Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says out on
Speaker 1: the wily Misty Moore's I'm John Constrictland and I'm Joe McCormick,
Speaker 1: and our other host, Lauren vocal baumb is not with
Speaker 1: us this time, but hopefully she will be back next time. Jonathan,
Speaker 1: I I suspect you of equivocating on more maybe a
Speaker 1: little bit, teeny tiny bit. Okay, So today we are
Speaker 1: going to be talking about Moore's Law. It won't be
Speaker 1: the first time on this podcast, probably won't be the
Speaker 1: last time, because it contributes very much to how we
Speaker 1: grapple with the future of consumer electronics and computer technology.
Speaker 1: And we've used, I mean we being the humans in general,
Speaker 1: have used Moore's Law to kind of be shorthand for
Speaker 1: the progression of computational power for pretty much as long
Speaker 1: as it's been around. So we are specifically focusing on
Speaker 1: how it's gonna go through a little bit of a
Speaker 1: transformation in the not too distant future. To quote MST
Speaker 1: three K. Jonathan recently wrote a piece for How Stuff
Speaker 1: Works Now about a recent development in the world of
Speaker 1: people thinking about Moore's law. Yeah, so first, before we
Speaker 1: even get into that, we should probably chat a little
Speaker 1: bit about what Moore's law is. The observation was originally
Speaker 1: made back in nineteen sixty five. That's when Gordon Moore
Speaker 1: came up with this. Uh, well, it really was just
Speaker 1: an observation, right, It wasn't a law. He was he
Speaker 1: noticed something and he wrote a paper about it. But
Speaker 1: maybe we should make it a law. Well, we certainly have. No,
Speaker 1: you're right that it is not a physical law. It
Speaker 1: is more a yeah, an observation or a prediction. Right,
Speaker 1: It's just a prediction that has held more true than
Speaker 1: not throughout the years, and so people referred to it
Speaker 1: like a law, kind of like Murphy's law. Murphy's law
Speaker 1: is not a real law. Moore's law is not a
Speaker 1: real law. But when Gordon Moore made that observation back
Speaker 1: in five, he was the director of research and development
Speaker 1: for fair Child Semiconductor. He was also a co founder
Speaker 1: of fair Child. He would go on to be a
Speaker 1: co founder of Intel. So here's a guy who was
Speaker 1: really really smart about integrated circuits. Uh, integrated circuits were
Speaker 1: a relatively new thing because transistors had not been around
Speaker 1: that long before then you're talking about vacuum tubes. And
Speaker 1: so he was taking a look at this and seeing
Speaker 1: that there was an interesting situation going on. He wrote
Speaker 1: an article for Electronics magazine and it has one of
Speaker 1: my favorite titles of all time for technical uh articles,
Speaker 1: it's cramming more components onto integrated circuits, because there's nothing
Speaker 1: delicate about that cramming. Cramming is such a great word.
Speaker 1: You get this image of Gordon Moore with a bag
Speaker 1: full of components and he's reaching in and pulling out
Speaker 1: fistfuls and trying to cram them in to a machine.
Speaker 1: And maybe he has like a real large wooden mallet
Speaker 1: sitting by just in case. Yeah, but that's not quite
Speaker 1: the actual way that this is being done. But he
Speaker 1: was talking in fact, I think he was talking as
Speaker 1: much about economics as he was about technology. In fact,
Speaker 1: he was talking more about economics. Yeah, more m O
Speaker 1: r E, not m o O r E in this case. Yeah,
Speaker 1: he was talking about economics primarily because he wanted to see, uh,
Speaker 1: where the price point fell on an individual component, At
Speaker 1: what point in a in the density of a an
Speaker 1: integrated circuit. Do you have the best price per component
Speaker 1: so that your your circuit that you're done with is
Speaker 1: at its lowest cost, right, the lowest price to to
Speaker 1: make it. And there's a certain rule that he saw
Speaker 1: that you had to hit a certain volume in order
Speaker 1: for that price to come down. It's kind of like
Speaker 1: if you're buying in bulk. Yeah, you know, if you're
Speaker 1: buying in bulk, you know, you buy more the individual
Speaker 1: cost of each individual item is lower. But that's why
Speaker 1: it makes so much more sense to buy that barrel
Speaker 1: of cheese balls, small little can of cheese ball by
Speaker 1: by the hot tub sized mayonnaise jar and put it
Speaker 1: in the garage. Yeah, it's always a great idea. Uh. Well,
Speaker 1: he noticed that there was a certain volume at which
Speaker 1: the the components would be at their ideal cost. But
Speaker 1: it was a sweet spot, like if you went too
Speaker 1: much over that, then the finished integrated circuit would be
Speaker 1: too expensive. If it were too too much under that,
Speaker 1: it would also be that the individual price per component
Speaker 1: would be too high, thus making the finished circuit too expensive.
