If AI is a bubble, what happens when it pops?

Summary

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Front Burner — "If AI is a bubble, what happens when it pops?"

Date: November 17, 2025
Host: Jayme Poisson (CBC)
Guest: Paul Kedrosky (Partner, SK Ventures; Research Fellow, MIT Initiative for the Digital Economy)

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Overview

This episode explores mounting fears that the current artificial intelligence (AI) boom is a financial bubble, scrutinizing what’s changed in the world of AI to fuel these anxieties, the real risks within the industry, and what could happen to the global economy if the bubble bursts. Jayme Poisson interviews venture capitalist and academic Paul Kedrosky, who draws parallels between the AI craze and historic bubbles—warning that, if it pops, the fallout may be far-reaching and tangled in not just technology, but also debt, real estate, and government involvement.

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Key Discussion Points & Insights

1. Is AI a Bubble? The Two “Bubbles” Explained

[02:54]

• Kedrosky distinguishes between the “bubble” in AI’s technology and the “bubble” in investment and infrastructure.
  • The technology is real and transformative (“It's actually a really useful technology. There’s going to be lots of uses for it…”).
  • The bubble is in the mania and overspending, particularly on data centers (“…this massive buildout underway…is very expensive…”), driven by investor excitement far outpacing real demand.

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2. Shifting Funding Models: From Cashflow to Debt

[04:14]

• The AI industry's infrastructure buildout is now funded by massive, opaque debt rather than operational profits:
  • Tech giants used to pay for data centers “out of their profits.”
  • Now, with estimates of $4 trillion needed in the next five years, companies finance expansion using private credit and special purpose vehicles—off-balance-sheet structures, which "makes things much riskier."
  • Kedrosky likens the system to a “shell game,” moving liabilities out of plain sight.

Quote:

“These are done in this very opaque and sometimes mysterious way… so when it comes time to say, well, how much exposure do you have to all of this? I get to say, hey, not my table, I don't own that, it’s over there. Which is kind of a shell game obviously.”
— Paul Kedrosky [05:21]

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3. Circular Money Flows and “Vendor Financing”

[06:17]

• The episode references a Bloomberg diagram with Nvidia at the center, illustrating cash flows looping between chipmakers and AI companies.
• Kedrosky compares this vendor financing to the dot-com era, calling it "circular" and a way to exaggerate growth and risk:
  • Companies give credit to their own customers to buy more of their products, which “masks the size of things and creates this illusion of even faster growth.”

Quote:

“It becomes very circular and very hard to tell where the reality is anymore, because all the money just goes… round and round and round.”
— Paul Kedrosky [07:33]

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4. Government “Backstopping” and Fears of a Fannie/Freddie Repeat

[08:32]

• OpenAI’s CFO floated the idea of seeking government guarantees or subsidies for AI funding, sparking panic.
  • Some see parallels with government-supported housing lenders (Fannie Mae, Freddie Mac) before the 2008 crisis.
• Governments in Canada, the US, Europe, and China are all considering AI as a “sovereign” asset worth strategic intervention.

Quote:

“That’s what Fannie and Freddie were… making it easy for people to make really bad loans. Well, we don’t want to reenact that again, is the thinking.”
— Paul Kedrosky [09:42]

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5. Canada’s Push for “Sovereign AI” and the Logic of Subsidies

[11:25]

• Canadian political leaders frame data centers and AI as the new “economic payday,” arguing for major public investment.
• Kedrosky cautions this narrative is misleading—AI capacity could be supplied globally with a handful of data centers, and domestic investment mainly shifts private costs onto public books.

Quote:

“I defy you to come up with a rationale for why it’s strategically important to spend billions of dollars on data centers other than it takes the cost… off the balance sheet of the technology companies themselves.”
— Paul Kedrosky [14:18]

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6. Comparisons to Past Bubbles: Bigger, Broader, Riskier

[15:42]

• Unlike earlier bubbles, the AI infrastructure mania combines tech, credit, real estate, and government all at once. That, Kedrosky argues, compounds systemic risk.
• The bubble is not just about tech stocks but is intertwined with national policy, land values, public funds, and ever-more complicated debt structures.

Quote:

“This one has all of the pieces that made the worst bubbles in the last 150 years into one neat package.”
— Paul Kedrosky [16:51]

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7. Will Today’s Overbuild Become Tomorrow’s Backbone?

