Paul Saffo on The Revolution After Electronics

Paul Saffo spoke to Stanford’s Media X conference on the art of predicting the future. Specifically predicting which technology will come to dominate the next decade. Paul’s talk may at first seem somewhat contradictory in nature: Demonstrating how to do it, while simultaneously showing it can’t be done. This article summarises the talk.

30 Year Cycle

Every 30-50 years a new science turns into a technology. With approximate dates:

We are now on the cusp of a revolution from electronics to biology. The precise inflection point, the point of change, may not yet be clear.

Paul noted that Thomas Watson’s famous misquote, “I think there is a world market for maybe 5 computers”, was made in 1953, right on the cusp of the electronics revolution: Aside from the fact that he was talking about a specific machine, and not all computers, the quote is a good example of how it is difficult to predict the future at such points of radical change.

Forecasting the Future

The goal is not to be right, but “to be wrong and rich”: It is easy to take the view that one cannot forecast. If you do attempt to forecast you will still mostly be wrong, but the very act of trying will increase your chance of success over those that do not try.

The further away from a point in time you predict into the future, the greater the level of uncertainty. The difficulty in forecasting is finding a balance between being too narrow and too broad. Forecasting might use wildcards. The “hard part” is to be wild enough.

Typically forecasts for a new product or technology’s introduction are linear: The magnitude of the amount of use of the technology is forecast to grow steadily with time.

Reality tends to be represented as an S-shaped curve: In the early stages the magnitude of use is below the expectation generated by the linear forecast. Usage then rapidly grows, such that the actual usage rises above the prediction in the later stages. The result is that in the first part, forecasters tend to over-estimate performance, while latterly they under-estimate performance. Venture capitalists tend to have linear expectations, and so are disappointed in the early stages, while failing to see the later potential.

Robots and Inflection Points

Stanley, winner of the 2005 DARPA Grand Challenge. Paul Saffo used the example of DARPA’s annual competition for robot-driven cars. In the first year only a handful of competing robot drivers made it out of the starting gate. No car completed the challenge. The next year 22 out of 25 robots got further than the leader in the first race.

The example gives a quantifiable measure of how the technology is developing, year to year.

Spotting the inflection point, the place at which real, dramatic change starts to occur, can still be hard. Sometimes it can be spotted using data which has been ignored or hidden. Sometimes it is a case of looking for what does not fit. The anonymous quote, “history doesn’t repeat itself, but sometimes it rhymes”, is apt. Look back in time as far as you look forward.

The good news is that if you miss an indicator, you still have lots of time to spot another.

Sensors

Paul contested that the last three decades had been characterised by a dramatic cheapening of a component technology, which in turn had led to the widespread use of a product:

Biology and Electronics

Electronics is building biology, and Paul expects that eventually biology will rebuild electronics: These technologies are far from isolated.

An example of developments in electronics progressing biology can clearly be seen from work on the human genome. A well funded government-backed project was beaten by a far smaller project. The smaller project was able to successfully deploy robots, with the results that the cost of the work dropped by a factor of 10 each year. The government project had been funded based on the cost of technology at the outset, and initially failed to adequately respond fully to the changing cost structure.

The creation of the first artificial genome in January 2008 may yet prove to be the inflection point.

Trust Instincts at Your Peril

“Assume you are wrong**” (** and forecast often)

Paul used the example of the sinking of a US naval fleet near Honda, on the west coast of the United States, on 8 September 1923. The fleet had been navigating using a forecasting technique called “dead reckoning”. The coastline had a (then) new technology available to assist navigation - radio direction finding. This allowed a bearing to be given between a land station and the fleet.

The radio direction finding gave an unexpected result that did not match the forecasted position. The lead boat in the fleet concluded that their position was more favourable than anticipated (closer to their destination), and turned sharply… straight into the rocks they had been trying to avoid. The 11th boat in the fleet did not trust the judgement of the lead boat, and when the fleet turned, it hedged its bets, slowing and waiting to see what happened. It was one of only 5 ships from the fleet not to run around.

The morale of the tale: Hedge your bets, but embrace uncertainty. Or as written once on a tipping jar:

“If you fear change, leave it in here.”

Divergence of the Species

The question was asked, will biotech lead to a further aggregation of wealth? Yes. The electronics revolution had itself deepened inequality. Biotech raises a particularly ugly spectre which extends beyond wealth, to life itself. The wealthy would be likely to use their wealth to extend their lives. The ultimate outcome - species divergence. Currently the rich tend to benefit from better health care, and so extend life. But biotech is likely to create a lot more options.

