The Rise and Fall of the Cloud – Again with Tom Lyon

Yeah, so what if I told you cloud computing was almost a hundred years old? If you define it as outsourcing your data processing to some company that owns the equipment that does the work, well, indeed, we're, we're nearly a hundred years old. Um, really in the '20s, but much more visibly in the 1930s, IBM started these service bureaus where you could bring your, your data, and they, they had key punch girls to punch it on cards and then do tabulating, et cetera, et cetera, all for people who couldn't afford the outrageous lease prices of the actual machi-machinery.

Well, it's similar today. You don't wa- you don't wanna build your own data center, you go to the cloud. Um, surprisingly, they started things like the Statistical Bureau at Columbia, which wa-- immediately was overrun w-with requests from astronomers to do basic arithmetic because it had some machines that could do things like multiply, starting in 1931.

You could multiply two numbers on a card and get ano-another card with it, so that was amazing. Um, in the late 1930s, this book over here, When Computers Were Human, talks a lot about this. They were lite-literally hi-hiring people off the streets to be part of a massive computing mechanism where you worked your calculator and somebody else had this parallel program about how you pass the numbers around to, to the other people.

Um, punch cards made all this possible. That POGS there is my nickname, and that's a relic from when I was heavily involved with punch cards in the '70s. So the '30s, of course, was all about the Great Depression, and so there was no boom, particularly until the war hit, and not really a cloud, there were just skyscrapers 'cause IBM was headquartered in New York City at that time.

So World War II, nothing interesting happened in terms of service bureaus, but of course, the-- a lot of other stuff. These are my favorite books about the World War II era. And the, the middle one is about radar.

It's really fascinating 'cause that's where all the high frequency electronics was developed. Post-war, it was still all punched cards, but getting really fancy now. I have these two books, The Proceedings, which are thick, thick, documents with all these papers about how to do fancy computation on punch cards with the then available equipment.

Still no computers. And this marvelous industry survey from 1952, the punch card is like the peak of the punch card and hundreds of different uses and applications, et cetera. It's mar-marvelous stuff.

1950s, of course, when, when computers really became part of the popular consciousness with UNIVAC, and starting around 1954, IBM actually made computers that would stay up for more than an hour or so. And so they, they started to, to percolate. But, in 1956, there was this IBM consent decree with the government, and it wasn't about computers, it was about service bureaus and data processing.

So still, we're very much in the punch card industry and, and s-outsourced data processing was a very big deal that the government had to get involved to, to make things more competitive. And so IBM had to start this, subsidiary called the Service Bureau Corporation and have hands-off operations with it. 1956, the Dartmouth AI Workshop, so that's really the birthplace of a lot of AI stuff, along with the first neural net in 1957.

1959, the first SAGE site, SAGE, huge defense installation stuff, the birthplace of a huge amount of technology and, and software. And really the, the enabling standards in this timeframe were the transistor for reliability, core memory for reliability, and magnetic tape, so you don't have to carry boxes of cards around anymore. You get a lot more dense information.

And of course, everything was government funded due to the, the wars and the Cold, the Cold War particularly. So in the early '60s, the whole service bureau thing started really spreading out. Every hardware company now had service bureaus for people who couldn't afford to buy, buy the full thing.

Um, independent companies started arising to do data processing. In particular, ADB w-was a big one, and they're still the guys who provide all your paychecks, probably. EDS was born in 1962.

That's H. Ross Perot and, started really coming into companies and taking over all their data processing needs. So it's kinda reverse outsourcing, but, very interesting company.

Timesharing was born and software becomes a business. Um, it was very unclear to the world what software was worth and who pays for what until the, the mid '60s. Um, Applied Data Research had one of the first products, Autoflow, which would create flowcharts out of your code, so you could try to understand it.

Mm. That, that could be, could very well be the first software product. And a little trivia fact, ten years later in 1977, I had a part-time job with ADR.