[17:04]

• Poisson notes that overbuilding in the dot-com era (e.g., fiber optics) paid off later. Kedrosky flatly disagrees.
  • Railways and canals lasted a century; GPU-based data centers become obsolete in years, often even faster if used for AI “training.”
  • Overbuilt GPU farms won’t be useful long-term (“You’re going to have to make all those capital expenditures a second time.”).

Quote:

“The notion that we can build all of this and… figure it all out four or five or six years down the road? No, it’s actually the reverse.”
— Paul Kedrosky [19:16]

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8. Potential Consequences: Recession, Wealth Loss, Debt Contagion

[19:43]

• If the bubble bursts, the pain could be deeper than 2008 or 2000 because AI is so central to current markets and growth.
  • Over half recent S&P 500 gains are AI/tech-related.
  • Unwinding would swamp markets in negative wealth effects, recession, and potentially a debt crisis as private credit is “sliced up and ends up everywhere.”

Quote:

“The idea that somehow we can remove all of this… and have that not have consequences, is naive.”
— Paul Kedrosky [21:42]

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9. Are Warning Signs Already Here?

[22:49]

• Poisson presses on timing. Kedrosky notes that credit default swaps on major tech names like Oracle have doubled in three months—historically “unthinkable.”
  • This signals the market now sees tech company debt as risky, and “it’ll get riskier before the wheels really come off.”

Quote:

“Technology companies’ risk of defaulting on debt could double in three months. There you go. There’s a sign that… something dramatic has changed.”
— Paul Kedrosky [23:32]

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Notable Quotes & Memorable Moments

• On bubble mechanics:

  “It’s sort of the bubble of bubbles… all the things that made the worst bubbles in the last 150 years into one neat package.”
  — Paul Kedrosky [16:51]

• On government’s role:

  “Now, saying you’re going to backstop loans maybe have been a one step too far. But listen, the US… is really pushing the idea this is the new global international arms race… so it really is just saying the quiet part out loud.”
  — Paul Kedrosky [10:40]

• On survivability of data centers:

  “60% of the cost of a data center is the chips and their lifespans are very short… So, most of what you spent previously will be useless…”
  — Paul Kedrosky [19:29]

• On the unwinding:

  “I think it’ll get riskier before the wheels really come off. But is it more than two years away? A year away? I doubt it.”
  — Paul Kedrosky [23:35]

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Timestamps for Key Segments

• [02:54] – Defining the two types of AI bubbles
• [04:14] – Shift from profit-funded to debt-funded expansion
• [06:17] – The “circular” vendor financing problem (Nvidia example)
• [08:32] – OpenAI CFO’s comments on government backstopping; policy alarm
• [11:25] – Canada’s sovereign AI investment pitch
• [15:42] – Distinguishing the current bubble from 2008 and others
• [17:04] – Will today’s AI infrastructure be tomorrow’s asset?
• [19:43] – Potential impacts if the bubble pops
• [22:49] – Real-time signs: tech credit spreads widening

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Tone and Style

• The discussion is analytical, occasionally urgent, with Kedrosky often punctuating tough economic analysis with wry, matter-of-fact observations.
• Jayme Poisson maintains a probing, curious tone, frequently surfacing analogies and counter-arguments, drawing out clarification for a non-specialist audience.

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Final Thoughts

The episode starkly illuminates how the AI industry’s explosive growth comes with structural risks eerily reminiscent of past crises—but now concentrated and amplified. Government subsidies, off-balance-sheet debt, and the ultra-fast obsolescence of AI infrastructure mean that, if the bubble pops, repercussions could reach further and cut deeper than most observers expect. As Kedrosky warns, “The idea that…we can remove all of this… and have that not have consequences, is naive.”

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Transcript

[A]

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[B]

This is a CBC podcast.

[C]

Hey everybody, I'm Jamie Presson. You know, how can the company with 13 billion in revenues make 1.4 trillion of spend commitments?

[B]

You know, and you've heard the criticism, Sam. First of all, we're doing well. More revenue than that. Second of all, Brad, if you want to sell your shares, I'll find you a buyer. I just. Enough.

[C]

If you are OpenAI CEO Sam Altman, you're clearly getting a bit tired of having to answer the question of whether the AI industry is a bubble. Well, simply saying enough hasn't put the question to bed. Here is Bill Gates.

[B]

There are a ton of these investments.

