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Hi, I’m Brad Feld, a managing director at the Foundry Group who lives in Boulder, Colorado. I invest in software and Internet companies around the US, run marathons and read a lot.

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The Future of Transportation

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William Hertling is one of my favorite science fiction writers. If you are in the tech industry and haven’t read his books Avogadro CorpA.I. Apocalypse, and The Last Firewall, I encourage you to go get them now on your Kindle and get after it. You’ll thank me later. In the mean time, following are William’s thoughts on the future of transportation for you to chew on this Sunday morning.

There’s always been a sweet spot in my heart for flying cars. I’m a child of the 1970s, who was routinely promised flying cars in the future, and wrote school essays about what life would be like in the year 2000. Flying cars are a trope of science fiction, always promised, but never delivered in real life. In fact, at first glance, they seem no closer to reality now than they did back then.

But maybe they’re not so far away. Let’s look at some trends in transportation.

Electric Cars

Hybrids vehicles, with their combination of both gas and battery power, represent 3% of the cars on the road today, up from zero just ten years ago. Fully electric cars like the Nissan Leaf and Tesla are mere curiosities, representing only 0.1% of all cars purchased in the U.S.

It might seem like a slow start, but electric cars will soon form the majority of all vehicles. Here’s why:

Except for early adopters of technology and diehard environmental customers, most people aren’t buying a fuel type, they’re buying transportation. They may want speed or economical transportation or family-friendly minivans, but how the vehicle is powered isn’t their main concern.

Examples like the Tesla have shown that electric vehicles perform on par with gas-powered cars. What limits their adoption then? Two factors: cost and range (and charging infrastructure, to a lesser extent, but that will be remedied when there is more demand).

The Nissan Leaf battery pack alone costs about $18,000 (though government incentives bring down the overall vehicle cost to the customer). When comparable gas-powered cars are about $20,000, the high cost of the battery pack alone is a huge barrier to widespread adoption, whether the cost passed on to the customer or the government, or hidden by the manufacturer.

Ramez Naam, author of The Infinite Resource: The Power of Ideas on a Finite Planet, recently explained that lithium-ion batteries have a fifteen year history of exponential price reduction. Between 1991 and 2005, the capacity that could be bought with $100 went up by a factor of 11. The trend continues through to the present day.

This exponential reduction in battery cost and improvement in battery technology, more than anything else, will affect both the cost and range of electric cars. By 2025, that Nissan Leaf battery pack will cost less than $1,800, making the cost of the electric motor plus battery pack less than the price of a comparable gasoline motor. Assuming even modest increases in storage capacity, the electric vehicle will rank better on initial cost, range, performance, and ongoing maintenance and fuel costs.

With both lower cost and better performance, electric vehicles will likely overtake gasoline-powered ones by about 2025.

Autonomous Cars

Even ten years ago, most of us couldn’t imagine a self-driving car. When the first DARPA Grand Challenge, a competition to build an autonomous car to complete a 150-mile route, was held in 2004, the concept seemed audacious and it was. Of the fifteen competitors, not a single one could complete the course. The farthest distance traveled was 7.3 miles.

The following year, twenty-two of twenty-three entrants in the 2005 Challenge surpassed the 7.3 mile record of the previous year, and five vehicles completed the entire course. Sebastian Thrun, director of the Stanford Artificial Intelligence Laboratory, led the Stanford University team to win the competition.

Sebastian Thrun went on to head Google’s autonomous car project, which first received press coverage in 2010 and continues to captivate our imagination. Yet despite Google’s technology proof point, and the development work now being done by many vehicle manufacturers, most people still imagine self-driving vehicles to be a long way off.

But Google has essentially shown that self-driving cars are already here: their vehicles have been accident-free for half a million miles whereas human drivers would have had an average of two accidents in the same miles driven.

