The 1961 number has gotta be extrapolation, not actual data, or else there was some serious underground-city scale computing center buried in Antartica . . . Man from U.N.C.L.E. anyone? :-)
Not sure why you got downvoted, but just a small nitpick. It's FLOPS for FLoating-point Operations Per Second. Otherwise $8300 per FLoating-point OPeration would be quite pricy for almost any time period. ;)
There are few domains that can't benefit from parallelization at all. And the domains which consume the most computing power tend to be embarrassingly parallel and require lots of floating point ops. Graphics and AI especially. Your word processor might not get much faster in the future, but it's speech recognition will.
One thing that they didn't quite touch on is the incredible increase in the performance of web browsers.
They did talk about more and more people using a computer as a window to the internet, but didn't talk about how much more efficient the software powering that window had become. Apple launched Safari in 2003 and Chrome/V8 was released in 2008. So even as we've wanted our web apps to do more, the engines running them have gotten so much better.
When most software that people were using was being written in C/C++, an increase in the amount of work the program wanted to do needed to be matched by an increase in performance. Compilers have gotten better, but nothing near the impact that modern web browsers have had. So older computers have benefitted from a huge increase in browser performance (the browser being the thing executing most "applications") over the past decade in a way that we never really saw in previous computing generations.
Firefox performance on my older laptop just gets worse and worse. Just a few tabs open to the wrong websites can peg CPU usage at 100%. As far as I can tell this is mainly due to Mozilla "improving" the guts of their rendering at some point and basically no longer supporting rendering with the laptop's terrible Intel graphics chip, forcing software rendering instead.
Older hardware definitely often pays a price for supporting newer features.
I really wish people would use log scale for this kind of graph.
Trying to study rates of growth on a linear scale is basically impossible during exponential-ish growth periods. All but the rightmost end of the chart gets squished flat along the horizontal axis.
Similarly, charts of e.g. currency exchange rates, stock indices, comparative economic growth between countries, etc. should most of the time be plotted with a log-scaled vertical axis. Otherwise there’s no way to accurately compare slopes in different parts of the chart, which can be deeply misleading.
Log scales are extremely misleading. It looks like computing power has only increased a bit since the 1980s, when in fact it has increased orders of magnitude.
Especially for a general audience, but even people familiar with log scales, it doesn't give an intuitive feel of the actual numbers.
I agree with you that not enough people are familiar with log-scale charts, but that’s a problem with experience, not with “intuition”. Log-scaled charts give perfectly fine “intuitive feeling” for anyone who is used to reading them. Which would include most people if log-scaled charts were more ubiquitous.
I am lamenting that they are not, because for many, many purposes they are easier to read and facilitate more useful intra-chart comparisons than linear-scaled charts. In particular, they make the slope of lines meaningful in many cases where linear-scaled charts do not. They also fit much more useful information about relative magnitude and allow us to read a couple significant digits of numbers across a wide range of scales. Any time there is more than 2 orders of magnitude difference among values a chart, a linear scale becomes nearly useless.
Reading charts at all is an acquired skill, and takes a lot more practice than you might expect. Just like doing arithmetic with fractions, decimals, or angles of a circle is an acquired skill, or driving a car is an acquired skill. If you study young children or people from non-literate cultures, you’ll see all kinds of difficulty reading linear-scaled charts.
Edit: here, I spent a couple minutes making a linear-scaled and a log-scaled chart of the wikipedia $/gigaflops table from elsewhere in this thread, using Matlab. See how much useful information you can get from the linear-scaled chart:
(On the log-scaled chart, if we penciled in a grid, you could get at least one significant figure out of each data point, despite the data spanning 11 orders of magnitude. On the linear-scaled chart, you get 2 significant figures for a single data point, but there’s no way to make any distinction at all between the rest of the values, which span about 8 orders of magnitude.)
IMO neither chart gives an intuitive feel for the data. That nice straight line does nothing to emphasize just how rapidly the price dropped over the last 10 years. That's why I listed a table of the raw numbers instead of a graph. People understand the scale of numbers, and the scale isn't lost by trying to cram it into a chart.
The video used a zooming chart, which solves both problems.
> That nice straight line does nothing to emphasize just how rapidly the price dropped over the last 10 years
Sure it does. The number goes from 10^12 down to 10^-1, 13 orders of magnitude. You can easily read these off the side or count the number of grid lines crossed. (Whoops, I wrote 11 in my comment before.) If you’re used to reading a log-scaled chart, counting the powers of ten along the vertical axis is perfectly “intuitive”, or at any rate just as “intuitive” as any other way you could write these.