Um, SDC, the system builders, they're-- they did all the software for that SAGE systemAnd that's really-- they have a good claim to inventing system programming as opposed to computational programming or data processing. You know, they're tying together big, big complicated systems. And this last book by Campbell-Kelly is a really marvelous history of, of the software industry.

Late sixties were the go-go years. I'm sure we all re-all remember, Rowan and Martin's Laugh-In. Um, the whole notion of a computer utility came around, so this is very much a cloud concept based on time sharing and remote access.

Um, and hundreds of companies got on this bandwagon and started building out crazy infrastructure. Um, in addition, the overall market was very favorable to software companies. You know, the first software IPO in nineteen sixty-eight.

The first soft-- first computing billionaire, H. Ross Perot, in nineteen seventy. And so we had a, the classic irrational exuberance going on in the market.

Um, the enabling standards were things like teletypes and modems and the fact that you could move Fortran and Basic programs around and hard drives let your data actually be online when you needed it. And of course, a huge amount of war spending due to V-Vietnam. I have these books in my collection that all talk about how wonderful the, the u-utility computing world is going to be.

Early seventies, major depression, so all that stuff was gone. Um, RCA and GE left the computer business entirely. Um, because money dried up, all of a sudden the capital and the people required to provide services didn't work out anymore, so there was a big shift to, to from services to software companies.

The rise of the mini computer didn't help any of the outsourcing stuff because now more and more people could afford a computer. Mm. Uh, things like word processors and calculators, same thing.

And really, semiconductor memory is what had the biggest effect in terms of new things entering in the market. Late seventies, this is where all the gloriful-- glory stuff happens. I'm sure we've all heard about all these different things happening, except maybe the last.

Um, the-- in the late seventies, people started having statistical multiplexing-based networks where the networks got a whole lot cheaper and a lot more reliable. And things like Tymnet and Telenet, CompuServe all grew up as new, new types of not just time sharing, but information sharing systems, all well before the internet. And this was...

This is where I really cut my teeth. So I, I graduated from Princeton in seventy-eight, already a Unix expert and went on from there. Early eighties, the whole desktop era.

I'm sure we're all pretty familiar with this, but, some of you may not know this TV show, Halt and Catch Fire. Mm-hmm. Really good.

Really captures, captures the essence of the, the time period. One of the, one of the guys who was on my board of directors at DriveScale, Carl Ledbetter, was a, the technical advisor for this. In addition to being a wizard mathematician and a venture capitalist.

" Clearly all about steps towards the cloud. It wasn't-- Didn't quite have the computing in the cloud at that point. Um, this diagram is, something I, I drew at the NFS architecture offsite when we were sorting out how to do NFS.

And if you look very closely, there's a cloud over there. The cloud was, was already commonly used to represent a network of stuff. Mm.

Right? But, but really computing wasn't in the network yet. Mm.

Late eighties and early nineties, that's when networking really took off. We had the, the Wild West of protocols. We had Cisco, 3Com, Novell, all this stuff happening.

And then of course, the real emergence of, of the internet. And this book, by Pelkey, a huge amount of detail, personal interviews of all the key players and stuff. Excellent book.

Late nineties. All right, here we go. The dot-com boom.

The web boom. I'm sure we all know a lot about this, but, I, I peg nineteen ninety-five as a key point because Windows ninety-five finally included TCP/IP. Mm-hmm.

So I was like, "Okay, we're done. " Mm-hmm. Um, ISPs took off.

ASPs, application service providers, if you remember those guys. I guess they'd be more like software as a service these days or something like that. And we, we had, the beginnings of actual infrastructure as a service companies.

LoudCloud and TerraSpring are the two examples I know. Um, and you could truly ask them to build your data center in the sky for you, and you'd never, never actually see it. Now, this was before X eighty-six virtual machines came around, so it was a, a little bit harder to do.

But this was a period of irrational exuberance. I remember discussions in, in the board meetings about how we were going to pave Milpitas with data centers. You know, kind of like the same discussions going on now with AI.