[C]

That will be dead ends at the end of October. Hedge fund manager Michael Burry, the guy who predicted the 2008 housing collapse, posted on X. Sometimes we see bubbles. Sometimes there is something to do about it. Sometimes the only winning move is not to play. And these are not the only signs. So why? What has changed in the world of AI that has got investors and experts feeling so spooked? If it's a bubble and it pops, what economic damage will it cause? Paul Kadroski is with me today. He's a partner at the venture capital firm SK Ventures. He's also a research fellow at MIT's Initiative for the Digital Economy. So he's been watching all of this very closely. Paul, hi. Thank you so much for making the time today.

[B]

Sure. Glad to be here.

[C]

So, as I mentioned, people have been saying that AI is a bubble for a while now. This isn't new, but why have we been seeing this renewed wave of headlines and people being concerned about the bubble popping over the last couple of weeks?

[B]

Yeah. So the way I try to frame it for People is that there's really two different ways to talk about a bubble and they get very confused. And so there's the idea that AI itself is a bubble or a mania, or it's some kind of, you know, furry animal that we're going to one day be embarrassed we all got excited about. And that's a mistake. It's not. It's actually a really useful technology. There's going to be lots of uses for it, blah, blah, blah. The way in which it's a bubble is in the sense that it's got people so excited that they're overspending in building out all of the things that go along with AI. You know, most notoriously, these things called data centers, these big hulking warehouses full of chips. But there's a whole host of things that go along with that. And so there's this massive build out underway. And that build out is very expensive. That very expensive build out is financed in a bunch of different ways, which is increasingly making people nervous. So that's the latter part of the bubble. The second bit about how all of this is going to get paid for and to my mind, and maybe more importantly, are we building an absolute boatload too much of this stuff, given what we know about what's going on. So the second part is what got people excited in the last, say, couple of weeks.

[C]

Yeah. And just could you tell me a little bit more about the concerns people have around the lending. Right. And, and how the funding model, as I understand it, has changed somewhat.

[B]

Yeah. So whenever we were only, and I say only with air quotes, spending tens of billions of dollars building data centers, these large technology companies, the Googles and Metas and others, and even the private ones like an OpenAI, were spending mostly out of cash flow, meaning out of their profits. These are hugely profitable companies on the order of hundreds of billions of dollars and they can ante up tens of billions for data centers without having to go out and ra money because they just take some of their earnings and use it exactly for this. But what's happened is this has become a much more expensive project. You've probably seen some of the numbers get bounced around. There was a report last week from JP Morgan that on current projections that might cost $4 trillion over the next, say five years to build out all of these data centers and related infrastructure for AI. Well, that's not going to come out of company's earnings, just mathematically can't happen. And so what's happened begun to happen this year and really accelerated in the second Half of the year was the technology companies increasingly went out and found partners to raise money. And those partners are usually what are called private credit firms. So these are like banks that aren't regulated. There's a host of them out there. Most of them emerged, ironically enough, after the last financial crisis. And they go into these partnerships with the large tech companies to build the data centers. And these are done in this very opaque and sometimes mysterious way using things like special purpose vehicles, these legal entities that only exist to build the data cent. And I have a stake in it as a technology company, the private credit company does. We put in debt, but I don't have to put it back on my balance sheet as a tech company. So when it comes time to say like, well, how much exposure do you have to all of this? I get to say, hey, hey, not my table, I don't own that, it's over there. Which is kind of a shell game obviously. But nevertheless, that's the change that's made things much riskier, really accelerated things in the last, I'll say six months. But even in the last two or three months it's really dramatic how this has changed and it's this change to a more debt financing away just using cash flow.

[C]

I remember back in October, I think it was, there was this diagram in Bloomberg that was making the rounds, right? And it was showing the way that a lot of money seems to be flowing in the industry. At the center of the diagram was Nvidia, which is the main provider of the chips that use that these AI models run on. And of course the single biggest company on the US Stock exchange. And then there are all these arrows, right, showing the cash flow in various directions between Nvidia and like all These other companies, OpenAI, Microsoft, Oracle, et cetera, et cetera. And you know, it just struck me as just huge amounts of money moving around between different players in the industry. And like it just seemed incredibly circular. And is that different than what you're talking about now? Is it? Because I know people were concerned about that at the time too.