In distance scale terms, this is the same as going from nanometers to tens of kilometers. Or in time scale terms from milliseconds to hundreds of years.
Numbers with such a big difference in scale are to some extent inherently incomparable: We don’t interact with such a range of scale with most of our naked senses, but need tools to understand and compare such divergent numbers. (Though if you’re hunting for an analogy, comparing volume or mass might be slightly easier, as they scale with length cubed. 13 orders of magnitude gets you from the mass of a human egg cell up to the mass of a tank.)
For log scale charts aimed at the general public, the key is very clear labeling along the axis. I think most viewers can handle it okay; the bigger problem is that charting tools default to linear scale and most chart makers don’t think about it.
It’s in some ways too bad that slide rules have been replaced by electronic calculators. I feel like the general public coming out of high school used to have more fluency with logarithms, which until the last generation or two were the basic tool for all calculation in science and engineering.
To me the obvious answer is: you only need a certain amount of system resources to do most things, and consumer computers reached that point around 2005. (Sort of like his theory, but the software doesn't need to use any more resources at all any more.)
Later improvements make it faster, easier to use, higher-resolution, better at multitasking etc., but none of that changed the yes/no question of "Will it work?"
I like to mention soundcards as a great example of this. For almost 20 years, the sound chip included on motherboards is all most people need. Sure, there's a hardcore niche of enthusiasts who want to plug in their keyboards and whatnot, but my motherboard can do 5.1 surround sound at a quality I could not distinguish from perfection, the bottleneck are my speakers.
Back in the 80s and early 90s, sound was a huge deal, going from synthetic beeps to full audio playback and you needed to fiddle with a dedicated soundcard to enjoy the latest breakthroughs. Nowadays, hardly anyone bothers because all computers have "good enough" sound out of the box.
I find it quite fascinating to think whether something similar might be possible for, say, graphics cards. It seems impossible now, but imagine a monitor at the max resolution your eye can see and real time 3D graphics with enough polygons to never see an edge and all the basic shading/lighting problems solved to Pixar levels... that might be less than 10 years away.
The same thing is pretty much true for graphics as of about 10 years ago. Most consumers who don't play games are fine with integrated graphics, or don't understand / care about the difference discrete graphics would make. If all you're doing is browsing the web, email, Facebook, chat apps, etc, a dedicated GPU probably isn't worth it for most people. This might be changing as more non-games utilize GPU compute power, but integrated graphics are also getting somewhat more powerful as compared to entry-level discrete GPUs. If anyone has a good comparison for this change over time, that'd be interesting to see.
That's a rather unimaginative future you're painting. Things don't just keep getting incrementally better, but instead we get paradigm shifts.
We're currently on the cusp of the machine learning paradigm shift. As ML keeps getting better, it will start showing up more and more in consumer interfaces. I'm willing to bet that within a few decades natural language interfaces will become the norm. The desktop paradigm will simply go away at that point.
You'll just tell the computer what you want to do instead of having to juggle windows using the mouse and keyboard. You'll be able to say find that youtube video, play this song, or reply to that email.
The computer will most likely turn into a personal assistant, and these kinds of machines will require a lot more power than what we currently have in desktops.
You are so wrong about this. So wrong that I made a HN account for the first time in years just to respond to this. You're not the only one who thinks this, but you were the one who said it, so here you go:
Your phone/computer's speech recognition is done by your phone/computer recording your voice and sending it to Microsoft/Apple/Google, then Microsoft/Apple/Google transcribing it on THEIR computers, storing your data, and sending the transcription back to your phone/computer.
It doesn't require any processing power on your computer, besides the power to record and send your voice.
You seem to think that because you bought a computer, that means that it's going to be your "personal assistant" unconditionally. It's not. It's going to give your voiceprint, location information, and other personally identifying information to MS/AAPL/GOOG. F/OSS might get similar features but they will lag years behind, and will not have the same impact on society that large corporations with datacenters/server farms can have. Eventually, you'll start seeing friendly suggestions and helpful tips, like your traffic schedule (this is already the case).
You'll receive SUGGESTIONS on where to go. Friendly suggestions like, "Did you mean to go to the strip club/bar? How about going to church instead?"
This could amplify inequality. Imagine Microsoft Clippy saying, "Hi, I noticed that you need $350 for crack rocks. May I suggest Honest Achmed's Pawn Shop only 1.2 miles from your trap house? We compared prices of the Internet of Things enabled devices you have on Amazon and we noticed that your 202X Macbook has a resale value of about $350."