Speaker 1: But he also knows that not only was there a
Speaker 1: sweet spot, that sweet spot moved over time. There was
Speaker 1: an incentive for companies to develop more powerful processors. There
Speaker 1: was a need in the marketplace, which meant that there
Speaker 1: were economic factor is that pushed the development of more
Speaker 1: sophisticated um approaches to manufacturing transistors and and UH and
Speaker 1: integrated circuits. That meant that the individual component prices began
Speaker 1: to shift over time. So at the time that he
Speaker 1: made this observation, he said that the ideal number of
Speaker 1: components for an integrated circuit was fifty. Folks were in
Speaker 1: the billions now, but at the time it was fifty
Speaker 1: fifty components. That was where you hit the lowest price
Speaker 1: per component. But he said, this was twice as good
Speaker 1: as the year before, and then that and that was
Speaker 1: twice as good as the year before it. So he said, well,
Speaker 1: I think this is going to continue. I project that
Speaker 1: within five years the lowest cost per component on an
Speaker 1: integrade circuit would be realized with circuits having around a
Speaker 1: thousand components. So to the day he makes the observation,
Speaker 1: it's fifty five years from then, he's saying it's going
Speaker 1: to be a thousand that we're just gonna see this
Speaker 1: trend to continue. He said, there's no reason you can't
Speaker 1: project this out further, and he said that by the
Speaker 1: time you would get to nineteen seventy five, it would
Speaker 1: be sixty five thousand combinans. He was talking about it
Speaker 1: essentially doubling more or less doubling every single year. He says,
Speaker 1: eventually you would probably have to make some adjustments. You
Speaker 1: might hit a point where this this trend continues, but
Speaker 1: it slows down. And in fact, that is what we saw, right.
Speaker 1: We saw it go from uh, every twelve months to
Speaker 1: really more like between eighteen and twenty four, depending upon
Speaker 1: the times span you're looking at um. And that was
Speaker 1: incredible that he made this observation and that it was
Speaker 1: so prescient and so accurate. And that's why we often
Speaker 1: call it Moore's law, although we don't necessarily I think
Speaker 1: the wider interpretation isn't focused so heavily on the economic
Speaker 1: side of things. So as a point of illustration of
Speaker 1: exactly what this looks like in terms of the hardware
Speaker 1: in our devices. There was a great example I found
Speaker 1: in a piece that was in The Economist, and it
Speaker 1: highlighted this fact. When Intel released its first microprocessor in
Speaker 1: nineteen seventy one, this was called the four zero zero
Speaker 1: four and the four thousand four That chip was twelve
Speaker 1: square millimeters and it had two thousand, three hundred transistors
Speaker 1: and the gap between each transistor was ten thousand nanimeters,
Speaker 1: which is about the width of a red blood cell.
Speaker 1: They say, you know, a kid with a decent microscope
Speaker 1: could see the individual transistors. Intel's Skylake chips in six
Speaker 1: are a little bit different. These chips are the spacing
Speaker 1: of fourteen nanimeters between transistors, so that's ten thousand nanimeters
Speaker 1: to fourteen and they're not only invisible to the naked eye,
Speaker 1: they're they're even invisible to any normal microscope right light.
Speaker 1: The wavelengths of light are too big for us to
Speaker 1: be able to see this. You have to use like
Speaker 1: a scanning microscope, electron scanning microscope. So here, here's two
Speaker 1: factors that are important in this. Like one is that
Speaker 1: the individual components, we were able to shrink them down
Speaker 1: to increasingly smaller sizes, which is a weird way of
Speaker 1: putting it, but it gets a point across, and we
Speaker 1: were able to pack them together more densely. We were
Speaker 1: able to decrease the spaces between those components. Because we
Speaker 1: both both miniaturization and architecture. Right, we're getting really, really
Speaker 1: good at various types of lithography and other methods of
Speaker 1: developing and laying out these these circuits. Um and that
Speaker 1: was what allowed us to continue on to a ridiculous degree.
Speaker 1: When you think about it, I mean you're talking about
Speaker 1: from a time where it was fifty components to literally billions.
Speaker 1: Now it's it's hard to wrap your mind around it.
Speaker 1: And again this was really all about how More was saying,
Speaker 1: this only makes sense if it makes economic sense. Right,
Speaker 1: It will only work to the point where companies can
Speaker 1: do this and have it be a product that they
Speaker 1: can make a profit selling if it gets to a
Speaker 1: point where it's too difficult to do. Too difficult translates
Speaker 1: to too expensive. Right, the harder something is to do,
Speaker 1: the more expensive it is to do it. Well, there's
Speaker 1: all kinds of stuff you can do in the lab,
Speaker 1: and it just doesn't make sense from a consumer perspective. Yeah,
Speaker 1: and it if it's one of those things where you
Speaker 1: get to a point where it's just too much trouble,
Speaker 1: then what it's worth then Moore's law breaks down, because
Speaker 1: again it's not that it's technically impossible, it's that it's
Speaker 1: economically not productive. And if it's not economically productive, no
Speaker 1: one's going to no one's gonna lose money just trying
Speaker 1: to uh to get across an engineering hurdle that has
Speaker 1: been problematic. It's sort of like saying, you know, could
Speaker 1: you in near me a car that goes three hundred
Speaker 1: miles per hour? You know, I bet somehow if we
Speaker 1: were gonna be willing to pour all of our resources
Speaker 1: into that, we could do that. Just why, there's no
Speaker 1: reason to do that. It doesn't make any economic sense.
