We Asked T. Rowe's $8 Billion Tech Manager Why We Are in 1998 — And Why Software Is in Trouble

Summary

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Excess Returns Podcast: "We Asked T. Rowe's $8 Billion Tech Manager Why We Are in 1998 — And Why Software Is in Trouble"

Released: August 11, 2026
Host: Jack Forehand, Kai Wu
Guest: Dom Rizzo, Portfolio Manager, T. Rowe Price Global Technology Equity Strategy & Technology ETF

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Overview

This episode features Dom Rizzo, who oversees T. Rowe Price’s $8 billion global technology strategy, for a deep dive on the parallels between today’s AI-driven tech markets and the late 1990s, the dynamics of the current capital cycle, and why he believes traditional application software is in trouble. Rizzo shares his “1998 thesis,” discusses the CapEx supercycle among hyperscalers, analyzes profit distribution along the AI value chain, and reflects on the future of work and portfolio construction in this new era. The conversation is candid, granular, and unafraid to challenge market orthodoxy.

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Key Discussion Points & Insights

1. Are We in 1998 Again? Drawing Parallels and Differences

[00:58-05:03]

• Dom’s “1998 Thesis”:

  • Rizzo points out similarities between today’s tech market and the late-1990s — particularly high momentum, volatility, and large drawdowns (30%+), driven by both macro/geopolitical events and technical trading.
  • “It rhymes, it doesn’t repeat”: While 1998 saw an 8% decline in semis revenue, today there’s a 64% increase, so the fundamentals differ even if price action looks familiar.
  • "Thinking about this as a 1998-style correction that results in an even stronger follow-on is...a good mental framing." (Dom Rizzo, [04:51])

• We’re Only Halfway:

  • Rizzo uses the performance of Nasdaq post-Netscape and the run from ChatGPT’s release as a comparative “halfway marker” for this current capital cycle.
  • “If you look at that, and then you put the launch of Nasdaq from the launch of ChatGPT through today, it's literally the halfway point, right?” (Dom Rizzo, [05:22])

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2. The Hyperscaler CapEx Supercycle

[05:14-08:05, 14:34-18:29]

• AI CapEx Is About to Accelerate

  • Hyperscalers (Amazon, Microsoft, Google) are expected to increase CapEx from $800B in 2026 toward $1.5–$1.6T next year—potentially outpacing even this year’s record 75% growth.
  • “I actually think we’re going to see an acceleration in CapEx next year...because the ROIC is just so attractive for the hyperscalers.” (Dom Rizzo, [07:19])

• Earnings Season: Signs of an Inflection

  • Amazon’s clear articulation of CapEx payback periods (2–3 years to break even, 5–6 years useful life), plus AWS’s accelerating growth at 50% margin, suggest strong ROIC is already arriving.
  • Microsoft’s Azure (guiding for 43–45% growth) and Google Cloud (+82%) have also hit inflection, with Nadella promoting the Morgan Stanley “30% ROIC” analysis.
  • Rizzo cautions, however, that a major chunk of this is driven by just two labs: OpenAI and Anthropic, which could threaten hyperscaler dominance over time.

• Quote:

  • “Even on the shortest lifecycle piece of this, the company should have very, very strong ROICs.” (Dom Rizzo, [08:55])
  • “OpenAI is already something like 25% of Azure revenue. It’s probably, you know, mid-30s or higher percentage of the backlog.” (Dom Rizzo, [10:41])

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3. The AI Value Chain & “Token Manufacturing”: Who Gets the Profits?

[14:34-24:52]

• ROI and Downstream Demand:

  • The end-user’s productivity gains from AI (especially in code generation) are massive:
    • 30 million coders × $100K salary = $3T spent on coding; even a 20% productivity lift is $600B of value, half of which could accrue to the labs.
    • “Coding in and of itself is as big as all of application software.” (Dom Rizzo, [16:53])

• Open vs Closed Weights Debate:

  • Open-weight models (open source) are not “free” since they require huge compute, limiting who truly benefits.
  • In a vertical integration scenario (“token manufacturing” akin to semiconductors), profit will accrue mostly to the labs (OpenAI, Anthropic) and chipmakers (TSMC, ASML, Nvidia).

• Role of Tech Giants vs Upstarts:

  • Tech-native companies like Uber/Coinbase may run their own models, but Fortune 500s likely stick with vendors; the field is polarizing into “lab-based” vs “model-agnostic” camps.

• Quote:

  • “My gut is just being at the frontier with the Frontier Labs is the best way to do it. But, but, but let’s see. I think open question.” (Dom Rizzo, [20:44])
  • “All roads lead to TSMC and ASML and maybe Intel too.” (Dom Rizzo, [26:18])

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4. Financing the Build-out: Equity, Debt & the Next Phase

[30:05-37:55]

• We’re in the Capital Cycle:

  • Until recently, the supercycle was “funded by the free cash flow of the most successful organizations of all time.” Now, even Google is raising equity for AI expansion — a clear sign of entering a new cycle.
  • Funding gaps are “not that big in context of [companies’] 3, 4, 5 trillion dollar market cap,” and higher yields are just a “crowding out effect.”

• Risks:

  • The main short-term risk is a market downturn so severe that it would “freeze the equity or the debt markets in terms of funding the build out.”
  • “My job is not to miss bubbles, but to navigate them responsibly for our clients by trying to capture upside and blunt downside.” (Dom Rizzo, [32:09])

• Self-funding Dynamics:

  • As returns (ROIC) materialize, there’s a growing capacity for self-funding further buildout via operating cash flow, though external funding will remain key.

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5. Semiconductors: Supercycle or Just Another Cycle?

[37:55-44:32]

• Semis Are Still Cyclical:
  • Rizzo cautions: “Semiconductors are always cyclical and will always be cyclical. Let’s be very clear. We just happen to be in a great upcycle right now.” ([38:44])
  • While corrections are inevitable, the shift from software- or internet-driven demand to “frontier intelligence” (AI) makes this cycle unusually capital intensive and enduring.
  • “AI is token manufacturing and token manufacturing is capital intensive, right? That’s the big difference between software or Internet.” ([40:21])

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6. Why Software Is In Trouble

[44:32-48:51]

• Software Faces a Structural Shift:

  • Traditional SaaS/application software (Salesforce, ServiceNow, Workday) risks being “dumb data pipes” into AI/agent platforms, with the user interface and value shifting to agents, not applications.
  • “Traditional application software is in trouble.” (Dom Rizzo, [45:20])

• AI Native Startups Winning:

  • “I haven’t seen a single compelling AI version of traditional enterprise software from a traditional enterprise software company.” ([45:53])

• IT Budgets are Changing:

  • Agents and model usage could become the dominant item in IT budgets—“what if [that’s] 100% of your IT budget?”