The real barrier to adoption is cost. In 2010, the cost of Google’s self-driving technology was $150,000, of which $70,000 was just the lidar (a highly accurate laser-based radar). German supplier Ibeo, which manufactures vehicular lidar systems, claims it could mass-produce them as soon as next year for about $250 per vehicle. Computational processing is likely another large component of the overall price, and it has a long history of exponential cost reduction.

If costs come down, are there other barriers?

Some concerns in the media include:

  • Legislation. Will self driving cars be legal? Nevada, Florida, and California have already legalized them, suggesting this may be less of an issue than anticipated.
  • Litigation. Who will take the risks and pay up if and when there is an autonomous vehicle fatality?
  • Fear & Control. Some humans will fear self-driving cars while others will insist on their own manual control of their vehicle.

However, these oppositions aren’t unbreakable laws of physics. They are resistance to change, and they are subject to the forces advocating for autonomous vehicles, such as:

  • Fewer accidents reduce overall risk and liability, which will cause insurance companies to favor self-driving cars.
  • A reduction in the number of people killed in motor vehicle accidents (currently 3,200 people are killed every single day) makes a compelling social benefit.
  • Greater convenience and the recapture of drive time will lead to strong consumer demand.
  • As a feature differentiator, manufacturers will be eager to sell a profitable new option.
  • Reduction in drunk driving and increased alcohol consumption will make alcohol companies and restaurants strong supporters.
  • More efficient use of roads will save governments money in reduced infrastructure costs.

Simply put, the money is with the forces for autonomous vehicles. Insurance companies, liquor companies, vehicle manufacturers, customers, and governments will all want the benefits of self-driving cars.

There’s been talk about halfway solutions: semi-autonomous vehicles that are hands off but require an attentive driver, or need a human to handle certain situations. It’s both cheaper and easier to build an assistive solution than to have full autonomy, which is why we’re starting to see them show up in luxury cars like the Mercedes S-class, which has a driver assistance package (just $7,300 over the starting $92,900 price!) that can help maintain your lane position, distance from drivers ahead of you, and avoid blind-spot accidents.

But the driver is still in control and responsible.

In some ways, this semi-autonomy may be the worst of all worlds. It could encourage drivers to pay less attention to the road even though the vehicle isn’t really up to the task of taking control. As it stands, drivers don’t get much practice with emergency situations. So when emergencies do occur, our reflexes are slow or wrong. How much worse would the average emergency response handling be if drivers got even less practice, and were only called into action when they were either not ready or in a situation so bad that the AI couldn’t handle it? Under these circumstances, it’s unlikely that a human driver would respond in a correct, timely manner. If even airlines pilots fall asleep when the autopilot is on, how likely is it that regular drivers will be attentive?

So when will it happen?

One rule of thumb I learned upon entering the technology industry was that it takes seven years, on average, for new technology to go from laboratory proofs to sellable product. I’m not sure where that rule comes from, but by that measure, we should see the first self driving cars on sale in 2017.

From a cost perspective, we’ve already seen that lidar is likely to drop from $70,000 to $250. We don’t know the breakdown of Google’s other costs, but it could decrease by a factor of ten in ten years (pure computing technology falls faster – about 50x in ten years, more mechanical things slower). That would drop the total price under $10,000 by 2020, a reasonable luxury car option.

By 2030, another ten years out, the price will fall under $1,000, at which point the autonomous option will cost probably less than the annual savings in insurance.

In sum, we already see some limited assistive capabilities now, and should see partial self-driving capabilities around 2017, available as expensive options, with full autonomous capability around 2020, still at a significant cost. By 2030 or slightly earlier, all vehicles should be fully autonomous.

Dude, Where’s my Flying Car?

Now we get to the long-promised but not-yet-realized flying car.

The barrier to flying cars is not in the design or building of a viable airframe. We’ve built small flying vehicles for a while now. A quick Google search shows their amusing variety. We have manned quadcoptershover bikes, and lots of flying car-like things.