Speaker 1: There's no economic imperative for it. I can do that easily.
Speaker 1: You just have to find a height tall enough to
Speaker 1: drop the car off. And then I mean, you know,
Speaker 1: I'm just say it. Technically I can get it up
Speaker 1: to three hundred miles per hour. Yeah, has you're not
Speaker 1: gonna be able to do anything with it. Well. You also,
Speaker 1: if you were to make a car, a consumer car
Speaker 1: that goes three hundred miles per hour, you you'd introduce
Speaker 1: problems based sort of on physics and the limitations of drivers,
Speaker 1: Like I would say, you'd, how should I put this?
Speaker 1: You'd you'd increase the error rate of that car, and
Speaker 1: that in fact leads us into one of the fundamental
Speaker 1: issues that are that we face now in our production
Speaker 1: of these incredibly complicated microprocessors. Now, one thing I should
Speaker 1: say is that we haven't necessarily seen the number of
Speaker 1: components double every eighteen to twenty four months over the
Speaker 1: past few years. Right, That was the original definition of
Speaker 1: Moore's law, though in MOR's law does still seem to
Speaker 1: hold in a more abstracted sense, which is the way
Speaker 1: it trickles down to the consumer, is that you can
Speaker 1: expect every eighteen to twenty four months, you're computing devices
Speaker 1: will be twice as fast, twice twice as fast, or
Speaker 1: the processing power is twice as much some some variation
Speaker 1: of that. That tends to be how we define More's
Speaker 1: law now, is that every eighteen to twenty four months,
Speaker 1: like if you buy a computer today, in two years,
Speaker 1: the computer you buy that day will be twice as
Speaker 1: powerful as the one that you just bought, And that
Speaker 1: it just shows the rapid development of technology and the
Speaker 1: speed at which we can improve processing. That that tends
Speaker 1: to be how we focus on More's law. But even
Speaker 1: that is starting to get difficult. So even when we
Speaker 1: got to the point where we were no longer saying
Speaker 1: all right, We're not not so much worried about how
Speaker 1: many components are you adding, but how you how you
Speaker 1: best utilize those so that you get the most out
Speaker 1: of them. We still run into problems. So one of
Speaker 1: the things we've seen is that we've seen companies like
Speaker 1: Intel take a TikTok approach. That's what they actually call
Speaker 1: their strategy when they're creating new types of microprocessors and UH.
Speaker 1: The TikTok approach means that you have two different generations
Speaker 1: of processors UH that are kind of piggybacked onto each other,
Speaker 1: and in fact they chain up to the previous generations.
Speaker 1: So the TICK generation is not blue and nine vulnerable
Speaker 1: and has a sidekick named Arthur. The TICK generation is
Speaker 1: when you shrink down those components to a smaller size.
Speaker 1: We're talking on the nano scale now, right Like it
Speaker 1: used to be that forty now cometers was considered the
Speaker 1: the super small components. Now we're talking about getting down
Speaker 1: to like less than ten nanometers per component. That's insanely small.
Speaker 1: When you get down to that size, that becomes the
Speaker 1: TICK where you say, all right, we're gonna build it
Speaker 1: on the same architecture as the previous generation's microprocessors. But
Speaker 1: now everything is just smaller, so we can pack stuff
Speaker 1: in more more pieces in so we're using the same chassis,
Speaker 1: but now we've got way more components in there. The
Speaker 1: talk is when they figure out, hey, now we know
Speaker 1: how to design all those little components so that they
Speaker 1: work best they work, and because the architecture from one
Speaker 1: generation to the next may not be the most efficient, right, like,
Speaker 1: you may need to change that, especially for stuff like
Speaker 1: heat dispersal. Heat is a real problem with microprocessors. So
Speaker 1: the talk that would be when you figure out, all, right,
Speaker 1: here's the architecture that works best with this eyes and
Speaker 1: then you would go to the next tick, which means
Speaker 1: everything is even smaller, and then the next talk where
Speaker 1: everything has been laid out in the most ideal way possible.
Speaker 1: So uh so here's a question though, Yeah, is that
Speaker 1: TikTok of the clock counting up or counting down that's
Speaker 1: coming down? Buddy? So we're hitting some fundamental limitations just
Speaker 1: because of our good old buddy physics, right, Like, you know,
Speaker 1: it's not like the nano scale is as small as
Speaker 1: it gets. You can go down to the atomic scale, right.
