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Rich Jaroslovsky on the Future of News

There’s an on-going series of video interviews with journalists on the futureof.news site. Two recent interviews were with Rich Jaroslovsky, my boss at SmartNews. Rich and I crossed paths years ago. He not only has a good instinct for what works for media online but also a history in both the print and online journalistic worlds and the deep memory for how things are put together and came to be the way they are today.

It is a huge vote of confidence that he’s working for SmartNews and, as you can see from the clips below, he’s here for all the right reasons. Some key quotes to call out:

excessive personalization is a rabbit hole. It at some point becomes an active negative, because what ends up happening is that you never discover anything new, you never discover anything that didn’t know ahead of time you would be interested in, and instead your worldview gets narrower and narrower.

. . .

When we launched WSJ.com, one of my conclusions was, serendipity is very hard to do in a digital environment. One of the great charms of SmartNews is that it has reintroduced that concept of serendipity, of finding things that you didn’t know you’d be interested in, and they turn out to be very interesting.

. . .

I’ve had many epiphanies over the years about digital journalism and how it’s different than print journalism, and one of them is that there is a craving in the audience for authenticity, for hearing things as close to the original source as possible. There are people who want to be able to access content that is from international sources, even when they are reading about stories that are being heavily covered by US media because it provides a different viewpoint.

. . .

In some ways news has been disintermediated the same way that music was. When I was in my record buying heyday and CD buying heyday, if there was a song I really liked, I had to buy the record. I had to buy the CD. And the fundamental unit was that CD, that package. I had to buy the whole package to get that one song. Now if there’s a song I like, I can buy that one song. That’s a very different model, as the music industry has learned somewhat to its despair but is adapting to. In news the same thing has happened.

The brand is no longer a destination, a place that people go to to get news. The brand is a mark of quality on that story. This is a USA Today story, I know what USA Today standards are, therefore the fact that it says USA Today, which is one of our valued partners, on top of that story—that’s a brand of quality. I know what I’m getting here. Or an NBC story, or a Huffington Post story, or a Fox News story. So it’s a very different environment, and the brand is still extremely important, but the meaning has changed quite fundamentally.

finally

My greatest hope is the the flip side of that coin—that as journalism evolves, as new forms of journalism evolve, as new delivery mechanisms evolve, that the end product is a more informed person and a more informed populace. Because I think that an informed populace is the critical element to a successful, thriving democracy. So my great hope is that as journalism works through this period of turmoil and uncertainty, that we come out the other end with models that keep citizens informed, where people can always get the information they need to make informed decisions.

You can see the entire text of the interview on the futureof.news site. I’ve also embedded both video clips below.

Part One

Part Two

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Do what you love. Love what you do.

SmartNews SF had over 50 Japanese university students visit the office to learn about doing business at a US startup and learn about how to start their career. This trip is part of the Japanese Ministry of Foreign Affairs-funded Kakehashi Project to promote greater understanding and opportunities between the US and Japan.

I’m always looking for a chance to practice my Japanese so I jumped at the chance. I tried to give as much of it in Japanese as I could but, as you can see, the slides are in English.

The main topics were:

  1. SmartNews, how it works, why it’s interesting and why it’s a cool company.
  2. My career, how I ended up at SmartNews, and what I learned along the way.
  3. How to get a job at a US company, what tools to use, and how to use them.

kakehashi group
Kakehashi visitors at SmartNews SF on March 9, 2016

Thank you Dennis, Jessica, Naoki, Chika, and Shunan for helping set up and handling the crowd and thank you Ken Funabashi from the Japan Consulate, Stacy Hughes, and Shimizu-san for giving SmartNews the opportunity.

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SmartNews Hidden Gems from 2015

We all remember the biggest stories of 2015, El Chapo’s escape, Ronda Rousey’s KO, and who can forget The Dress? In the spirit of discovery, we at SmartNews would like to highlight the stories that you might have missed. Following on the hidden gems theme, I took a look at each of the SmartNews categories and looked for the outliers. My somewhat unscientific methodology looked for stories from sources that would not normally appear in the category but were picked up and featured based on a topic analysis, hopefully introducing a source to a new audience that would not normally be exposed to that publication.

cats-in-box

GQ describes itself as a men’s fashion and style magazine. When Marshall Sella tests the Bitcoin waters, SmartNews puts his piece in front of the Business readers. Marshall describes his time with Charlie Shrem, an early Bitcoin entrepreneur (bitrepreneur?) whose LinkedIn profile now shows him cooling his heels at Lewisburg Federal Prison.

