Jordi Visser / VisserLabs

Silver Went Parabolic — The Next Trade Is Still Forgotten — Jordi Visser (25 enero 2026)

🇬🇧 EN🇪🇸 ES
AIMacroMarketsTrading
51:53 min youtube 2026 Week 4 🇬🇧 EN

TL;DR

  • The AI trade is shifting from software abundance to physical scarcity, making materials (silver, copper) and energy the primary structural investment drivers.
  • Investors should focus on buying scarcity—favoring commodities and emerging markets like Brazil over pure large-cap tech growth.
  • A massive $85 trillion infrastructure buildout is underway, creating long-term, non-cyclical demand for critical minerals needed for AI hardware.

Summary

YouTube: https://www.youtube.com/watch?v=CEtUJN2nfEI  |  Duration: 51 min

â—† Market Dynamics and Sector Shifts

The market has been flat, with the S&P 500 being stagnant for the last 60 days. While year-to-date performance is up one percent, growth is primarily driven by energy and materials sectors. The Mag 7 saw a slight gain of around one percent.

Market leadership is quietly shifting toward materials, energy, and industrials due to a physical upgrade cycle. Small caps are expected to significantly outperform large caps this year as the AI trade reaches an inflection point and decentralization takes hold away from the MAG 7. Political events are considered directional shocks but not economically important for trading decisions.

The speaker advises focusing on these physical sectors rather than expecting major bounces in tech or software alone, noting that current valuations support a prolonged ramp-up period for essential resource sectors.

â–¶ The Core Investment Framework: Scarcity vs. Abundance

The core investment framework centers on identifying persistent bottlenecks rather than trying to predict market bottoms in abundant sectors like software. Software development is characterized by massive abundance, making attempts to time its cycles highly speculative.

The next major bottleneck involves the conversion of raw energy sources into intelligence. This process has two distinct tracks: power infrastructure (energy conversion) and compute infrastructure (scarce advanced GPUs and high bandwidth memory).

⚠️ Critical Risk Alert: The primary bottlenecks for AI growth are physical scarcity in commodities like silver and copper, and the slow speed of enterprise adoption. Intelligence cannot scale without massive infrastructure buildout, creating long-term shortages in essential materials and energy.

★ Critical Minerals, Geopolitics, and the AI Arms Race

The global arms race for military supremacy is heavily reliant on critical minerals needed for advanced AI components. Securing these supply chains is a major geopolitical focus, involving strategic concerns around nations like Greenland and Venezuela.

Despite having abundant fossil fuels, the world faces significant shortages of essential critical minerals. This mineral scarcity was instrumental in recent trade negotiations regarding rare earths with China. Industrially, companies are entering mining sectors, such as Amazon's involvement in copper extraction.

â–º Silver: From Precious Metal to Industrial Input

Silver is shifting its role from a precious metal to a critical industrial input driven by AI and advanced technology. It is essential for numerous high-growth sectors including solar, data centers, NPUs, EVs, edge devices, robotics, and drones.

These technological applications are currently in simultaneous S-curves, creating massive structural demand for the metal. The speaker argues that silver's demand is price inelastic; its necessity remains even if market prices fluctuate significantly. Therefore, mining stocks are expected to maintain underlying support due to this fundamental industrial need.

â—† The $85 Trillion AI Infrastructure Buildout

Jensen Huang highlights a massive $85 trillion infrastructure buildout over 15 years driven by AI, spanning energy, chips, and data centers. He argues that this investment cycle is in its early innings, mirroring historical commodity booms like silver and copper.

The shortage of essential materials is non-cyclical and long-term, contradicting current analyst skepticism regarding miners. Materials and energy stocks are predicted to be the top performers this year due to this structural demand shift. Investors should recognize that the market is moving away from a pure growth focus toward value and commodities.

â–¶ Structural Under-Ownership in Materials and Energy

Materials and energy sectors are structurally under-owned due to the shift toward building out the massive infrastructure of the AI factory world. Market indicators suggest breakouts in healthcare, energy, and materials stocks, with Euro stocks being especially favorable.

Commodities such as silver, copper, and DAMS are expected to rise alongside DRAM prices. Emerging Markets benefit significantly from this commodity demand. The speaker anticipates a weakening dollar because US large caps may underperform, driving capital into manufacturing and commodities which favors Europe over the US.

★ Emerging Markets and Investment Strategy

The speaker identifies Brazil as a critical-mineral winner due to the global push for secure supply chains amid US-China trade tensions. He suggests that emerging markets, particularly Brazil, offer strong investment potential in the current environment.

Regarding inflation, he is skeptical of major spikes because current economic weaknesses and fossil fuel abundance differ significantly from past oil-driven inflationary periods. Despite this, he recommends being long energy stocks due to increasing power scarcity and commodity sector booms. The central investment strategy advocated is buying scarcity, meaning investors should favor commodities and energy over abundance sectors like software.

â–º The Threat to Traditional SaaS Models

Agentic AI and vibe coding are fundamentally threatening the traditional enterprise SaaS model by demanding hyper-customization. Traditional SaaS platforms rely on standardizing workflows for a mass audience, but large enterprises require 100% tailored automation that rigid systems cannot provide.