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7. Productivity, Labor, and the Future of Work

[48:51-53:49]

• Productivity > Displacement:

  • Rizzo is candid: “I think it’s dishonest to say that AI won’t displace some jobs. I think AI will clearly display some jobs just like all technology innovation has always displaced jobs.” ([49:19])
  • Net, he sees the age of abundance winning out, with greater productivity and GDP growth likely creating more jobs, though churn, retraining, and political strife will occur.

• Unknowable Future Jobs:

  • “If I had told my great grandfather … he would … it wouldn’t even resonate, right?” ([51:11])

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8. Portfolio Construction in a Tech Revolution

[53:49-61:51]

• Dom’s Strategy:

  • “We’re going to put together a strategy that is an easy button in tech...looking for linchpin technologies innovating in secular growth markets with improving fundamentals at reasonable valuations...” ([54:42])
  • Technology portfolio management is now about balancing subsector allocations (semis vs software vs internet) as much as individual stock selection.
  • “I think a lot about factor construction and I think a lot about subsector bets.” ([59:04])

• Global Scope:

  • T. Rowe Price tech is 70–80% US, but includes global “linchpin” players like ASML and TSMC: “If those [great] stocks happen to be in Japan or the Netherlands or the U.S., we’ll go find them.” ([60:27])

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Notable Quotes & Moments

• “It rhymes, it doesn’t repeat, but we are clearly not seeing that decline that we saw in 1998 from an overall revenue perspective.” (Dom Rizzo, [04:45])
• “Even though the vast majority of tokens may actually be open weight...the vast majority of the value accrues to those frontier models as they orchestrate the other models as they make it more efficient.” (Dom Rizzo, [19:26])
• “All technology innovation has always displaced jobs... but on net I think we are going to grow a lot of jobs because of AI.” (Dom Rizzo, [49:19])
• “Traditional application software is in trouble.” (Dom Rizzo, [45:20])
• “AI is token manufacturing and token manufacturing is capital intensive, right? That’s the big difference between software or Internet.” (Dom Rizzo, [40:21])
• On risk, reflexivity, and market cycles: “The power of reflexive cycles is underestimated... I think we may be going through one of the greatest reflexive cycles of all time.” (Dom Rizzo, [62:04])
• “Accelerating fundamentals is usually a great place to hunt.” ([63:00])

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Timestamps for Key Segments

• AI Markets vs. 1998 Tech Cycle: [00:58–05:14]
• CapEx Supercycle: [05:14–08:05], [14:34–18:29]
• Earnings Takeaways, ROIC, Downstream Risks: [07:51–12:23]
• AI Value Chain & Token Manufacturing: [14:34–24:52]
• Open Source, Enterprise Adoption, Profits: [18:29–24:52]
• Financing and Capital Cycle: [30:05–37:55]
• Semiconductors: Cyclical or Supercycle?: [37:55–44:32]
• Traditional Software's Challenges: [44:32–48:51]
• Labor, Productivity, and Jobs: [48:51–53:49]
• Portfolio Construction & Global Approach: [53:49–61:51]

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Closing Lessons

• On Market Reflexivity:
  “The power of reflexive cycles is underestimated... You have to read The Alchemy of Finance.” (Dom Rizzo, [62:04])

• On Investing:
  “Accelerating fundamentals is usually a great place to hunt.” (Dom Rizzo, [63:00])
  “You don’t need the highest IQ to be in this business. You need a high enough IQ.” (Dom Rizzo, [63:43])

---

This conversation offers a masterclass on why 2026’s market may echo 1998 — but with important, actionable differences. Rizzo’s framework highlights the imperative for investors to look for “linchpins” with accelerating fundamentals, rethink traditional software, and focus on who wins in a capital-intensive AI revolution.

---

Transcript

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[Jack Forehand]

Welcome to Excess Returns. I'm Jack Forehand, joined by the better half of our hosting team today, Kai Wu of Sparkline Capital. And today we're really lucky to have Dom Rizzo on Dom's the portfolio manager of T. Rowe Price's Global Technology Equity Strategy and the firm's technology etf. And we are going to talk about the thing everybody's talking about these days, which is AI and everything that's going on there. And we're going to get deep into the technology and what it might mean for the economy and a lot of different things. But we're going to start at a high level because we have had. It's funny when I put these questions together, it was like three days ago and I was going to talk about the big correction we're having these types of stocks and then two days later we're back to a rally. But I did want to ask you about that because one of the things I read in our prep for this is you had a quote that said this feels a lot like the 1998 sell off to me, which proved to be an incredible buying opportunity. So I'm wondering if maybe you could talk about what we've seen recently in that quote.

[Dom Rizzo]

Yeah. Well, first off, thanks for having me guys. It's great to be, to be here and for the listeners, I think it's really important, you know that I love the question list that you guys send home over. So I'm really excited for this. Really, really excited for this pod. Look. Well, first off, let's take this with a grain of salt. I, I was five years old in 1998, so this is not personal stock picking experience. But from Someone who loves markets and cycles and studying bubbles and reflexivity. Look kind of, kind of, if you did rewind to 97, 98, I think there's a couple similarities. So one, one is, you know, really high momentum factor in, in terms of day to day trading volatility and trade, month to month trading volatility. And if you just look at the statistics from June and July, June was literally in the top 4% for momentum factor for monthly returns and then July was in the bottom 1%. So we're in a high volume, high momentum driven market either on the way up or on the way down. And that, that has some similarities. You have some geopolitical shocks. So Asia financial Crisis was more 97 heading into 98, but Iran War and questions around oil pricing and where that's going to end now. So I think that there's some similarities there. I think there's some similarities in the technical trading around, you know, large hedge funds having some issues as well. Right. Long Term Capital Management was many, many, multiple times bigger from a systemic risk perspective to situational awareness. But you know, I've seen estimates of situational awarenesses, public gross exposure being, you know, north of $100 billion. And that was obviously unwinding through much, through much of July now in hindsight. So I think there's some similarities there. There is a fairly big fundamental difference with 98 and that is actually in the fundamentals themselves. So if you go back and look at the semiconductor industry in 1998 you saw an 8% revenue decline, right. That was mostly driven by the decline in memory pricing. But then if you look in 2026, you know, industry sources have something like a 64% revenue increase. So you know, there's similarities like, like all great things in history, it rhymes, it doesn't repeat. But we are clearly not seeing that decline that we saw in 1998 from an overall revenue perspective. Yet we're seeing almost similar price action. Right. You know, the stocks went down 40% in 1998 and we saw a 30% drawdown recently in the stocks. So some similarities. Not perfect, but I think overall thinking about this as a 1998 style correction that results in an even stronger follow on is kind of a good mental framing.