No, the real problem is that piloting is hard. Less than one third of one percent of Americans are pilots. A pilot’s license costs $5,000 to $10,000 and requires months or years of time and study. (Even if a pilot could fly a car in an urban environment, it’s not likely to be an enjoyable experience: think about the difference between a drive on a two-lane country road versus commuting in an urban grid. One is pleasure and the other utility.)

So it’s really the piloting barrier we need to overcome to see flying cars.

That will happen when autopilots, not humans, have achieved the necessary level of sophistication. Companies like Chris Anderson’s 3D Robotics have built, along with the open source community, the ArduPilot, a sub-$500 autopilot for unmanned drones. The ready availability of these consumer-grade autopilots suggests that navigation in open air by software is no more challenging (and may be less so) than navigating ground-level streets.

There will be substantial legislative barriers and not as many forces pushing for flying cars, but we should see at least see concept vehicles, prototypes, and recreational models (possibly outside the U.S.) in the late 2020s, just following the mass-market production of fully autonomous cars.

What about cost? An entry-level plane like the Cessna Skycatcher is a mere $149,000, a price point that’s lower than that of forty currently available automobile models. While entry-level helicopters are twice as expensive as comparable fixed-wing aircraft, quadcopters significantly simplify the design and add fault tolerance at a lower cost than single-rotor copters.

If the legislative barriers can be overcome, flying cars might not be as common a sight as a Ford or Toyota, but they could be more common than a Lamborghini or Aston Martin.

Trains & Hyperloops

I love the train ride between Portland and Seattle, and I’ve taken it dozens of times, including just riding up and back in a single day. Trains are relaxing and roomy, and their inherent energy efficiency appeals to my inner environmentalist.

On the other hand, they also have shortcomings. They’re locked into a track that is sometimes blocked by other trains, leading to unpredictable arrival times, and they go according to timetables that aren’t always convenient.

Elon Musk’s hyperloop may reduce new infrastructure cost, boost speeds, and reduce the timetable problem while maintaining energy efficiency, but I think the hyperloop is a stop-gap measure. That’s because we’ll soon reach an era of cheap electricity.

Photovoltaic cost per watt continues to drop (from $12 per watt in 1998 to $5 per watt in 2013, 14% annually over the long term) at the same time that we’re seeing new innovations in grid-scale energy storage. Ray Kurzweil and others predict that we’ll meet 100% of electrical needs with solar power by 2028. So while efficiency of passenger miles traveled is a key element to sustainable transportation right now, it may be less important in the future, when we have abundant and inexpensive green power.

Green power reduces the energy efficiency advantage of trains and the hyperloop. Of course, the other major benefit of mass transit is freeing the passenger from the tedium of driving, but self-driving vehicles accomplish that just as well.

Transportation Singularity: 2030

In sum, we have several key trends converging on the late 2020s: fully electric fleets, cheap electricity, autonomous vehicles, and flying cars.

Transportation will look very different by 2030. We’re likely to have many autonomous, personal-use vehicles. Since car sharing services are even more useful when the cars drive themselves to you, we may have much less personal ownership of the vehicles. Airline travel is likely to change as well, as self-piloting fast personal vehicles will compete for shorter trips, while the reduction in fuel costs may change the value structure for airlines.

And yes, we’ll finally have our flying cars.

About the Author

William Hertling is the author of Avogadro CorpA.I. Apocalypse, and The Last Firewall, science fiction novels exploring the role of artificial intelligence and social networks in the near future. Follow him on twitter at @hertling, or visit his blog at www.williamhertling.com to learn more about his writing.

Ignore Trends and Predictions

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This first appeared in the Wall Street Journal’s Accelerator series last week under the title Don’t Believe the Hype.

Every year, at this time, I get a flurry of requests for my “predictions for 2013” or “exciting, hot, new trends for 2013 that I’m looking at.”

I respond with “I don’t care about trends and my only prediction is that one day I will die.”

This is usually not a particularly satisfying response to whomever sent me the request. One of two things happen: They either ignore my response and drop me from their prediction request list for whatever article they are writing. Alternatively, they press a little further, usually with something like “c’mon, you’re a venture capitalist — you must have an opinion about what is going to be hot next year.”