Only it's not Milpitas, it's Texas. Um, these books are marvelous 'cause it talks about the boom, but they were written before the bust. So it's like it captures the exuberance.

Two thousands, the dot- the dot-com bust. So again, the shift from services to software. Both LoudCloud and TerraSpring shifted to softer- softer product.

LoudCloud ended up at HP, TerraSpring at, at Sun. But the thing that was going on behind the scenes was creative accounting and outright fraud by the telcos. So peop- people always call it the dot-com boom, but I, I think of it as the telecom boom because it was the telecom fraud that just removed a huge amount of money from the market and affected all the investors, the, the, the ones who thought they were buying safe stuff like telecom stocks.

The, the irrationally e- ex- exuberant people got what they deserved. So you're saying that it was really a telecom bust, but it got renamed or is named the dot-com bust? Yeah.

I... Clearly there was too much exuberance about the dot-com stuff. Yeah.

But the real pain, I think, and the long recovery came from the telecom part. Mm. But ironically, at least that's what left the assets in the ground- Right ...

overbuilding on the telecom front- Right ... right, to pave the way for the- Right ... we came on the other side.

Yeah. So we have hope that someday all these brand-new data centers that are being built might actually be used. Yeah.

Yeah, so outright fraud, caused a lot of the pain. Two thousands, it took till two thousand and two for, to get to the low point of the S&P, so it was a long, painful decline. Um, I like the fact that in the two thousands, AI stood for American Idol, not for artificial.

AWS got launched in two thousand and six with pretty basic services, but very popular even then. Um, this ImageNet database is the first very large image database which enabled a lot of AI processing. Mm.

But again, there was another recession, but it was all real estate fraud this time. So that slowed things down a bit. I'm gonna be way ahead of time as well.

Twenty tens, AI got, gets real. So Watson wins on Jeopardy. That was a key, key thing in the popular space.

AlexNet proves the unreasonable effectiveness of neural networks. I saw that, that phrase comes from, what's his name, Dean at Google. He gave a talk about how they were applying AI and how just these neural networks were just unreasonably effective at doing things.

Mm. And that, I saw that talk fifteen years ago, so it's all, all gotten better. Nvidia up six hundred times since then.

Google acquired DeepMind, OpenI- OpenAI got founded as a not-for-profit. I think most people forget that now. And of course, the enabling technology was all this GPU processing and big data.

Too recent for me to know of any good books about this area. And here we are in the twenty twenties. AI is totally unreal.

Uh, insane data center build-out. It, you know, even, even for the level of the data centers and power and water, I don't think there's enough money in the world to cover what people are saying they're gonna do, let alone fill the data centers with electronics. Nvidia, of course, rules the roost.

It's... They, they kind of deserve it. I have nothing bad to say about them.

But here we are again with total irrational exuberance, lots of creative accounting, all kinds of circular investments and revenue going on. Um, and, you know, it can't last. And of course, the government is not exactly enforcing anything related to business or whatever.

So they're leading by example, and there's got, there's got to be massive fraud brewing in this kind of environment. And then there's the huge data sovereignty and copyright issues. You know, how can you trust an LLM if it's gonna share all your data with somebody else?

And they've al- they're already being sued by all the authors for snarfing all the, the books. So I think it's... I think we're due for a- another crash.

Mm. So things are looking ugly. There has to be an AI recession, right?

But when? How soon? The sooner, the better.

But I'm no good at predicting. It's gonna be ma- made much worse by the fraud and the wars and the tariffs and all that crazy stuff. But there is real stuff behind AI, and we'll get back to normalcy someday, but it's probably in the five to ten year timeframe.

The end is near, so that's that's all I got. Can, can you go back to the previous slide? So if you were to take ju- just, uh-To me, we had this discussion earlier.

I, I think a lot of the... It, it becomes this, you know, kind of like the military industrial complex, right? Um, it...

and it's almost like there, there's so many people that stand to benefit from an AI build-out as opposed to the internet. The internet build-out was kind of contained to the telecom world and the, you know, the, the internet world. Uh, but this, so many people stand to benefit from building data centers, building physical, concrete things, right?

