Compiled & Organized: Deep Tide TechFlow

Hosts: Ejaaz Ahamadeen (EJ), Josh Kale (Josh)

Original Title: What The Best AI Investors Are Buying Right Now

Podcast Source: Limitless Podcast

Broadcast Date: May 28, 2026

Editor’s Introduction

This episode of the podcast mainly discusses the investment philosophy of Gavin Baker, founder of Atreides Management and a long-term investor in Nvidia and Cerebras. His core judgment is that AI is not a bubble, but a supercycle driven by electricity, wafers, and computing power. Real excess returns are not found in large models or chatbots but in the "shovels" of GPU connections, memory, inference chips, advanced processes, and power supplies.

Gavin Baker hedges against broader market downturns using QQQ puts while focusing on investments in AI bottleneck assets such as Astera Labs, Unity, Micron, Nvidia, Cerebras, and Positron. He pulls the discussion of the "AI bubble" back from the emotional level to supply and demand constraints, believing that as long as TSMC, ASML, high bandwidth memory, and the power grid do not quickly become oversupplied, AI capital expenditures are unlikely to repeat the 2000 Internet bubble.

Key Quotations

AI Bubble or Supercycle

• "AI is not in a bubble; on the contrary, it is in a supercycle."
• "The biggest returns are not in SaaS, not in chatbots like OpenAI or Anthropic, but in electricity, computing power, and silicon manufacturing."
• "This is not an internet bubble because the buyers are mainly the smartest, most cash-rich companies globally, not buying computing power on debt leverage."
• "If the overall market cannot be oversupplied, it is hard for it to suddenly collapse like traditional bubbles."

The Real Bottlenecks: Electricity, Wafers, Tokens

• "Gavin's theory is simple: focus on the bottlenecks in AI infrastructure; whoever can increase performance per watt and decrease token costs will be valuable."
• "AI labs are increasingly concerned with how many tokens can be generated per watt of electricity."
• "Electricity and wafers are two brick walls and two key constraints limiting the rapid acceleration of AI."

From Pre-training to Inference and Post-training

• "Just because a model is pre-trained doesn’t mean it will be a genius for life; it still needs to absorb new information in the post-training phase."
• "Inference essentially requires a lot of computation, which is why inference chips and infrastructure will become the focus of the next phase."
• "The cost or revenue opportunities brought by inference alone may be 5 to 10 times the pre-training computing power investment."

Vertical Small Models, Edge Models, and Sovereign Infrastructure

• "In the future, you may not need to interact with Claude every day; what you really need is a personalized AI agent trained on your own data."
• "The speed of infrastructure deployment itself is a moat; the iteration speed in the digital world far exceeds that of physical infrastructure."

"Whoever can compress physical deployments from months or years to weeks can sell at high prices in AI infrastructure."

Gavin's Investment Approach: Long on Bottlenecks, Short on Overall Market Risk

• "He strongly believes that AI winners will emerge, but that doesn't mean he is optimistic about the entire market; QQQ puts are his hedge against overall downside risk."
• "TSMC effectively limits the speed at which bubbles can accelerate; as long as chip production cannot suddenly expand, capital expenditures are unlikely to spiral out of control."
• "Gavin is like a more seasoned, stable, and cyclical investor than Leopold: the former's successes are measured in decades, while the latter's are more quarterly."

Assets Worth Betting on in the AI Supercycle

EJ: Gavin Baker is an extremely prolific AI investor that the public has hardly heard of. Over the past 20 years, he began investing in some AI companies that later became household names before they rose to prominence. He early invested in Nvidia (a supplier of AI GPUs and accelerated computing cores) and Cerebras (an AI chip company), holding a very clear viewpoint that AI is not a bubble; on the contrary, it is a supercycle.

He believes that by observing watts (electricity), wafers (silicon), and tokens (units of model generation and computation), which are the foundational infrastructure of AI, key bottlenecks and constraints can be identified. His conclusion is simple: the biggest returns in AI come from power, energy, and silicon manufacturing, with little relation to SaaS (software as a service) and chatbots like Anthropic and OpenAI. The entire industry will ultimately funnel down to semiconductors, which are the picks and shovels that support the entire AI industry.

While many claim that the AI industry is already a bubble, he views this as a generational buying opportunity, particularly in AI infrastructure. He expresses this judgment using a fund size of about $4.1 billion.