Tim Howgego, 8th March 2008. Related topics: Analysis, Biotech, Future, Media X, Robots. 1 Comment »

Dave McClure on Social Networking and Web 2.0

Dave McClure addressed a Edinburgh Entrepreneurship Club/Edinburgh-Stanford Link event on 29 January 2008. He outlined some of the advantages of “Web 2.0″, talked extensively on the use of real-time metrics to evolve web services, developed a history of social networking websites, and highlighted the interesting aspects of Facebook. This article summarises Dave’s talk, with some additional commentary from myself.

Advantages of Web 2.0

Web 2.0 is characterised by the:

Dave McClure did not actually try and define the term, which was probably wise. Generally the term is applied to websites and services where users collaborate or share content.

Web 2.0 has a number of advantages (although it could be argued that some of these apply to earlier iterations of the internet too):

Metrics for Startups

I believe the ability to very accurately analyse website usage, implement changes, and then analyse the results, is a key advantage of web-based services. It is an advantage often overlooked by information technology professionals and programmers. I’m not sure why - possibly because web service developers:

Or perhaps Web 2.0 just isn’t mature enough yet for developers to have to worry too much about optimisation: A new concept for a site will probably either fail horribly or generate super-normal profits. The sector isn’t yet competing on very tight margins, where subtle optimisation can make or break profitability. Of course, optimisation of websites can deliver substantial changes in user behaviour. For example, I have found that a relatively subtle change to the position of an advert can alter the revenue generated by over 20%.

Dave McClure developed the AARRR model. AARRR segments the five stages of building a profitable user-base for a website:

  1. Acquisition - gaining new users from channels such as search or advertising.
  2. Activation - users’ first experience of the site: do they progress beyond the “landing page” they first see?
  3. Retention - do users come back?
  4. Referral - do users invite their friends to visit?
  5. Revenue - do all those users create a revenue stream?

For each stage, the site operator should analyse at least one metric. The table below gives some possible metrics for each stage, with a sample target conversion ratio (the proportion that reach that stage).

Category User Status (Test) Conversion Target %
Acquisition Visit Site - or landing page or external widget 100%
Doesn’t Abandon: Views 2+ pages, stays 10+ seconds, 2+ clicks 70%
Activation Happy 1st Visit: Views x pages, stays y seconds, z clicks 30%
Email/Blog/RSS/Widget Signup - anything that could lead to a repeat visit 5%
Account Signup - includes profile data 2%
Retention Email or RSS leading to clickthrough 3%
Repeat Visitor: 3+ visits in first 30 days 2%
Referral Refer 1+ users who visit the site 2%
Refer 1+ users who activate 1%
Revenue User generates minimum revenue 2%
User generates break-even revenue 1%

These metrics become critical to the design of the product. Poor activation conversion ratio? Work on the landing page(s): Guess at an improvement, test it out on the site, analyse the feedback, and iterate improvements. Gradually you’ll optimise performance of the site.

I find this attempt to structure analysis and relate it back to core business performance, very interesting. However, the sample metrics can be improved on a lot, depending on the nature of the site. For example, to track virality (referral), I might watch the monthly number of del.icio.us adds, or monitor the number of new links posted on forums (Google’s Webmaster tools allow that). Tracking users all the way through the tree from arrival to revenue generation needs to done pragmatically where revenue is generated from very infrequent “big-ticket” sales: With minimal day-to-day data, it can take a long time to determine whether a change genuinely has improved long-term revenue, or whether natural fluctuations in day-to-day earnings just contrived to make it a “good day/week/month”.

Now I know this approach works, but why it works is less clear. We might like to think that we are genuinely improving the user experience, and maybe we are. However, it could be argued that merely the act of change is perceived by users as an improvement - a variation of the Hawthorne effect. The counter argument to the Hawthorne effect can be seen on sites with low proportions of repeat visitors: The majority of those experiencing the improvement will not know what was implemented before.