It’s not often that Scientific American shows up in the Entertainment section. Cindi May’s The Problem with Female Superheroes took a look at how characters such as Storm and Dazzler in the recent X-Men films may be adversely affecting the young audiences who watch them. “Saving the world in spiked heels” may not be giving young girls a realistic expectation of their abilities. We hope the upcoming Dawn of Justice does a better job.

We all cringed when we saw the video of the 12-year-old boy who tripped and punched a hole in the 350-year-old painting valued at $1.5 million. Oliver Holms of The Guardian covers the restoration effort (thankfully it was insured) and points to other mishaps such as when a pair of Qing dynasty Chinese vases and a Picasso did not fare as well. SmartNews placed this one in the Lifestyle section which is where our Art & Culture are featured.

“I thought it was a CIA surveillance device,” said Brett McBay in Modesto, California after instructing his son to shoot his neighbor’s drone from out of the sky with a 12-gauge shotgun. Cyrus Farivar at Ars Technica brought up a number of issues including the right to privacy (the skies around your home) and the respect for private property, (Eric Joe’s homemade hexacopter drone), and of course the right to fire off buckshot into the sky. SmartNews to placed this story into the US category where much of our gun violence stories have been running. Inquiring minds want to know if this Brett McBay of Modesto is the same Brett McBay whose twitter profile states he is the District Representative for a California State Assemblymember.

SB Nation covers sports and, yes, there is a basketball in this bit but it’s used to explain the Magnus Effect from physics and, for that reason, this article showed up in Science.

The Nation likes to dig (and sometimes poke) which usually lands them in the US section for political coverage. Back in May, Dave Zirin asked why mainstream sports sites were not covering the case of NBA player Thabo Sefolosha, who was tackled by NYPD outside a nightclub, injured, and subsequently missed the playoffs with his team. This story introduced Sports readers to The Nation style of media inquiry. Seven months later ESPN published an in-depth investigative piece on this same story.

Finally, in Technology, there’s this fun piece blowing the cover on arcade claw machines. Did you know that they were rigged? Phil Edwards at Vox posts PDFs from the operator manuals to prove his point and caused great consternation in the gaming industry.

We hope you had a great 2015 and learned something new. Stay curious and see you in 2016.

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Recommended from SmartNews

SmartNews has built a sophisticated, duplicate content filter so that when the latest press release from a presidential candidate, disaster, crime, or culinary sensation hits the proverbial viral loop, the breaking news from multiple outlets does not overwhelm the app and crowd out other stories. SmartNews strives to promote only the best and unique stories to our readers.

But there are times when you want to dig deeper on an issue or read an alternative take. Introducing the Recommended widget.

You can find the Recommended widget at the bottom of the SmartView of any article in the SmartNews app. Swipe left on any article to get to the simplified SmartView of that article. Scroll down to the bottom of the article to get to the Recommended widget.

Take, for example, the recent Rolling Stone story about Ringo Starr auctioning off his personal copy of The Beatles’ White Album.

smartnews-recommended-widget

The first three headlines (in purple) are from the publisher of the original piece (Rolling Stone). If the publisher has continued coverage of the story, you’ll see past stories about the topic giving you deeper context around what you just read. In this example, there is a link to an earlier story about the auction followed by an interview with Ringo Starr and then a piece about The Beatles and their album Rubber Soul.

The bottom two headlines (in green) are culled from our daily crawl of 10 million+ headlines and matched entirely based on a custom SmartNews algorithm. Here we see two other stories about the Ringo Starr auction, one from The Guardian and the second from NME.

How do we do it? That sophisticated de-dupe filter we built to reduce articles that are too much alike? Turn it around and it makes a fantastic related-articles algorithm!