If you listen to him discuss these constraints, especially around AI infrastructure, you'll find this theory is familiar. We previously discussed an investor, Leopold Aschenbrenner, who has also made many configurations around similar directions. The difference is that Leopold has only been at it for about three years, while Gavin has worked in this field for over 20 years.

Leopold manages about three times the assets that Gavin does, but the show's producer Luke reminded us of a good point: you might win against Warren Buffett in a year, but can you do it continuously for decades? Gavin Baker’s track record suggests that he may have a different perspective on this investment theory.

For those unfamiliar with Gavin Baker, he is the founder of Atreides Management (an investment fund) and has been investing in Nvidia for the past 20 years. If you can hold Nvidia for 20 years and continue to work, that in itself is incredible as it should yield astonishing returns.

Some of his recent wins include Cerebras and Astera Labs (an AI data center connectivity chip company). Cerebras is an AI chip company, mentioned in the show to have an astonishing valuation post-IPO. There are also some companies you might not have heard of; we will explore his portfolio and judgment in this episode to see where he believes the AI investment opportunities lie.

The question then becomes: what exactly has he invested in and why? If you look at Atreides Management’s recent 13F (a quarterly filing disclosing U.S. institutional investors’ holdings), this fund has about $4 billion AUM (assets under management). Breaking down some of its largest holdings reveals that these companies point to the AI development bottlenecks Gavin frequently mentions.

He holds significant positions in some less glamorous companies, many of which people may not have even heard of. For instance, Astera Labs accounts for about 9% to 10% of the fund. You can understand Astera Labs as a connection layer between GPUs. If you imagine a data center as a system, GPUs are the engines, responsible for model pre-training, post-training, and inference. But for GPUs to operate, they must exchange large amounts of data with each other and access the memory chips where the data is stored.

To achieve this, a "pipeline system" is needed. I am speaking very generally here, as I do not pretend to understand all the underlying details. Astera Labs addresses precisely this issue. When the AI cluster expands to hundreds of thousands of chips, the bottleneck is no longer just the GPUs themselves, but the data transmission window, figuring out how to send the correct data at the right time and access the correct data. Astera Labs builds that kind of pipeline system.

I had not heard of Astera Labs before researching for this episode. But I recall that Cerebras was in a similar situation. Gavin had mentioned Cerebras about six months ago, and considering the time scale for AI, six months is quite a long time. It subsequently went public, with a valuation mentioned in the show to be around $60 billion, and it rose 40% after the IPO. This suggests that Astera Labs may also be an important name in a similar trend.

Josh: Cerebras was an extremely early investment for him. He entered the company early in its lifecycle, meaning he has been betting on this theory for many years. There are a few other companies in which he has also bet long-term, with Nvidia being the flagship.

To have been involved in Nvidia for more than 20 years and to maintain conviction throughout is impressive. Recently, I listened to two podcasts featuring Gavin, where he clearly expressed a judgment when discussing his Nvidia position, believing that Nvidia can maintain its current profit margins and sustain demand. This means he believes Nvidia has the opportunity to approach a market cap of nearly $10 trillion, while it is currently only halfway there.

Another noteworthy mention is Micron (Micron Technology, a major global memory chip manufacturer). In our last episode, we talked about the AI investment stack and the positions of these companies within it, and I highly recommend revisiting that. Micron is one of the largest memory makers. During the show, an astonishing figure was mentioned: a year ago its market cap was under $100 billion, while at the time of recording, it had surpassed $1 trillion in value—a tenfold increase in one year. This indicates how critical the memory problem is.

There are also some less conspicuous but interesting companies. EJ, I particularly want to mention one to you: Unity Software. Those familiar with gaming know that Unity is a game engine; many popular games are created using this 3D rendering software.

So why would an AI investor invest in Unity, this "video game maker"? The answer lies in the 3D game engine. Unity is a world model builder with a deep understanding of physics, how the world operates, materials, and lighting. When AI companies build AGI (artificial general intelligence) and humanoid robots, a critical step is to simulate virtual environments and datasets, allowing the robots to train within them. Unity happens to be one of the strongest tools available. So as a world model maximist, you would likely appreciate this example of a company famous for its game engine, having a clear path to becoming an important player in the AI world.