History of Social Networking

Dave McClure’s interpretation of the timeline of the development of social networking sites is as interesting for what it includes, as for what it omits: No Geocities; no usenet; no forums; no MUDs… The following timeline shows key services in chronological order, except without dates - all the services shown were created within the last ten years:

The future may be “Social Prediction Networks”. This is a variation on the theme of using trusted networks to filter content: Instead of Blogging meets Search, I characterise Social Prediction Networks as Digg meets Facebook. Shrewd observers will note Facebook has already implemented Digg-like features, while simultaneously topic-specific, community-orientated Digg-clones are being launched. People gather into interest groups around a topic, and then through use of tagging and rating, the community filters content. The system effectively predicts what other people in the group will find useful. This may be an optimum approach for groups above the Dunbar number (or an equivalent number representing the maximum number of people a person can form stable relationships with).

Interesting Aspects of Facebook

Three were discussed:

  1. Social graph (friend list) - email and SMS (mobile phone) service providers have rich data on the frequency of communication between people, yet aren’t using this information to form social networks. Dave noted that two major email service providers, Yahoo and AOL, are currently struggling to thrive - this could be an avenue for their future development.
  2. Shared social activity streams - knowledge of what your friends think is important. Friends are more likely to influence you than people you do not know.
  3. API/Platform - dynamic behaviour and links across your social network.

Further Observations

Will growth in social networks continue? Yes - the friend list adds value to the content.

Will others compete? Probably, as a “long-tail” of networks, likely topic-specific.

Can social networks be monetarized better? Currently social networking services generate far less revenue than search services. The challenge for social networking sites is to move towards the wealthy territory of search services. At the same time, search services are moving towards becoming more like social networking sites.

How can traditional companies engage with social networking sites? Social networking sites work best for sales where a product has a strong aspect of peer pressure in the decision to buy. The most important advice is not to create a copy of a website: Instead provide less complex content that uses social networks to draw users to a website.

Applications for social networks tend to be over-complicated, normally because programmers attempt to implement functions found in software they have previously written for other platforms or websites. Generally the successful applications are very simple. Some developers have opted to break complex applications into a series of smaller applications, and use the virality of social networking sites to build traffic for one application from another.

Social network applications are exceptionally viral. They can gain users very rapidly, yet also loose users just as fast. Much of this virality comes from feeds, which typically alert friends when a user installs an application. Within a few years the feed is likely to be based on actual usage of an application.

Facebook now allows applications to be added to “fan pages” (or product pages) - so individual users need not now be forced to install an application to use it.

Those using email lists for retention are best to focus on the title of the email, and not the content. Merely make it easy to find a URL in the content. The key decision for the reader is whether to open the email. What the email says is almost irrelevant - they’ve already decided to visit the site based on the title.

Tim Howgego, 31st January 2008. Related topics: Analysis, Edinburgh, Information Management, Social Networking, Web 2.0. 1 Comment »

Mike Masnick on Techdirt, Information and Consultancy

These are notes from a talk given by Mike Masnick, CEO of Techdirt, a “technology information company”. Mike addressed a small Edinburgh Entrepreneurship Club/Edinburgh-Stanford Link gathering on 22 January 2008. He outlined the company’s history and philosophy - “use what’s abundant to solve what’s scarce” - and outlined an interesting approach to the delivery of expert/consultancy business services.

Brief History of Techdirt

In 1997 Mike started running a technology-orientated email newsletter and then website, purely as a hobby. In 2000 he found himself looking for a job, and decided to develop Techdirt into a business. He applied the ethos of “taking a major problem and solving it” to information: Businesses found that there was a lot of information being published on the internet which they could not filter and use effectively. Techdirt’s original business model gathered up information, and then filtered it out based on what they knew each customer would be interested in.

Techdirt was funded by revenue from business clients, not advertising, so survived the “Dot Com” crash. Between 2000 and 2004 there was no money available for investment. However there were plenty of good people willing to work, and a lot of excess infrastructures (servers, etc) available. “Use what’s abundant to solve what’s scarce”: The initial business model used the abundant labour and technology, and did not rely on external funding. That model might now be reversed.

Insight

By 2005 blogging had become more widespread, with news-readers increasingly used to aggregate data. Techdirt can still add value for businesses by managing information: I’m glad to know I’m not the only one with 1000+ unread ‘blog posts heaped up in Google Reader…

While most blogs were of little value for the information they contained, some bloggers were insightful experts on topics. All these insightful people were a resource that needed to be connected to companies. What developed was the Insight Community - essentially eBay for technology experts and freelance consultancy services: Companies announce requirements for analysis, with a set amount of money available for the best (or best 2-3) pieces of work that meet their requirements. Freelance experts then compete with one another to provide the best analysis at the price offered. Companies gain multiple points of view, and access to a wider community of experts on the topic.