Actually, I don’t. I have never been a short term investor, and I don’t think entrepreneurs should be short term thinkers. Creating a company is really hard and it almost always takes a long time. Sure, there are occasional short term success stories — companies founded two years ago that get bought for $1 billion, but these are rarities. Black swans. Things you don’t see in nature and can’t count on.

So don’t. If you are an entrepreneur and following a trend, you are too late. You want to be creating the trend that other people are following. And then you need to work your butt off to stay ahead of them. Every single day. For a very long time. Through many product cycles and multiple trends.

As a VC, I feel exactly the same way. At Foundry Group, we have a set of well-defined themes. We believe there will be investment opportunities in these themes for the next ten to 20 years. We are constantly tuning the themes, learning from our investments, and exploring new themes. But these themes aren’t trends and we don’t predict anything around them, other than they are constructs in which we think great companies can be created and built.

So I don’t really care about the predictions for 2013. I don’t care about hot new trends. I don’t care that some people think the world is going to end on 12/21/12. I take a much longer view. And I encourage you to as well.

How To Predict The Future

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Today’s post is a guest post from William Hertling, author of the award-winning Avogadro Corp: The Singularity Is Closer Than It Appears and A.I. Apocalypse, near-term science-fiction novels about realistic ways strong AI might emerge. They’ve been called “frighteningly plausible”, “tremendous”, and “thought-provoking”. By day he works on web and social media for HP. Follow him on twitter at @hertling or visit his blog williamhertling.com.

I’m a huge fan of William and his writing as you can see from my review of his book Avogadro Corp. So when William offered to write a guest post on how to predict the future, I enthusiastically said yes. Take a look – and take your time.

Pretty much everyone would like a sure-fire way to predict the future. Maybe you’re thinking about startups to invest in, or making decisions about where to place resources in your company. Maybe you just care about what things will be like in 10, 20, or 30 years.

There are many techniques to think logically about the future, to inspire idea creation, and to predict when future inventions will occur.

I’d like to share one technique that I’ve used successfully. It’s proven accurate on many occasions. And it’s the same technique that I’ve used, as a writer, to create realistic technothrillers set in the near future. I’m going to start by going back to 1994.

Predicting Streaming Video and the Birth of the Spreadsheet

There seem to be two schools of thought on how to predict the future of information technology: looking at software or looking at hardware. I believe that looking at hardware curves is always simpler and more accurate.

This is the story of a spreadsheet I’ve been keeping for almost twenty years.

In the mid-1990s, a good friend of mine, Gene Kim (founder of Tripwire and author of When IT Fails: A Business Novel) and I were in graduate school together in the Computer Science program at the University of Arizona. A big technical challenge we studied was piping streaming video over networks. It was difficult because we had limited bandwidth to send the bits through, and limited processing power to compress and decompress the video. We needed improvements in video compression and in TCP/IP – the underlying protocol that essentially runs the Internet.

The funny thing was that no matter how many incremental improvements we made (there were dozens of people working on different angles of this), streaming video always seemed to be just around the corner. I heard “Next year will be the year for video” or similar refrains many times over the course of several years. Yet it never happened.

Around this time I started a spreadsheet, seeding it with all of the computers I’d owned over the years. I included their processing power, the size of their hard drives, the amount of RAM they had, and their modem speed. I calculated the average annual increase of each of these attributes, and then plotted these forward in time.

I looked at the future predictions for “modem speed” (as I called it back then, today we’d called it internet connection speed or bandwidth). By this time, I was tired of hearing that streaming video was just around the corner, and I decided to forget about trying to predict advancements in software compression, and just look at the hardware trend. The hardware trend showed that internet connection speeds were increasing, and by 2005, the speed of the connection would be sufficient that we could reasonably stream video in real time without resorting to heroic amounts of video compression or miracles in internet protocols. Gene Kim laughed at my prediction.