The value is shifting from the software itself to the data and the results achieved through customized agentic AI solutions. Consequently, workflows are no longer the competitive moat; success depends on replacing people with highly customized automated processes. This forces enterprise SaaS companies to evolve into agent-native models or face severe structural value compression.

â—† Crypto and Long-Term Patience

The speaker notes that assets like silver move rapidly and emphasizes the importance of patience in investing. While Bitcoin currently shows short-term negative signals, he maintains a long-term bullish view for the crypto space.

He distinguishes Ethereum as representing utility while positioning Bitcoin primarily as a store of value. Using resources stocks and a custom proxy involving copper, gold, and Q's, he predicts that similar strong upward movements are imminent for Bitcoin and Ethereum.

âš¡ Actionable Recommendations

  • Focus on structural demand: Favor commodities and energy over abundance sectors like software.
  • Invest in scarcity: Target critical mineral winners, such as companies in Brazil or mining stocks benefiting from silver's industrial role.
  • Look beyond the MAG 7: Expect small caps to significantly outperform large caps this year.
  • Be long energy stocks: Due to increasing power scarcity and commodity sector booms.

â—† Search for the alpha

The core thesis driving capital allocation is a decisive shift from betting on software abundance and pure growth in large-cap tech to investing in physical scarcity. The massive, non-cyclical $85 trillion AI infrastructure buildout fundamentally redefines investment risk, favoring essential resource providers (Materials, Energy) over companies whose value proposition relies solely on standardized digital workflows.

  • Avoid the Software/Semis Trade: Do not pursue the popular trade of shorting semiconductors and being long software; the speaker predicts that software relative to the NDX will continue declining for at least five years due to increasing enterprise competition and agentic AI disruption.
  • Focus on Physical Bottlenecks: The primary investment focus must be on sectors addressing physical scarcity—namely Materials, Energy, and Critical Minerals—as these are the binding constraints of the global AI factory buildout.
  • Silver's New Role: Silver is no longer primarily a precious metal but an industrial input essential for high-growth applications (solar, data centers, EVs), meaning mining stocks maintain fundamental support regardless of short-term price fluctuations.
  • Emerging Market Exposure: Emerging Markets, specifically Brazil, are highlighted as critical mineral winners due to the global geopolitical push for secure supply chains amid US-China tensions.
Asset Signal Reading
Materials & Energy Stocks Structural Demand Predicted top performers this year due to AI infrastructure buildout.
Silver (Ag) Industrial Input Shifting from precious metal status; demand is price inelastic for tech applications.
Brazil / Emerging Markets Geopolitical Advantage Strong investment potential as a critical mineral supplier in secure supply chains.
The twist: The market is still framing AI as an exponential software revolution, but the guest views it through a hardware and power lens. The implicit message is that the true economic bottleneck isn't intelligence capability itself, but the physical ability to convert raw energy into usable compute infrastructure, making resource companies the ultimate beneficiaries of the AI cycle.

â–º Chapter Summaries

Why this week marks a turning point in the AI trade (0:00)

The speaker is focusing attention on silver, copper, and minerals, viewing them as a critical transition point for the physical world. He notes that investors are concerned about parabolic commodity moves while simultaneously shorting semiconductors and buying software. Market-wise, S&P, Q's, and small caps were flat this week despite initial volatility. The Mag 7 saw a slight gain of around one percent. While year-to-date performance is up one percent, growth is primarily driven by energy and materials sectors. Tech stocks are currently near the bottom, contrasting with industrials, materials, and energy being among the best performing areas.

Markets are flat, but leadership is quietly shifting to materials and energy (2:05)

The S&P 500 has been flat for the last 60 days, suggesting it is spinning its wheels while market leadership quietly shifts toward materials, energy, and industrials due to a physical upgrade cycle. The speaker advises focusing on these sectors rather than expecting major bounces in tech or software alone. Regarding macro trends, small caps are expected to significantly outperform large caps this year as the AI trade reaches an inflection point and decentralization takes hold away from the MAG 7. While rates remain a key theme, the speaker does not view shorting bonds as a viable trade, noting that junk spreads have remained stable. Political events are considered directional shocks but not economically important for trading decisions.

Software abundance vs physical scarcity: the core investment framework (5:15)

The core investment framework centers on identifying persistent bottlenecks rather than trying to predict market bottoms in abundant sectors like software. Software development is currently characterized by massive abundance, making attempts to time its cycles highly speculative. The next major bottleneck involves the conversion of raw energy sources into intelligence, a process with two distinct tracks. A key trend disrupting the software industry is the move toward hyper-customization, similar to personalized medicine for patients. This customization will specifically apply to business workflows as technology evolves. Additionally, physical scarcity remains a critical factor in investment analysis, particularly concerning shortages of critical minerals.

Critical minerals, geopolitics, and the new AI arms race (7:18)

The global arms race for military supremacy is heavily reliant on critical minerals needed for advanced AI components. Securing these supply chains is a major geopolitical focus, involving strategic concerns around nations like Greenland and Venezuela. Despite having abundant fossil fuels, the world faces significant shortages of essential critical minerals, which remains a key policy focal point. This mineral scarcity was instrumental in recent trade negotiations regarding rare earths with China. Industrially, companies are entering mining sectors, such as Amazon's involvement in copper extraction. The speaker also noted the parabolic nature of silver's recent price movement despite anticipated pullbacks.