[Jack Forehand]

Another implication of your 1998 thesis is that we are nowhere near the end

[Kai Wu]

of this capital cycle.

[Jack Forehand]

I mean, I think you've talked about, we're about halfway there. So this would look more like a speed bump on that thesis than the end of the road.

[Dom Rizzo]

Well, one of my favorite charts is Actually just the performance of the Nasdaq from the launch of Netscape through the end of 1999, 2000. And if you look at that, and then you put the launch of Nasdaq from the launch of ChatGPT through today, it's literally the halfway point, right? So ChatGPT was launched roughly three years ago. I know this because I took over our global technology strategy on December 1, 2022, which was the day after ChatGPT was released, November 30, 2020. So, you know, in life there's great. Sometimes you get lucky on timing and that ended up being one of them, right? Semiconductor background, taking over the global technology strategy. And then we were AI on and semiconductors on. Right. And so, so that's, that's been, in hindsight, the right place to be. But, but if you think about where we are from the spending cycle, I think we are actually right at the point of acceleration on high numbers. Right? And I think that's hard for some people to grok because the numbers are already so stupendously big. If you look at the hyperscalers in capex spend, they're going to grow say 75% in 2026 to roughly $800 billion of spend. The street, broadly speaking, thinks that we're going to see 20 to 30% capex growth next year, which say, you know, you know, obviously depending on where you end up, but call it like 1.1, 1.2 trillion of, of CapEx. I actually think we're going to see an acceleration in CapEx next year, so growing faster than the 75% growth we're going to see in 26, which will put us at kind of 1.5, 1.6 trillion of CapEx spend for the hyperscalers. And what's so amazing about that is I actually think that will happen in a relatively moderate pricing environment for memory. So a lot of the increase in 2026 was memory going from mid to high single digits of the overall capital budget to say mid-30s of the overall capital budget. As pricing went up hundreds of percent in memory. I don't think we see nearly the memory price inflation that we do in 27. And despite that, I still think we see an accelerating capex number next year because the ROIC is just so attractive for the hyperscalers.

[Jack Forehand]

Was there anything we learned from these recent earnings reports? We just had all the big tech earnings reports and it seems like the market like Microsoft and like Amazon and they like, dislike other ones like Meta and we don't talk about the individual companies. But was there anything at a high level you learned about the overall ecosystem from those reports?

[Dom Rizzo]

Yeah, well, I'm very happy to go into why. I think the, the, the stocks reacted the way they did and I think, yeah, I think it's really a function of what the market's perceived ROIC is on each of them on this capital build out. Right. And so let's take Amazon first, because that was the clearest articulation by any of the hyperscalers on what the capex really would translate to into revenue and cash flow out of Jassy. So one, they, they broke out the different pieces of the capex, right? There's, there's long life cycle assets and short life cycle assets, right? You know, long lifecycle assets, you know, being property and, and you know, actual infrastructure and then the short lifecycle assets being, you know, the chips, the networking equipment, the, the data center spend, right? And, and Jassy laid out on the call that, on that short life cycle piece, which is the piece that everyone would naturally worry about the most, that they break even in roughly two to three years and then they last five to six years of useful life, right? So two to three years to get your money back and then two to three years of, of great cash flow. And when you, when you think about what that means, it means that even on the shortest life cycle piece of this, the company should have very, very strong roics. Right? And then when you take that in the context of AWS growth accelerating to 37% at 50% incremental operating margins, you don't just have management saying ROICs are going to be strong. You see that coming through in the acceleration of the revenue growth and the EBIT margin expansion as well. And then you take that in context of Microsoft Azure growing 43% guiding to accelerating to 45% into context of Google Cloud Platform into GCP growing 82% also at 50% incremental operating margins, I think you have a lot of evidence that the hyperscale capex business is hitting that inflection point. And then I think the cherry on top of all this was Satya Nadella kind of, you know, retweeting the Morgan Stanley analysis of 30% ROIC for the broader hyperscaler universe. I, you know, that tacit blessing, I think gives people comfort that this immense capital build will also be coupled with revenue acceleration and operating margin expansion out of the major hyperscalers. Now, if you wanted me to take a step back and say, hey, that sounds really good, where are Some potential pitfalls that we should really think about. The main pitfall is how much of this is actually just driven by the AI labs, OpenAI and Anthropic. Right. OpenAI is already something like 25% of Azure revenue. It's probably, you know, mid-30s or higher percentage of the backlog, probably even higher as a percentage of net new bookings. So I think there's a real scenario where the labs grow so ridiculously quickly that they, you know, could eventually aggregate frontier intelligence and, and put pressure on the hyperscalers. But that scenario is, you know, a few years out at least. And in the short term we should have very, very strong fundamentals out of the cloud companies. Now you kind of alluded, hey, some, some, some of the companies reacted well to their earnings and some companies, but obviously the company that acted poorly after was primarily Meta. And the thing that's tough for the market to get around with Meta is that they're investing all of this to just make the core business better. And the core business has already accelerated. Right. If you go back a few years ago, the world was thinking of, you know, we used to talk about Meta and we used to talk about, oh, what about the, the, the, the China, China quick commerce comps.

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Right.

[Dom Rizzo]

There's a world where because of advertising from Temu, you know, Meta may decelerate to high single digits. Okay, well that's not the world we're living in obviously. Right. We're going to see mid-20s growth out of Metta this year. But this is about making their core business better and AI being an existential platform that Meta has to win in order to remain very strong going forward, not necessarily accelerating a business that's so obvious to see like it is Azure or AWS or gcp. And so that's the slight difference. I actually think all these companies are making the right decision by spending. When you have a platform shift like AI and it's structurally capital intensive, one, you have no choice but to spend. But then two, I think the returns of spending are actually extremely high as we've already seen demonstrated. So that's what gives me confidence in this acceleration in capex kind of heading into next year.

[Kai Wu]

I think you are right to point out that, you know, downstream of the near term demand are the, the Labs, Anthropic and OpenAI and then they themselves of course have customers. Right. So going back to your question on roi, so the cloud companies have obviously been realizing some roi, but the question I guess is more on sustainability of that demand vis a vis the end Users, Right, because ultimately the end user pays anthropic for the tokens. Who then pays the cloud hosting companies? You know, we've obviously seen some news around, you know, pullbacks, let's say Uber, maybe even coinbase around, you know, people thinking less about token maxing and more about, hey, you know, how much are we actually getting in ROI per unit of token spend? You know, how do you think that trend, you know, and then of course pressure from Chinese labs and you know, Chinese companies and open source as potentially being an alternative pricing wars, maybe even with OpenAI and some other model companies introducing lower tier models that are more, you know, price efficient and compute efficient. How do you feel like that plays into this thesis on not just with the hyperscalers in general, but the whole AI complex all the way down to memory and chips.