Gavin's Investment Theory and Strategy

EJ: The theory of world models is simple: current AI models or LLMs (large language models) primarily understand the world through text and books, much like a student sitting in a library, but they lack real-world experience. What world models aim to unlock is this issue: placing a game character in a simulated environment to understand how physical reality operates. For instance, what happens if I drop a phone or kick a ball? What are the subsequent steps? What should you do? World models address this problem.

Currently, not many players can produce this capability on a large scale. The leading contenders might be Google, which has models like Genie 3 (Google's generative interactive world model project). The show also mentioned that Google recently released Gemini Omni, but these kinds of models have yet to experience their own ChatGPT moment.

What I appreciate about Gavin is that his portfolio resembles a barbell strategy. On one side, it is very traditional; everyone needs GPUs and storage, so he invests in major players like Micron and Nvidia. On the other side, it is very avant-garde. He feels the puck will go there, so he invests in Cerebras because he believes inference will become very important; he also invests in Unity because he believes world models will be the future way to train robots and the next generation of LLMs.

His portfolio also includes Positron, which develops inference chips. If this sounds similar to Cerebras, yes, they both revolve around inference. Recently, Gavin has repeatedly discussed a trend where the infrastructure stack for AI models, especially the training stack, is shifting from pre-training to a greater emphasis on post-training.

If you are in the AI circle, you would know this shift has already occurred. Gavin is very focused on this. A model still needs to understand new information and data, and to update itself. Just because it finished pre-training on some dataset does not mean it will be a genius for life. It still needs to learn new information, which happens in the post-training layer and requires a lot of computation.

Moreover, if you want an AI model to think through problems, much like we do after receiving new information, questioning whether an angle holds, or if another theory could explain it, that's reasoning. Reasoning also requires a lot of computation. Current estimates suggest that the cost or revenue opportunities arising solely from inference could be 5 to 10 times the investment in pre-training computing power.

Therefore, AI labs and chip makers are going through significant shifts. You are already seeing Nvidia launching many GPU solutions aimed at inference to support agentic applications. Gavin also expresses his bets on inference through a series of investments.

Lastly, I find it very interesting that Gavin talks about China. In the AI race, the narrative has consistently been China versus the US. China has a unique configuration, having relatively ample energy supply and the capability to expand chip manufacturing. The US is currently struggling in this area, which is why many processes are outsourced to TSMC (Taiwan Semiconductor Manufacturing Company, the world's most important advanced semiconductor foundry).

His explanation is that China has a unique opportunity to create a very different kind of AI infrastructure or chips from the US due to its strong focus on inference. One could say Gavin is leading the charge on US bets for building inference infrastructure through his investments. I believe this may turn into a massive opportunity in the future.

Josh: It is also worth noting that this bet is not solely upward. He also holds a significant QQQ put position. QQQ is an ETF tracking the Nasdaq 100, representing a basket of stocks and is the second-largest ETF by trading volume in the US. It performed very well: up 55% in 2023, 25% in 2024, 20% in 2025, and has risen 17% so far in 2026.

In other words, as an index fund, QQQ has performed remarkably well; it's easy to buy as it includes the top 100 stocks. Meanwhile, Gavin is hedging against it. He is not suggesting that AI won’t win; rather, he is saying: he wants to invest in the critical manufacturers that solve bottlenecks but isn't overly optimistic about overall market sentiment. QQQ puts are downside protection: if the entire market collapses adversely, even if AI wins in the long term, he still has this hedge.

Four Categories of Investment Directions

Josh: We can break down what he considers the most important investment bottlenecks into several categories. The first category is verticalized small language models. Regular LLMs, like chatbots Claude and ChatGPT, are generalized LLMs that have a broad understanding of the world and can answer specific questions. However, training models around a specific vertical field or particular questions is another matter.

These specific problems typically exist within enterprises, especially those that delve deeply into certain issues or form niche companies within a specific lane. Verticalized SLMs address precisely this issue: they are frontier models but highly optimized, capable of running efficiently on specific enterprise data or locally on devices.

We have previously discussed on-device or locally run models. The reason is that your phone or other devices contain a lot of highly personalized data that you may not wish to share, and companies may not be able to access it. For example, medical records, financial details. I remember OpenAI released a financial AI agent that can access your bank account but cannot truly operate on your behalf because it contains a lot of personally identifiable information, such as social security numbers and banking details.

Local models or SLMs can resolve these issues. Gavin bets significantly that they will become very important in the future. There is one company he is particularly optimistic about: Apple. Although he may not have explicitly expressed investment interest, he believes Apple will be one of the primary device makers enabling local models to run on devices.