You'll eventually be CONTROLLED by some algorithm somewhere.
There's nothing PERSONAL about the assistance they'll provide.
>It doesn't require any processing power on your computer, besides the power to record and send your voice.
Sure, however this approach has a lot of downsides, such as utter lack of privacy. As we've seen many times historically the pendulum swings between thin and thick clients.
Right now the computational power needed to do this kind of processing is very expensive so it's done on server. However, faster hardware will make it possible to do it on personal devices as well.
Also worth pointing out that privacy doesn't exist for most people already. The mode of interaction with the computer isn't going to change any of that one way or the other.
Or, it could also be that natural language interfaces aren't going to become much better than they are. Siri and cortana have been around for a few years now, but they haven't actually gotten all that much smarter. We've got the most powerful companies in the world investing the resources of thousands of the smartest programmers to build these agents, and the best they can do is siri? That doesn't bode well for the intelligent agent future.
The primary bottleneck for intelligent agents is not voice recognition, it's semantic insight. They need to have an innate ability to learn the meaning of new things without being programmed to do so. As far as I know siri, cortana and google do not do this. Meaning must be programmed into them explicitly. That doesn't scale. Yes, they can learn new things, but they learn them slowly. You can even build advanced intelligent things like self-driving cars (with great effort) because the required level of understanding about the world is very constrained. However, you cannot scale it up to general purpose assistants because the amount of code required cannot be built in any reasonable amount of time. Until we have algorithms that create algorithms, software rewriting itself and evolving based on higher goals, we won't see the promised star-trek-level personal assistants come to fruition.
It could go either way. Let us hope you are right.
> Until we have algorithms that create algorithms, software rewriting itself and evolving based on higher goals, we won't see the promised star-trek-level personal assistants come to fruition.
There's a massive jump you're making here, I feel. From where we are to "computer, invent a new novel for me" there's a huge range of useful mid-points.
One level is just understanding more about human-written unstructured data to answer questions.
Another is to be better at sending commands to other humans.
This then gets you to the level of essentially free personal assistants for a wide range of general tasks. That would be valuable to a very large number of people.
There are two parts to this. First one is about parsing the speech, and we're getting pretty good at that. The second part is actually understanding the context of the speech and that's obviously harder problem.
We're just starting to explore machine learning in any meaningful sense. I think the progress we've seen already bodes tremendously well for AI assistants.
>The primary bottleneck for intelligent agents is not voice recognition, it's semantic insight.
I absolutely agree. We won't see general purpose assistants in the near future, but that doesn't mean we won't see specialized assistants. Something like the desktop is a fairly constrained environment.
A lot of what you'd need to do is already being done by systems like Google search. You can type in a random term and it will often do an excellent job of finding exactly what you wanted.
Here's an example of just how good this tech is now. I forgot a name of a tv show one time and I typed in "movie with a clock and the key" into Google. Sure enough it found the show as the first result.
While we don't know how to make a general purpose AI right now, but there is a ton of work happening in that domain. I would be very surprised if we don't see a lot of progress there in the next few decades.
The AI doesn't have to be self improving, our brains aren't and we can do all the things that a digital assistant would need to do.
> I'm willing to bet that within a few decades natural language interfaces will become the norm.
They have pretty bad discoverability. Users can only find out what works by a try->fail->try something else approach or by being told that it works like that.
What of those who don't like to tell or non-English (or other popular languages) speakers? I hate voice control and I don't see myself using it on PC in 10 years or ever.
If majority of people decide that's how they want to interact with a computer then that's the way things will go. Whether one individual likes it or not isn't going to make a difference I'm afraid.
Do you think that MS/AAPL/GOOG will go out of its way to collect your user data in particular, when it can get millions or billions of people's user data?
Do you think that MS/AAPL/GOOG doesn't have voice recognition for other languages? Did you look it up at all?
Do you think that MS/AAPL/GOOG won't get voice recognition for other languages in "10 years", given how much personally identifying data they can scoop up?
No, but I also know very few typical users that use voice controls extensively.
"Do you think that MS/AAPL/GOOG doesn't have voice recognition for other languages? Did you look it up at all?"
Voice controls in Lithuanian (my mother tongue) on Android didn't work until very recently (2015 Sep, according to Wiki) and it still works very poorly. If your primary language is not a mainstream one, you are out of luck. I'm sure it will get better, but it's not among the reasons I don't use it anyway (see below).
