Monthly Archives: July 2012

The third age of cyborgism

cy • borg  |ˈsīˌbôrg|  — noun  — a fictional or hypothetical person whose physical abilities are extended beyond normal human limitations by mechanical elements built into the body.

I think we are entering the third age of cyborg technology. In the first, humans (unknowingly or not noticing) became part-machines with improved vision (eyeglasses), digestion (fires that heat and pre-digest food, then tooth fillings), strength (levers) and speed (the wheel). Once we became robots in the first stage (sitting in rolling cars each morning), we then moved on to primary sense — vision and the mind that holds it — and how to augment that. This brings us to today, the second age of cyborgism, with flat panels surrounding us from far away (55-inch Samsungs) to tool level (iPads) to whispered love (cell phones), all backed by augmented memories (books and Google search). This second age is ending as panel design becomes, well, panels, and artificial memories become seamless (hi, Siri).

The third age of cyborgism will be one of true virtual reality, 3-D imagery floating around us, in which humans, tired of augmenting just their body and vision, decide to amplify the entire world. As that takes off, the design fights from Apple and Nokia will leave the tools with QWERTY keys behind to joust over the applications in the air. (Smartphone market-share reports already commonly rank operating systems like iOS and Android, and not the hardware. When you think about it, OS doesn’t really exist.) It’s a bit surreal, to realize that soon we will merge ourselves entirely into virtual worlds that make Second Life look like a 2001 Microsoft tablet computer. Haptic gloves, body sensors, and eyeglasses or contacts that put a fake reality in the air around us will lead to a new form of progress. Apple already has a patent for glasses-less 3-D that picks up ambient shadows in the room to make the stereoscopic images seem completely real. Somehow, I suspect not many people in that new world will still have abs.

From my comment at What Would the Internet Do? 

Image by Abraxas3d at Burning Man 2009.

The great Facebook ‘Like’ currency devaluation

Humans will make 1,168,000,000,000 “Likes” and comments on Facebook in 2012. That’s an almost unfathomable number, so we can put it in perspective in several ways — if each Like and comment were one dollar, that’s slightly more than U.S. annual military spending ($929 billion); it’s 3.2 billion per day; it’s 1,296 per year per Facebook user; or, to bring it all the way home, the average Facebook user makes 3.5 Likes or comments each day. Since the average Facebook user also is connected to 234 friends, and you see what your friends do, this means if you’re on Facebook you are exposed to 819 “Likes” or comments each day.

819 Likes and comments per day is a lot.

Now, to be fair, Facebook doesn’t break out “Likes” and comments separately, and to parse only the “Likes” that brands are now so enamored with is difficult. Facebook is a chatty medium, so if we assume charitably that 90% of activity on the network is comments, that’s still about 82 Likes per day that you would see.

82 Likes is a lot, too.

All this means is “Likes” have become a commodity — and they face the same challenge that advertising “impressions” have for decades. There are more coming your way than you know how to respond to. This is not a mathematical nuance, but a real problem for marketers who hope to scale organically inside social media without paying for it, because the old challenges of communication really have not changed — there is more supply of messages being pushed out than there is demand for consumers willing to receive them.

I’ve written elsewhere that the desire to “go viral” inside social networks faces tremendous friction that precludes success. The formula for going viral — Viral spread = (Message generation rate – Absorption rate) * Cycle time — means if more people “absorb,” or refuse to pass along, the message than the number of people who share it,  your meme will die. Like a story passed from person to person at a party, if your message is deemed boring, the conversation will flow somewhere else.

The problem social media faces is really that its rapid adoption by users, who spend hours a day sharing on Facebook and Twitter, has created a growing glut of messages. Like email or TV spots or radio ads, there are more people braying at you than you choose to listen to.

The supply of marketers chasing the demand of consumers has always been out of whack. Facebook “Likes” now face exactly the same challenge that other forms of outbound communications do, too. All of this explains why advertising expenditures in the U.S., especially among digital advertising, continue to grow. The dream of consumers organically listening to your pleas for engagement is a nice one. The trouble is, you need to get in line for organic growth, so it may be easier to just pay for it.

Zombies will win in the apocalypse. Math proves it.

A while ago I wrote about the formula for “going viral,” or how marketers can ensure that their message will scale to the masses. It’s simple math, really:

Viral spread = (Message generation rate – Absorption rate) * Cycle time

Or put another way, for each cycle of contact between people, the message generation (passalong) must exceed the absorption (people who don’t give a damn and stop passing it along). This is how some memes — cultural units of society such as the current fad among youth to wear baseball caps with straight brims instead of curved — move through society. It’s how you get sick with the flu and how computer viruses spread.

Unfortunately, viral passalong also works incredibly well for zombies.

Zombies, a.k.a. the living undead, have been a concept for nearly 4,000 years, dating back to the ancient poem the Epic of Gilgamesh in the 18th century BC. That story was about battles with monsters, quests for immortality, and the goddess Ishtar breaking down the gates of the Netherworld to “let the dead go up to eat the living.” (That’s right, there are no new ideas coming out of Hollywood.) Mary Shelley rebooted the idea with her novel Frankenstein and we’ve been afraid of zombies ever since. But the basic construct is most interesting today, with our planet swarming with nearly 7 billion people and fears of potential pandemic flu rising: In most modern tales, zombies begin with a lethal outbreak of a virus that rapidly spreads through the population, leaving small bands of uninfected human survivors trying to, well, survive.