[Dom Rizzo]

It's a great question. It's probably the thing I think about most right now, right, because it has so many ramifications to what happens to the chip ecosystem, what happens to the hyperscaler ecosystem, what happens to the application software ecosystem, what happens to infrastructure? What happens? Every single question is really downstream from this concept of what do the frontier labs look like in a few years? And let's just take a step back, right, and think about the past year. When I did the anthropic round last year in the global technology strategy, they were doing $5 billion of run rate. ARR, this is last summer, right? The latest rumors, you know, on Twitter or whatever, any third party data source would put that number well north of $70 billion. Okay. I've never seen a company grow that fast. I mean, how many servicenows is that? That's like three servicenows or something, right? It's really stunningly fast growth. And why is that? It's because coding as a use case went completely vertical. And then Claude Code and OpenAI Codex really just caught complete virality within the enterprise. Right. And why did coding go vertical is because you clearly made your average coder, you know, 20 to 30% more productive and your most successful AI native coders probably like many fold more productive. And what's so amazing about that is like ask any engineer in the world right now, has that resulted in any decline in their day to day work or are they working harder? I mean, every software engineer is working the hardest they've ever worked right now, despite being 20, 30, 40% more productive. Right. And so when you're in the world of making, you know, white collar labor more productive, I think there's very, very high returns because enterprises are willing to pay for that productivity inherently. Right, and so how big is the coding, tam? Right. It's a question I get all the time. Well, there's 30 million people who write code for a living. Let's say we pay them each a hundred thousand dollars a year. So we spend $3 trillion on coding knowledge work as a society, globally. Let's say we make them each 20% more productive. That means that the $3 trillion of spend is $600 billion more productive. And then you say, okay, what right does OpenAI or anthropic or cursor, I would argue maybe have in that $600 billion spent? I don't think it's crazy to say half. It may even be more than half. Right? And so that's 300 to $400 billion. And then you compare that to the overall application software business being 300 to $400 billion of revenue, and you're like, wow, coding in and of itself is as big as all of application software. And that doesn't even include hr, legal, finance, all these different tabs. Okay, so then your question is, what if you have open weight models? Commoditize that, Tam. What if you have, you know, N minus 1 models come in and the returns aren't to frontier intelligence, but they're to N minus 1 models that are simply good enough? Or what if, you know, that's way too much money to spend on coding and enterprises aren't seeing the return? Right.

[Podcast Sponsor Voice (Accenture)]

So.

[Dom Rizzo]

So one, I think, I think enterprises are not stupid in general. I think actually most enterprises are quite thoughtful and clearly they're getting some value where they wouldn't be spending this money. Right. This concept that people would spend money and not get a return on it I find a little silly, but let's put that to the side. I do think what we've learned is that almost all the economic return is accruing to the frontier from a revenue perspective. Even though the vast majority of tokens may actually be open weight or open Source or N minus 1 to be more efficient. Right. And I think a really good analogy may be Apple versus Android. But broadly speaking, I think we're going to live in an 8020 world where 80% of the tokens are probably open source or openway or N minus 1 and 20% of them are frontier. But then the vast majority of the value accrues to those frontier models as they orchestrate the other models as they make it more efficient. And then I think there's a real question structurally whether or not OpenAI and Anthropic are best positioned to give you that whole spectrum of models. Are they actually the most profitable and willing to give you? You know, I don't know if you guys saw the the chat GBT pricing decreases for the lower models last week. I think that was Sam's bet that you want to be the most efficient at every level. The most efficient per task. The at Seoul, Luna and Terra, right. Like at all the different levels of the model. Or do you want a Palantir or a Microsoft to sit on top and help you kind of model swap to the most efficient model? I think every enterprise is going to make a different decision on that. My gut is just being at the frontier with the Frontier Labs is the best way to do it. But, but, but let's see. I think open question the last thing addressed on your question. I mean and I'm going for a while but I think this is probably the most important question so I could do a full podcast on this is what about examples like Uber and Coinbase? And I would say, hey wait a second. Uber and Coinbase are not the average Fortune 500 company, right? Uber and Coinbase are tech native companies that have the expertise to either buy billions of dollars of GPUs on their own and run these open weight models, get the most efficient pricing and efficient utilization out of the hyperscalers and build their own harnesses on top of the models which they can hot swap underneath. I think that is like quite rare. And you know, I'll just pull a random I don't think Pfizer is going to do that. Right? I have no clue what Pfizer's plans are, but I my, you know, random generic fortune 500 company I don't think is going to do that. I think they are going to either rely on a Microsoft and Palantir to try to do it in a model agnostic way or simply rely on the Labs and that battles remain to be seen. But I think you're seeing the world starting to line up on either side of that and that's really exciting from my perspective. I think I answered the question.

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[Kai Wu]

That was a great response. And I think you're right to point out that you know, end user rois, that's, that's the game, right? If there's no pie to slice up, then you know, there's nothing. What are we doing here? And then as you step through productivity,

[Dom Rizzo]

right, that comes from making the labor force more productive. That's what unleashes the tam.

[Kai Wu]

That's right, yeah. I mean if we're not seeing productivity gains, then the debate over how that pie is sliced up is moot. So let's presuppose for now that there are productivity gains, since obviously that's a philosophical debate that would take three podcasts to cover. But as we think about more about the value chain. You already brought this up a couple times. I think this is really interesting. You mentioned memory over the past year because of the bottlenecks, they're taking a larger share of that pie. But you said something interesting earlier on that you actually think that will alleviate. So that's one thing that'll be interesting to discuss. You mentioned the hosting companies, the applications, the model labs, maybe system integrators, the palantirs of the world, Microsoft. I guess that's my question, which is as we think about where there are moats and what will be commoditized. Because if you look back and you mentioned you're a historian, you've studied these past technological cycles. One of the weird things you see, and it's kind of ironic, is that in pretty much all cases, or maybe almost all cases, you find that the actual builders of the infrastructure don't actually make the profits and that the profits actually flow downstream to maybe users or other parts of the ecosystem. So I think, let me ask you that, which is obviously the frontier labs, what everyone focuses on. But if we step back and ask what are the non obvious areas where profit pools might accrue, where there actually might be surprisingly more moat than, than, than one might think, where do you think, what do you think would fall into that category?