If the future unfolds this way, we may no longer think Claude must be the model you interact with every day. What you may need is a personalized AI agent trained on your data—this is what SLMs may ultimately become. The general version could run on your phone, while many enterprises would also run highly optimized, specialized models trained on their proprietary data to better sell or market their products.

EJ: Apple is in a prime position for this. I am looking forward to WWDC (Apple's Worldwide Developers Conference); it's just around the corner.

Josh: Yes.

EJ: With only a few weeks until Apple's developer conference, they will release new AI software and how it integrates with hardware. This will be extremely important, and we will continue to cover it; I'm looking forward to discussing this matter.

Josh: The second pillar is sovereign infrastructure. We often say that bits travel much faster than atoms. Looking at AI infrastructure, the quality of models is increasing almost exponentially; the intelligence generated per watt and each token will only continue to rise.

However, the speed of physical deployment has not increased at a comparable rate, which in itself is a moat. Hardware is incredibly complex; transistor precision is approaching atomic levels. Scaling physical deployments in a world where existing infrastructure is already under pressure is not easy. After the rapid proliferation of electric vehicles, the power grid is feeling greater pressure, with many places nearing full capacity. Now AI is further introducing the energy problem and chip problem.

Gavin strongly bets on the fact that infrastructure is hard to build and requires many days, many months, or even many years. He is investing in those who can compress this cycle into weeks. Therefore, the speed of physical deployment itself is a moat. He is narrowing his focus, looking for companies that can deploy quickly.

The first example that comes to mind is SpaceX, and their speed in constructing Colossus (xAI's large AI supercomputing cluster) and renting it to Anthropic will likely extend to other companies in the future. This infrastructure pillar is one of Gavin's key foci.

Looking at Leopold's portfolio, this is also a core part. The reality is that building things is incredibly difficult, and those who can produce them can sell at very high prices. The show mentioned that SpaceX's largest revenue source now is renting data centers, not rockets. This illustrates the importance of this pillar.

EJ: He is concerned about speed but also cost. He repeatedly mentions one metric: performance per watt, which refers to performance per watt of power. What he truly aims to convey is that AI labs are becoming increasingly concerned with how many tokens can be generated per watt.

If you think about it, only about five companies are spending billions or even tens of billions of dollars this year on GPUs, computing, and the power that drives these systems; you will want to ensure that bang for buck is adequately high. Especially when hyperscalers expand to this scale, cost becomes a core issue.

For instance, if I ask Claude a question and the cost of the answer is 2 cents, and I ask ChatGPT a question and the answer costs $1, even if Claude is only 95% as intelligent as ChatGPT, I would most likely choose Claude. Because I can ask a few more times and ultimately get the answer at a lower cost.

Therefore, the cost of accessing this intelligence is critically important. Just this week, Microsoft and Uber announced they are actually reducing their usage of Claude Code (the AI coding tool from Anthropic), as their annual budgets are getting exhausted in about four months.

You can see this reflected in Gavin’s portfolio: Cerebras, Positron, Astera Labs. He identifies very segmented infrastructure bottlenecks, then makes a simple bet: if this company alleviates this bottleneck, raising performance per watt to a certain level, and reducing token costs to a certain level, AI labs will buy more GPUs, more products, or more of such items.

So his theory is quite straightforward, even though the specific technology is complex: I only focus on bottlenecks within the AI infrastructure level. If I can find a company that raises performance per watt and reduces token costs, I bet it will be valuable in the future, either going public or being acquired at a high price.

Josh: For anyone looking to replicate Gavin’s trades in this segment, you need to know a few names: Astera Labs, Cerebras, SiFive (a RISC-V chip design company), and Positron. These four companies are critical in this sector.

The fourth and final direction is the intersection of energy and space. As we mentioned earlier, the terrestrial grid significantly restricts energy supply, and new energy sources are tough to establish. The show highlighted a statistic that about 40% of new data centers face strong opposition from people lobbying and protesting against their establishment.

There are two types of solutions. One is to create out-of-the-box energy, meaning portable energy. You can bring a data center along and power it with a small energy device. Blue Marble, which Leopold is very optimistic about, falls into this category.