Silver is not a precious metal anymore — it’s an AI industrial input (9:52)

Silver is shifting its role from a precious metal to a critical industrial input driven by AI and advanced technology. It is essential for numerous high-growth sectors including solar, data centers, NPUs, EVs, edge devices, robotics, and drones. These technological applications are currently in simultaneous S-curves, creating massive structural demand for the metal. The speaker argues that silver's demand is price inelastic; its necessity remains even if market prices fluctuate significantly. Therefore, regardless of specific price targets, mining stocks are expected to maintain underlying support due to this fundamental industrial need. This transition highlights silver's growing importance in both civilian technology and modern military applications like drone warfare.

Jensen Huang on the $85T AI infrastructure buildout (12:07)

Jensen Huang highlights a massive $85 trillion infrastructure buildout over 15 years driven by AI, spanning energy, chips, and data centers. He argues that this investment cycle is in its early innings, mirroring historical commodity booms like silver and copper. The shortage of essential materials is non-cyclical and long-term, contradicting current analyst skepticism regarding miners. Materials and energy stocks are predicted to be the top performers this year due to this structural demand shift. Investors should recognize that the market is moving away from a pure growth focus toward value and commodities. Current valuations support a prolonged ramp-up period for these essential resource sectors.

Why materials and energy are structurally under-owned (14:44)

Materials and energy sectors are structurally under-owned due to the shift toward building out the massive infrastructure of the AI factory world. Market indicators suggest breakouts in healthcare, energy, and materials stocks, with Euro stocks being especially favorable. Commodities such as silver, copper, and DAMS are expected to rise alongside DRAM prices. Emerging Markets benefit significantly from this commodity demand, making them a natural investment area. The speaker anticipates a weakening dollar because US large caps may underperform, driving capital into manufacturing and commodities which favors Europe over the US. This trend confirms that the next phase of artificial intelligence will be defined by hardware, power, and materials rather than just software.

Emerging markets and Brazil as critical-mineral winners (16:27)

The speaker identifies Brazil as a critical-mineral winner due to the global push for secure supply chains amid US-China trade tensions. He suggests that emerging markets, particularly Brazil, offer strong investment potential in the current environment. Regarding inflation, he is skeptical of major spikes because current economic weaknesses and fossil fuel abundance differ significantly from past oil-driven inflationary periods. Despite this, he recommends being long energy stocks due to increasing power scarcity and commodity sector booms. The central investment strategy advocated is buying scarcity, meaning investors should favor commodities and energy over abundance sectors like software.

The two bottlenecks: power infrastructure vs compute infrastructure (19:43)

The primary bottlenecks in AI buildout are power infrastructure, which handles energy conversion, and compute infrastructure, involving scarce advanced GPUs and high bandwidth memory. The speaker argues that while raw energy is abundant, the conversion process into intelligence presents a persistent issue. He advises against the popular trade of shorting semis and being long software, predicting that software relative to the NDX will continue declining for the next five years. Instead, investors should look at shortages in materials or consider cyclical rerating opportunities in the drug, healthcare, and energy sectors. While AI coding is accelerating app releases and Anthropic is strong on the enterprise side, competition within enterprise software is rapidly increasing. Furthermore, commodities like copper and silver remain important indicators, especially if oil prices do not rise.

Why agentic AI and vibe coding threaten traditional SaaS (23:52)

Agentic AI and vibe coding are fundamentally threatening the traditional enterprise SaaS model by demanding hyper-customization. Traditional SaaS platforms rely on standardizing workflows for a mass audience, but large enterprises require 100% tailored automation that rigid systems cannot provide. The value is shifting from the software itself to the data and the results achieved through customized agentic AI solutions. These new agentic platforms are designed to handle complex tasks without predefined scripts, offering adaptability that incumbents find costly and difficult to match. Consequently, workflows are no longer the competitive moat; success depends on replacing people with highly customized automated processes. This forces enterprise SaaS companies to evolve into agent-native models or face severe structural value compression. The primary risk in this transition is not current AI capability but human underestimation of exponential technological progress.

Adoption, not technology, is the real AI monetization bottleneck (30:12)

The primary bottlenecks for AI growth are physical scarcity in commodities like silver and copper, and the slow speed of enterprise adoption. Intelligence cannot scale without massive infrastructure buildout, creating long-term shortages in essential materials and energy. While AI models are rapidly improving, Dario Modi notes that current enterprises struggle to deploy technology that is potentially ten times more capable than what they can currently use. The market shows a widening gap between the top 12% of companies successfully embedding AI into core workflows and those who lag behind. Furthermore, the focus is shifting from pure software models to a hardware stage driven by robotics and autonomous vehicles, with electrical power being the binding constraint. This combination of physical scarcity and adoption friction creates an air gap between massive economic potential and current revenue realization for many tech companies.

Bitcoin, crypto structure, and why patience matters (48:32)

The speaker notes that assets like silver move rapidly and emphasizes the importance of patience in investing. While Bitcoin currently shows short-term negative signals, he maintains a long-term bullish view for the crypto space. He distinguishes Ethereum as representing utility while positioning Bitcoin primarily as a store of value. Observing historical correlations between precious metals and cryptocurrencies, he points to recent parabolic moves in basic resource stocks. Using these resources stocks and a custom proxy involving copper, gold, and Q's, he predicts that similar strong upward movements are imminent for Bitcoin and Ethereum. He advises investors not to wait for these major parabolic shifts to occur.