[B]

Yeah, so that's another piece of the problem. So there's this other aspect which used to be called back in the days of the television telecom bubble, back in the dot com days this was called vendor financing where I extend a loan to you as Nvidia. It's not really a loan though, it's just money I'm giving you so you can buy my stuff. Now back in that period, Nortel Canadian company was at the epicenter of that. So that has gone on previously. It rarely ends well. But it's just another aspect of the current mania that we also in addition to this prodigious amount of debt that's flowing into these technology companies to allow them to go out and buy and build data centers, there's all also an aspect via which some of the money is very circular in that I extend credit to you. It could be in any different form, as an Nvidia, say to OpenAI, but in exchange for that, guess what? You have to buy all my stuff. So it becomes very circular and very hard to tell where the reality is anymore because all the money just goes as the Bloomberg diagram showed, it just goes round and round and round. So that's another aspect of the risk. It just masks the size of things and then creates this illusion of even faster growth, which in turn drives more funding, fundraising, which in turn drives more debt. And the more debt there is, the more risky things get.

[C]

I wanted to also talk to you about something that caused a stir earlier this week, and it was a comment from OpenAI's chief financial officer, Sarah Fryer. She was talking at this Wall Street Journal event and she essentially said that.

[D]

This is where we're looking for an ecosystem of banks, private equity, maybe even governmental the ways governments can come to.

[C]

Bear meaning like a federal subsidy or something.

[D]

Meaning like just first of all, the backstop, the guarantee that allows the financing to happen. That can really.

[C]

She then walked that back. Did she ever in a LinkedIn post saying OpenAI is not seeking government backstopping? And Sam Altman chimed in as well.

[E]

Saying that they are on track for a $20 billion run rate this year and will hit hundreds of billions by 2030, money that will fund its $1.4 trillion in compute contracts.

[C]

The Trump administration AIs are. David Sachs came in and was like, we're not going to bail out anybody if you kind of implode. Right. And why did this talk of government backstopping seem to freak out so many people?

[B]

Because it's got gives you acid flashbacks to the financial crisis, I would think, because in a sense, that's what Fannie and Freddie were, the big federal, if you will, mortgage banks in the United States. They were backstopping very risky mortgages, which in turn all were packaged out and syndicated and kind of blew up the global economy. But at the back of it all were these two federal entities that were in a sense making it easy for people to make really bad loans. Well, we don't want to reenact that again. Is the Thinking, not surprisingly, and so extent that government would be in any way backstopping loans to data centers, which are inherently a very risky proposition given how rapidly these things become obsolete. Our inability to know what future demand looks like, all those kinds of pieces makes these loans very, very risky because again, they have to pay off the loan using the proceeds from the center. And if the centers are turning out, we're building 2x too much, well, that's not going to work. The math doesn't work on that. And so here's the thing, though, she only really said the quiet part out loud. There is this idea, and it's not just in the us, it's in Canada and it's worldwide, that there's a. We have a national interest is the claim in AI. There's this idea of sovereign AI and so on. So the idea that OpenAI would kind of take it to the next step and say, well, if this is so strategically important, you should be willing to stand behind what we're doing in some form or another. Now, saying you're going to backstop loans maybe have been a one step too far. But listen, the US in the form of executive orders and in the form of explicit policy, is really pushing the idea that this is the new global international arms race. And you know, I see the same thing happening in Canada, I see it in Western Europe, I see it in China. So it really is just saying the quiet part out loud.

[C]

So our AI Minister, Evan Solomon, has talked about sovereign AI quite a bit. The federal budget that was being voted on this week promises 1 billion in funding to the AI industry over the next five years. And then also recently, our Prime Minister, Mark Carney even called pipelines so boring.

[E]

It's not.

[B]

It is. It is. No, but it is.

[F]

It is because it's. Look, it's. Don't worry, we're on it. We're on it. Like, we're on it. But there is this, you know, big.

[C]

Topic of conversation here. He suggested that not boring pipelines are not boring to a lot of people in this country. But yeah, he suggested that the real economic payday for Canada right now is in data centers and AI.

[F]

What, what some in the room will unlock on the data center side, the intelligence infrastructure side, will have a much bigger impact on productivity in this country, will have a much bigger impact on our standard of living. It's an easy conversation to have about a pipeline because it's one thing we can see, but the reality is that there's much, much more of the Canadian economy and there's much, much more to the future Canadian economy. And so we're attacking.

[C]

What do you make of him framing it like that? That Canada desperately needs to build up its domestic AI industry or get, or risk getting left behind or, or not as profitable as other. As other countries.

