Welcome to the Acceleratron

Amidst the worst economic crisis in generations (which conceivably may not be over), it’s easy to lose sight— not of the recovery, but of what’s really going on. The “new normal” is likely not a hopelessly stuck economy, but a technologically-fueled pace of change that’s far more dramatic, a future that delivers a constant stream of surprises, both “good” and “bad”.

The “new normal” seems to be an acceleration of change driven by red-hot technological innovations that interact with each other exponentially on a truly global scale, as Moore’s Law meets Metclafe’s Law meets learning curves. The result transforms the pace of scientific research, creative destruction, and cultural evolution.

Two recent articles highlight the change. John Hagel’s and John Seeley Brown’s research on the “The Big Shift” illuminate and quantify the impact on businesses and the economy. Their work is detailed, thoughtful, and ongoing.

As I have been reflecting on what’s happening, I begin with a look back. In the last fifteen years we have witnessed and experienced massive changes, such as: the rise of the consumer internet, adoption of cell phones by more than 3 billion people, completion of the mapping of the human genome, and cloning of the first mammals.

But do you believe that the next 15 years will produce changes that are very likely to exceed these? Do you believe that the rate of technological change is continuing to accelerate? Do you believe that no other period of history is remotely close to what we are about to experience?

This thinking stretches me, but I believe it. Here’s why: information technologies now in place globally are feeding on each other, creating an accelerating wheel of advancement and creative destruction. The technologies fall into (at least) three categories, the dynamics of which are individually mostly understood, but which collectively are creating new dynamics that are difficult to fully grasp since they are evolving in real-time. Three of the powerful technological forces are:

1. IT Infrastructure— Moore’s Law type effects. This includes computing power, storage, bandwidth, and all of the derivative devices, such as an increasing array of personal computing devices (such as tablet computers) and massive scale cloud computing. These powerful effects have been running strong for decades and will continue.

2. Meta Tools-- fueled by acceleration in open source software, meta-tools continue to accelerate the pace of software development. Building on the shoulders of those that came before them, software engineers in healthy situations can be 3-5X more productive than 10 years ago.

3. Collaboration and radical network effects—Metcalfe’s Law type effects are combing with learning curve effects to create viral adoption of new methods, a catalyst for the next ratchet-up of the speed of change. John Hagel and John Seely Brown have called this effect the “collaboration curve”, when traditional learning curves are multiplied by massive scale collaboration. While not limited exclusively to the IT sector, the best examples are companies like Apple, Google, and Facebook, who often have more third-party (external marketplace) engineers working on improving and extending aspects of their software technologies than they employ themselves. They’ve opened critical parts of their systems and toppled market leaders.

These three mega-trends— simultaneous radical improvements in infrastructure, tools, and collaboration— work together to create a constant flow of discontinuous change, an “acceleratron” that may be only beginning.

The acceleratron enables developments such as:
- iphone applications being written at the rate of 25,000 to 50,000 per year, essentially none of those by Apple itself
- Google becoming a $100 billion market cap company in under a decade, while old media dies
- Amazon becoming one of the most valuable retailers in the world in just over a decade
- Wikipedia becoming the world’s largest database of actively curated and moderated content with only a few employees

The acceleratron speeds up all industries and processes. You can find examples in agriculture, education, energy, healthcare, manufacturing, and retailing.

The acceleratron's reach will remake industries in small fractions of the time it took to create them (see newspapers); it will unleash even more fantastic scientific progress (see genetics); and it might remake a “have/have not” world order to a “know/know not” world order (which will bring new issues).

Are we ready for all of this change? As companies, countries, cultures, and citizens, are we prepared? Absolutely not. The changes will test us, strain us, and push us in entirely new ways.

Will our essential humanity, our fuzzy brains, our cultural proclivities, and our basic desires for the simple things, provide a “natural brake” to the full effects of the acceleratron? Will some communities do a better job of digesting and moderating the pace of changes than others? In my view, yes and yes.

I’m optimistic about our ability to harness the acceleratron, unlocking an exciting period of advancement and understanding, while holding on to things most dear.

I know not how to end this post. Perhaps, with this: more to come.


Credits: Joe Rosenblum, INET’s CTO, helped shape my thoughts on “the acceleratron”.

The Muse Comes Quietly

Our "eureka!" moments are a literal burst of mental energy emerging from calm. Recent research shows that the two main steps in the problem solving process, analysis and insight, use completely different parts of the brain and rhythms of thought. Brain monitors show that epiphanies arise from relative quiet in the brain, followed by a flash of activity.

I sometimes close my eyes for a brief moment when I need to think really hard. Why?

Since about 90% of the brain's input is visual information, I sometimes need to turn off the giant firehose of noise to clear my thoughts and invite the muse.

Reading the research helped me assess my own quirky methods, which people have politely asked about over the years. I try to be very conscious of the shift from analysis to insight. First, I evaluate and synhthesize information, then I try to figure out what to do with it. I find analysis and insight to be related like a house and its foundation, useless without each other and made of really different stuff. I try hard to tune into the shift between the two modes-- they even feel different. Analysis feels like seeking, a hot pursuit of knowledge, a process of elimination and inclusion, of making contextual choices on a path. Insight feels just the opposite, like receiving, a cool process where ideas arrive.