Generated with algorithm v1-chunked · model google/gemma-4-e4b · 2026-02-17T11:00:00Z

Transcript

[0:00] uh in Miami this week for a Bitcoin
[0:03] conference. So,
[0:06] let's go through this. Um
[0:12] week started off uh as a lot of these
[0:15] weeks seem to start off with uh uh some
[0:19] issues involving Davos and Greenland and
[0:22] that lasted for a day. Uh I am going to
[0:25] finally spend a lot more time on
[0:29] silver, copper, minerals. We've talked
[0:32] about uh this being a transition point
[0:34] for the physical world, but I am going
[0:36] to really start to focus the attention.
[0:38] I'm writing a piece on silver for next
[0:40] week. And most of that is inspired by
[0:41] what I've heard at this event as people
[0:44] are trying to pick a top in uh
[0:48] commodities or at least worried about
[0:49] the parabolic moves. At the same time,
[0:52] they're looking to short semiconductors,
[0:55] which have had a parabolic move, and buy
[0:57] software. That seems to be what
[0:59] everyone's doing. I'll cover all of that
[1:01] this week and kind of go through my
[1:03] thoughts uh on where we are in the AI
[1:06] trade and especially for this year on
[1:08] the themes of making money. So, quickly,
[1:10] S&P flat for the week despite the uh
[1:14] horrible start. flat for the week for
[1:15] the Q's, flat for the week for small
[1:19] caps, and the Mag 7 up about 1%. Heading
[1:22] into earnings, uh we did have uh the
[1:26] second largest sigma fall
[1:30] uh in the market in the last, you know,
[1:33] this is uh over the course of the last
[1:36] 18 months uh or sorry, last year. But
[1:39] the main point I think to say is that
[1:41] most of this occurred. It was a big move
[1:43] down but it was also coming off of a uh
[1:46] a low V realized V time. So year to date
[1:50] now we're up 1% but I just want to again
[1:52] highlight that we are being led by
[1:54] energy and materials. So you're getting
[1:56] a lot of alpha there and you can see
[1:58] tech near the bottom. Uh you can see
[2:01] industrials, materials and energy. Three
[2:03] of the best performing stocks. We are in
[2:05] the physical upgrade cycle. I expect
[2:07] this to continue. Um, this to me is a
[2:10] preview for the year. Regardless of any
[2:12] bounce we get in tech or any bounce in
[2:14] software, I think you need to be
[2:16] focusing on energy, materials, and
[2:18] industrials in different components of
[2:20] it. And I'll go through the areas. Um,
[2:23] last 60 days in the S&P, uh, lowest rate
[2:26] of change since we broke out in May,
[2:29] which basically says the S&P is kind of
[2:31] spinning its wheels here for the last 60
[2:33] days, 60 trading days, which is three
[2:35] months. Um when we did get the move
[2:38] lower on Tuesday, uh the turbulence
[2:42] model basically showed more signs of a
[2:45] bottom than than anything. Uh but most
[2:48] importantly, I did get a few reachouts
[2:50] from people on what the turbulence model
[2:53] did and if there was anything there. And
[2:55] effectively from the analysis, it was
[2:57] very consistent with you had no movement
[3:00] in credit whatsoever measured on real
[3:02] time with HYG versus IF. Here are the
[3:05] junk spreads still sitting down there.
[3:07] Uh, as long as anything that happens
[3:10] with Trump is not economically important
[3:13] or earnings important and nothing that
[3:16] was going on with Greenland to me was
[3:17] more than a directional shock to people.
[3:20] Uh, I think I would be on the side of
[3:22] using those events to find the names
[3:24] that you haven't been able to get into
[3:26] yet because they've moved against you.
[3:28] Again, we had more people talking about
[3:30] rates. This will be a theme for me all
[3:32] year. um you know since 2022 when chatpt
[3:37] was launched basically tenure rates in
[3:39] the US have stayed between four and four
[3:41] and a half for the majority of the time.
[3:43] So we're at 423 uh as of Friday. Anyone
[3:47] focused at this being the big trade when
[3:49] you're seeing things like micron
[3:50] technology and silver and all of this
[3:54] stuff is just a waste of time to me. And
[3:56] again I have no care whether tenure
[3:58] rates go to 475 or 375 uh breaking out
[4:01] of this range. I think they're going to
[4:03] be stuck around this level. If I had to
[4:04] guess, we're going to see an upward move
[4:07] in rates, but I just don't think
[4:08] shorting bonds is a trade. Michael
[4:10] Hartnett had a lot of good things out
[4:12] this week in terms of kind of the charts
[4:14] and things that I agree with. Uh, so I
[4:17] just wanted to highlight them, not
[4:18] because we're in agreement, but because
[4:20] I think these are important charts to
[4:21] keep in the back of your mind for what I
[4:22] think is going on. First thing is is
[4:24] just the returns between small cap and
[4:26] large cap. As I've talked about, I
[4:27] expect small caps to be up significantly
[4:29] this year relative to large caps. You
[4:31] can see the inflows. You can see all
[4:33] this. This is the year where I think
[4:34] people are basically going to have a
[4:36] hard time making alpha the way they have
[4:38] in the past.
[4:40] And this is all related to the fact that
[4:42] I think we're at an inflection point in
[4:44] the AI trade. And I really think people
[4:46] are going to have to uh adapt uh in
[4:49] understanding what's happening as the