Each article is automatically “read” and key terms, companies, people, and other entities are extracted along with data around the author, publisher, length of the piece and many other factors that are used to make a data representation of the article. When two representations overlap significantly we give them a similarity score. The higher the score, the more similar the two articles are for the purposes of filtering or recommending.

I like to think of the Recommended widget as a jumping off point for further exploration. Headlines 1-3 go deeper into the past with a specific source while headlines 4 & 5 go broader along the same topic but across different publications. Choose your adventure.

The similar articles feature is not new. I use a WordPress plugin on this blog to power the Related box you see below each post. Most news sites have something similar, usually driven by keyword or tag matching, against a limited content set. SmartNews has a more sophisticated matching algorithm across a much broader universe of articles and I think you’ll notice the difference right away.

Download SmartNews have fun exploring and let me know what you think!

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Ghost in the Machine

There are at least two sides to every story. The Planned Parenthood videos were a polarizing topic that monopolized the news cycle several weeks ago. How do you teach an algorithm a point of view? How do you optimize for discovery and strike the right balance for diversity while avoiding duplication?

SmartNews is a news aggregation app driven by machine learning algorithms. The platform is tuned for discovery (as opposed to personalization). After using it regularly, I began collecting screenshots of my favorite examples when the app taught me something new or showed me two items side-by-side that suggested a subtle intelligence.

Two candidates and their technology.

The science and application of artificial intelligence to personalization is well understood. From Amazon’s people-that-bought-this-also-bought-that to Pandora’s Music Genome Project, software has been recommending what you’ll like next best based on what you’ve liked so far for years.

The new frontier in artificial intelligence is machine learning. Companies such as Spotify and Netflix are hard at work trying to predict future tastes based on an evolving understanding of collective tastes. Sure, learning assumes knowledge of the past, but projecting that learning into the future is much harder as you build a model based on an understanding of something that does not exist. Rather than showing you something we know you’ll like based on what you liked in the past, machine learning discovers things you didn’t know you would like.

First a little context. SmartNews, while deceptively simple, has a lot going on under the hood. At any time, the SmartNews app shows around 250 headlines across 8 categories. These headlines are selected from millions of stories that are scanned each day. In order to ensure that the stories featured in the app are the most important and interesting, a number of things must take place.

SmartNews Engine

After harvesting URLs, the text of each article is run through a classifier that examines things such as the headline, author byline, publication date, images and video embeds. These pieces are analyzed by a semantic engine that extracts data so the algorithm can map the article to a topic cluster and place it into the appropriate subject category. (I wrote about how this is done in an earlier post)

Importance estimation is where we rank an article and determine where it will go in the app relative to other articles. Does it go towards the top of a section or towards the bottom? If the top, does it deserve featured treatment? Maybe it’s so topical it needs to be pushed to the Top page, which is reserved for only the most important stories of the moment.

Finally, diversification ensures there is a good mix of stories in each category. If there are 40 stories about guacamole and peas, here’s where we determine which to show and which to push to the background. If there’s a new development on a story, the update will push its way in and take prominence over an older story.

These are just details to give you context. The most amazing thing to me is when the app surfaces a “hidden gem” that I would not normally run across if I were using an RSS reader hard-coded to a collection of feeds, or a social network that is limited to news shared by my friends.

The best way to appreciate SmartNews as a discovery engine is to use it daily, but if you haven’t had a chance, here are a few more of my favorite Gems below:

Planned-Parenthood

While the Center for Medical Progress’ undercover video interviews with Planned Parenthood staffers may have been shocking, the representation of two points of view helped me see both sides of the issue. What was interesting was the Cosmopolitan article (a source I normally do not read) had the best measured rebuttal.

alternate science

Much of the climate change news ends up in the Science category. As that story grows in relevance to us all, more publications dig into it. If you haven’t read this terrifying Rolling Stone piece, read it now.

Hulk Hogan

Here’s an example of a developing story getting an update. ESPN reports that WWE is cutting its relationship with Hulk Hogan his comments that were offensive. People Magazine follows up with the story of his apology. Oh, also notice that the algorithm put both stories into the Entertain section.