Speaker 1: The only problem is that as you're getting down to
Speaker 1: the nanoscale and the atomic scale, something else starts to
Speaker 1: play a role in your designs, and that's quantum physics.
Speaker 1: You don't need to worry about that. On the classical side. Really,
Speaker 1: quantum physics at that point becomes negligible. You're not you're
Speaker 1: not so much worried about weird quantum effects to bring
Speaker 1: back the car. I mean, you don't have to go
Speaker 1: to relativity to understand the physics of designing a car, right, Yeah,
Speaker 1: we we don't have components on our cars that go
Speaker 1: down to such the nanoscale where you have to think, oh,
Speaker 1: wait a minute, quantum tunneling. But we do have to
Speaker 1: worry about it with microprocessors and quantum tunneling in particular.
Speaker 1: I mean, there are a lot of quantum effects that
Speaker 1: we could talk about, but quantum tunneling in particular is
Speaker 1: a problem. Uh. It leads to what some folks call
Speaker 1: electron leakage, which just sounds gross. But here's what's happening.
Speaker 1: So quantum tunneling is this phenomenon you get where you
Speaker 1: have a quantum particle. In this case, we're talking about electrons,
Speaker 1: and a quantum particle has uh kind of a range
Speaker 1: of of probabilities of where it can be at any
Speaker 1: given time. So we've talked a little bit in previous
Speaker 1: episodes about the Heisenberg on certainty principle. Right, we don't
Speaker 1: know where it is. We know where it could be, right, Right,
Speaker 1: We've got a general idea of the area where within
Speaker 1: that area somewhere, that's where the particle is. But it
Speaker 1: could be anywhere within that one area. So instead of
Speaker 1: thinking of an electron as a point, think of a
Speaker 1: a kind of a nebulous fog of war like circle,
Speaker 1: and the electron could be anywhere within that circle. All right. Now,
Speaker 1: with microprocessors, you have these things called gates. The gates
Speaker 1: allow either allow electrons to pass through or do not
Speaker 1: allow electrons to pass through. In terms of effects, I
Speaker 1: would say their logic gates. The gates in a in
Speaker 1: a process or sort of the the ability of your
Speaker 1: brain to tell the difference between yes and no. Right,
Speaker 1: And so if you think about computer processing ultimately, you're
Speaker 1: talking about very very very very complicated approaches to just
Speaker 1: having lots and lots of yes or no questions, Right,
Speaker 1: I mean logic gates. You build logic gates by having
Speaker 1: all these different channels, and by opening some channels and
Speaker 1: closing off others, that's where you create the the language
Speaker 1: that ultimately becomes the commands for the computer and you
Speaker 1: of getting to play your Call of Duty game or
Speaker 1: whatever it may be. So let's say you've got this
Speaker 1: Heisenberg uncertainty principle at play, and let's say you've shrunk
Speaker 1: down the components to such a degree that when you
Speaker 1: get close to one of these gates, it's so thin
Speaker 1: that part of the cloud of probabilities that that electron
Speaker 1: could inhabit overlaps the gate, so that part of it
Speaker 1: is on the other side of the gate. So if
Speaker 1: you can think of it like a flashlight, imagine that
Speaker 1: you're shining a flashlight on on an actual little like barrier,
Speaker 1: and part of the flashlight you can see like the
Speaker 1: circle of light. Part of it is on the is
Speaker 1: on one side of that barrier, and part of it's
Speaker 1: on the other side of the barrier. Think of that like,
Speaker 1: that's the potential of where the electron could be. Now,
Speaker 1: if there's a probability for an electron to be in
Speaker 1: a specific location, I mean sometimes the electron is in
Speaker 1: that specific location. So if the probability field of the
Speaker 1: electron actually overlaps the gate. That means sometimes the electrons
Speaker 1: on the other side of the gate whether you've opened
Speaker 1: the gate or not. And so sometimes the brain of
Speaker 1: your electronic device can't tell the difference between yes and no. Right,
Speaker 1: It may think that it's a yes, when in fact
Speaker 1: it was supposed to be a no. That's where you
Speaker 1: get electron leakage, where electrons are leaking through the system
Speaker 1: and inserting errors into your calculations. For computers, this is
Speaker 1: what we call a bad thing, Like you want your
Speaker 1: calculations to be reliable, and if they're not, then programs
Speaker 1: are gonna crash, files will get corrupted, you won't get
Speaker 1: you won't get good behavior out of your computer. And
Speaker 1: we've been hitting up against this. But I was trying
Speaker 1: to say, no, don't go into the room with the group.