[B]

I think that's a dangerous line of patter that's aided and abetted by the technology industry, which likes the tax subsidies that come from getting relief on capital expenditure to build data centers, from getting relief with respect to real taxes on the real estate they're building getting with respect to long term power agreements all over the US this is happening constantly. And the claim is that these are the factories of the new industrial revolution. And so we have to have these things, which is of course nonsensical because all say inference supply. Whenever you ask a question to a chat model like OpenAI, all of the Canadian inference supply could be done with, I don't know, two data centers in the corner of Abilene, Texas. So the notion that I have to do this domestically or it won't happen is demonstrably wrong. There's infinite capacity to supply all of that. So then you say, well, we have a uniquely Canadian way of doing this. It's possible, I suppose, but it seems unlikely because training these models is a prodigious exercise requiring exabytes of data and years and years of effort. It's not something you're going to do just because you have a couple of extra data centers in northern Alberta. So it's not about training and it's not about inference and it's demonstrably not about the job creation because these things basically run lights out after you finish the construction phase. So I defy you to come up with a rationale for why it's strategically important to spend billions of dollars on data centers other than it takes the cost, again, off the balance sheet of the technology companies themselves. Knock knock.

[G]

Ooh, who's there?

[E]

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[G]

You called that a knock knock joke.

[E]

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[B]

Okay.

[G]

It's just that when people say knock knock, there's usually a joke to go with it.

[E]

Like I said, this isn't a joke.

[G]

So the knock knock was just you knocking?

[E]

Yeah, that's how doors work.

[G]

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[H]

The Supreme Court opened its new term with a pro racial profiling wrecking ball, and they're just getting started on the docket. The justices weigh whether cops can storm your house without a warrant, states can do racial gerrymandering, and if bans on conversion therapy for LGBTQ minors violate free speech. Oh, boy.

[C]

But don't worry.

[H]

Our podcast Strict Scrutiny is here to break it all down with sharp analysis and just the right amount of shade. New episodes drop every Monday. Listen wherever you get your podcasts or watch on YouTube.

[C]

You mentioned the 2008 crisis earlier, and I just wonder if you could talk a bit more about it, because I think it's probably on a lot of people's minds listening to you. You mentioned some similarities, but, like, how. How might what's happening right now be different from 2008?

[B]

So it's different only in the sense that it's all of that and more. And this is the thing that I think people miss about the current episode. They say, oh, you know, these crazy technologists, it's another goofy bubble. They're doing crazy stuff again. Those, those. It's not dot com, it's something else. It's data centers. No, that's not really the case. So if you go back through the history of modern financial euphorias, bubbles, if you will, they tend to localize around a couple of things. So there's a great technology story. Well, this one has that. There's loose credit, the easy availability of financing. Well, this one had that, and so did the global financial crisis. And it was mostly used for real estate. It tends to have an element of government involvement in many cases. Well, this one has that, and so did the financial crisis. There's all these pieces that come together, but usually each of those are discrete bubbles. This one has all of the pieces that make a single bubble combined in one Real estate technology, loose credit, government. And this is what makes this bubble different. What makes it different is sort of the bubble of bubbles, which is why it's so much larger than people and growing so much more quickly than people realize and why people all seem to see it from different perspectives. Real estate people see it as a real estate thing. Tech people see it as a tech thing. Government sees it as the new industrial revolution. There it is right there. This. This bubble combines all the things that may have made the worst bubbles in the last 150 years into one neat package.

[C]

I. I suppose one kind of counter bubble argument that I've heard is that we could get this build out of infrastructure in these data centers. That seems a tremendous overkill at the moment, but that it might be useful later. Right. So for example, in the dot com era, you saw the massive expansion of fiber optic networks which are now the backbone of the modern Internet. Or even further back, you've got the railway mania.

[B]

Yep. Or canals. Or take your pick.

[C]

Yeah. And it did actually lead to something useful later on. And what do you make of that argument? That we might be overbuilding these data centers now, but that they will be useful someday?

[B]

It actually mostly shows why this bubble's different. Because there's nothing, there's no commonality between building out a fiber optic network or laying railroad tracks or building canals and building data centers full of GPUs. Because GPUs have very short lifespans. Not just because technology changes really quickly. And that's true. You're probably turning over the chips on a three to five year basis, generally speaking, inside of data centers. Railroads are not like that. I don't have to turn over the rails every three to five years. I may have to weed them now and then, but that's about it. This has nothing to do with turning over the technology changing. So even if I don't use a railroad line for five years and I come back and I say, you know what, I want to run some trains down that track, you can do that with impunity. That is not the case with the GPU driven data center. They can't just sit there and then be turned on and suddenly come back to life and everything will be fine. The technology has changed and there's an even more insidious component which is that if the data center is used for training. So let's say I was using it to train some of these large language models. That's kind of like using your car for like a 24 hour race at Le Mans. It's running that thing so hot that it breaks. It's just like a car breaks down quickly from being used for endurance racing. Chips degrade quickly if they're used for training. So it's not three to five years anymore. If I've used my data center for training, it's often 18 months to 24 months. So now again we have the problem that given that 60% of the cost of a data center is the chips and their lifespans are very short, the notion that we can build all of this and it'll all be, we'll figure it all out four or five or six years down the road. No, it's actually the reverse. You're going to have to make all those capital expenditures a second time or most of what you spent previously will be useless for this reason of the really short, relatively short lifespan of the technology underneath it, as opposed to canals, railroads, fiber optic cables.