Which brings us to 2009 and the brilliant academic research paper produced, half in jest, by the University of Ottawa. The theme may be silly, but the math is real. The authors predicted that in any timescale, a population of uninfected people if paired with a population of infected zombies would eventually be wiped out. Or as the paper cheerfully stated, “coexistence between humans and zombies/infected is … not possible.” The math is complex, but basically if there are two populations and in any encounter the bad can infect the good with any contact — a 100% zombie virus “message” passalong rate — statistically the outbreak will continue to spread. You will die. The only hope would be if humans developed an antidote, which could raise the absorption rate (the portion of humans touched who don’t pass zombification along), which in turn would not allow humans to overtake the zombies but a small population of normal people could survive.

Zombies are a cultural meme that cannot be stopped. Zombies are the ultimate viral meme that wins, because in any contact, nothing slows it. This also explains stupid straight-brimmed baseball caps.

On the fallacy that digital response rates are low

Talk to people slightly familiar with digital advertising and you’ll hear “oh, yes, response rates are low.” Everyone knows most people don’t click on display ads, that websites face a glut of banner inventory, that those colorful little boxes promoting vitamins at the side of don’t really work at all.

Except this isn’t true. Digital advertising response rates are terrific, and I’ll explain why in just a few words:

1. Strategically, advertising is a game of what you catch, not what you spill. If you pay for ads that are seen by thousands of people, and only a few respond, all that matters is what you pay for response. You got on a vast road network to drive to work today, and yet you ended up in the office, missing all those other roads. Like driving, all that matters in advertising is the result, not the miss.

2. Tactically, in most cases, digital advertising outperforms TV, radio and print in terms of the percent of people who saw the ad who then respond.

3. Here’s the math. If you spend $3.50 CPM (cost per 1,000 impressions) for online display ads, a $100 media budget gets you 28,571 impressions. If you have the standard click-through rate on those ads of 0.07%, you get 20 respondents. Your cost per response is $5.00.

4. By comparison, a typical radio ad has a $35 to $50 cost per response. Radio costs about $7.00 CPM, so if you reverse engineer the same $100 media budget in radio, you get 14,286 impressions — and your response rate to achieve a $35 response would be 0.02%, far below that of digital ads.

The same goes for television. Go home tonight and turn on your TV, and if you’re a typical American, you’ll leave it on for nearly 5 hours — being exposed to 160 television ads in one day, or about 5,000 ads per month. If you respond to 3 of those TV ads each month, and I bet you don’t, that’s a 0.06% response rate — not quite as good as little banner ads.

Direct mail has higher response rates, but it also costs a lot more on a per-thousand basis. And the response cost is higher than digital — a 1.3% average response for a 50-cent mailer costs you $38.

Understanding why digital beats other media in cold, hard response evaluation requires math, yes. But you get the gist. Most advertising never catches anyone. But that doesn’t matter, because as in love, marriage, kisses and fishing, all that counts is what you catch.

Image: Stuart Anthony

How to predict the end of the human species

Back in 1999 when I still subscribed to the print edition of The New Yorker, it ran a profile of Princeton astrophysicist J. Richard Gott III, who claimed to be able to predict anything. Gott was a big thinker, starting his career writing about open universes and the real possibility of time travel, but this profile shook me because Gott turned science against the big, meaningful question — how long the human race will survive.

Gott’s approach was simple: He took an old idea from Copernicus, that in almost all cases there is nothing special about where you are when you observe something, and then applied it to time. In other words, if you’re alive today, July 4, 2012, that’s just as random an outcome as if you had been alive when America’s Founding Fathers declared independence on July 4, 1776. Like pennies tossed in the air to land on the floor, Ben Franklin just happened to be there, in that year, and you and I just happen to be here, in 2012. If things, including people, land randomly in time, then statistics can easily predict how long a series of events will last — because the bell curve of random distribution shows the curve of how many got to this present point, and how many others will be in the future before the event curve peters out.

Everything, you see, has a beginning, middle, and end, and if your location in time is random, you are most likely to land in the thick middle part of a bell curve.

Gott gave the example of the Berlin Wall. He thought of his prediction idea in 1969, back when the big wall was eight years old. If his location in the history of the wall was random, there’d be a 50 percent chance that he’d be in the middle two-quarters of the wall’s duration. Running some numbers in 1969, he calculated there was a 50 percent chance the wall would only last between 8 months to 24 more years. The wall was torn down in 1989 — 20 years after his prediction, and well within the predicted range.

This sounds complicated until you think of our human population. Today there are 6.89 billion people in the world, the highest number ever, and those bodies have climbed rapidly in recent decades. In 1804 the population was only 1 billion. In 1000 A.D., the population was a meager 275 million. Humans, stacked in a bell curve, are today riding near the top of a huge wave.

But if everything has a beginning, middle, and end, and you are randomly located in the series, you are most likely near the top of the curve. You can visualize this by imagining our species lasted only three centuries; say, century 1 had 10 people, century 2 had 80 people, and century 3 had only 10 people, totaling 100 humans ever living. The odds of you landing in the “thick” middle century — among the 80 people out of the 100 total — would be 80 percent.

You’re alive at the biggest, thickest part of human history. Which means the bell cure should begin sloping the other way, with humans dying out.

Gott noted that our species, homo sapiens, has been around for 200,000 years. Running his calculations, he figured we are 95 percent likely to last 5,100 to 7.8 million years longer. A bit depressing, but a normal lifespan for a species; most mammals die out after 2 million years, and our forefathers, homo erectus, lasted only 1.6 million years. Neanderthals were even more fleeting, lasting only 300,000 years.

We can predict endings, just like anything else. Perhaps humans sense that we’re crested the top of the hill, with our collective imaginations enraptured by end-of-the-world movies, stories of apocalypse, arguments over whether global warming is harming the planet. What we do with the present is our choice, but odds are, the future won’t always be there.

Image: Jacsonquerubin