The Nimbus of Never-Neverland
Where are the flying cars? In an imaginative tone, The Future is Faster Than You Think, Peter Diamandis and Steven Kotler say auto–aeros will be flocking in any moment now over the horizon from such companies as Uber Elevate, pioneering what it calls “aerial ridesharing.”
Following in the flock will be Zee Aero, Opener, and Kitty Hawk, collectively funded by a billion or so of venture capital. Ornithopters, among other favorite things, may follow on gossamer wings.
Jeff Holden, then Uber’s chief product officer, declared to a crowd in May 2018 at LA’s Skirball Center that Uber could “demonstrate flying car capability in 2020 and have aerial ridesharing fully operational in Dallas and LA by 2023.”
Almost nine months to go before car–birth. We’ll see. Or rather terrestrial geezers like me will not see.
The Curse (or Gift) of Knowledge
In prophesying new technologies, it is often worth exploring the disappointments of the past. Many promising technologies fall short because they are introduced before their time. Intrinsically feasible, they may be temporally inopportune. Their introduction is out of phase with their environment.
Observing that in some ways that technology is moving backwards, Peter Thiel has gently chided the proponents of flying cars, public space travel, and the coming of a “singularity” when computers usurp human minds. He points out that with the withdrawal of the Concorde from service, passenger air flight has actually slowed.
We might also point out diminishing energy efficiencies and utilities due to the widespread deployment of archaic windmills and sporadic sunhenges. Vastly wasteful of the surface of the planet, intractably volatile in their output, and vulnerable to sabotage, these totems seem futuristic chiefly to the gullible folks at Fortune magazine and The Economist. Journalists follow cues from scores of thousands of academic and political green grant–anglers.
Touted from time to time are magical “million–mile” battery technologies, but Tesla’s still take roughly 1,000 pounds or more of battery to replace 60 pounds of gasoline. With new low oil prices, the prospects for these “green raw deals” become increasingly fanciful. Materials science seems to be more intractable than software engineering. However, we can be cautiously optimistic; batteries do improve.
No clear rule will enable you surely to filter the merely untimely from the nimbus of never–neverland.
What we might call “anachronicity” also afflicted such now venerable technologies as digital computers, prophesied in the early 19th century by such prophetic figures as Charles Babbage and Ada Lovelace, nanotech anticipated by Richard Feynman in 1951s in his speech “There’s a Lot of Room at the Bottom,” and pioneered today by James Tour, artificial intelligence and cloud computing foreseen by John McCarthy in the 1950s and now ubiquitous, and integration of tera–scale computer systems on whole wafers rather than tiny chips pursued by Gene Amdahl of IBM and Amdahl in the 1990s (maybe achieved now for deep learning at Cerebras corporation).
I am not laughing at premature prophets. I have predicted many technologies before their time.
Included are smart phones (check), teleputers (on their way), virtual and augmented reality rendering over the internet (experimental at Otoy), ultra–wideband wireless (nearly successful at Israel’s Camaro to see through walls, and now adopted for limited uses in the Apple iPhone 11), software defined radios (experimental in the military), cognitive radios (arguably emergent in Wi–Fi), Foveon analog “silicon–eye” cameras (niche under Japan’s Sigma), quantum dot programmable matter (projected still), predictive computer models of genome–proteome cascade (at Compugen in Israel a promising but slow work in progress).
In my correct pursuit of bandwidth abundance, I made flawed predictions of all optical networks. The photonic signals on our fiber optic lines still are converted regularly to electronic forms to be boosted by erbium doped amplifiers. Infinera still thrives in the conversion business.
Photonic computers are application specific only. It turns out that photons are perfect for communication because they do not collide or interact with one another. But computation requires interactions. Banned for consumer use by the Environmental Protection Agency when I pursued the technology with Seldon Laboratories were carbon nanotube filters. But banning them was a crazy chemophobic mistake. The filters worked well for the Special Forces and will come back to extract pure water from fetid pools or even sewage.
Carbon nanotube non–volatile memories remain still delayed at Nantero and Fujitsu. There are so many memory technologies in play that it is hard to be sure that any one will prevail. But Moore’s Law continues its two–year doubling rate in computer storage.
Blockchain–based internet security (in the sky), and blockchain based digital “gold” (in preparation by influential alchemists in the Cryptocosm).
Looking back at this medley of mixed outcomes, I am encouraged by the recent successes of ultra–wideband, carbon nanotubes, and blockchain. But I am most excited by the new ways of measuring innovation through time–prices.
Measured by the hours of work needed to earn money to buy a thing or money price over hourly income, time–prices are the true prices. As calculated by Gale Pooley and Marian Tupy, time–prices show that contrary to believers in a recent slowdown of innovation, we are in fact in the midst of a golden age of global creativity and abundance. Since 1980, the time prices of the fifty key commodities for human life have dropped 71% while population has grown 72%.
More important for my purposes is the effectiveness of time–prices as a measure of innovation. At one time, innovation reduces hours of work and increases incomes. Time–prices capture both these vectors of advance in one number, measuring hours and minutes to buy a commodity.
A recent report on artificial intelligence describes a breakthrough by our favorite Chinese plays in The George Gilder Report. (Go here to learn how you can join, if you are not already a subscriber.)
These companies use a Ding Sun Bao app to take a smartphone picture of automobile damage in an accident and upload the picture to the AI adjuster. The app measures the damage, calculates the repair cost, and provides a repair plan. The company even tested the app against human claims adjusters. The app took six seconds while the humans took seven minutes.
This process of learning across the economy is not readily measured by economists estimating inflation rates. But it is enriching the world at an ever more rapid pace even during a time of viral challenge.
Our investors get to participate in this anti–fragile process of learning and growth.
Editor, Gilder’s Daily Prophecy