Lion Hunter

As news of the killing of Cecil the Lion went viral, the algorithm was smart enough to surface a side of the story from a local Minnesota paper.

too-many-guns

The screenshot above, more than any of the others, shows the freaky intelligence working behind the scenes. Like those times when an algorithmically generated playlist just nails the transition of one song into the next, drawing the causality between gun violence in the US to how such an environment might have prepared an off-duty soldier to do the right thing shows how a well-designed system can be greater than just the sum of its component parts.

Do you use SmartNews? Have you had the same experience? Send along some of your own Hidden Gems and I’ll add them to the gallery.

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SmartNews Pro Tip: Save it for Later

smartnews_iconSmartNews is focused on today’s news. Because of this the app is optimized for showing you the most important stories of the moment. The idea is to get you up to speed on what’s going on and then on with your day. If we do our job well there, the thinking goes, you’ll be back.

That said, there are times when you’re glancing at the latest headlines and you run across a meaty profile in Vanity Fair or a lengthy speech transcript in Medium. I’ve seen comments in the App Store where people are looking for a way to save articles for later. There are a couple of options that I’d like to share.

Read it later with Pocket

SmartNews is integrated with Pocket. Create an account at Pocket or login with your existing one. When you share from the article page on SmartNews (another pro tip, a long press on any headline will go directly to the save menu), you have the option to Save to Pocket. Once you’ve saved it here you can go back to Pocket on the Web and read the full text of the article later. If you upgrade to Pocket Premium, they will even download, index, and archive the full text of anything you save to Pocket making later retrieval easier.

Hear it later with Pocket

Pocket TTS

Pocket recently added Text-to-Speech to their mobile app. I ride my bike to work so sometimes it’s better to have a long article read to me. This afternoon I listened to the transcript of Jennifer Granick’s excellent keynote at Black Hat 2015, The End of the Internet Dream which was posted on Medium.

It somehow seemed appropriate to have the same voice that speaks to me as Siri explaining how important it is to keep the internet open and decentralized.

Show more, is that an archive?

SmartNews Read More

Well, kinda. While we try as much as possible to keep things lightweight in the SmartNews app, we recognize that you might sometimes go more than several hours in between SmartNews fixes. We hear you. But if you’re hearing about that great story in the morning and it’s no longer there, we’ve got your back!

Scroll to the bottom of any tab other than Top and you’ll see a “Show more” link that will show you more articles in the channel. We can’t store everything but it’ll at least extend your horizon a few more hours if you want to dig in a little further.

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Popping filter bubbles at SmartNews

It’s now just over a month since I joined SmartNews and I am digging into what’s under the hood and the mad science that drives the deceptively simple interface of the SmartNews product.

smartnews

On the surface, SmartNews is a news aggregator. Our server pulls in urls from a variety of feeds and custom crawls but the magic happens when we try and make sense of what we index to refine the 10 million+ stories down to several hundred most important stories of the day. That’s the technical challenge.

The BHAG is to address the increased polarization of society. The filter bubble that results from getting your news from social networks is caused by the echo chamber effect of a news feed optimized to show you more of what you engage with and less of what you do not. Personalization is excellent for increasing relevance in things like search where you need to narrow results to find what you’re looking for but personalization is dangerously limiting for a news product where a narrowly personalized experience has what Filter Bubble author Eli Pariser called the “negative implications for civic discourse.”

So how do you crawl 10 million URLs daily and figure out which stories are important enough for everyone to know? Enter Machine Learning.

I’m still a newbie to this but am beginning to appreciate the promise of the application of machine learning to provide a solution to the problem above. New to machine learning too? Here’s a compelling example of what you can do illustrated in a recent presentation by Samiur Rahman, and engineer at Mattermark that uses machine learning to match news to their company profiles.

Samiur Rahman on Machine Learning

The word relationship map above was the result of a machine learning algorithm being set loose on a corpus of 100,000 documents overnight. By scanning all the sentences in the documents and looking at the occurrence of words that appeared in those sentences and noting the frequency and proximity of those words, the algo was able to learn that Japan: sushi as USA : pizza, and that Einstein : scientist as Picasso : painter.

