular set of default priorities. What is the best set of default priorities for general use?

There is also the question of how to best represent the “footprint of a meme” in geographic space. We can detect mentions of memes and using the above methods we may be able to associate each mention with a particular geography (the geographic region of the publisher and/or the intended audience — if the source has an audited audience demographic survey — as most publications that sell advertising do — then it is easy to associate any memes that occur within its content with particular geography and demography). Now the question is suppose we are tracking a particular meme — can we determine its geographic trajectory over time? Can we determine the vector of each meme at each sector in a geographic map? And can we represent that in an animated map for exampe, perhaps with something like a fluid flow animation?

Another open area to study is to analyze the higher order distributions of memes in order to automatically detect memes that are “interesting” (ie. not “noise” according to our priorities). One easy way to do this is to automatically ignore any memes that have a random distribution. We may also want to de-emphasize memes that have regular distributions — such as memes for which the dynamics have been the same for a reasonanble period of time. In other words, we want to filter for memes that have dynamics which deviate from being predictable or stable (randomness and regularity are both predictable). My hypothesis is that the really interesting memes — the memes that represent important emerging trends or current hot issues — will exhibit high volatility. For example, imagine for a moment that we are tracking memes related to “digital music” — if we look back in time there will be a point where the word “Napster” suddenly appears — at first it is a relatively “small” meme but gradually it spreads and gains momentum. Then there is a critical point where it begins to grow exponentially. Then it probably levels off for a while or even inflects after the initial hype phase ends. Next another dramatic increase in momentum should be seen around the time of the music industry’s lawsuits against Napster. Then following the resolution of these we should see Napster fall off dramatically. Later we see momentum increase again as the new commercial version of Napster is announced. This type of pattern is what we are looking for. Can we characterize these patterns well enough that we can detect them automatically?

Perhaps one way to do this is by training a neural network to recognize the types of patterns that interest us — we could do this for example by taking historical content (such as the last 10 years of the Associate Press) and then telling a neural net what memes are most important to us. The neural net can then learn from this training data. We can then run the neural net on current or more recent news and let it guess what is important to us based on the patterns of past important trends. We can rate these guesses to provide further feedback to improve learning. This approach could be used to train intelligent agents that specialize in detecting particular types of trends — for example, we could train agents to alert us when a major new technology trend is about to erupt, or when we should invest in a technology stock, or when a company we track is experiencing a major change of some sort, or to tells us when a new competing product emerges or when an existing competing product overtakes our own product, etc. We could also potentially train agents to recognize the early signs of important cultural or political issues, significant changes in sentiment or focus for a given community we are interested in, or even signs of emerging threats.

Are There Ideal Meme Distributions?

Perhaps one of the most interesting questions I have thought about in relation to the physics of ideas is whether or not there are perhaps “ideal distributions” of memes that get the best response from humans? In other words, do the higher order distributions of memes that become major trends, or that get the most attention in noisy environments, have similar characteristics? If it turns out that this is the case then it could provide a powerful new technique for advertising, information filtering, and even for user-interface design. I believe we can analyze memes to answer this question. Here’s how we might do it:

Approach 1: We choose a representative set of memes for major trends. We analyze their higher order distributions in the media. We then attempt to figure out whether these distributions have anything in common that we can isolate. We then search the media for other memes that have distributions with similar properties and test whether they are in fact major trends. We can provide feedback by scoring the output of these trials and using an evolutionary algorithm to evolve successively better filters. Eventually through such a process we can evolve an agent that is good at discovering major trends in the media.

Approach 2: We can do a perceptual psychology experiment to discover and evolve memes that get the most attention. Create a noisy environment in any sensory modality — let’s use visual information for the moment. Put 100 human subjects in a room and show them a computer generated slideshow. Our slideshow consists of 100 images. We change slides rapidly. Each slide is shown many times in the course of the slideshow, with a frequency according to one of many different distributions we wish to test. For example, one slide is shown such that it has low mass, low velocity — a low momentum. Another is shown to have high momentum. Others are shown to vary such that their momentum inflects and is volatile. We can test a number of different momentum curves in this manner — such as linear or nonlinear momentum growth, etc. At the end of the slideshow we give each subject all the slides and ask them to prioritize them in order of most important to least important — we ask them to tell us what they think the most important slides in the slideshow were. This effectively tests the various distributions we ran in the experiment to see which ones had the strongest cognitive effect on the subjects. Two weeks or a month later we repeat this rating test to see which distributions have the strongest long-term effect as well. By doing this experiment many times with many distributions we can experimentally determine which memetic distributions have the strongest cognitive impact. The next step would be to test whether the distributions we discover are applicable across sensory modalities — for example, do the distributions we found for vision also work for the auditory system. My hypothesis is that they do hold across modalities. If this is the case then we have discovered a key underying meta-pattern in the human perceptual system — the pattern by which humans recognize what to tune their attention to.