"Do you think that MS/AAPL/GOOG will go out of its way to collect your user data in particular, when it can get millions or billions of people's user data?"
I'm very pro-privacy, but it's not my main concern when talking about voice controls. The main drawback and reason why I hate it, is that talking breaks concentration and disrupts workflow. Another reason is that I don't like people around hearing what I'm doing, I can imagine a lot of anecdotal situations regarding "Computer, load PornHUB" etc. Also, it annoys other people around -- imagine a shared, dormitory like flat where all people are using voice controls at once.
This is absolutely not true. More computing power opens doors to software solution that wasn't even possibly before even to this day. It may be GPU accelerated now but even at this point we are op/s and W/hr limited.
Your turning point of 2005 might simply be because programmers stopped developing applications that require faster serial processing. Those programmers might have stopped developing such applications because they noticed that chip clock speeds haven't increased since 2004.
The author left out a comparison on graphics cards. There is a recent trend in computing (starting around 2008 when GPUs became freely programmable) where GPUs are the new real measure for horsepower in a high-performance computer due to their raw speed and usefulness in scientific computing and games.
Well, to be fair these devices have fairly different purposes. One might argue that CPUs are in fact sufficient for what they do in most cases and all other cases should likely be somehow be ported to accelerator devices such as GPUs.
For non-gaming, non-video-editing tasks, I partially see it as just the core tasks aren't changing that much. An operating system isn't doing that much more than it was 5-7 years ago.
I'm using my 2011 Macbook Pro for Protools, and it feels just as fast as my 2014 model does for the rather paltry tasks I throw at it (I use outboard hardware for almost everything, mixing 16 tracks of audio was something a G3 Mac could handle easily). I don't see any reason I'll realistically need to upgrade it anytime soon (loaded with memory, SSD, etc)
The thing that stuck out to me was the Geekbench score. 2,287 vs 6,350 over the course of a decade isn't that impressive improvement. Given it's reflective of what a computer can do, comparing metrics such as Ghz or gigaflops seems superfluous in the context to the average user. During the exponential improvement in 80's or the 90's, it seems like we have hit a brick wall.
Not to say there hasn't been massive strides of improvement over the last decade, SSD and GPUs in particular have been spectacular. But many consumers have the decision they'ed oven rather have a power efficient processor than a heavy duty CPU.
As developers it means being conscious of bloat and writing more efficient software. Already we're seeing many libraries that are closer to the hardware or 'metal'.
The biggest reason for old computers still being usable is that the market was long ago overshot by computer power.
Most people simply don't need a lot of machine to do what they need on a computer. Where as early on computers were struggling to do things like display text and images the peak of user demands was the point where all computers could run Flash Player 9.
This skirts close to being a circular argument: computers are "fast enough" because people don't need a faster computer; but people don't "need" a faster a computer because faster computers aren't available. You could make that argument about every phase of personal computing from the late 1970s onwards, because what people "need" is determined by their expectations of technology. Once a product comes along that exceeds those expectations (the Apple I; the Macintosh; the Amiga; etc.) then people suddenly "need" a lot more.
It has nothing to do with the availability of faster computers. People just don't need that much machine to do the things they'd want to do with a computer.
What people want to do with computers is really misleading, ask about virtual reality from people and maybe they don't want that.
But when good product comes out things will change, are you going to bet against virtual reality?
Always with new technology has come new ways to consume which required more processing power. Right now it's smartphones, but maybe soon it will be VR/AR.
It's true people might want something in the future that would require faster processing (I doubt VR is going to be in high demand). But that is talking about a hypothetical future; not the present.
Currently, a 5 year old computer can easily meet most demands. Today several times as many people that have ever bought a computer are buying smartphones. Yet they are less powerful.
In 2014, I upgraded both my parent's computers to SSDs. Dad has a 2006 desktop. Mom has a 2010 laptop. They both boot extremely quickly, faster than anything new with a spinning disk.
Since then there has been an explosion in number of cores. Seems like coding for distributed systems is the way to go. I suspect that increasing the transistor count will remain beneficial to things like scientific computing and machine learning.
Is there one with data past 2008 for single-threaded performance? I thought that was improving quite a bit, just with more instructions per clock rather than higher clock rates. Core counts don't seem to be going up that fast. A typical computer now might have four cores, which is far from an explosion.
Single threaded performance has gone up but not by that much. It is due to some pipelining, some reduced latency, some SIMD, some deeper reorder buffers, some more cache, better prefetching and probably other factors I'm forgetting. All have diminishing returns and Intel has no competition.