Those of you paying close attention will notice that some the relationships are off slightly – France : tapas? Google : Yahoo?  This is the power of the human mind at work. We’re great with pattern matches. Machine learning algorithms are just that, something that needs continual tuning. Koizumi : Japan? Well that shows you the limitations of working with a dated corpus of documents.

But take a step back and think about it. In 24 hours, a well-written algorithm can take a blob of text and parse it for meaning and use that to teach itself something about the world in which those documents were created.

Now jump over to SmartNews and understand that our algorithms are processing 10 million news stories each day and figuring out the most important news of the moment. Not only are we looking for what’s important, we’re also determining which section to feature the story, how prominently, where to cut the headline and how to best crop the thumbnail photo.

The algorithm is continually being trained and the questions that it kicks back are just as interesting as the choices it makes.

The push and pull between discovery, diversity, and relevance are all inputs into the ever-evolving algorithm. Today I learned about “exploration vs. exploitation”. How do we tell our users the most important stories of the day in a way that covers the bases but also teaches you something new?

This is a developing story, stay tuned!

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Getting the Band Together Again at SmartNews

Following a month off after my unexpected liberation from Gigaom, I started this week as Director of Media & Technology Partnerships at SmartNews. I feel very fortunate to have discovered this company at a time when I believe I have a lot to offer.

First, some recent coverage,

While researching the company, I was delighted to learn they had hired Rich Jaroslovsky. Rich and I crossed paths a few times when I was working at Dow Jones as he was getting wsj.com off the ground. We both have a fascination with technology’s impact on media and I shared his mission to bring The Wall Street Journal online. We had since gone our separate ways but I always admired his love and respect for good journalism as a writer, editor, and business guy.

Rich explained to me that SmartNews thinks of itself as a machine learning company with a news front-end which is right in the nexus of what makes me tick. The co-founders, Ken Suzuki and Kaisei Hamamoto, are super-sharp engineers who see news discovery as an interesting problem to solve and hugely important for society to get right. To give you a sense for how they think, as they look for real estate for their San Francisco office, Ken and Kaisei each created their own interactive maps showing the locations of high tech startups and compared notes to determine that the area of 2nd and Howard was the ideal spot to focus their search.

I made my pitch (excerpted below) and here I am!

Two of the hardest challenges for the publishing industry are distribution and advertising. When publishers moved online, they had to reinvent their traditional distribution channels and navigate a new landscape.

Initially it was the portals such as Yahoo and AOL that would curate the best of the web. Advertising was also sold this way, manually curated and matched to broad channels of interest maintained by the portals.

As technology improved, search engines such as Google automated discovery and matching a reader’s interests to a publisher’s content. Advertising was automated and optimized via keyword matching and auction systems to extract maximum value. Distributed widgets allowed publishers to embed advertising into their sites and a combination of publisher tags and indexing that allowed them to take advantage of an ad network’s inventory.

Social media platforms have recently taken over as a source of traffic for publishers and content snippets shared via these networks represent the fastest growing segment of inbound readers for a publisher.

A common thread to success across all these channels is attractive representation of a publisher’s content within each distribution channel. Whether it’s meta-data, SEO, or “social media optimization,” each new distribution channel has spawned a new method of representing your content to the service which is doing the crawling and aggregation.

For a new distribution channel both the crawling and aggregation algorithms are key to successful presentation of content and relevant advertising to the reader.

Technology has enabled effortless distribution of news so the looming challenge is not so much the distribution of content but more its discovery and presentation. Social media burnout and personalization algorithms are still very basic and often push more and more similar content to the reader resulting in a “filter bubble” which shows the reader only what they want to see or worse, what they already know.

Working with publishers to find them new sources of readership and readers to teach them something they didn’t know is an important goal that aligns with my interests. The fact that the team is based in Japan, a culture with a strong culture of news readership, is attractive to me as I am a big fan of introducing Japan to the rest of the world.