[C]

You know, let's say this bubble pops. Like I just. How could this play out? And you know, in your estimation, how bad could it get? Because I'm just thinking too that at the center of this are companies that also are quite diversified, right. And do make money doing a lot of other things. So could it be as catastrophic as 2008 or the dot com bubble? Or do you think? Or are you saying it could be worse?

[B]

Oh, it could be way much worse. For me, it's more like the late 1920s where you have the combination of this. So at the time there were two big technologies that were making people incredibly optimistic. And that led in part to the crash of 29, the subsequent economic decline. And one of them was electrification. This idea that we have this ubiquitous technology, rural electrification that everyone's going to connect to and it's going to change everything. And it did. But we vastly overspent, built too many speculative companies, many of which were packaged into forms that all failed after the crash of 29. And the car was another technology that came along at the same time that helped the two together conspired to make people incredibly optimistic, drive this incredible stock market gains. Well, we've seen something similar in the lead up to this current episode. Something like 35% of the S&P 500 is AI related companies. Now from a market cap standpoint, something like pushing 50% of the gains of the S&P 500 over the last four years are all tech related. I mean, this is insane. We didn't even see this at the peak of the dot com period. This concentration in such a small number of companies. So that unwinding has a negative wealth effect, makes people feel poorer and they spend less. But at the same time, something like 50% of recent GDP growth in the US has been data centers. So it's been like a private sector stimulus program that nobody talked about. The unwinding of that is the exact same. It's a contraction in the economy and takes you into recessionary, into a recessionary period. So the idea that somehow we can remove all of this, the thing that was more than half of GDP growth, the thing that's driven all the stock market gains and have that not have consequences, is naive. And then the thing that makes it even worse of course, is that now that it's been significant debt component attached to it, well, the debt then becomes collateral for other things. And that is increasingly metastasizing through the economy because the financial providers who are providing the debt, they turn around and securitize it. They cut it up into pieces and sell it to other people. So the debt finds its way. It's not just sitting with a bunch of billionaires at private equity firms. That debt gets sliced up and ends up everywhere. So you've got pieces of this not just in equity markets, but debt markets and everywhere else.

[C]

Wow, that makes me feel quite nervous what you just said. Really? The question I want to know is when do you think we're going to start to see it unwind? But I suppose that's not a fair question to ask.

[B]

No, I mean, that's the problem. And I hear a lot from hedge fund managers and others who have the same question. And of course the answer is a. If I knew, I wouldn't tell you because I'd probably just. I'd go out and play Michael Burry or something. But even Michael Burry apparently doesn't know and he's decided that it's too complicated for him.

[C]

He's just quitting. Yeah, he's just like, I don't understand this.

[B]

Right. I'm taking all my toys and I'm going home.

[C]

Home.

[B]

So I think what we saw in the last two months was a really nice sort of an opening realization of people saying, you know what? These technology companies are more at risk than we thought. There are these esoteric instruments called credit default swaps that are used to measure how risky your debt is. And historically, technology companies, people didn't even realize that their debt could be risky. And Oracle, one of the larger technology companies, their credit default swaps have doubled in the last three months. That's a sign that it costs roughly twice as much to insure their debt. Again, this all sounds very wonky, but the point being the idea that technology companies risk of defaulting on debt could double in three months. There you go. There's a sign that finally there's a growing realization that something dramatic has changed. And it's that an entire sector of the economy is much riskier than it once was. And I think it'll get riskier before the wheels really come off. But is it more than two years away? A year away? I doubt it.

[C]

Okay, Paul, thank you so much for this. I really appreciate you coming by. I hope you'll come back soon.

[B]

Sure. Thanks.

[C]

All right. That is all for today. I'm Jamie Poisson. Talk to you tomorrow.

[B]

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