Speaker 1: Yeah exactly, Like yeah, so I wanted I wanted to
Speaker 1: go east and open the mailbox. Um. Yeah. Know, when
Speaker 1: we get to that point, then that's that's an issue,
Speaker 1: and that's where we've been bumping up against Over the
Speaker 1: last few years now. We keep seeing engineers come up
Speaker 1: with new materials that help edge us away from that
Speaker 1: but it's something that has been an issue for the
Speaker 1: last several generations of microprocessors. Another issue with the design
Speaker 1: of microprocessors you already touched on, but I was gonna
Speaker 1: mention wasting energy's heat, Yeah, and not just wasting energy. Yeah,
Speaker 1: so there's wasting energy, but also just the problem of
Speaker 1: accumulating heat. Right, if if a chip gets too hot,
Speaker 1: it'll it'll lock up, it'll shut down exactly. So you
Speaker 1: continually shrink these components and increase transistor density, but this
Speaker 1: cuts into your ability to disperse all of the waste
Speaker 1: energy that they create as heat. Yeah, how do you
Speaker 1: end up cooling all those billions of components so that
Speaker 1: they can continue to operate without getting to that that
Speaker 1: critical threshold of heat. It's not a perfect analogy, but
Speaker 1: I would say it's sort of like trying to cram
Speaker 1: more computers into a server bank. Sure, and if you
Speaker 1: just keep cramming more and more in, well, that's great.
Speaker 1: You can fit more computer is into that tiny little room.
Speaker 1: But eventually the room's gonna get really hot, and if
Speaker 1: you don't have a good capacity to cooling that room down,
Speaker 1: then eventually those machines break down. They stop. Um, you know,
Speaker 1: you may have heard that in overclocking competitions, where people
Speaker 1: are trying to massively push the limits of what their
Speaker 1: processors processors can do, they might go to such extremes
Speaker 1: as cooling their systems with liquid nitrogen in order to
Speaker 1: get rid of that heat, because since they're running them
Speaker 1: so hard, they're generating even more heat than they normally would.
Speaker 1: And these are like top of the line processors. Oh.
Speaker 1: I I have a friend actually who is buying himself
Speaker 1: an insane computer, and he's going to get a liquid
Speaker 1: cooled computer. Liquid Cooled is pretty standard for things like
Speaker 1: a high end gaming rig these days because it's it's
Speaker 1: more efficient than air cooling. I did not know that.
Speaker 1: I mean I knew that you could do that, but
Speaker 1: I did not know that liquid cooled computers were things
Speaker 1: somebody just have in their house as opposed to in
Speaker 1: a labs. Right. Yeah, I would say that about maybe
Speaker 1: four or five years ago, it became kind of like
Speaker 1: the gold standard of how to cool your gaming rig.
Speaker 1: Before that, it was all you know, GPUs that had
Speaker 1: their own dedicated fans, so you get more and more
Speaker 1: fans inside your computer, which would make it louder and louder. H.
Speaker 1: But uh, it's not as unusual now as it used
Speaker 1: to be. Used to be. Like that was, if you
Speaker 1: had ridiculous amounts of money and you wanted to really
Speaker 1: increase your swagger around the PC gaming circles, you would
Speaker 1: invest in a water cooled system. It's it's less, it's
Speaker 1: less extravagant. Now it's still pretty extravagant, but it's not ridiculous.
Speaker 1: It's not like the Rolls Royce like it used to be.
Speaker 1: But yeah, we've seen engineers work really really hard to
Speaker 1: get around these problems. But that can only go so long, right,
Speaker 1: you do start to bump up against this issue, and
Speaker 1: and by by saying engineers work really really hard to
Speaker 1: get around these problems, that's where we're starting to see
Speaker 1: where the the issue really is. Like we said at
Speaker 1: the very beginning, Moore said, as long as it's economically advantageous,
Speaker 1: as long as it makes sense from a money standpoint,
Speaker 1: this will continue. So those engineers working really hard, somebody's
Speaker 1: got to pay their salaries, exactly, And if they have
Speaker 1: to work really hard for a really long time, that
Speaker 1: technology gets really expensive. So this kind of leads us
Speaker 1: to a recent report called the International Technology Roadmap for
Speaker 1: Semiconductors or i t r S, which was an annual
Speaker 1: report or ITTERS. It was an annual report until until
Speaker 1: this most recent one, which was technically the two thousand
Speaker 1: fifteen i t r S, but it came out this year.
Speaker 1: I'm sorry, I didn't mean to step on your emphasis.
Speaker 1: It was you're saying. It was like it's done. This
Speaker 1: was the last one that came out. That the most
Speaker 1: recent one is the final one. Bye bye, shed it
Speaker 1: here for ITITTERS. Yep. So they found that by twenty
Speaker 1: twenty one, so not far at all, we will no
Speaker 1: longer be shrinking transistors. We will have reached a limit.