There is another interesting and related question to the above experiments: Do certain distributions retain attention better than others? The human perceptual system attenuates to signals very quickly — we tune out anything regular or predictable and focus on identifying novelty. But what is “novelty?” Any new meme that occurs is novel at first, but whether or not it remains novel or gets tuned out is another question. Which meme distributions do NOT get tuned out as quickly, or ever? Is there an optimal way to vary the distribution of a meme such that it continues to remain novel? In thinking about this, are there any meta-patterns to the memes that have gotten your attention in the past? For example, is there something about the way that particular technology trends or celebrities have moved through the media that made them appear to be hotter and more important to you? Having high momentum at a given time is part of this, but it may in fact be the change in momentum over time — the “meme impulse” — that really makes the difference. For example in my own experience I notice that trends that exhibit exponential growth in momentum quickly get my attention — but as soon as the growth becomes predictable I lose interest. So it seems that the trends that retain my interest the best are the ones that have more variable graphs — graphs that are neither random nor regular. Is there an ideal balance between randomness and order? What patterns have this balance — can we quantify this and define it more concretely?

A better understanding of the cognitive effects of various higher order distributions of memes in various human sensory modalities could be particularly useful for advertisers, marketers, and user-interface designers. An advertiser or marketer could use this knowledge to design campaigns that get the most attention and that are not “tuned out” by people as quickly. A user-interface designer could use this information to design interfaces for manging changing information in which the signal-to-noise ratio is optimized so that users can quickly focus on just the most important changing information — for example the information display of a stock-trading terminal, executive information system, military situation room, or fighter jet cockpit user-interface could perhaps be improved using these principles.
Concluding Remarks

Given that memes are now among the most powerful “hidden” forces shaping our individual minds, our relationships, organizations and our world, wouldn’t it be great if we could really measure them and analyze them empirically?

That is what I hope the basic techniques provided above will help to catalyze. By making this hidden layer visible we can gain a much better understanding of our world. Let me know if you end up using these techniques for anything interesting (and hopefully you will make your ideas open-source too so everyone can benefit).

What these basic techniques provide is a way to measure the movement of ideas in time and space. For example, we can track the trajectories of ideas in our workspaces, our teams, enterprises, cities, nations or interest-communities. We can also track them across geography or any other set of dimensions.

Because we can compute basic physical properties of memes we can start to apply Newtonian physics to analyze them. Perhaps by doing so we can really develop a “Physics of Memetics” with which we may begin to predict the outcomes of interactions among memes, the future trajectories of memes, and the influence changes to memes have on events in the so-called “real world” and vice-versa. With this in hand we could potentially teach systems to learn to detect memetic patterns of interest to us — for example the early “fingerprints” in the media that indicate the outcome of a proposed act of legislation or a vote, or a stock price, or a political change. We could also use it to detect emerging cultural trends, and to measure and compare the dynamics of brands or competing technologies in various markets in order to predict winners.

By putting this information into the public domain I hope to see these techniques in use as widely as possible. They will provide dramatic benefits in managing large volumes of information, improving knowledge worker and team productivity, and in discovering and measuring trends in communities.

Ultimately, I would like to see this embodied in a “grand cultural project” — a real-time map of the memetic dynamics taking place around the globe. This map would be filterable in order to show relative memetic dynamics in different places, communities, etc., and to show how various memes are spreading and interacting over time around the world. The data would be open and accessible via an open API so that all services that manage information could provide information to it and query it for stats when needed.

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radarnetworkstowardsawebos-rotated.jpg

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February 09, 2007

How the WebOS Evolves?

Here is my timeline of the past, present and future of the Web. Feel free to put this meme on your own site, but please link back to the master image at this site (the URL that the thumbnail below points to) because I’ll be updating the image from time to time.
graphic image: http://novaspivack.typepad.com/RadarNetworksTowardsAWebOS.jpg

Radarnetworkstowardsawebos

This slide illustrates my current thinking here at Radar Networks about where the Web (and we) are heading. It shows a timeline of technology leading from the prehistoric desktop era to the possible future of the WebOS…

Note that as well as mapping a possible future of the Web, here I am also proposing that the Web x.0 terminology be used to index the decades of the Web since 1990. Thus we are now in the tail end of Web 2.0 and are starting to lay the groundwork for Web 3.0, which fully arrives in 2010.

This makes sense to me. Web 2.0 was really about upgrading the “front-end” and user-experience of the Web. Much of the innovation taking place today is about starting to upgrade the “backend” of the Web and I think that will be the focus of Web 3.0 (the front-end will probably not be that different from Web 2.0, but the underlying technologies will advance significantly enabling new capabilities and features).

See also: This article I wrote redefining what the term “Web 3.0” means.

See also: A Visual Graph of the Future of Productivity

Please note: This is a work in progress and is not perfect yet. I’ve been tweaking the positions to get the technologies and dates right. Part of the challenge is fitting the text into the available spaces. If anyone out there has suggestions regarding where I’ve placed things on the timeline, or if I’ve left anything out that should be there, please let me know in the comments on this post and I’ll try to readjust and update the image from time to time. If you would like to produce a better version of this image, please do so and send it to me for inclusion here, with the same Creative Commons license, ideally.

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