[4:50] MAG 7 and the deconentration takes hold.
[4:54] Uh just again AI is an important thing
[4:57] and now we're getting kind of the first
[5:00] fear factor I guess uh with where storms
[5:04] connect back to the grid. We'll see what
[5:06] happens but I wanted to bring this up in
[5:07] case something goes on. So this is a
[5:09] visual that you're going to start seeing
[5:11] me spend more time on the entire year
[5:14] because I believe to make money from
[5:16] this point. This is the software boom.
[5:18] We have abundance. Um everyone can try
[5:20] to pick a bottom in software. I view
[5:22] this as the same thing as people trying
[5:24] to predict when the equity market is
[5:26] going to collapse on the back of the
[5:27] debt. Uh the ego that sets in with
[5:30] trying to go against something that has
[5:32] worked for 15 years that now has an
[5:35] insane amount of abundance in it. Uh
[5:38] coding is abundant. Uh apps are now
[5:40] being built in fast time. All of these
[5:42] things which I'll go through as it goes.
[5:44] But just keep this in mind. This is
[5:46] about scarcity. Now I every investment
[5:49] you have to go through and look for
[5:50] bottlenecks and decide whether this is
[5:52] going to be there over the next 5 years
[5:54] and if it is how much of has already
[5:56] been discounted I think the data center
[5:57] scarcity side is known um I think the
[6:01] next stage is really as I'll go through
[6:04] something that fits more into a
[6:06] different visual which I'll highlight
[6:08] which is the conversion from raw p raw
[6:11] energy so natural gas solar coal
[6:15] whatever you want into intelligence
[6:17] There are two two tracks of that of that
[6:20] uh bottleneck and I'll break them down.
[6:21] I've talked about them separately but I
[6:23] have a visual to go through. Uh again we
[6:26] start the economist finds a way to make
[6:29] sure that there's a bottom in the market
[6:30] at least for a period of time but the
[6:32] Donald Trump bare thing to start off the
[6:34] week. I do want to highlight a new drug
[6:36] for each patient. I'm going to highlight
[6:37] this now. This was a story in there.
[6:39] This is again going to be important to
[6:41] me when we get to the software side and
[6:43] being in an age where now you're
[6:45] customizing. Uh think of a new drug for
[6:48] each patient as different than Advil for
[6:51] everyone or drugs that are made for
[6:54] everyone goes through an FD everyone
[6:56] takes it. um customizing things
[6:59] specifically for a patient is going to
[7:00] be the trend, but customizing workflows
[7:03] specifically for businesses is going to
[7:05] be the solution that will disrupt
[7:07] software over as time goes on. Uh so the
[7:09] Greenland side, I talked about this a
[7:12] year ago, you're going to continue to
[7:14] hear me talk about this. We have a
[7:16] shortage of critical minerals. We are in
[7:18] an arms race around the world for AI.
[7:20] We're in an arms race for military
[7:22] supremacy. All of these things require
[7:25] critical minerals. In the past, we have
[7:26] talked about rare earth as a major
[7:29] factor in this. So, anyone that doesn't
[7:32] want to connect Greenland to critical
[7:33] minerals in Greenland to the strategic
[7:36] implications of making sure that the
[7:38] other AI uh components with the
[7:40] military, China, Russia, Iran, they are
[7:43] involved in all this decision making.
[7:45] So, it's not just about the rare earth,
[7:47] it's also about security. Same thing
[7:49] went for Venezuela. I had some people
[7:51] reach out to me here and say that people
[7:53] had said that it was ridiculous for me
[7:54] to post something about Venezuela's
[7:56] importance with crit critical minerals.
[7:59] I don't think people fully grasp this.
[8:01] 100% everything in the world is about
[8:03] securing the supply chains right now in
[8:05] the military making sure that Iran and
[8:07] Russia in particular when it comes to
[8:09] the US along with China are in their
[8:11] hemisphere. Um we are in a different war
[8:14] type situation with drones over tanks.
[8:16] We're in a different war situation with
[8:18] drones over ships and making sure that
[8:20] bases are at a particular pace and also
[8:22] that we have access to critical minerals
[8:24] because these will be in a shortage for
[8:26] a long long time. And Howard Luck Lutnik
[8:29] confirmed it. He told reporters
[8:30] Venezuela possessed significant amounts
[8:32] of critical minerals and a great mining
[8:34] history that's gone rusty. He believed
[8:36] the Trump administration could fix. So
[8:38] again, the administration talks about
[8:39] it. So for people that are not fully
[8:41] grasping this and talking about oil, we
[8:43] have abundance of energy. We have more
[8:45] fossil fuels than we need for the
[8:47] environment we're in. But we have
[8:48] shortages of and this was announced by
[8:51] the White House um on January 14th
[8:55] again around critical minerals. This is
[8:57] a focal point. This will remain a focal
[8:59] point. Don't forget it. It's what
[9:00] stopped the tariff situation with the
[9:02] rare earths and China showing that they
[9:04] had this monopoly in exchange for the
[9:06] chips. That is our monopoly.
[9:09] So you also had Riotinto and Amazon
[9:11] collaborate to bring copper. So
[9:13] basically, Amazon's in the copper mining
[9:15] business as of this week, the silver