Speaker 1: That's five years, folks, five years, five years. We will
Speaker 1: no longer be making smaller components for these microprocessors. We
Speaker 1: will have gone as small as we can go. Now,
Speaker 1: when I say as small as we can go, I
Speaker 1: don't necessarily mean that we'll have reached a physical limitation
Speaker 1: like as in the laws of physics, will deny us
Speaker 1: the ability to go any smaller. But from an economic standpoint,
Speaker 1: that will be as small as we can go, because
Speaker 1: at that point it will be so complicated that to
Speaker 1: try and go smaller would be more expensive than you
Speaker 1: could ever recapture. So I've got an idea. I think
Speaker 1: instead we need to be paying these engineers to design
Speaker 1: a simulated universe that we can all plug into, in
Speaker 1: which there are ways to make cheaper, smaller microprocess But
Speaker 1: if they do that, then it's clear that we already
Speaker 1: exist in a simulated universe, and then we all have
Speaker 1: existential dread. Well, of course we do. We all got
Speaker 1: to wait until that time somebody gets bored and turns
Speaker 1: us off. If we get to a point where we
Speaker 1: can create a simulated universe, the argument from a philosophical
Speaker 1: standpoint is that we must already be in a simulated universe. Yeah,
Speaker 1: I know that argument, because because the simulated universes will
Speaker 1: outnumber the real universe, is right, and the odds of
Speaker 1: us being the first universe to create a simulated universe
Speaker 1: would be incredibly small. Well, we're getting off topics, Yeah
Speaker 1: we are. But that's a fun, fun thing to talk
Speaker 1: about anyway. So existential dread set aside. Let's take real
Speaker 1: dread by twenty one. We cannot get those those components
Speaker 1: any smaller for whatever reason, but that doesn't necessarily mean
Speaker 1: that we've reached the end of the advancement of computer
Speaker 1: power computer speed. Yes. It So some people have said,
Speaker 1: does this mean Moore's law itself would end? And the
Speaker 1: answer is not really. And the reason for that answers
Speaker 1: because again, we have tweaked our definition of Moore's law
Speaker 1: enough so that if we're taking it from the definition
Speaker 1: of every eighteen to twenty four months, we're able to
Speaker 1: double the processing speed or power of a computer. That
Speaker 1: is something we could probably continue to do for a
Speaker 1: few more years. Uh So, uh, what we would have
Speaker 1: to do is find new ways of designing microprocessors beyond
Speaker 1: what traditionally we have concentrated on. So your typical micro
Speaker 1: processor you can think of as essentially two dimensional, right,
Speaker 1: it's laid on an x y grid essentially. Uh. And
Speaker 1: we've made the various little components very tiny, so you
Speaker 1: can think of the actual squares and that grid are
Speaker 1: on the nano scale. They're really really small. So we've
Speaker 1: packed a ton of them into that little form factor.
Speaker 1: But we've still really only focused on width and length.
Speaker 1: We haven't gone into another dimension, the height dimension. Now
Speaker 1: that sounds like that introduces a whole other world of
Speaker 1: design constraints and yeah, yeah, and when you want to
Speaker 1: go vertical, which is exactly what a lot of people
Speaker 1: are talking about now doing vertical stacks of transistors. So
Speaker 1: you're building transistors on top of transistors, not just left
Speaker 1: and right, but on top of each other. How do
Speaker 1: you have them communicate with each other? How do you
Speaker 1: build vertical transistor gates and logic gates? How do you
Speaker 1: cool that? Because now you're increasing the density of transistors
Speaker 1: even more by going up, not just going out, and
Speaker 1: by that you're going to create more heat. So you've
Speaker 1: gotta figure out how to disperse that heat. One of
Speaker 1: the the arguments I've seen is using microfluidic channels and
Speaker 1: some form of magic heat dispersal liquid. I think I've
Speaker 1: seen stuff about microfluidic channels. One of the I should
Speaker 1: I guess maybe insert here that to manage the heat issue.
Speaker 1: I have seen proposals of getting away from silicon. Yeah.
Speaker 1: Now I don't know if that's ever going to be
Speaker 1: an economically feasible alternative, but that's one thing people talk about,
Speaker 1: is like going to carbon nanotube based computing or something
Speaker 1: like that, which they think can do a better job
Speaker 1: of of managing heat problems. Yeah, and that would that
Speaker 1: would definitely at least buy us some more time. In fact,
Speaker 1: that I I t R. S says that we will
Speaker 1: still hit a fundamental limit, and in so just a
Speaker 1: few years after our first fundamental limit. But this limit
Speaker 1: would be due to the fact that we would reach
Speaker 1: peak heat density in a silicon based chip. And if
Speaker 1: we are using silicon and we're building vertically, we won't
Speaker 1: be able to pack more transistors into a space after
Speaker 1: because we'll be generating so much heat that we will
Speaker 1: not be able to disperse it fast enough to allow
Speaker 1: it to continue to work. It will break down just
Speaker 1: because the heat it generates will be too much for
Speaker 1: it to handle. So, uh, that's a real issue. We
Speaker 1: probably will see a lot of innovation around those cooling systems.