Nine years later, in February 2005, YouTube arrived. Streaming video had finally made it.

The same spreadsheet also predicted we’d see a music downloading service in 1999 or 2000. Napster arrived in June, 1999.

The data has held surprisingly accurate over the long term. Using just two data points, the modem I had in 1986 and the modem I had in 1998, the spreadsheet predicts that I’d have a 25 megabit/second connection in 2012. As I currently have a 30 megabit/second connection, this is a very accurate 15 year prediction.

Why It Works Part One: Linear vs. Non-Linear

Without really understanding the concept, it turns out that what I was doing was using linear trends (advancements that proceed smoothly over time), to predict the timing of non-linear events (technology disruptions) by calculating when the underlying hardware would enable a breakthrough. This is what I mean by “forget about trying to predict advancements in software and just look at the hardware trend”.

It’s still necessary to imagine the future development (although the trends can help inspire ideas). What this technique does is let you map an idea to the underlying requirements to figure out when it will happen.

For example, it answers questions like these:

- When will the last magnetic platter hard drive be manufactured? 2016. I plotted the growth in capacity of magnetic platter hard drives and flash drives back in 2006 or so, and saw that flash would overtake magnetic media in 2016.

- When will a general purpose computer be small enough to be implanted inside your brain? 2030. Based on the continual shrinking of computers, by 2030 an entire computer will be the size of a pencil eraser, which would be easy to implant.

- When will a general purpose computer be able to simulate human level intelligence? Between 2024 and 2050, depending on which estimate of the complexity of human intelligence is selected, and the number of computers used to simulate it.

Wait, a second: Human level artificial intelligence by 2024? Gene Kim would laugh at this. Isn’t AI a really challenging field? Haven’t people been predicting artificial intelligence would be just around the corner for forty years?

Why It Works Part Two: Crowdsourcing

At my panel on the future of artificial intelligence at SXSW, one of my co-panelists objected to the notion that exponential growth in computer power was, by itself, all that was necessary to develop human level intelligence in computers. There are very difficult problems to solve in artificial intelligence, he said, and each of those problems requires effort by very talented researchers.

I don’t disagree, but the world is a big place full of talented people. Open source and crowdsourcing principles are well understood: When you get enough talented people working on a problem, especially in an open way, progress comes quickly.

I wrote an article for the IEEE Spectrum called The Future of Robotics and Artificial Intelligence is Open. In it, I examine how the hobbyist community is now building inexpensive unmanned aerial vehicle auto-pilot hardware and software. What once cost $20,000 and was produced by skilled researchers in a lab, now costs $500 and is produced by hobbyists working part-time.

Once the hardware is capable enough, the invention is enabled. Before this point, it can’t be done.  You can’t have a motor vehicle without a motor, for example.

As the capable hardware becomes widely available, the invention becomes inevitable, because it enters the realm of crowdsourcing: now hundreds or thousands of people can contribute to it. When enough people had enough bandwidth for sharing music, it was inevitable that someone, somewhere was going to invent online music sharing. Napster just happened to have been first.

IBM’s Watson, which won Jeopardy, was built using three million dollars in hardware and had 2,880 processing cores. When that same amount of computer power is available in our personal computers (about 2025), we won’t just have a team of researchers at IBM playing with advanced AI. We’ll have hundreds of thousands of AI enthusiasts around the world contributing to an open source equivalent to Watson. Then AI will really take off.

(If you doubt that many people are interested, recall that more than 100,000 people registered for Stanford’s free course on AI and a similar number registered for the machine learning / Google self-driving car class.)

Of course, this technique doesn’t work for every class of innovation. Wikipedia was a tremendous invention in the process of knowledge curation, and it was dependent, in turn, on the invention of wikis. But it’s hard to say, even with hindsight, that we could have predicted Wikipedia, let alone forecast when it would occur.