[Dom Rizzo]

Yeah, well, I think so much of it does depend on this open weight versus closed weight debate. Right. Because first off, I think it's important that everyone realize when you say open weight, it does not mean free. Right. You can't take the Kidney K3 model and run it on your laptop. Right. It's a 2.7 or 8 trillion parameter model. I can't remember where they recommend that you use 64 different accelerators to run this properly. Right, okay. In order to run a kidney K3 model effectively at enterprise scale, if you want to do so on prem, you need to go invest in like billions of dollars of Nvidia gear to make that work. Right. And so what's happening, the hyperscalers or, you know, some of these new AI companies are saying, hey, we'll do that for you and you just rent it for us. Right? That's, that's one business model. The other business model is no, we'll keep the close weights. We're going to build the amazing harness on top and then actually we're going to build different applications on top of it for you. And that's what Anthropic and OpenAI I think are trying to do over time. And I'm partial, I'm far more partial to the concept that they will be the best position to build magical products that delight the enterprise and go viral. And I actually think, you know, one analogy I've been playing around with my head and it's not perfect yet, so if it sounds choppy, you know, that's okay. Is what, what if AI manufacturing and like outcome manufacturing is actually far more akin to semiconductor manufacturing. Right? Structurally capital intensive, increasing cost curve. And what if there's a real synergy between the design of the harness and the manufacturing of the token itself through the model? And that would actually lend itself to a vertically integrated solution. Just like it lended itself to intel dominating the CPU market of the 90s. Right. And so that would lend itself to OpenAI and anthropic running away with it, which I think is a real possibility. And I think that's why everyone's freaking out on the other side. Jensen's lining up, Alice Karp is lining up, Satya is lining up. Because they all know if OpenAI and Anthropic run away with the game, it's a really big issue for their core businesses and they are eventually commoditized. So why that matters though is if it's truly vertically integrated token manufacturing, then the economics go to two places, right? They basically go to labs and to the chip infrastructure companies. Right. Eventually all roads lead to TSMC and ASML and maybe intel too. Right. Eventually in that world, in the world that Nvidia, Microsoft and Palantir would, would prefer, you know, there's, there's a lot of economic profit left for Nvidia and the clouds and, and a lot of those applications will be built by smaller application companies that sit on top of say the Microsoft Azure platform or the Amazon AWS platform. And that token manufacturing is actually just who can get the lowest cost per token and a pure commodity market. And then the economic profit can kind of flow differently. First off, my gut is that this is just so big that they're all going to be pretty successful. And I don't know if it's 6040 vertically integrated versus open or 7030 or maybe 6040 the other way. I'd say at the moment, as we stand on August 4, 2026, I'm probably in the camp that'll be 60, 70 in favor of the Frontier Labs over the long run. But let's see, I mean we can, I can change my mind right after this podcast. So it kind of depends what does that mean in the short term to medium term? I do think that means that chips lead us higher because whether it's open weight or, or closed weight models, the investment in chips or immense. And then what type of chips? Right. There's memory chips, there's logic chips, there's optical chips and there's semiconductor equipment, there's semiconductor manufacturing, there's EDA for design software. There's a million different places. Right now I think the most attractive place is the logic semis, so particularly the CPUs. But I'm also, we're also recording this right before they all report. So let's get the incremental information of the reports. But right now I think as memory prices normalize into next year, logic chips can accelerate. And when I see memory prices normalizing, I don't mean that we see a decline but just a deceleration from this incredible pricing growth that we've seen in memory.

[Kai Wu]

So yeah, I want to follow on. You mentioned the short term as opposed to long term and you also brought up the hedge fund situational awareness in the beginning, which obviously we'll see. Jury still on whether or not the long term view is correct. Many people believe it is, but obviously in the short run he did not survive or his public book did not survive. So that gets me to the next question, which is around the financing of the build out. Obviously there is a period in time when building out data centers AI data centers was a rounding error on Google's free cash flow and we're now well past that, in which case we're approaching the point where this needs to be funded. Not out of free cash flow, out of equity, out of debt. Right. And we're increasingly seeing debt financing, some people point out to circular financing and derogatory way, but there's clever ways that this is being financed. You see that as a near term risk. Maybe even if in the end game this, this build out does occur successfully and this technology is game changing. But that there's a hiccup along the way, as we've seen so many times before, around the way that this is actually financed because, you know, several trillion dollars, that's a pretty big number.

[Dom Rizzo]

Yeah. So look on Leopold specifically and the situational awareness blow up. I would just say I think the paper that they wrote, I mean was really quite prescient. I think the podcast he did two years ago with Dwarkesh really laid out almost to a T what happened to the next two years fundamentally. And then there was clearly left portfolio construction leverage mistake. Right. Which resulted in, you know, I, who, who knows if it's full? I, I, I don't, we don't know the details of the situations. Right. But, but, but clearly substantial, substantial capital destruction. So you know, leverage helps on the way up and it really hurts more on the way down.

[Kai Wu]

Right.

[Dom Rizzo]

As, so as so many people have learned before in terms of equity and debt financing of this build out. You know, my job was easier last year because when people would bring up where we are in the bubble and you know, I would always say AI has the potential to be the biggest productivity enhancer since electricity. Productivity enhancing technologies come with speculative bubbles. My job is not to miss bubbles, but to navigate them responsibly for our clients by trying to capture upside and blunt downside. I know that's a mouthful, but I've practiced it a few times on. But last year my job was really easy because I would just say, oh well, this is just being funded by the free cash flow of the most successful organizations of all time. That changed this year, Right. Google issued equity, you know, $85 billion of equity. Here it is either the most profitable or the second most profitable and third most profitable company in the world issuing equity for this capital build out. Right. So we're clearly in a different stage. Right. And when we talk about where are we fourth inning, fifth inning, are we halfway through, we are in the capital cycle part of the build out. And so if you ask Me what worried me in July, I would say the only thing that worried me was if the price correction could be so material that it could freeze the equity or the debt markets in terms of funding the build out. Right. Could the price correction cause a reflexivity shock to the negative side where people wouldn't be willing to believe. Right. CDS spreads everything. Talking about, you know, all these different companies, where are we at in terms of debt versus equity? Where should their bonds trade? Look, the reality is if you do the math, it's actually not that big for these companies, right? The funding app, I know it sounds like, it sounds like a big number when I'm about to say oh, the funding gaps, a few hundred billion dollars each, you know, and each company's different to get to those higher end scenarios even less. But, but, but it's actually not in the context of a 3, 4, 5 trillion dollar market cap, right. In terms of the, the funding gap between how much they actually need versus their equity and then it's really not that big in the context of their net debt to ebitda. Right. Just in terms of their leverage. I do think you can make a compelling argument that the amount that needs to be spent is so much bigger than what the investment grade market is used to, that maybe it overpowers the IG market. But I actually think that's why you've seen yields go up. There's a crowding out effect, right? Google's willing to pay you, Google is willing to pay you a real number now. And what's the difference to Google of 50 basis points, right? Nothing actually in the, in the grand context of things. So I think we are clearly in the equity and debt financing portion of the build out. I don't think the spread is that wide between what they need to raise versus what they want to spend or what I think they probably should spend is probably a better way to phrase that. And then as the ROIC kicks in, you start to see the inflection on the operating cash flow that can, that can continue to fund a lot of this build out. And then the question, I think the fundamental question is is a structurally capital intensive or not? Or do they, or are they able to, you know, grow revenue materially faster than capex over time?