[9:18] chart. And yes, there will be a
[9:20] pullback. And it makes sense that we're
[9:23] just above 100. Uh this was before the
[9:25] close on Friday, but I wanted to
[9:27] highlight the parabolic nature of this,
[9:29] which is these are impossible charts to
[9:32] buy. Where do you buy something that has
[9:34] already broken out? you had basically 14
[9:36] years to buy silver and in particular up
[9:39] until about September October it really
[9:42] wasn't moving and then this move
[9:44] occurred. So the question is that being
[9:46] at this conference and hearing people
[9:48] talk about silver and thinking about it
[9:49] as a precious metal. I just want to make
[9:52] the connection. It is not a precious
[9:53] metal is directly related to the AI
[9:55] trade. It is an industrial metal and
[9:57] more importantly the fact that people
[9:59] think of this as something that is just
[10:01] representative of what gold has done. I
[10:03] think we have to dig deeper in this.
[10:05] This is the overlay of micron with
[10:06] silver. These are parabolic moves. These
[10:09] are the types of things that if you
[10:10] don't get in early you have a hard time
[10:12] getting into. I posted this because Elon
[10:15] Musk during the holiday season posted
[10:17] about silver. This is not good. Silver
[10:19] is needed in many industrial processes.
[10:21] That's an understatement. It is huge in
[10:24] solar, in NPUs, in switch gear, in
[10:27] circuit breakers, data center, advanced
[10:29] packaging at chips, EVs, FSD, edge
[10:31] devices, drones, radar, electronic
[10:34] warfare, robotics, humanoids, and
[10:36] sensors. All of these are in S-curves.
[10:39] They're all happening at the same time,
[10:41] which is why I'm writing a paper because
[10:42] I want to make sure people realize
[10:44] regardless of your belief in where
[10:45] silver should be, whether it should be
[10:46] 100 or 75, the mining stocks are going
[10:49] to continue to have a bid underneath
[10:50] these. This is not a cyclical move in
[10:52] silver. And that's the point of this.
[10:53] Forget buying into it at this point. It
[10:56] is price inelastic. It does not matter
[10:59] whether it's $300, $400, $500. It is
[11:02] such a small portion of
[11:04] data centers that it's a small portion
[11:06] of AIS. It is not huge in this, but it
[11:10] is necessary in this. So, it has become
[11:12] a critical part in drones. Uh, it is a
[11:15] critical part in a lot of different
[11:16] things. But when you're talking about
[11:18] drones versus tanks, massive amounts of
[11:20] drones versus very limited dranks,
[11:22] extremely high replacement rate. This is
[11:24] just showing the importance or the
[11:26] transition in silver for the military
[11:28] side. And this is just with drones. This
[11:31] doesn't include the other side of it. It
[11:32] does include the data centers and the
[11:34] places that it's necessary uh for NPUs
[11:37] and edge devices for humanoids. All of
[11:39] that's there. Uh I'm just bringing this
[11:41] up because Cuba would be the I guess the
[11:44] last place where there's a Russia China
[11:48] situation and where there's critical
[11:49] minerals. So don't be surprised if Cuba
[11:52] becomes uh a story at some point this
[11:54] year as well. Now we did have Davos
[11:58] going on and Nvidia and Jensen Yuang
[12:01] spoke many many times. I just wanted to
[12:03] highlight this thing that he said on
[12:05] Friday which is the largest
[12:07] infrastructure buildout in US history or
[12:10] human history with estimates pointing to
[12:12] $85 trillion of investment over the next
[12:14] 15 years spanning energy chips, data
[12:15] centers and AI factories. The buildout
[12:18] is still in the early innings. The
[12:19] reason I bring up the silver thing, the
[12:20] reason I bring up the copper thing,
[12:23] we're at the very early stages. So when
[12:24] I wrote the paper this year about us
[12:26] being at the transition point away from
[12:28] software and into hardware, we're at the
[12:30] very beginning stages of building AI
[12:32] factories. We're at the very beginning
[12:33] stages of having a shortage in energy of
[12:36] needing the micron. That is why you're
[12:37] seeing these parabolic moves because
[12:39] we're in a commodity bull market and
[12:41] where there's shortages you buy as much
[12:43] as you can particularly when they're
[12:45] price insensitive uh for the demand. So
[12:48] this came out uh Friday. Bernstein is
[12:51] showing what the copper shortage will
[12:53] look like. So I want you to think back
[12:55] to Jensen Yuang's thing and he basically
[12:57] said for the next 15 years. So I wrote
[12:59] my paper. This is 2026. Here's 15 years.
[13:03] This is the predicted shortage. This is
[13:06] not cyclical. This is not okay copper
[13:09] goes to a certain level and it's going
[13:10] to collapse. You have to think about
[13:12] that because that's the way these stocks
[13:13] have been priced and that's the way they
[13:15] show up. So right now to show how much
[13:17] people doubt the non-yclicality of
[13:19] miners here is the move in basic
[13:21] resources in the uh euro stocks I want
[13:25] you to highlight we're at the same level
[13:27] we were in the great financial crisis.
[13:29] This is going much higher and this will
[13:31] be as I said materials and energy will
[13:33] be the two best performing stocks this
[13:34] year. I said this the first week, it