(If one had the idea of an crowd curated online knowledge system, you could apply the litmus test of internet connection rate to assess when there would be a viable number of contributors and users. A documentation system such as a wiki is useless without any way to access it. But I digress…)

Objection, Your Honor

A common objection is that linear trends won’t continue to increase exponentially because we’ll run into a fundamental limitation: e.g. for computer processing speeds, we’ll run into the manufacturing limits for silicon, or the heat dissipation limit, or the signal propagation limit, etc.

I remember first reading statements like the above in the mid-1980s about the Intel 80386 processor. I think the statement was that they were using an 800 nm process for manufacturing the chips, but they were about to run into a fundamental limit and wouldn’t be able to go much smaller. (Smaller equals faster in processor technology.)

But manufacturing technology has proceeded to get smaller and smaller.  Limits are overcome, worked around, or solved by switching technology. For a long time, increases in processing power were due, in large part, to increases in clock speed. As that approach started to run into limits, we’ve added parallelism to achieve speed increases, using more processing cores and more execution threads per core. In the future, we may have graphene processors or quantum processors, but whatever the underlying technology is, it’s likely to continue to increase in speed at roughly the same rate.

Why Predicting The Future Is Useful: Predicting and Checking

There are two ways I like to use this technique. The first is as a seed for brainstorming. By projecting out linear trends and having a solid understanding of where technology is going, it frees up creativity to generate ideas about what could happen with that technology.

It never occurred to me, for example, to think seriously about neural implant technology until I was looking at the physical size trend chart, and realized that neural implants would be feasible in the near future. And if they are technically feasible, then they are essentially inevitable.

What OS will they run? From what app store will I get my neural apps? Who will sell the advertising space in our brains? What else can we do with uber-powerful computers about the size of a penny?

The second way I like to use this technique is to check other people’s assertions. There’s a company called Lifenaut that is archiving data about people to provide a life-after-death personality simulation. It’s a wonderfully compelling idea, but it’s a little like video streaming in 1994: the hardware simply isn’t there yet. If the earliest we’re likely to see human-level AI is 2024, and even that would be on a cluster of 1,000+ computers, then it’s seems impossible that Lifenaut will be able to provide realistic personality simulation anytime before that.* On the other hand, if they have the commitment needed to keep working on this project for fifteen years, they may be excellently positioned when the necessary horsepower is available.

At a recent Science Fiction Science Fact panel, other panelists and most of the audience believed that strong AI was fifty years off, and brain augmentation technology was a hundred years away. That’s so distant in time that the ideas then become things we don’t need to think about. That seems a bit dangerous.

* The counter-argument frequently offered is “we’ll implement it in software more efficiently than nature implements it in a brain.” Sorry, but I’ll bet on millions of years of evolution.

How To Do It

This article is How To Predict The Future, so now we’ve reached the how-to part. I’m going to show some spreadsheet calculations and formulas, but I promise they are fairly simple. There’s three parts to to the process: Calculate the annual increase in a technology trend, forecast the linear trend out, and then map future disruptions to the trend.

Step 1: Calculate the annual increase

It turns out that you can do this with just two data points, and it’s pretty reliable. Here’s an example using two personal computers, one from 1996 and one from 2011. You can see that cell B7 shows that computer processing power, in MIPS (millions of instructions per second), grew at a rate of 1.47x each year, over those 15 years.

 

I like to use data related to technology I have, rather than technology that’s limited to researchers in labs somewhere. Sure, there are supercomputers that are vastly more powerful than a personal computer, but I don’t have those, and more importantly, they aren’t open to crowdsourcing techniques.

I also like to calculate these figures myself, even though you can research similar data on the web. That’s because the same basic principle can be applied to many different characteristics.

Step 2: Forecast the linear trend

The second step is to take the technology trend and predict it out over time. In this case we take the annual increase in advancement (B$7 – previous screenshot), raised to an exponent of the number of elapsed years, and multiply it by the base level (B$11). The formula displayed in cell C12 is the key one.