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Gigaom Search & Alerts

I never got around to writing about the Search and Alerts products I worked on while at Gigaom. Using native WordPress features and extending it just a bit, we were able to build a full-fledged faceted search engine and notification platform at a fraction of the cost of what it cost to do when I was at Factiva.

search.gigaom.com pulled in content from across gigaom.com, research.gigaom.com, and events.gigaom.com and presented results in a way that allowed you to filter by tags and explore relationships between tags applied on to the content. Built in was a well structured taxonomy and basics smarts which would map a keyword to the appropriate tag.

Gigaom Alerts solves a different problem. While search allows you to search back in time through the archives (which at Gigaom were a significant portion of their total traffic), Alerts let’s you, in a sense, look forward. One of the problems of a media site is that it is often not a destination. Visits come by way of an app or aggregator so the challenge is getting your readers to return. Newsletters are one way but we are experiencing a proliferation of newsletters competing for readers’ attention.

Alerts was built as a way to store a standing query which would deliver notification if and only if there was new content which matched that query. Results are highly relevant because the alerts are constructed by those who read them. If you explicitly state your interest in “Nest” or “Tony Fadell” then there is a high likelihood that you will click thru on a notification of new articles about those topics. Indeed, we did see high engagement from readers that came in via Gigaom Alerts, they stayed on the site longer and read significantly more pages per session the our average readers.

Gigaom Alerts leverages the native WordPress post-taxonomy architecture so that you can have scale to a large number of individual alerts without a significant cost.

  1. Each saved alert is a post
  2. The terms for the alert are taxonomy terms on the post
  3. The author of the post is the user to be alerted

WordPress VIP kindly archived a talk that Casey Bisson did at one of their meetups which I’ll share here along with a link to the slides.

Hat tip to the folks at Followistic.com who let me know that Casey’s session was posted. If Gigaom Alerts sounds interesting to you, I’d check them out. They have built a plug-in which works much the same and is super-easy to install if you’re running WordPress.

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Stewart Brand and the Pace Layer Model

Update: The full audio of the talk is now up on the Long Now blog.

I had the good fortune to see Stewart Brand speak the other night with futurist Paul Saffo as moderator at the The Interval, a bar/salon built by The Long Now Foundation. Brand edited the original Whole Earth Catalog (which Steve Jobs famously called, “Google in paperback form”), founded The Well which pre-dated (and set the tone for) internet newsgroups, and weaved his life in amongst Ken Kesey’s Merry Pranksters and the Haight-Ashbury scene in the 1960s. I am a huge fan of Stewart’s philosophy and knew this would be a special talk. I was not disappointed.

Steward Brand & the Pace Layer Model

“What does this even mean?”

So posted my neighbor in response to my Facebook posting of the diagram above.  I would describe it as a rubric to apply to the way change happens over time.  There is a natural order to systems where some elements move faster than others. This fractal pattern of fast & slow is repeated in all things, best described in Stewart’s book, Clock Of The Long Now

Consider, for example, a coniferous forest. The hierarchy in scale of pine needle, tree crown, patch, stand, whole forest, and biome is also a time hierarchy. The needle changes within a year, the tree crown over several years, the patch over many decades, the stand over a couple of centuries, the forest over a thousand years, and the biome over ten thousand years. The range of what the needle may do is constrained by the tree crown, which is constrained by the patch and stand, which are controlled by the forest, which is controlled by the biome. Nevertheless, innovation percolates throughout the system via evolutionary competition among lineages of individual trees dealing with the stresses of crowding, parasites, predation, and weather.

Stewart Brand – Clock of the Long Now

This model of thinking can be applied to all systems, natural and man made, and is useful to understand inter-dependencies. The model first came together in another book by Stewart, How Buildings Learn: What Happens After They’re Built

Pace Layering

Stewart noted that different layers in a building had different rates of change. The furniture (stuff) gets re-arranged freely while other layers such as the structure or skin are much less malleable. The most immutable is the site which is the plot of land upon which a building is standing, in cities bounded by streets and sight lines.

Huge skyscrapers dance to the choreography of (a city) street plan.