That really wasn't the case back then. So if you were to- Oh. Y- you know what I mean?

Like, um- I mean, there, there's only a handful of companies capable of building the really big ones, right? Yeah. But I mean, but- But, but, but there, there are lots of users who want the- There are lots of users.

And if you think about all the periphery around it, right? I mean, if you're making generators, if you're, you know, concrete, what- whatever, right, fire- Yeah, yeah ... suppression systems, right?

Anything that... You know, physical infrastructure that, that's on a massive scale exponentially larger than what we saw with the internet. So there's, there are just so many more people that have a vested interest to make this more than what it is.

So say if, if you could ma- w- wave a magic wand and the infrastructure piece was solved. Say, you know, somebody figured out a way to, you know, cut, cut the infrastructure build by 10 times. So take that off the list.

Take that as, as, as a given. The insane data center build-out, take that one off the list. Would those other bullet points be enough to derail everything, right?

The, you know... I- if, if you just had to deal with data sovereignty and copyright issues, would that be enough to derail it? Um, would, you know, a massive fraud, would that be enough to de- Yeah ...

to derail it? Or does it take all those things in combination to do it? Well, I think it's the, the exuberance plus the fraud are the, the big ones.

Okay. Um, and people, people are, like, you know, people are expecting way too much out of AI too soon, right? So let's go lay off half the people in the company- Yeah ...

'cause we're gonna do AI, right? Well, you know- Seems like it's a- You need AI ... question of, like, is there a there there?

Yeah. 'Cause the, the dotcom bust was really that there were all these companies that had not a bad idea, but they- But they were way too soon ... they were too early.

Right. The consumer wasn't ready 'cause they didn't- Yeah ... have the web yet.

Yep. Yeah. So all this stuff had to get built out, and people had to adopt the web before they could actually sign on and start using these- Yeah ...

websites. I'm wondering if AI is gonna follow a similar trajectory where we're overbuilding- Mm ... there'll be a bust- Right ...

but then there'll be another, like, sort of delayed boom. Yeah. That, that, that's what I think.

And I, I think right now people are expecting it, you know, these, these 1,000- Magic unicorns ... 1000% per year growth- ... or whatever.

Yeah. Yeah. When nothing in history has ever grown that fast.

Yeah. Right. Right?

And you need to dial it down to maybe 100% per year, right? Mm. But...

What do you think? Do you trace any of this back to when, when money got cheap? You know, I remember after the, you know, after 9/11, interest rates pretty much went to one, 2% 'cause they were trying to boost the economy, and we never really came out of that it seems like.

You know, I can remember before then, you know, interest rates typically were four, six, 7%, and then it was this normal cycle of the economy slowing down, all the interest rates to, you know, juice it back. So y- y- you know, when interest rates were high, you didn't have to chase these dangerous returns, right, to get, to get a return on your investment. It just seems like we've lived in s- such a long cycle of, of cheap money.

Yeah. I mean, I mean, even, even before AI and all this stuff, we're overdue for some kind of recession. Yeah.

The, the basic S&P has been growing too far too fast. Um, but, we... You know, the interest rates did, did...

We did get a good chunk of inflation with COVID- Yeah, yeah ... 'cause the go- government started giving money away for free. Mm.

We benefited at DriveScale from some of that. Yeah. It let, it let our painful life last another six months.

But, uh... Go ahead. Go ahead, Jeff.

So one thing interesting when we did our podcast, Right Then- Mm-hmm ... we talked about last week, was the idea of the energy build-out that's necessary for the data centers, right? Right.

Because estimates are somewhere between half to two-thirds of the data center proposals that are coming out are enormous over capacity. But the reason they're doing it is to guarantee that, you know, maybe one out of two or one out of three of the data centers might be able to get built, right? So you're seeing that trend.

