Here's what the HN homepage looked like on the day it launched (Ocb 9, 2006)
https://news.ycombinator.com/front?day=2006-10-09
(Funny timing, I just discovered this feature yesterday.)
https://news.ycombinator.com/front?day=2006-10-09
(Funny timing, I just discovered this feature yesterday.)
The lesson from this type of articles is usually that you can make linear extrapolations and look cool as an author, but not everything is linearly extrapolatable in a positive way. Trends continue linearly for a while, but so do problems that eventually require a major shake up. It's seeing the growing problems that's difficult.
Who would have thought before 2006 that the world doesn't need any more powerful and bulkier computers in their homes but instead, we'd need lightweight pocket computers that would put new requirements on energy efficiency of software for example? That operating systems won't go away but instead will take a whole new direction towards managed environments and more performant programming languages. Etc. etc.
Among them are Alvin Toffler's Future Shock, which turned 50 last year, read for the first time, Lawrence Lessig's Code and Other Laws of Cyberspace (22 years), a re-read, and Andrew Shapiro's The Control Revolution, also for the first time.
All have hits and misses. Some of them are more slanted one way than the other.
Toffler has aged surprisingly well, in my view, despite some weaknesses. Lessig, at least in his introduction and first chapter, similarly. Shapiro is rapidly shaping up to be an excellent Really Bad Example. (Lessig references Shapiro early in Code, hence my looking into it.)
I've been toying with a notion of an ontology of technological mechanisms, which looks not at fields of technology, but rather how they achieve their effects. I've come up with nine basic categories: materials, fuels, process knowledge (what's generally meant by "technology"), causal knowlege (roughly, "science"), power transmission and transformation (ask if you're curious), networks, systems, information (accessing, storage/retrieval, processing, disseminating), and a final category I've tended to call hygiene functions -- dealing with unwanted or unintended consequences.
It's that last aspect which seems to dominate considerations ultimately, because all technologies can be thought of as interventions in some system to an intended effect, but having several dimensions, including:
- Benefit / harm
- Near term / long term
- Clearly evident / non-evident
Generally, we tend to choose technologies with clearly evident near-term benefit, and strictly avoid those with clearly evident near-term harm. In cases where a mixed set of benefits and harms of varying evidence or perceptibility, and of differing timeframes is present ... things get more complicated.
And as interventions in systems become more complex, the odds of a negative interaction tend to increase.
The upshot is that cautions are often more significant than ethusiasms, and in the case most especially of Toffler, the concerns he raises, most especially of psychological and sociological impacts of increasing information flows and rates of change, do seem to have been reasonably prophetic. Sections dealing with specific technologies and their presumed social benefits are the weakest. In some cases the promised benefits have come to pass, occasionally to such a degree that it's hard to even see them from the present vantage point --- they've very much become part of our world in a way that is like water to a fish: so ubiquitous it's easy to forget it exists at all. In particular, the developers or advocates of a specific technique (or occasionally, scientific advance) seem to be exceedingly poor at conceiving of, or at least sharing any conceptions of, downsides. There's an extraordinarily strong positivity bias. Not universally, but as a general rule.
Lessig is similarly concerned with harms, and (at least in its introduction) seems to focus accurately on the right problem areas and dynamics.
Shapiro has read to me through the first part of his book as, charitably, remarkably oracular in the sense of "this is a prophecy which might be read two ways", as in "if King Croesus crosses the Halys River, a great empire will be destroyed." (Spoiler: one was. It wasn't the one Croesus had in mind.) For the most part, Shapiro reads to me as childishly naive, credulous, fatuistic, uninsightful, concerned with the trivial, self-parodying, and often foreshadowing but apparently with absolutely no self-awareness in doing so. It is remarkable how many of the specific actors and situations what are front-of-mind today are mentioned or alluded to in the text. But the references don't seem aware of their own significance.
In his defence, Shapiro occasionally evidences some degree of awareness or perception, though these feel like brief moments of lucidity in a once sound mind. And it's possible that the latter parts of the book refute the naivete of the first chapters, though I'm skeptical (and reviews I've read suggest otherwise). It's very much a case of The Author To Whom I Must Constantly Scream as I read the text though.
But yeah: be very skeptical of self-involved proponents of technological or other initiatives. Whatever expertise they may have is strongly moderated by deliberate or unconscious self-serving bias.
> - Benefit / harm > - Near term / long term > - Clearly evident / non-evident
I still think that in addition to these things it's the growth function, the linear segments of it, then sudden non-linearity that we fundamentally always get wrong in our predictions.
Think of the 1960-1970s that was considered the era of space exploration or the beginning of it, and how people wrongly extrapolated it up to the year 2000 (A Space Odyssey, yep).
Or when we try to predict non-linearities, we get it wrong too. Taleb predicted the end of Google in 2007, also made fun of e-scooters back then. But if I'm not mistaken he also praised segways. Well, everybody did.
In other words, we are wrong most of the time and we are wrong about the non-linearities in the growth functions. So much so that you could probably even say: if a prediction is a linear extrapolation then it's definitely wrong. If it's a prediction of a breakage then the chances of it being correct are purely random.
Look at all the predictions of the next market crash made in the past 10 years. It's as if the market is listening and doing the opposite!
Anything that functions as a network, and this includes information itself, can see periods of exponential growth. Which itself doesn't mean "very fast", but as we all (re-)learned last year, "imperceptibly slowly at first, then very suddenly".
The other elements tend to have other sorts of interactions, and a key reason for splitting up the mechanisms as I did was around not only the technical operations, but the economic implications. In particular, hygiene factors, unlike (but also intrinsically tied to) network mechanisms, behave very differently. That's explored somewhat in this post (principally in comments): https://joindiaspora.com/posts/e045d2304aec0139cee5002590d8e...
(That's very preliminary, I'm developing thoughts, and need to do a proper write-up. The fact that I was thinking along those lines spurred a draft version after one line of responses in the thread.)
And you could add all kinds of other elements. So long as you're discussing nonlinearity, there are chaotic systems which can suddenly transition between states, or collapse entirely. There are long-latent potentials waiting for a trigger or opportunity which suddenly spread forth. There are interactions effects. There are subtle accidents of space and time. (Historians and anthropologists tend to haet haet haet heavily on geological determination, but to ignore it entirely strikes me as a peculiar fetish.)
Prediction is hard, especially about the future. But there do seem to be some trends which emerge. And the role of negative unintended consequences does seem to me a significant one.
Markets and financial systems ... that's a whole 'nother game.
That's pretty cool too and I just found out about it this week ( this account is young but my soul is old ... )
https://web.archive.org/web/20050804002153/http://www.reddit...
Interesting day: https://news.ycombinator.com/front?day=2017-10-09 Microsoft gives up on Windows 10 Mobile the same day Librem 5 was funded.
Google Acquires YouTube For $1.6B
with : 12 points - 0 comments ...
Think about that when in the early days of a new project.