I also like to use a sanity check to ensure that what appears to be a trend really is one. The trick is to pick two data points in the past: one is as far back as you have good data for, the other is halfway to the current point in time. Then run the forecast to see if the prediction for the current time is pretty close. In the bandwidth example, picking a point in 1986 and a point in 1998 exactly predicts the bandwidth I have in 2012. That’s the ideal case.

Step 3: Mapping non-linear events to linear trend

The final step is to map disruptions to enabling technology. In the case of the streaming video example, I knew that a minimal quality video signal was composed of a resolution of 320 pixels wide by 200 pixels high by 16 frames per second with a minimum of 1 byte per pixel. I assumed an achievable amount for video compression: a compressed video signal would be 20% of the uncompressed size (a 5x reduction). The underlying requirement based on those assumptions was an available bandwidth of about 1.6mb/sec, which we would hit in 2005.

In the case of implantable computers, I assume that a computer of the size of a pencil eraser (1/4” cube) could easily be inserted into a human’s skull. By looking at physical size of computers over time, we’ll hit this by 2030:

 

This is a tricky prediction: traditional desktop computers have tended to be big square boxes constrained by the standardized form factor of components such as hard drives, optical drives, and power supplies. I chose to use computers I owned that were designed for compactness for their time. Also, I chose a 1996 Toshiba Portege 300CT for a sanity check: if I project the trend between the Apple //e and Portege forward, my Droid should be about 1 cubic inch, not 6. So this is not an ideal prediction to make, but it’s still clues us in about the general direction and timing.

The predictions for human-level AI are more straightforward, but more difficult to display, because there’s a range of assumptions for how difficult it will be to simulate human intelligence, and a range of projections depending on how many computers you can bring to pair on the problem. Combining three factors (time, brain complexity, available computers) doesn’t make a nice 2-axis graph, but I have made the full human-level AI spreadsheet available to explore.

I’ll leave you with a reminder of a few important caveats:

- Not everything in life is subject to exponential improvements.

- Some trends, even those that appear to be consistent over time, will run into limits. For example, it’s clear that the rate of settling new land in the 1800s (a trend that was increasing over time) couldn’t continue indefinitely since land is finite. But it’s necessary to distinguish genuine hard limits (e.g. amount of land left to be settled) from the appearance of limits (e.g. manufacturing limits for computer processors).

- Some trends run into negative feedback loops. In the late 1890s, when all forms of personal and cargo transport depended on horses, there was a horse manure crisis. (Read Gotham: The History of New York City to 1898.) Had one plotted the trend over time, soon cities like New York were going to be buried under horse manure. Of course, that’s a negative feedback loop: if the horse manure kept growing, at a certain point people would have left the city. As it turns out, the automobile solved the problem and enabled cities to keep growing.

So please keep in mind that this is a technique that works for a subset of technology, and it’s always necessary to apply common sense. I’ve used it only for information technology predictions, but I’d be interested in hearing about other applications.

Ecstatic Metapattern Rants

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Jason Silva is a total stud. Every time he does another amazing video ecstatic metapattern rant” on Vimeo, he tweets me about it. Here’s his lastest.

TO UNDERSTAND IS TO PERCEIVE PATTERNS from jason silva on Vimeo.

Here’s what’s so amazing about this to me. I don’t think I know Jason. Maybe we’ve met once – I don’t know. I do know that he’s jason_silva on Twitter, is a Host / Producer for Current TV (so I might know him via the TechStars segment CurrentTV did a few years ago on TechStars), but I don’t recognize his picture. We might have met a few times – that’s my issue, not his, since I’m out of namespace in my brain (I have to forget someone to learn someone new.)

All that said, I think Jason is just awesome. Every video I’ve seen of his lights me up. They are beautiful, thought provoking, and something I wish I had the talent to do.

I just tweeted him back that I want to get together with him. I think he’s in NY (Twitter says he’s on 53rd between 5th and 6th) so hopefully he’ll respond and we can hang out the next time I’m in NY. He certainly has gotten my attention!

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