Right. But also acknowledging that, especially in the US, our energy infrastructure is in disastrous shape. We haven't done anything dramatic- Mm-hmm ...

since the 1930s with the Tennessee Valley Authority. I mean, you've got stuff that's literally 80 to 100 years old that needs to be, you know, rebuilt. And maybe the idea of not necessarily the huge nuclear plants, but things like small modular reactors and things along those lines, maybe that would be the overcapacity that, well, now electricity is basically free, and once we get past the current time, it'll be easier to feed renewable energy into that network, because we were headed in that direction in our discussion about EVs and things like that offline.

Yeah. You'll have a grid. You know, you'll have to build out a grid.

You'll have that grid. Yeah. Which we don't have today.

Yep. Exactly. The ability to exchange things actually between Texas and Illinois, you know?

Right. Um, so- Yeah ... maybe is that what's gonna be the fallout or the positive fallout of this?

I like that analogy to, like, what happened with the fiber build-out. Exactly. Yeah.

We overbuilt capacity- Yeah ... and then-We found a use for that capacity. Yes.

You mentioned. So if we overbuild our power capacity- Yeah ... in, in the short term, we'll find a use for it, and maybe that could be fixing our ailing infrastructure.

Yeah. That might be- Yeah, but I, I, I've seen a lot less hype about fixing the transmission than I have about- Mm-hmm ... spinning up new- Mm-hmm ...

new power sources. Right. And it's, it's a lot more political.

I, I would argue the one thing, that political, I, I think that hits the nail on the head, 'cause one thing, the, the stuff we did before was under the ground. You didn't see it, right? Mm-hmm.

You burned fiber into the ground. It, it wasn't a data center humming in my backyard, right? So, data centers that used to be a, a pro forma sign-off, we'd love to have you come to town, they're like the prisons of this decade.

Nobody wants them in their backyard, right? Mm-hmm. Right.

So I, I think that, I think the political dynamic is really gonna play out big in the next two or three years. Well, it's playing out big now, you know? Yep.

Things, things that were signed off on before- Mm-hmm ... you know, y- you get a new council in, in place and, and it's not the rubber stamp it used to be, right? Exactly.

And if, if, if, if people had a more realistic growth rate expectation- Yeah ... I think solar could cover a lot of the, the stuff. " But, but that makes no sense 'cause it's gonna take 10 years for any new nuke- Yeah ...

to happen. Yeah. Well, and even, even once we build the nuke plant, we still have no way to get the power where it needs to go.

The grid, yep. 'Cause the grid is, what? 60 years old, most of it.

Yeah. Yeah. And, and never been changed.

Right. Well, but the, you know, the... I'm all in favor of new nuclear plants.

Oh, yeah. No ... I'm not so keen about the tech bros being in charge and- ...

you know, moving fast and break things and, but yeah, they, they probably- Hail, but it does not work with nukes. If you had the SMRs, you could just put them next to the data center. Yeah, that's true.

But, you know, there's... Like I- I'm from El Paso, Texas originally. True.

Facebook is currently building a giant data center which was planned quite a while ago. But across the board in New Mexico, they're buying up I don't know how, how many acres and planning data centers larger than anything that's ever been planned be- Mm-hmm ... p- planned before, and there's basically no water there any- Yeah.

Right ... so what are they doing for cooling? And then Eric Schmidt's got some new deal with one of the largest landowners in West Texas- Hmm ...

where they're gonna pave it with data centers, so... And again, it's like, how does cooling work? I don't know.

Yeah. Yeah. Em- empty data centers that get turned into, living space with- Yeah ...

power right on hand. Like malls. Yeah, I was just gonna say, I think that a lot of times after crashes happen, we get left with something and then we benefit from it, so yeah, it might be better electricity.

Yeah. That would be kind of cool. Yeah.

Mm-hmm. Yeah. Especially Texas, 'cause it's- It's on your own grid down there, right?

It's, it's duct taped together, but... Duct tape trailing wires which don't go together. That's how the, the railroad- Not connected to everything else ...

the railroad system was built that way, too. Yeah. Right.

Build a railroad. Oops, we're out of business. Do something else.