Cores have gone up, just not in the narrow spectrum of consumer processors since not a lot of software takes advantage of them. Knights Landing will have 72 cores, all 4 way hyper threaded, all out of order cores. GPU's internal cores have gone up as well.
Transistor budgets haven't stalled quite yet but lots of cheap journalism proclaims the end of moore's law because of consumer chip benchmarks. Intel has a roadmap to 7nm which will quadruple the amount of transistors we have now, after that it is unclear.
They've been available on the high end for a long time. I have a 2013 Mac Pro with 6 cores, for example. But it seems like the more average computer has not changed nearly as much in this area.
Single-threaded performance hit a wall around 2011, especially if you take overclocking into account. The difference between an i7-2600K (2011) and an i7-6700K (2015), both at max overclock, is at most about 25% and often much less depending on the workload.
Not to mention that the i7-2600k sold for around 250€ whereas the i7-6700k costs more than 370€!
Nowadays for 250€ you can only buy a i5-6600k which is around 25% faster than the i7-2600k in single core performance, but slightly slower in multi core performance due to lack of HT.
Only the GPU inside these new CPUs has improved greatly.
Quite frankly, Im surprised the reason behind this isnt common knowledge, at least among us geeks.
The reason is hardware designers have hit a wall, they cant make single threaded applications run any faster.
So your brand new gaming PC is no faster at running single core applications than your 10 year old computer is. You can copy over any single core application from your new PC to your old one, and it will run just as fast.
What your new computer has is more cores though. Ideally you want linear scaling. That is if you have 2 cores your program should run twice as fast, and if you have 256 it should run 256 times as fast. This kind of scaling is almost impossible to achieve. Most programs don't scale beyond a certain number of cores.
Your multithreaded program might run a bit faster if you throw an extra core at it, but very quickly throwing more cores at it doesn't make a difference. It might even get slower. You have all those extra cores idle, or worse they are busy waiting.
Most programs dont scale well to many cores because any synchronization between cores kill scalability. If you have ANY sequential steps in your application that will put a limit on scalability.
As an example say you have 4 cores. You have a main loop on one core, it posts expensive work to the other (3) cores. And when they are done, you collect all the results and use them somehow. This final step is sequential and it kills your scaling. Your application wont run 4 times faster. And it probably wont run any faster if you throw 16 cores at it. This is because that one core is synchronizing with all the other cores. So those other cores do a lot of waiting around. And any sequential steps in that one core becomes the bottleneck, as those other cores will all end up waiting for it.
This message is already too long. So long story short. Single threaded programs are no faster on new computers, and most multi threaded programs dont scale well to many cores. Its a huge wasted opportunity to let cores be idle or underutilized. We have to fundamentally change our programming style and tools to take make use of all available cores.
I'm writing this on a Core2Duo E6750 that has a single thread score of 1000 - most new CPUs are at least twice as fast. Also Instructions per cycle going up.
It's really a hard problem. You can't just spawn threads and put mutexes in front of you data structures. This is terribly slow, even slower than single core without locking for some tasks.
We need better lockless data structures that minimize synchronisation.
The E6750 is a 2.66Ghz CPU that was introduced in Q3 2007. That means that single core performance went up only 8% per year on average.
Compared to the improvements we made in the decades before that, that's really poor.
I have a i7-2600k (quad core 3.4Ghz, 95W TDP, HT) that was introduced in 2011. Five years later, it still is not significantly slower than recent CPUs by Intel unless they take advantage of new CPU features. It sold for around 250€ back then. Today, for 250€ you can buy an i5-6600k (quad core 3.5Ghz, 91W TDP, no HT).
The only big improvement in the i5-6600k is the GPU (which I don't use).
The best example to bring this idea home is $70(~$40 used) Pentium G3258 on ~$30 motherboard. It gets single thread score of 2400, so almost top of the chart. In single core games like World of Tanks (python ...) there is no differenve between this bottom of the barrel CPU and i7
Getting pretty clear already in the video that there is more than one effect. What is mentioned not clear enough/missing is the more difficult usage of the newer processors and their additional cores.
You can see it in games. Until recently it was equally possible to play on an overclocked Pentium G3258 as on an i5-4690, a way more powerful quadcore processor, because most games just did not use the additional threads the i5 provided. That is changing now, so far that even the Hyperthreading of an i7 gets very useful in games.