[13:36] continues to be the case. And right now,
[13:38] earnings estimates, the analysts are
[13:41] still freaking out about cyclicality.
[13:45] This will be a long-term play. From a
[13:47] valuation basis, it's they're not only
[13:49] rejecting kind of earnings estimates,
[13:52] we're at valuations which say you got a
[13:54] long ramp run rate into here. Even if
[13:57] nothing else happens, as long as silver
[13:59] stays around the same level, you can see
[14:01] where the money's going to go. Now, I do
[14:03] believe this is the 1970s. Now, before
[14:05] you get into the inflation stuff, I'll
[14:07] get into that. But these are the types
[14:08] of things that Michael Hartnett put out
[14:10] that you want to be invested in. Look
[14:11] where tech is. So, we just entered a
[14:13] point where you wanted to be the
[14:14] opposite. You didn't want value. You
[14:16] wanted growth. Growth is here. It's
[14:18] still going to work, but it is way
[14:19] behind a lot of these. I completely
[14:21] agree with small cap, the value side,
[14:23] commodities, all of that. So, if you're
[14:25] a mutual fund sitting out there and your
[14:26] portfolio is weighted heavily towards
[14:28] growth, remember your current waitings.
[14:30] Yes. technology sector 33, but when you
[14:32] add in the communication services,
[14:35] you're up at 43. When you add Amazon and
[14:37] other things from the consumer
[14:38] discretionary side, you're basically at
[14:39] 50%. Energy, materials, 5%.
[14:44] Nobrainer.
[14:46] Get your money in these things. We need
[14:48] and we cannot convert raw energy into
[14:51] intelligence. So, we were using GPUs for
[14:54] the data center. That was for the
[14:55] training models, but we are now entering
[14:57] the AI factory world. that
[14:58] infrastructure buildout that Jensen Yu
[15:00] Wong talks about, who better to kind of
[15:02] say what's going on. Now, from a
[15:03] charting basis, you've got healthc care
[15:06] ready to break out. I've talked about
[15:07] the drug and the pharma side. You've got
[15:08] energy ready to break out of a big base,
[15:10] John Rog style. You have materials ready
[15:12] to break out of a John Rog big base
[15:14] style. And I highlighted the euro stocks
[15:16] are even better. So, technically, this
[15:18] is not just an AI story. This is also
[15:20] the technical picture, the underweight
[15:22] picture, everything. DRAM, another super
[15:24] spike. So, this is the old DRAM prices.
[15:27] This is overlaid with PMIs. Commodities,
[15:30] commodities, commodities, silver,
[15:31] copper, DAMS. You're going to have
[15:33] everything along those lines going
[15:35] higher. EM a natural play on this. I'm
[15:38] going to spend a lot of time on EM. I'm
[15:39] going to spend a lot of time on Brazil
[15:40] today. EM is a natural place for this to
[15:43] be. They benefit a lot from what we're
[15:45] talking about, but in particular
[15:47] commodities. I want to be long emerging
[15:49] markets. I wrote this in Brazil in June
[15:51] of last year.
[15:53] The dollar to me will be weakening on
[15:55] the back of the capital s uh the capital
[15:57] account surplus reversing because the
[15:59] mag 7 will be underperforming. If
[16:01] materials do well look at the material
[16:03] side in Europe versus material side in
[16:05] the US compare them as a waiting in the
[16:07] portfolio and you get very very
[16:09] different things. Technology in the US
[16:10] and particularly the mag 7 if they're an
[16:13] underperformer and large cap is an
[16:14] underperformer you're going to have a
[16:15] massive move into the manufacturing and
[16:17] commodity stuff that's better for
[16:18] Europe. It leads to capital outflows. uh
[16:21] the next phase of artificial
[16:22] intelligence will be defined by
[16:24] hardware, power, materials, not
[16:25] software. I wrote this in June of last
[16:27] year. If you would have followed this
[16:28] with silver and everything and
[16:30] everything along those lines and semis
[16:32] and micron and all all of this stuff as
[16:34] opposed to software, you'd be doing very
[16:37] well since there. And finally, a global
[16:39] push to secure critical minerals due to
[16:41] the trade war between China and the US
[16:42] where Brazil is rising fast as a
[16:44] strategic supplier. Just think about the
[16:46] countries we're seeing and how much
[16:47] critical minerals are bringing up. This
[16:49] is a new story. Brazil breaking out.
[16:52] This is what it's done since I wrote
[16:53] that paper. It's up 30
[16:56] 30% since there. I think it has a lot
[16:59] further to go. This is the relationship
[17:01] historically over the last 14 years
[17:03] between silver and EWZ. Obviously,
[17:06] there's a dollar component. There's a
[17:07] Brazil component, which gets back to
[17:09] commodities. Well, here's EWZ with the
[17:12] most recent thing of silver. If you're
[17:13] looking for lagards, get into Brazil. We
[17:16] just had one of the best performances on
[17:19] a weekly basis in EWZ relative to the
[17:21] S&P. Brazil is up in one of the best
[17:24] performing markets in the world right
[17:26] now. Now, this is the negative side and
[17:28] why I said earlier, do not worry about
[17:30] the inflation component. So, I just want
[17:33] to highlight he put this as well. This
[17:35] is not going to happen. And the reason I
[17:37] can say this with certainty or at least