[Jack Forehand]

To your last point, is there a case for more and more self funding here? Like I know you listen to it as well this, this podcast. Gavin Baker just recently came out and he was talking about this idea that the cost of compute is going way up and as these contracts kind of roll it's going to go up and up. And he was making more and more a case that more of this could be internally funded versus needing external funding. I mean, do you think that's, do you think that's fair?

[Dom Rizzo]

I think it's, I think it's going to be a combination of both external and internal. Look, I mean, if you see continued acceleration under the hyperscalers, your ability to borrow is easier too, right? Because the market is willing to lend you more money if you're, if your core business is accelerating or your ability to issue equity at more reasonable rates is, is easier too. I do think there is a self funding mechanism which is, which is simply the, the operating cash flow, the OCF acceleration, right? As that accelerates as the ROIC kicks in, there's just more dollars to go spend. And then do they, you know, do I believe in the world where we see material price increase? The part of the podcast, which was a great podcast you're talking to, is when they're talking about pricing increasing for older GPUs as the returns to frontier intelligence continue to go up, and then there's a scarcity of GPUs. I like the idea a lot. It would go against everything I know about GPU pricing historically. But I think it's a, let me put it like that. I think it's a distinct possibility that that's the case. And what happens is there's a race between demand and supply. But the weight is so high on the demand side right now. But every time you get supply increases, you know, actually demand increases faster. And I, I don't see that coming because I think, you know, we're so early in AI. You know, Ben Horowitz said something like he thought we were 3% penetrated in AI. And I heard that number and I thought it was right. You know, I think, I think that's probably where we're at.

[Kai Wu]

You obviously have a semis background and this is your bread and butters.

[Jack Forehand]

Maybe.

[Kai Wu]

I know what you're going to say, but some have argued that while historically chips have been a cyclical industry, we're now entering this kind of golden age of a super cycle. Right. Where do you fall on that? Obviously there's significant near term demand, 3% penetrated. That of course stands the reason. But we live in a capitalist economy where profit margins attract competition, investment capital cycle, you already mentioned, and potentially innovations with regards to, you know, more efficient models that require less compute per unit of intelligence. You know, do you, do you, do you see downside risk in terms of, you know, chip stock kind of regaining their cyclical characteristics? Or is that not really something you're worried about over the next, you know, several years?

[Dom Rizzo]

Well, semiconductors are always cyclical and will always be cyclical. Let's be very clear. We just happen to be in a great upcycle right now. I think this is why I love semiconductor investing, because both things can be true at the same time, which is we will have a correction. Whether it is inventory driven or demand driven or supply shock driven, we will clearly have a correction at some point. How aggressive is the correction? Where does it show up in the supply chain?

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[Dom Rizzo]

Will be a function of a lot of different factors at the end use case and mo. Most notably, I'd say okay, AI is structurally different than software. AI is token manufacturing and token manufacturing is capital intensive, right? That's the big difference between software or Internet. These companies could scale to, you know, tens of billions, hundreds of billions of dollars of revenue without material capex. Now the software guys had to do it with a big sales and marketing budget, right? Which they used a lot of stock based comp to get around. But the Internet guys didn't have to do that, right? The Internet guys just hit 2 billion users and they were able to monetize the world. And that was like the beauty of Ben Thompson's aggregator theory, right? It was a zero marginal cost industry and then it was who had demand. You know, in the end eyeballs ended up being right, right? If you had $2 billion, 2 billion eyeballs, you were able to monetize, you know, incredibly effectively through, through either advertising or I mean advertising obviously being the main one on the Internet. So okay, AI is different and why is AI different? AI is different because the scaling loss, roughly speaking, if you throw 10x more compute at a problem, you get 2x more intelligence, right? And so naturally people say, well, isn't there a limit to intelligence? What's the difference? You've probably talked to someone with a 130 IQ and probably someone with a 140 IQ and they may have both been a little boring. What's the difference between a 130 and a 140 IQ person? And I would say like you know, versus a 150 IQ person, right. I would actually say don't think about it as iq, think about it as task completion. And once you start thinking about it as task completion, a million use cases open up your mind of what the models can't do today, which is what they can. So I'm a big user of codecs internally. I have all these agents running around making me charts, you know, giving me data, all this stuff. I could think of a million things that it still can't do that I wanted to do right. And, and it's funny, I, my, my Codex wasn't working the other day. I had to use traditional ChatGPT and I felt like I was going back in time. I, you know, I, I, I, I, I'm living in this agentic world. I had to go to a non agent world, I had to go to a chat based world, not an agent world and I couldn't ask it to do tasks on my behalf. And it was really frustrating. It was really frustrating. And so I actually think what ChatGPT is doing, stuffing Codex into the ChatGPT user interface, I mean it's a, it's a big bet, but I think it will end up playing out really well for them. So is AI structurally capital intensive? I think so, I think so because the scaling laws and there's just been no evidence that the scaling laws or ending okay, doesn't mean that at some point we can't see a blip in the scaling laws. Doesn't mean at some point we can't see a supply shock out of China. It doesn't mean at some point that we may not have enough physical space to put the chips. All of those are real things that could result in an inventory correction. And semiconductors are at the end of the bullwhip and you get Google sneezes and small cap optical stock, you know, catches not just a cold, I mean potentially almost bankruptcy historically. Right. So that, that's the, that's the part that's hard for people. But that's, that's the part I love about semiconductors too. So I, I don't think semiconductors are no longer cyclical. But I do think you've gone from a world of software and Internet being the driver via PCs and smartphones to a world of frontier intelligence being the driver and frontier intelligence being structurally capital intensive.

[Kai Wu]

So what do you think is then the role for software? Right, so SaaS and software was at one point the kind of growth darling of the stock market. Obviously they've been punished the past year, two years, stock's down 50 to 80%. And you make the correct point, right, that AI is structurally different, unit economics are different than software. But. And you kind of make the case that, you know, through many vertical integration and harnesses, you know, a lot of what it sounds like traditional software would have done can be done or should be done through agents and through, you know, OpenAI Anthropic and folks like that, you know. So what's left over I guess for the software companies? Or is that maybe too bearish a framing of your view here?