When I make the shift from analysis to insight, I deliberately quiet my mind, step back from the facts, and set my intent to create. I invite new thoughts and try to wait for them. This isn't always easy, as I'm not especially patient.

"Eureka" strikes, or it doesn't. If I'm alone and tackling more complex issues, I am more patient. Often drawing maps of the concepts helps.

Sometimes I want to shout, "Hurry up, damn muse!". But, shockingly, that doesn't really seem to help. And when insight does arrive I fight my next lovely impulses. Like, "Why didn't I think of that before?!". Followed by the inevitable, "Now I have to make up for lost time, stupid".

I think I need to treat myself better-- and certainly the muses. Good ideas like to be invited, not chased.

Zen Venn: How to Strive to be Happy in Business

I love this Venn diagram by Bud Caddell-- it seems to have caught fire on Twitter. To be fair, it's almost a perfect copy of thinking by Jim Collins years ago, which he called the hedgehog concept. I loved the concept then (as applied to corporate strategy) and I love it now (as applied to finding happiness in business careers), because in both cases it made me think.
The more I thought, the more the three circles collapsed (which I believe is where both authors want us to go).

Here's what i mean. First, my intent is to align what I "do well" as much as feasible with "what I want to do". Although we are all endowed with some innate gifts, what we truly "do well" is largely the result of an enormous amount of practice (think the 10,000 hour rule popularized in Gladwell's book Outliers). I try to invest time as much as possible into those skills that "I want to do" better.

This brings me to the "paid to do" circle. This one is much more difficult, since what the world pays for is very specific and and constantly evolving-- and not always exactly what I "do well". But it is what it is. In a Zen-like way (well, I try), I first accept economic reality, then think of ways to uniquely contribute, and then ideally develop ideas that are entirely new.

So for me, staying valuable, relevant, and hopefully cutting-edge is a dance between the commercial state of the world "as is" and what I can uniquely deliver-- either with my current skills or, increasingly for all of us, skills that I have yet to fully develop. If not Nirvana, my intent is to make that tango a joyful search for "hooray".

The Rapidly Evolving Twittersphere

Twitter's rapid adoption is forcing change on the big guys. First, Twitter's success casuses Facebook to accelrate adoption of more features; today a report that Google is readying Twitter search.

What happens next? There are a myriad of possibilities for Twitter. While the business models for all of this remains unclear, the level of user activity has everyone's attention. This type of explosive change often attracts a round of over-investment in the gold rush phase (11,000+ Twitter apps already), but an eventual shaking-out as the winners emerge.

Google's Real Advantages Are Not the Algorithm

In results of a clever blind test of search engine queries, Google is running slightly ahead of Bing and Yahoo, but not by much.

This shouldn't be too surprising. The algorithm itself can be approximated. Idealab experiments for the search engine Snap proved this many years ago. On a blind basis, it's difficult to tell the differences. Google's conumser-facing advantages aren't so much the algorithm as the brand. Google has come to mean search. Google has become a verb. Google also brilliantly protects its brands by offering a huge set of other consumer tools and services for free.

Equally important as Google's brand are its enormous economies of scale and scope. These advantages include:
-- A much, much larger penetration of advertisers and their budgets
-- Lower operating costs per dollar of revenue
-- A technology platform that allows very rapid introduction of new services
And all of these advantages are self-reinforcing.

I'd expect Bing to take some market share from Google and Yahoo and perhaps cause Yahoo to update its negotiating stance with Microsoft. But I wouldn't expect things to change too much very fast.

Pixel This: Google Analytics at 80%+ Penetration

If you had a tracking pixel on more than 80% of internet websites, what would you do with the data? The mind reels.

You could conceivably know the most about:
- People’s interests throughout the world
>>>what do they buy?
>>>where do they shop?
>>>who do they trust?
- Changes in fortunes
>>>which products and services are hot, which are not?
>>>which consumer trends are emerging and which are fading?
- Organizing all of the world’s information
>>>what do people want?
>>>how do they like consuming it?

Which brings us, of course, to Google. A new study of 400,000 domains reveals that the Google Analytics (“GA”) pixel is now deployed on about 88% of them. While the growing ubiquity of the Analytics pixel is relatively well known, these numbers were a bit higher than I thought. But if the study is overstating the reach now, it won’t be for long. Rapid GA adoption continues.

I hear a lot of concerns about Google’s enormous might, but I’m not a reactionary or conspiracy theorist about Google’s “dark side” or their ability to become “big brother”. I’m only observing that the aggregate GA data is an enormous and extremely insightful tool; it contains important knowledge mapping data on a scale unlike any other.

I’m not sure how Google is or isn’t using this data now, but I assume they will ultimately use it to help with their mission of organizing all of the world’s information. With the data, you can build better search tools, better knowledge maps, and better services. (This is a great example of Google's advantages and of economies of scope .)

The other striking data point from the study is that the nearest “competitor” to GA, StatCounter, has only 7% penetration of the 400,000 domains in the study. I would expect other competitors to increase their penetration, since the cost/benefit ratio of installing tracking pixels is generally improving. But 88% vs. 7% sure is a commanding lead.

I’d love to hear your thoughts about where Google might take its GA position.

Finally, a quick shout out to Christa Quarles for tweeting the NYT article, which brought the new data to my attention.