If that was true in games, that was probably also true for other software. Meaning that the new processors where not that more powerful for all those software not being able to use the multi-threading capabilities.
How about, because operating systems, browsers (& web sites) and a lot of other things have to run on mobile devices, programmers can no longer rely on specs increasing, they have to keep code more efficient.
As other have touched on, the apparent slowdown in processor speeds is due to computers have enough CPU power for most users needs.
Today computing power in personal computers is basically a commodity, with companies now having to focus on other aspects to be able to sell their computers. Namely weight, battery life, network connectivity etc.
Having said that, the chips are still developing, but now the focusing is on the new user demands rather than raw speed. E.g. we now have native decoding of audio, video, networking, low power modes, etc to increase battery life. (And thereby decrease weight)
While the consumer GPU's are also getting faster, it's not due to more advanced use cases, but rather because computers now have screens with higher screen resolutions.
I believe that processing power became a commodity around a single core GeekBench Browser score of about 2500 (Think Late 2011 Macbook pro - note using browser score here to be able to compare apples to apples). Interestingly, this is now pretty much exactly where the newest iPhones are. (Android only just now reaching the 2k's).
In essence, with the latest and greatest chips on mobile, we are reaching the point where processing speed is now a commodity on this platform. However as with PC's we will likely see a bit of overshooting, so expect the mobile CPU race to continue until around a GeekBench Browser score of 3000-3200.
Hopefully this will result in companies starting to compete on battery life of the device as was seen with laptops. E.g. the newest MacBook Pro claims 9 hours of battery, compared to the 4 hours promised for the Late 2011 Macbook Pro.
The only obvious unknown I can think of, is if the average consumer embraces Virtual Reality, rather than it becoming a niche such as high quality PC gaming. Then we might once again see a new strong focus towards raw single core computing power, both on Desktop and Mobile.
One thing that was not mentioned was scheduling of processes especially on mobile devices. Many operating systems employ timer coalescing that along with other techniques serve to maximize power efficiency instead of CPU utilization
In most features, my smartphone is about x1,000 more powerful than my ZX81 (from '81). RAM, "ROM", clockspeed. Accounting for no hardware fp (Z80), I estimated that my smartphone's GPU is about x1,000,000 the FLOPS.
And high-end consumer video cards are about x1,000,000,000 ... which is about, or a little less, what one of the Moore's law corollaries predicts for 35 years, 1981 to 2016, per $.
a computer increases performance 10 fold:
what takes 10 minutes now takes 1. we notice a difference of 9 whole minutes! wow!
a new computer increases performance 10 fold:
whoaaaa, that minute of waiting is now merely SECONDS! we saved like almost a whole minute of waiting!
an even newer computer increases performance 10 fold:
nice, thats like noticeabley faster:
today, the newest computer is available:
meh something from like 10 years ago would run this fine...
we are getting to a point where other aspects of performance are also getting harder and harder to notice. things like rendering, supported display sizes and refresh rates, the ability for machines to understand us (voice recognition and computer vision)
My 2008 PC is still performing well.
I think engineers should work more on the OS since sometimes we need to upgrade just because of the trash and useless things kept by the OS.
>The question I'm asking is why does a 10 year old computer basically run current software OK while that wasn't true in the past.
I've got a theory that it's a bit related to human brain IO limits. There's only so much text, sound and video we can take in, video being the highest bandwidth and computers got to a stage where they could do video OK a decade of so ago. So increasing the output dramatically does not have a huge effect on user experience. I mean 4k video is nice but it's a similar experience to 360p.
Or may be we are approaching a wall that is harder to climb than usual.
With unlimited computing power we could render real time highly interactive super accurate simulations which takes months to render. But we can't because we don't have the computing power.
The truth is, we are hitting multiple wall at the same time. It's harder to shrink gates any further, it's harder to increase clock speed anymore and it's harder to dissipate the heat generated by the cpus. All the increase we are now getting are incremental not exponential and that is why we can run a current software on a 10 year old cpu, if the software is not optimized for multicore cpus.
Or a bit of both. You have a point that the performance of a single core has stopped increasing so fast.
On the other hand if people really wanted performance they could get high powered desk top computers but the trend has been more towards using phones. About the most processor intensive thing I do personally is edit video and even that works OK on my 2 year old phone.
I have no idea what type of video editing you are doing but editing 2.7k60 video on my high end gpu accelerated desktop is a pain in the ass.
The adoption of mobile phones as computing platform is a very different event. It allows use of computing in a very versatile manner. That is why it's usage exploded.