[Dom Rizzo]

I think there's a really, I mean, I think if you're an established system of record, you get to be a dumb data pipe into do OpenAI or Anthropic in the world I'm describing, right? The salesforces of the world, the servicenows of the world, the work days of the world get to put their data into, into OpenAI Anthropic and then the intelligence can work on top of it. I think you're already seeing that, right? This whole push by Salesforce to go headless is a concept that they, you know, know the direction of travel is that the user interface is going to migrate away from salesforce.com that the actual app to, to, you know, either agents or to, to a chat interface. Right. And so I think traditional application software is in trouble. And the last thing I'd say is I haven't seen a single compelling AI version of traditional enterprise software from a traditional enterprise software company. Right. I could name, you know, dozens of fast growing startups that are AI native. I don't look at the products that the major software companies have put out and say, wow, that's really compelling. And it's not just bundling to get them to those reported ARR numbers. Right? So, and I think that may be something to do with designing AI native software actually being quite different than traditional software and understanding how the models play, how the models play with the harness. Designing to an ever increasingly smarter model is Actually quite different than the world of traditional application software. I do think that there's different types of enterprise software companies. So as an example, like SAP has a massive data gravity. That's a really interesting asset that they have through their erp. There's consumption software like Snowflake. Hey, dump all your data of your organization into Snowflake and then let the models run on top or use our own coding agent to make things better within Snowflake. Don't get me wrong, I'm not saying that there's not areas. They're probably primarily in infrastructure software where you can feel more comfortable rather than application software because the entire application interface has changed from humans to agents. And so that's going to be a tough transition. I think we're already seeing it and then there's only so many dollars, right. If the IT budget is going to start going towards intelligence tokens. I, I do think IT budgets are going to grow. But I think that, you know, what percentage of your, your corporate budget is going to be intelligent tokens in five years is a really interesting, important question. And what, what percentage of, you know, your IT budget is that? Like, what if it's 100% of your IT budget? Your 2026 IT budget is tokens? I, I like, I think that's a possibility in, you know, some medium time horizon.

[Jack Forehand]

How about the other side of the coin from the productivity, which is, I guess the right way to call it is labor displacement instead of, instead of job loss. But like if we think about a continuum here in terms of a technology like this, I mean, I think we all think AI is going to grow dramatically. And then the question is, you know, if we're way on the productivity side that we kind of get the world of abundance people are talking about. If we're way on the job loss side, we get this treaty piece thing and then maybe reality we get somewhere in the middle. Like I'm just wondering if you have any thoughts on like that balance of those two things.

[Dom Rizzo]

Well, I think it's dishonest to say that AI won't displace some jobs. I think AI will clearly display some jobs just like all technology. Innovation has always displaced jobs. Right? How many bank tellers versus ATMs, right. The role of the typist pool versus email and word. Right? There's, there's clear jobs that change because the economy changes and technology changes. And I think to say that I won't displace jobs is dishonest. So people shouldn't say that on net. I think we are going to grow a lot of jobs because of AI. And I think that we've actually seen that happen so far, you know, particularly in blue collar jobs. Right. Electricians and plumbing for data center construction. But you know, we were kind of joking about ask your average software engineer, are they working more or less right now? I think they're clearly working more right now because I think there's so much to do. And so I do lean more towards this age of abundance camp, which is productivity results in revenue growth. Revenue growth is good for the economy and good for the whole. GDP growth should accelerate in the US and if GDP growth accelerates, that results in and more jobs. Right? And I always think of Payne saying that, you know, whatever his prediction was, 50 years from now, well, we'd only be working two hours, two hours a day for two days or whatever, whatever the exact prediction was, I don't remember. But obviously we're not doing that because humans are status creatures and we love building and you know, there's labor's a love and every startup's a laid a bear of love of the founder. And you know, we build things and it's exciting and we're project based and I think there's so many ideas that we haven't thought of. And then you say, dom, well what are those amazing jobs that you haven't thought of? And I say, I have no clue. And that's not like a great answer. But if I had told my great grandfather that, you know, hey, I know you just got off the boat at Ellis island and one day your great grandson is gonna, you know, have a personal trainer and he's gonna have all these, his job is gonna be sitting behind a computer screen and describe my day. I mean it would, it wouldn't even resonate, right? And I, and I think, and I think we've seen immense technology, innovation and productivity since then and I think that will continue. It doesn't mean it's not gonna come without political strife, economic strife, all these things that are real and there are real concerns and we should take them very seriously. So, so I'm neither in the dismiss it and age of abundance camp, but I'm not a jobs doomer either. I think we just have to be realistic that we're probably going through a period of high churn in the economy where the net job number will probably go up and revenue growth will probably accelerate, GDP growth will probably accelerate. But, but people are going to have to potentially do different jobs and I don't know what those are, but they always seem to pop up historically.

[Jack Forehand]

It's funny, one of our guests pointed out that computer was a human job at one point. And if you told those people that this machine is going to do the stuff you do, they probably. That'll never happen. So it's just hard to know.

[Dom Rizzo]

It's really hard to know. And it's not any more comforting to anybody whose job could be displaced by AI too to say, oh, well, what are you going to do? I don't know. But I am confident that this is going to result. Confident is a strong word. I think this should result in major productivity growth for the economy, which should result in GDP growth acceleration. And historically, GDP growth acceleration is great for everybody. It's when you don't grow that's dangerous, right? Because then you start thinking about not how do we grow the pie for everybody, but how do we divide up what we have. Right? And, and doesn't mean that you can't, you, you can't think about that already, but like, growing the pie is clearly the best way for, for everybody.

[Jack Forehand]

One of the things I'm jealous of you about is I'm a quant investor, so I'm, I'm running models all day and you're getting to look at one of the biggest technological revolutions in the world and like, decide how to build a portfolio. And so my question, I guess, is I want to. At a high level, like, how do

[Kai Wu]

you think about that?

[Jack Forehand]

I mean, there's so many different things you could be investing in. How do you think about, like creating a portfolio for the fund at a high level? Like, what are you looking for? Just. That's a very broad question. But just in general, how are you thinking about constructing?

[Dom Rizzo]

Well, look, I actually think that historically quantitative portfolio managers have done a better job of portfolio construction than traditional portfolio managers. So we have an amazing quant team internally and I try to learn from them on portfolio construction, risk management, my beta exposures, my momentum exposures, all my different factors. How do I build a portfolio?

[Jack Forehand]

We struggle is trying to invest during these productivity revolutions because we're looking at the past fundamentals and it's like they're not going to tell us what's going to happen in the future.