Besides rate of change, there are other properties of the layers of the model as you progress from the outside in,

OuterInner
FastSlow
LearnRemember
ProposeDispose
Absorb shockIntegrate shock
DiscontinuousContinuous
InnovationConstraint
RevolutionConsistency
Gets AttentionHas Power

The interplay between each layer in the model, the “slip zones” is where, as Stewart says, “all the action is.” The outer layers move more rapidly than the inner ones but each ring is not independent. There is tension of one upon the other so that something like fashion, which wiggles back and forth, revisiting and revising itself over time influences the other. As one ring moves, there is a viscosity between each layer and there is a tension that pulls and pushes neighboring layers so that changes in fashion lead to changes in commerce which then influences the infrastructure necessary to support that commerce and so on.

When the tension becomes too great, we get “slippage” that must be absorbed to prevent the system from breaking apart. Like tectonic plates along a fault line, if one layer gets too out of sync with another, the shock from rapid movement of a layer causes ripple effects felt throughout the system. Healthy systems can incrementally absorb movements at their own speed. Those that cannot, because of inflexibility, crack and break as a result of the stress. If a government cannot adjust, it will ultimately be overthrown. If commercial pressures  in pursuit of ever greater profits outstrip the ethics of a culture, that too may break apart a system.

The 1906 Earthquake in San Francisco impacted the insurance industry that was not prepared to underwrite damage on such a large scale. This led directly to the financial panic of 1907. This is an example of a rapid movement in nature impacting commerce which required later adjustments to infrastructure and governance after the fact to address the new, post-earthquake reality.

Jupiter

Another way to visualize the Pace Layer model is by looking at the rings of Jupiter. Each ring moves at a different rate and the “shear” on the boundaries of each layer causes the turbulence seen above. Stewart calls these boundaries areas of “productive turbulence” rich in innovation and evolutionary change, similar to the tidal zones of the ocean side.

The intersection of change in these zones gives birth to our greatest ideas. The combination of the counter-culture movement of the Sixties with the convergence of technical advancements made available by the space race in Silicon Valley gave us the internet.

When the Pace Layer model is applied to our world today, Stewart argues that some forces such as technology have permeated each layer to the point where they act as “gravity” pulling layers along and keeping them in sync. The trend in wearable computing are very much in the fashion layer and demands and constraints there are driving commerce to keep up. New infrastructure is required to support new devices such as Apple’s iWatch (think beacons) and debates about data privacy are driving debates in our governance. The impact of everyone sharing location are forcing us to re-think our cultural norms. Will the culture of selfies and “competitive happiness” of status updates from Facebook here to stay or will there be a backlash?

Concepts such as Democracy and Capitalism are accelerators. The transparencies of  these methods of organization help transmit information quickly from one layer to the next. Think of them as catalysts that help the slower layers to move in sync with the others, preventing the need for large adjustments of the slower layers which inevitably cause shocks to the system.

Stewart added that the layers also act as filters which sift ideas from the outside layers. His example was that both hula hoops and jogging were born in the same Summer but only one survived to have broader impacts to the inner layers. Jogging begat an appreciation for exercise which drove new industries in commerce, infrastructure changed to allow for jogging paths as an important part of city development and so on.

As a parting thought, Stewart Brand left us all with some homework.

Identify a global issue/challenge foremost on your mind…

Now ask yourself:

  1. Out of which pace layer did it emerge?
  2. In which pace layer are its impacts most felt?
  3. From which pace layer is a solution most likely to emerge?

Consider the time dimension for each of 1 – 3:

  1. How long did it take for the issue to emerge?
  2. How long will it take to resolve?

The challenge proposed was global warming. While changes to the upper layers in fashion and commerce have impacted nature, the feedback from the planet in the form of super storms and long term droughts are now being felt with increasing frequency. Will the upper layers be able to absorb these changes or can changes in the governance layer lessen the impact and put in place laws and infrastructure that can reign in the onward momentum of the upper layers?

One challenge before us is that our existing structures of governance are limited by geography. Local, State and Federal governments are coming together to address climate change but to impact changes as deep as the global climate change, a new form of global governance may be necessary, one that has the teeth to make and enforce global policy changes. If not, we may be in for a rough ride and can expect multiple shocks to the system.