One of my favorite quotes about startups/VC, from legendary VC Mike Moritz of Sequoia:
I rarely think about big themes. This business is like bird spotting. I don’t try to pick out the flock. Each one is different and I try to find an interestingly complected bird in a flock rather than try to make an observation about an entire flock. For that reason, while other firms may avoid companies because they perceive a certain investment sector as being overplayed or already mature, Sequoia is careful not to redline neighborhoods.
In traditional business thinking, generalizations about “flocks” i.e. markets/categories/sectors can be very useful. But when your job is to identify exceptions, generalizations can be dangerous.
Kevin Kelly thinks artificial intelligence is finally ready deliver on its promise:
The AI on the horizon looks more like Amazon Web Services—cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. Like all utilities, AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization. It will enliven inert objects, much as electricity did more than a century ago. Everything that we formerly electrified we will now cognitize. This new utilitarian AI will also augment us individually as people (deepening our memory, speeding our recognition) and collectively as a species. There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and now it’s here.
from Wired, “The Three Breakthroughs that have finally unleashed AI on the world“
Nikola Tesla predicted the development internet-connected smartphones back in 1926:
From the inception of the wireless system, I saw that this new art of applied electricity would be of greater benefit to the human race than any other scientific discovery, for it virtually eliminates distance. The majority of the ills from which humanity suffers are due to the immense extent of the terrestrial globe and the inability of individuals and nations to come into close contact.
Wireless will achieve the closer contact through transmission of intelligence, transport of our bodies and materials and conveyance of energy.
When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole. We shall be able to communicate with one another instantly, irrespective of distance. Not only this, but through television and telephony we shall see and hear one another as perfectly as though we were face to face, despite intervening distances of thousands of miles; and the instruments through which we shall be able to do his will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket.
We shall be able to witness and hear events–the inauguration of a President, the playing of a world series game, the havoc of an earthquake or the terror of a battle–just as though we were present.
Jaron Lanier discussing whether machine learning systems like Netflix recommendations, Facebook’s newsfeed, Google search etc are manipulative:
There’s no way to tell where the border is between measurement and manipulation in these systems. For instance, if the theory is that you’re getting big data by observing a lot of people who make choices, and then you’re doing correlations to make suggestions to yet more people, if the preponderance of those people have grown up in the system and are responding to whatever choices it gave them, there’s not enough new data coming into it for even the most ideal or intelligent recommendation engine to do anything meaningful.
In other words, the only way for such a system to be legitimate would be for it to have an observatory that could observe in peace, not being sullied by its own recommendations. Otherwise, it simply turns into a system that measures which manipulations work, as opposed to which ones don’t work, which is very different from a virginal and empirically careful system that’s trying to tell what recommendations would work had it not intervened. That’s a pretty clear thing. What’s not clear is where the boundary is.
If you ask: is a recommendation engine like Amazon more manipulative, or more of a legitimate measurement device? There’s no way to know. At this point there’s no way to know, because it’s too universal. The same thing can be said for any other big data system that recommends courses of action to people, whether it’s the Google ad business, or social networks like Facebook deciding what you see, or any of the myriad of dating apps. All of these things, there’s no baseline, so we don’t know to what degree they’re measurement versus manipulation.