Speaker 1: The ours Technica piece that I read that kind of
Speaker 1: inspired this episode had mentioned the possibility of electronic blood.
Speaker 1: H yeah, kind of a kind of electronic version of
Speaker 1: blood that would they would just recirculate throughout the system
Speaker 1: and pull heat away. Did not. I did not click
Speaker 1: on it to go into another piece to read up
Speaker 1: on exactly what that was because I love ours Technica.
Speaker 1: I love that site. Great site. Those guys know their stuff.
Speaker 1: And when you get through one ars technical piece, you're like,
Speaker 1: I might need to take a break before I read
Speaker 1: another one, but they're great. That that's not that's not
Speaker 1: that's more of a limitation on my own brain power
Speaker 1: than on ours. Technica. UM, I really do like that
Speaker 1: side a lot. So you need some of that electronic
Speaker 1: blood for your brain, obviously I do. It's so it's
Speaker 1: possible that we will hit a limit within our times
Speaker 1: that we just cannot work around using the silicon based technology.
Speaker 1: Right we may hit a point where we say, okay,
Speaker 1: that's it, that's as that's as many transistors as we
Speaker 1: can fit. That's well, we'll be able to play with
Speaker 1: the arrangement for a while, but eventually we're going to
Speaker 1: hit the idealized version of that, and then that's as
Speaker 1: that's as peak as we can get with this particular
Speaker 1: form factor. Now, it might mean at that point that
Speaker 1: we have to build bigger processors. Right like that, the
Speaker 1: size may increase, which means that computer sizes will increase.
Speaker 1: It also means that you will quickly get to a
Speaker 1: point where it's impractical for handheld devices. So we may
Speaker 1: see our handheld devices max out on their processing power well,
Speaker 1: before we see computers take what if we go back
Speaker 1: to desktop machines. What if we go back to machines
Speaker 1: that are taking up like an entire room or an
Speaker 1: entire floor of a building. We used to make jokes
Speaker 1: about that all the time. Yeah, and now how big
Speaker 1: computers used to be. Now they're fit in your hand. End, Well,
Speaker 1: that might be the future. Yeah, we may not be
Speaker 1: able to avoid it once we hit these fundamental limits, unless, again,
Speaker 1: like Joe was saying, we find a totally new way
Speaker 1: of creating either the chips that we're used to now
Speaker 1: but using different materials so that the heat dispersal ends
Speaker 1: up we were able to push that off a little further,
Speaker 1: or we come up with an entirely new way of
Speaker 1: processing information so that we can build on that. Well,
Speaker 1: that's something that I kind of wanted to end with here.
Speaker 1: Not that we have any answers on this, but I
Speaker 1: do think it's an interesting question to ponder, essentially, what
Speaker 1: will the future physical side of computing look like? Because
Speaker 1: we've entered a sort of stage of humanity where exogenous
Speaker 1: computing information processing taking place outside of the human brain
Speaker 1: is a fundamental part of our culture. And here we are,
Speaker 1: and I think that's always going to have to be
Speaker 1: a part of what humanity is is from now on,
Speaker 1: But will it always have to take place based on
Speaker 1: the same physical architecture. So right now we have silicon chips,
Speaker 1: people are playing with other things like you know, carbon
Speaker 1: nanotube computers and stuff like that. There are experimental things,
Speaker 1: but mostly it's still all these silicon microprocessors. But but
Speaker 1: what else could there be? I mean, computing is an
Speaker 1: abstract concept, and it's it's obvious how the concept of
Speaker 1: a semiconductor as an electronic device enables it by allowing
Speaker 1: sort of on and off switches that allow you to
Speaker 1: perform logical operations. But I wonder what are the things
Speaker 1: that are that are next, what's out there that we're
Speaker 1: not seeing, What are ways to use the material and
Speaker 1: the energy of the universe to process information for us?
Speaker 1: I think in the short term this is just me
Speaker 1: kind of talking off the top of my head. It
Speaker 1: would be offloading all of that computing to a third
Speaker 1: party that doesn't worry so about the space constraints of
Speaker 1: having enormous numbers of processors to use. So in other words,
Speaker 1: it's again that movement to having your your terminal being
Speaker 1: more like a dumb terminal like it would have It
Speaker 1: would have processing capabilities of its own, but it would
Speaker 1: be leveraging the processing capabilities of a much more powerful
Speaker 1: machine with lots of processors that is run by some
Speaker 1: company like Amazon or Google or something along those lines,
Speaker 1: and that at that point you're really more focused on
Speaker 1: the bandwidth issue between your device and home base that's
Speaker 1: doing all the computing. It's like streaming based gaming versus
Speaker 1: uh yeah, which is which has not worked out great
Speaker 1: so far because of things like latency and other issues.