Techdirt take 25-40% of the money paid by companies - Techdirt’s value added to the independent experts is in putting them in contact with companies - do the experts’ marketing and networking for them. Ultimately, companies can still employ the experts directly, and save Techdirt’s fee. This may happen once companies become aware of how good individual experts’ work is.

Attracting experts was far harder for Techdirt than attracting companies to supply jobs. Initially a lot of work was generated by technology start-ups, but has since shifted towards better-established businesses, which tend to bring repeat business.

I come from an environment where any competition is solely to attain the contract to start the work. I find the concept of the experts competing on the actual output delivered intriguing. I suspect it only works well in niches where all the expert’s value is in their ability to add unique perspective or insight. A lot of mainstream consultancy involves the management of processes, or deploying teams to gather information. More than one person/organisation trying to perform those tasks in competition with one another would needlessly duplicate cost, with the potential to cause chaos.

Mike “has plans” to roll this approach out beyond the technology sector, but did not detail them.

In Review

Mike Masnick summarised his talk as:

His biggest surprise? “That we’re not more successful!”

What would he have done differently? Hired someone that knew how to manage people… The best moments for the company in terms of outward successes also heralded the toughest internal conflicts.

Postscript: The Music Industry

In response to a question, Mike outlined how the philosophy of selling scarcity, not trying to sell what is abundant, needed to be applied to the music/recording industry. The industry believes it is still selling music as a physical product (the CD), yet in the age of the internet, music is abundant. While sales of CDs are in decline, most other aspects of the industry are very healthy. For example, revenue from concert tickets is at an all time [more-or-less] high. So, rather than fight and try to criminalize its own consumers, the music/recording industry should simply change its business model, and focus on scarce aspects: For example, access the artist. An interesting perspective on probably the best-known case of how not to react to the arrival of the internet.

Further Reading

Tim Howgego, 29th January 2008. Related topics: Analysis, Consultancy, Edinburgh, Information Management. 3 Comments »

El’s Extreme Anglin’ - 2007 Retrospective - Part II

This article continues my observations on running El’s Extreme Anglin’, a World of Warcraft (WoW) fishing guide, with a look at some of the trends in usage during 2007. You may also be interested in part I of the 2007 retrospective, which contained some observations on aspects such as thought leadership, quality and links.

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Tim Howgego, 3rd January 2008. Related topics: Analysis, El, Learn2Play, Video Games, WoW. 3 Comments »

Learn2Play, the new Real Money Trading?

Extract from advert for Luke's Gold Making Guide. Real Money Trade (RMT) is the buying and selling of virtual property or currency for real-world money. Many virtual worlds now embrace this trade in virtual currency and goods, often as a source of income for the world’s operator. Blizzard, the developer of World of Warcraft (WoW), does not:

“RMT is a TOS [Terms of Service] violation. The fanbase is pretty committed to being against it, and we’ve got a group of guys that are committed to stopping TOS violations. The game was never designed for that in mind - everyone starts off even. In the real world that’s not true, but in WoW everyone starts even, and the RMT stuff messes with that.”

Not just rhetoric. They have sued a leading supplier to prevent them advertising in-game. And they regularly ban large numbers of accounts used to “farm” gold.

That environment seems to have expanded another quite logical commercial market: Teaching players to play. “Learn2Play” in the vernacular, or “L2P” in shorthand.

Rather than buying gold (in-game currency), players buy the knowledge of how to make gold themselves. The market isn’t restricted to gold. Guides to power-leveling (advancing a character through the first part of the game as fast as possible) are also popular: Rather than pay someone else to level a player’s character, players can buy a guide containing instructions optimised for rapid leveling.

This article explains Learn2Play, and explores some of the history and trends in this “market”. It focuses specifically on World of Warcraft, in English, which is sufficiently popular to create a tangible commercial Learn2Play market. It draws on my own experience from selling these guides.

Superficial analysis suggests the World of Warcraft Learn2Play market is valued at over $3 million revenue per year. In spite of WoW being an online experience, revenue from physical book sales may still exceed revenue from the virtual equivalent. The market is far smaller than RMT. But the notion that people are willingly investing US dollars in knowledge and skills that are useful solely within one virtual environment, should perhaps deserve as much attention as other real-virtual money transactions.

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Tim Howgego, 9th September 2007. Related topics: Analysis, Learn2Play, Real Money Trading, Video Games, Virtual Worlds, WoW. 4 Comments »