The other is orbital compute, which is a direction Gavin is currently focusing on. The largest and most core company in this field is undoubtedly SpaceX. It is the only company capable of providing a high-speed highway to space, delivering payloads into orbit, transporting racks and data centers to low earth orbit, and generating sufficient intelligence and energy to send back.

I think the significance of SpaceX extends beyond the company itself. I am a bit surprised not to see more space stocks in Gavin's portfolio, considering he believes this is a massive industry. Perhaps the reality is that it is still too early, and SpaceX is the linchpin unlocking this industry.

The upcoming Starship V3 launch will be closely watched. We just witnessed a successful Starship launch last week. If Starship cannot operate effectively, there will be no space energy or racks to orbit. It is a necessary condition because the payload that needs to be launched is substantial. Therefore, SpaceX is certainly a company to focus on, although many secondary companies will also be affected.

Why This is Not Another Internet Bubble?

Josh: Next, everyone is bound to ask, why isn't this just another dot-com bubble? Gavin has been asked this question many times, and he offers a very strong answer, which I fundamentally believe; his argument is quite convincing.

His logic is roughly this: the Internet bubble of 2000 was debt-fueled. Many borrowed significant amounts of money to invest in unproven theories and products that no one truly used or cared about.

Comparing it with what Gavin describes as the AI supercycle, just OpenAI and Anthropic, this year alone, are expected to reach $200 billion in ARR (Annual Recurring Revenue). And this is not fabricated money but money already contracted, a large portion of which, the show notes indicate 40% to 60%, has already been prepaid by enterprise and retail customers. In other words, real money is flowing.

Looking at GPU computing power—not through model labs but observing who is buying products from Nvidia—Google, Microsoft, Amazon, and Meta are using their cash reserves to pay without borrowing. Amazon has just tapped into the end of its free cash flow; if they begin borrowing money, then we can start worrying. But the focus right now is that they are not leveraging.

Moreover, these are some of the top five companies globally, which are arguably also among the smartest companies, given their market capitalization, scale, and status. In contrast to the Internet bubble—in which numerous unknown companies raised substantial funds and burned cash in unreasonable ways—this cycle sees the world's smartest companies spending using unleveraged funds.

The quarterly reports we discussed in recent weeks also indicate that profits are optimizing around these activities, models are improving, and becoming smarter. Therefore, Gavin's core argument is: This is not an Internet bubble because it is not fueled by leveraged money; simultaneously, the bottlenecks we discuss are constrained by physical atoms.

Buying a bunch of memory chips and GPUs is one thing, but Nvidia cannot over-sell GPUs, and Micron cannot over-sell AI memory chips because they do not have enough chip manufacturing capacity. So his simple point is: If you cannot oversupply the entire market, then it is not a bubble. We are constrained by a lack of enough picks and shovels to complete it, and that is what he is investing in.

Another good point: Gavin believes that if TSMC could supply, Nvidia could have sold $2 to $3 trillion worth of GPUs this year and next. In other words, TSMC is a critical link within the bubble boundary.

The reasoning is that if TSMC can meet the demands of these companies and provide them with that many chips, it would consume enormous funds. Now looking at the charts, there has not yet been a significant disconnect between CapEx (capital expenditures) and operating cash; the cash generated by companies is still sufficient to support construction.

However, if TSMC were to tell Nvidia tomorrow that they could triple capacity overnight, Nvidia would not refuse; they would start to spend significant funds buying chips. Other companies would be forced to borrow money to purchase these chips, at which point a CapEx bubble would begin to expand, creating a gap with corporate operating cash flows.

But because all links face supply constraints—storage limitations, chip manufacturing constraints, energy restrictions, especially TSMC's constraints in advanced chip production—realistically, we cannot accelerate the pace of construction that quickly. Therefore, TSMC is blocking the acceleration of the bubble.

As long as TSMC’s chip manufacturing capacity remains limited, and as long as Samsung and other chip manufacturers do not surpass its market share, the growth rate is relatively sustainable. It appears rapid, but there remains significant unmet demand because we are simply not building fast enough. As long as this dynamic exists, I believe the short-term risk is low.

EJ: One more point: you cannot assume that demand remains static, because it won’t. AI-related demand is growing exponentially, and its growth is outpacing the production supply of these chips.

The only two ways I can think of to falsify this theory are: first, if someone miraculously replicates ASML (the core supplier of extreme ultraviolet lithography machine), suddenly a plethora of ASML competitors appear. For those unaware of ASML: it produces machines worth about $400 million, which TSMC and all major chip fabs need. The show mentioned that ASML has only one team manufacturing these things in Norway, and the cycle is very long, with a backlog of orders extending about five years.

