Steve Neiderhauser writes about "a presentation by Michael Kennedy regarding the concepts in his book - Product Development for the Lean Enterprise."

This book takes you on a journey that explores the Toyota product development system -- referred to as Knowledge-based Development (KBD). Why Toyota?

Toyota's new product engineers are 400 percent more productive than those employed by most companies. The result? Toyota has more net profit than all other auto makers.

Don’t let the phrase “Lean Enterprise” fool you. This book isn’t about linear improvements, it’s about a summersault into the future that generates a host of benefits that are off the charts for the typical American company:

- 4X increase in development productivity
- 2-3X decrease in development cycle time
- 2-3X decrease in development costs
- 2-10X increase in innovation
- 2-5X decrease in development risk

You see, if you embrace KBD and take the offensive, this strategy will enable you to checkmate the competition. As Kennedy said in his presentation, “When a company performs at this level, it’s game over.”

Ross Mayfield writes:

Social Software, a term coined by Clay Shirky, is the design of systems that supports groups with an underlying value proposition of building social capital…
Social Software is not that new, but its currently a growing and evolving sector marked by a high level of cross-polinization. The level of innovation defys easy categorization.

Properties include people-centricity, low communication costs, low transaction costs that encourage adoption, easy group forming, triads rather than pairs, treating groups as first class objects in the system and adapting to the social network (heterarchy) rather than requiring it to adapt it it (heirarchy). Second order effects include emergence, reputation, different values at different scales, transparency, decentralization and fun parties.

Other dimensions to view this space include enterprise vs. consumer, how connections are formed, different values at different scales, what markets are cannibalized, what cultures (not markets, but don’t reach for your gun) are served and open vs. closed.

These dimension easily blur. Take for example the distinction of enterprise vs. consumer. Social Software adoption is being driven in the enterprise from the bottom up. Initially, it users as developers bringing in their own tools like personal publishing and wikis plus (shameless plug here) enlightened companies serving both users and enterprises at different scales.

Adam Rifkin writes about an idea from Rohit Khare:

One of the primary thrusts of our work at CN Labs will be a new kind of internet-scale event notification service: an application-layer router. Just like there's an IP packet format at the network layer, there ought to be a new standard that unifies the welter of application-layer protocols: smTP, htTP, fTP, nnTP, and more.

TP, a Transfer Protocol, merely provides a best-effort delivery service for named, MIME-typed bags of bits. Rather than using IP addresses, those names are the endpoints that identify multiple services.

If I want a $5 increase in IBM stock price to pop up an alert in my browser, I ought to be able to request something like

"send all messages about
//nyse.com/IBM?delta>5
to javascript://rohits-laptop/window1/alert('sell!')"

There's a lot more to this idea, whether you call it bringing pub/sub to the web, or bringing programmable agents to mail, or some other unification of those messaging middleware modes.

[via Jeff Nolan] Matt Blumberg has an entrepreneur's perspective on company boards:

1. Building a board can be one of a CEO's greatest trump cards.

2. Don't think of managing your Board as a burden.

3. Figure out how to work differently with investor directors and outside directors.

4. Sit on a board yourself.

I first heard about Black Swan in the context of a paper mentioned by Chetan Parikh (of CapitalIdeasOnline) in one of Book Club meetings. The author of the paper, Nassim Taleb, is a Lebanese-born financial trader and the author of a book, “Fooled by Randomness.” He is currently founder and chairman, Empirica LLC, a research laboratory and financial products trading house in New York, and Fellow in Mathematics in Finance and Adjunct Professor at the Courant Institute of New York University. The title of the paper is “The Black Swan: Why Don’t We Learn that We Don’t Learn?” [Malcolm Gladwell, the author of Tipping Point, has profiled Nassim in The New Yorker.]

Nassim explains a black swan event in an op-ed page article in the New York Times (April 8, 2004):

A black swan is an outlier, an event that lies beyond the realm of normal expectations. Most people expect all swans to be white because that's what their experience tells them; a black swan is by definition a surprise. Nevertheless, people tend to concoct explanations for them after the fact, which makes them appear more predictable, and less random, than they are. Our minds are designed to retain, for efficient storage, past information that fits into a compressed narrative. This distortion, called the hindsight bias, prevents us from adequately learning from the past.

Black swans can have extreme effects: just a few explain almost everything, from the success of some ideas and religions to events in our personal lives. Moreover, their influence seems to have grown in the 20th century, while ordinary events — the ones we study and discuss and learn about in history or from the news — are becoming increasingly inconsequential.

Consider: How would an understanding of the world on June 27, 1914, have helped anyone guess what was to happen next? The rise of Hitler, the demise of the Soviet bloc, the spread of Islamic fundamentalism, the Internet bubble: not only were these events unpredictable, but anyone who correctly forecast any of them would have been deemed a lunatic (indeed, some were). This accusation of lunacy would have also applied to a correct prediction of the events of 9/11 — a black swan of the vicious variety.

A vicious black swan has an additional elusive property: its very unexpectedness helps create the conditions for it to occur. Had a terrorist attack been a conceivable risk on Sept. 10, 2001, it would likely not have happened. Jet fighters would have been on alert to intercept hijacked planes, airplanes would have had locks on their cockpit doors, airports would have carefully checked all passenger luggage. None of that happened, of course, until after 9/11.

Consider two types of randomness. The first type is physical randomness — in other words, the probability of running into a giant taller than seven, eight, or nine feet, which in the physical world is very low. The probability of running into someone 200 miles tall is definitely zero; because you have to have a mother of some size, there are physical limitations. The probability that a heat particle will go from here to China, or from here to the moon, is extremely small since it needs energy for that. These distributions tend to be "bell-shaped", Gaussian, with tractable properties.

But in the random variables we observe today, like prices — what I call Type-2 randomness, anything that's informational — the sky is the limit. It' s "wild" uncertainty. As the Germans saw during the hyperinflation episode, a currency can go from one to a billion, instantly. You can name a number, nothing physical can stop it from getting there. What is worrisome is that nothing in the past statistics could have helped you guess the possibility of such hyperinflation effect. People can become very powerful overnight on a very small idea.

Take the Google phenomenon or the Microsoft effect — "all-or-nothing" dynamics. The equivalent of Google, where someone just takes over everything, would have been impossible to witness in the Pleistocene. These are more and more prevalent in a world where the bulk of the random variables are socio-informational with low physical limitations. That type of randomness is close to impossible to model since a single observation of large impact, what I called a Black Swan, can destroy the entire inference.