Speaker 1: I mean, we've seen streaming based gaming services come and go. Uh.
Speaker 1: I think it was a fine idea. I just don't
Speaker 1: think that the the the realization of that idea was
Speaker 1: powerful enough to justify moving to one of those platforms.
Speaker 1: But that might not always be the case. We may
Speaker 1: see that change, um in the long term. If you're
Speaker 1: talking about like, well, that's at some point you're gonna
Speaker 1: hit the peak of that too. Rite You're like, it's
Speaker 1: just not gonna make sense that, Hey, we need to
Speaker 1: add eighteen new machines to the farm over there, um,
Speaker 1: you know, because we've got to be able to there's
Speaker 1: a new new Halo game has come out and they
Speaker 1: had a jet pack, so it's a thing. Now, it's
Speaker 1: I think we will be looking at truly experimental work
Speaker 1: ways to do computing well beyond what classic computer science
Speaker 1: has taught us. That it'll just it'll have to it'll
Speaker 1: be out of necessity, will have to develop that, and
Speaker 1: you know, you will see other variations like we'll see
Speaker 1: quantum computing. But as we've stated in previous episodes, quantum
Speaker 1: computing is great for certain types of computer problems, but
Speaker 1: is no better than classical computers with other types of
Speaker 1: computational processing. So if you wanted to do a classic
Speaker 1: computer problem, or you wanted to play a game, you know,
Speaker 1: let's say let's stick with gaming. You want to play
Speaker 1: a high end computer game. A quantum computer is not
Speaker 1: going to run it better. In fact, it may run
Speaker 1: it much worse than a classical computer. But if you
Speaker 1: have a great problem that is specifically designed to to
Speaker 1: be solved by things like parallel processing, the quantum computer
Speaker 1: could be a great job, could do a great job
Speaker 1: of that could also end up breaking all the encryption
Speaker 1: and all over the world in record time, which is
Speaker 1: kind of terrifying, but we've talked about that previous episodes. Um, yeah,
Speaker 1: so we've still got some time with Moore's law. Maybe
Speaker 1: a little less than a decade from now, we might
Speaker 1: actually say a well, guys, uh, don't make more complicated
Speaker 1: software for a while. Okay, you can't stop them. It's
Speaker 1: gonna it's gonna continue to bloat. You can't stop them. Well, yeah,
Speaker 1: but if you no longer have an increase in processing power,
Speaker 1: that bloat eventually gets to a point where you can't
Speaker 1: run it on the machine, and then you do have
Speaker 1: to stop. That's not their problem. Yeah, it kind of is.
Speaker 1: It kind of is when that PC magazine article comes
Speaker 1: out PC World Magazine comes out, software doesn't run because
Speaker 1: it's too complicated for any existing computer. You gotta back
Speaker 1: off a little bit. But this was an interesting subject.
Speaker 1: I'm glad that we tackled it, and it was fun
Speaker 1: to revisit Moore's law. It's one of those subjects I
Speaker 1: always love to chat about because one it's I think
Speaker 1: it's widely misrepresented, uh in in casual conversations and media coverage.
Speaker 1: A lot of people don't talk about at the the
Speaker 1: actual original intent was behind it. And also just you know,
Speaker 1: every few years you see discussions of Moore's Laws coming
Speaker 1: to an end. I wonder if we could find I
Speaker 1: actually tried to look for this before the episode, but
Speaker 1: I couldn't find anything. But I'm sure somewhere out there
Speaker 1: is the great first the end of Moore's Law. Yeah,
Speaker 1: I would love to see happen in the nineteen eighties,
Speaker 1: the nineteen I'm sure it was in the eighties. I'm
Speaker 1: sure it was in the eighties. I would love to
Speaker 1: see a timeline of all the major articles that have
Speaker 1: come out that said Moore's Law is over or whatever,
Speaker 1: because I'm sure it has happened at least a dozen
Speaker 1: times um. And you know, the remarkable thing is that
Speaker 1: engineers have found new ways to defy the end of
Speaker 1: Moore's Law to keep it going. So it's entirely possible
Speaker 1: that within a decade we're talking about a totally new
Speaker 1: technology that does continue the spirit of Moore's Law, even
Speaker 1: if it has moved away from what we think of
Speaker 1: as traditional uh integrated circuit components. Who knows, it's a
Speaker 1: tall order, but you know, once upon a time, the
Speaker 1: transistor didn't exist, So it's not like we're talking about
Speaker 1: something that is beyond the entire It's not that it's
Speaker 1: completely implausible. I mean, it's might be a long shot,
Speaker 1: but it's still possible. All right. Well, that wraps up
Speaker 1: this episode, and hopefully in our next one, Lauren will
Speaker 1: be back with us and we'll be able to jump
Speaker 1: back into the future again like we love to do.
Speaker 1: If you guys have suggestions for future episodes, we've got
Speaker 1: any comments or questions, send them to us. Our email
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