The second way would be to create a completely different type of LLM that does not require so many GPUs or much storage. But currently, we see no sign of such an occurrence.

Today I saw a news piece about SK Hynix (a major global supplier of high bandwidth memory). It is the top storage manufacturer and supplier for Nvidia GPUs and is almost the top dog in AI storage. It is currently reportedly receiving offers from Google and Microsoft ranging from $50 billion to $100 billion to lock in supplies they will need to pay for the equipment they need to ramp up production over the next three years.

This underscores how thirsty these big companies are for storage, and this is merely one sub-sector within AI components. Ironically, SK Hynix stated: I don’t want to provide you supply assurance, I’ll just raise prices directly. Their operating margin is about 70%, which is quite astonishing in the semiconductor industry.

So Gavin going all-in makes sense. It doesn’t appear as a bubble; perhaps the market may react that way in the short term. Before we recorded today, we opened the stock portfolio, and it was almost entirely down, but that’s more of a reactionary response. The directional goal of this matter is: we will need more GPUs, more semiconductor chips, while the supply is insufficient and the manufacturers are inadequate.

Gavin's Portfolio

Josh: The conclusion is power and wafers. Just these two. They are two brick walls and two limiting factors preventing us from accelerating too quickly. As long as power and wafers remain valuable, demand is strong, and supply is limited, there will continue to be good days ahead.

If you want a TL;DR (Too Long; Didn't Read) on Gavin's portfolio, I can summarize his largest holdings. Again, this is not investment advice. This is what Gavin holds, which does not represent what we hold. I do not know whether these stocks will go up, down, or remain stagnant.

His largest position is somewhat counterintuitive, being a QQQ put position. Overall, he is bearish on the market, which is quite notable. The second largest is Astera Labs, with a position of about 7.4%, ticker ALAB. The third is Unity, which is the 3D software company.

There are many more: Ciena (a company for optical networking equipment), Micron, Nvidia, Amazon, Lumentum (a company for optical communication and laser devices), Alphabet (Google's parent company), Coherent (a company for optoelectronics and materials), Roblox (a gaming platform), EchoStar (a satellite communication company), Twilio (a cloud communications platform), Wayfair (a furniture e-commerce company). He invests in everything.

If you are interested, you can check his 13F, and we will include the link in the description. But this is Gavin's viewpoint, and the bottlenecks are in power and wafers. As long as these constraints remain in place, it is essentially a one-sided upwards trend. EJ, how do you absorb this information? How would you process it?

EJ: Since Leopold’s 13F came out, the market has been quite volatile. As I record this episode, I am increasingly realizing that Gavin is like an older, smarter version of Leopold. He has been in the industry for a long time. He may not have $13 billion AUM, but I feel he will still be around in ten years.

If you are thinking right now that you do not want to track AI developments every minute, hour, or day, you just want to put your money there and see how it grows over the coming months or years. Then Gavin’s portfolio could be very meaningful as a reference. Of course, this is not investment advice.

He adopts a more cautious, long-term, and future-oriented approach. If his trend judgments ultimately materialize, just like his early bets on Nvidia and Cerebras, exponential returns may come in the following years. But all of this rests on his core viewpoint: we are not in a bubble.

I am curious if listeners agree. Obviously, most people won't delve as technically or deeply as Gavin. But after listening to this episode, do you feel we are in a bubble? Or not? What are the supporting and opposing arguments? Is there anything we missed? Josh, do you think it is a bubble before we wrap up?

Josh: I definitely think we are in a bubble. The question is, at which stage of the bubble are we? That can still be discussed. As it stands, it seems like an early stage, so hopefully, it continues to maintain that state. According to Gavin, as long as TSMC continues to limit chip production capacity, we are still okay.

That is the overall outlook. We have already discussed Leopold, whose success is currently measured quarterly; now we are discussing Gavin, whose success is measured in decades. Many people's answers may fall somewhere in between the two.

If you liked this episode, don’t forget to share it with your friends. Also, let us know which type of asset you are most optimistic about. Perhaps it's not a theory but a stock code that deserves our attention. I find this exciting, as everything is moving quickly, whether upward or downward, with a lot of fluctuations and a sense of involvement. See you tomorrow, good morning.

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