[Dom Rizzo]

I mean, I. Some of my best ideas screen terribly on my little codex agents for my little quant codex agents, right? And they're my best ideas. And so that's, that's where beauty, that's where alpha lies, I think for stock selection. Look, how do I think about portfolio construction? The global technology strategy was One of the top performing strategies in the country from 2010 to 2020 had a terrible 22 and interest rates went from 0% to 4% over that period and had way too much software. And we saw a meaningful correction. One thing that I wanted to do was make sure that my strategy was really like the easy button in tech. Right. And, and what do I mean by that? We're going to think about semiconductors versus software. We're going to think about Internet versus you know, cloud, we're going to, versus Fintech. We're, we're going to take all that into account. We're going to put together a strategy that is an easy in an easy button. And it's not an AI strategy, it's not a semiconductor strategy. It's a strategy that is a framework where I look for stocks with linchpin technologies innovating in secular growth markets with improving fundamentals at reasonable valuations to find bottom up stock selection ideas, service them to the top and then think about overall portfolio construction on top of that. Right. And so how much semis to have versus Internet? I mean that, that, that's a tough question. That's probably the thing I struggle with right now. And then if you look back the past few years, I've had a lot of semis and I've had a lot less software. And the software that I have had has primarily been in this infrastructure software space. And so you've got to really think about how, how to structure it. That you could go through periods of July or April of this year or April of last year and whether these downturns. Right. And so that's when you take into account your, your factors, right. Your beta, your mo. You really try to understand these things. And then the thing I talk about with my risk team all the time is yes, that is the risk I'm taking and that, that is the bet that I want to be making. So making sure that there's no unintended bets in the portfolio and that's something servicing that I, that I'm not aware of. Right. I'm very happy to take bets. That's how you generate alpha, is you, you take bets but, but you want to make sure you avoid the, the, the unintended bents. You know, Rumsfeld got so much flack for the, the quote. There's the known knowns, the. No, no knowns, but is the. Then there's the unknown unknowns and those are the ones that get you. It's the same with risk factors. There's, there's the known unknowns. There's stuff you know you don't know, but it's the stuff that you don't know that you don't know when it comes to portfolio construction and risk management, that those are the ones that get you. And so always try to be on the alert for those and get those into the second bucket at least.

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[Jack Forehand]

so do you think more about the weighting of the buckets than the individual names? Do you think that's more important, like in terms of construction getting?

[Dom Rizzo]

Yeah, I think a lot about factor construction and I think a lot about subsector bets. Those are the two. Like if you had to boil it down to those are the two because I think on individual names. Okay, so there's different types of semiconductor beta. There's memory beta, there's logic beta, there's high quality beta. I mean take the difference of like an Nvidia versus a SanDisk. They're radically different stocks even though they're both semiconductors. Right. And so, so there's different types of volatility in each individual name. And I think trying to understand that and then using, frankly using portfolio as a, using performance as a guide to understand where your risk factors are is actually a really important part of that portfolio construction as well.

[Jack Forehand]

One of the interesting things about your fund is it's a global fund. And you know, those of us in the US we tend to just think about the US Companies. So like how would you characterize the opportunity or what you're seeing like outside

[Kai Wu]

of the US in tech right now?

[Dom Rizzo]

Yeah, so both strategies, right. At any given point maybe 70 to 80% US and 20 to 30% OUS. I think there's great, you know, my framework, linchpin technologies innovating in secular growth markets with improving fundamentals at reasonable valuations. There are these linchpin technology. What is a linchpin technology? It's mission critical to the success of its customers. I mean ASML is a clear linchpin example, right? TSMC is a clear linchpin example. Clear linchpin technology, right? Innovating in these secular growth markets. Are you taking share and fast growing end markets? Do you have improving fundamentals? Do you have revenue that's accelerating, operating margins that are expanding or free cash flow conversion that's improving? That's the one that got the hyperscalers the last year. Right? Revenue is accelerating, but free cash flow conversion was going down. And then do you have a reasonable valuation? And then often the market will dare you to buy stocks because it will have a few of the factors, but not all of them. Right. And so, so memory is a great example. Fundamentals are decelerating. Revenue growth has to grow less from here, right? You can't grow 350% year, year over year forever. And they're trading at low single digit or mid single digit P Es, right? So that's the market daring you to make a choice between valuation and fundamentals. So put those all together and, and, and, and that's how you try to try to pick great stocks. And if those stocks happen to be in Japan or the Netherlands or the U.S. you know, we'll go find them. And you know, that's the team of 20 plus people all around the world trying, trying to, trying to, trying to find great names and surface great ideas.

[Jack Forehand]

Well, this has been awesome. Kai and I could go on for hours with this, but we, we evaluate your time so we won't do that. But we do have two standard closing questions we ask all of our guests. The first is what's one thing you believe about investing that most of your peers would disagree with?

[Dom Rizzo]

You know, that's a great question. It's a hard one because I do think some of my peers do believe in this. But I think the power of reflexive cycles is underestimated. And so that's why I said in July I was worried about what does it mean for the capital markets. I think we may be going through one of the greatest reflexive cycles of all time. And, and so, so when people ask me what book to read in, in stock stock picking, you know, you have, you have to read the Alchemy of Finance, right? So where Soros lays out reflexivity and

[Jack Forehand]

our Last question is, based on your experience in markets, if you could teach one lesson to the average investor, what would it be?

[Dom Rizzo]

You know that graph where it's the 80, 20 graph and it's got the, the. It's like they call it the mid twit graph, right? The, the.

[Kai Wu]

The.

[Dom Rizzo]

The dumb guy, the average person who thinks they're smart, and then the really smart guy and the dumb guy and the smart, the really smart guy think the same thing, and then the middle guy, you know, think some very complex topic. It would just be, you know, by accelerating fundamentals, right? And I think really smart investors and not dumb investors. But, but, you know, if you're simple about it, but accelerating fundamentals is, is. Is usually a great place to hunt.

[Jack Forehand]

Well, there's, there's hope for dumb guys like me out there.

[Dom Rizzo]

You know, it's. What did, what did Buffett always say? You know, you don't need the highest IQ to be in this business. You need a high enough iq, And I always like that.

[Jack Forehand]

Well, Dom, thank you very much. This has been awesome.

[Kai Wu]

We appreciate your time.

[Dom Rizzo]

Thank you, guys. Thank you for tuning in to this episode. If you found this discussion interesting and valuable, please subscribe on your favorite audio platform or on YouTube. You can also follow all the podcasts in the Excess Return network at excessreturnspod.

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[Dom Rizzo]

If you have any feedback or questions, you can contact us at excessreturnspod at gmail.

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No information on this podcast should be

[Dom Rizzo]

construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.