[17:39] where I'm willing to put my uh
[17:43] uh my uh my money on the line here, I
[17:46] highlighted this because I do expect a
[17:48] possibility that this could occur. This
[17:50] would take us back up to 4%. This to me
[17:52] is not going to happen because what we
[17:54] have right now is an employment
[17:55] situation which is weakening. So wages
[17:58] are not growing. We have a housing
[17:59] affordability issue. So house prices are
[18:01] going to be moving lower. They have been
[18:04] moving lower and I think the Trump
[18:05] administration is focused on that. But
[18:07] most importantly, this happened because
[18:09] of oil and this is the difference right
[18:11] now. We have an abundance of fossil
[18:13] fuels. So in 1974 when everyone scared
[18:17] the hell and said we're going to have
[18:18] another rise of inflation. This is the
[18:20] price of of oil per barrel. So we went
[18:23] from 10 to 35 to get that second wave of
[18:27] inflation. So you had the first wave
[18:28] here and then you had the second wave.
[18:30] We are not getting that this time. So if
[18:32] you think oil is going to go effectively
[18:34] from 60 to 180, then yeah, we're going
[18:36] to get inflation at those levels. If you
[18:38] believe that we have a surplus, it's
[18:41] fine. This is the overlay of CPI
[18:42] year-over-year with that oil chart in
[18:45] those years. So that was an oil thing.
[18:47] We have a very unique situation that is
[18:49] not about oil, but I do want to be long
[18:51] energy because we do need power. I do
[18:53] think oil has a floor because of how
[18:56] much the commodity stuff is booming and
[18:58] how much PMIs are going down. But if oil
[19:00] only goes to $70, you're still going to
[19:02] see XLE outperform. And again, I include
[19:05] this as software. Everyone is trying to
[19:07] pick the bottom in software, forget it.
[19:09] You can get a bounce. You can get a
[19:11] trade. You can go. That's an ego trade
[19:12] as far as I'm concerned. You want to buy
[19:14] scarcity. We have problems with
[19:16] commodities. We have problems with
[19:17] power, electricity. There are plenty of
[19:20] ways to make money. The electricity
[19:21] thing will be deals that Chevron, Exxon,
[19:23] all of them will benefit. I don't want
[19:25] to be short this chart. This is a
[19:26] beautiful looking thing. And this is XLE
[19:28] over software or over IGV. That's the
[19:32] trade you want to have. I showed it is
[19:34] Chevron versus um uh Salesforce.com last
[19:38] week. So back into the abundance
[19:39] scarcity, you want to be short abundance
[19:41] software. You want to be long everything
[19:43] associated with a buildout. This is the
[19:45] reason why and I want to make sure that
[19:47] you guys can fully see this. Oh, you
[19:51] don't need to see me. Um so this thing
[19:54] here is very simple. This is showing
[19:57] that we have plenty of energy. We're
[20:00] making it into intelligence. There are
[20:02] two tracks here where there are
[20:03] shortages. One is turning energy, the
[20:06] raw power into electricity. And that's
[20:07] where the turbines show up. That's where
[20:08] the switch gear shows up, the
[20:10] transformers. These are all bottlenecks.
[20:12] That is where the data center has been
[20:14] and this is where trades have worked. On
[20:17] the other side, you have all of the
[20:19] advanced GPU. You have the high
[20:21] bandwidth memory. All of this has also
[20:24] been in a shortage. This is the place
[20:26] where scarcity exists. So you are taking
[20:28] raw energy which we have an abundant
[20:30] amount for but it's the conversion
[20:32] process into intelligence where we have
[20:34] the issue that will remain the issue as
[20:37] we go forward. The place where you want
[20:39] to be this advanced GPUs that's why I
[20:41] showed this packaging thing that is a
[20:43] lot of names there. This right now
[20:46] becomes important as we get into edge
[20:48] devices. This here is really more for
[20:50] the cloud and the because of the data
[20:52] centers. you're going to get into the AI
[20:54] factories which are more for on premise
[20:56] things but I just wanted to bring that
[20:57] up because you want to invest in the
[20:59] shortages so everyone wants to take the
[21:01] other side of this it's in X every day
[21:04] everyone wants to short semis and be
[21:06] long software they think this is a
[21:08] bubble they look at it this way I just
[21:10] wanted to show who wants to be long this
[21:12] chart this is just that inverted
[21:14] whenever I see people kind of doing this
[21:16] they're looking at it and they're more
[21:17] comfortable shorting semis and uh versus
[21:20] and being long software as some trade,
[21:23] but nobody wants to catch a falling
[21:25] knife. And this just continues to go
[21:26] lower, and I think it will continue to
[21:28] go lower over the next five years with
[21:29] episodic shifts. This is software
[21:32] relative to the NDX. Again, who wants to
[21:35] buy stuff like this? Can we get one of
[21:37] these where it reaches a point and then
[21:38] it goes sideways? Sure. If that the best
[21:41] trade you can come up with to try and
[21:42] pick the bottom of software, it's not
[21:44] worth it. So, let me go through the
[21:46] reasons. I wrote a piece, why buying
[21:47] cheap software is now the new AI bubble
[21:49] trade. I fully believe in hearing people
[21:51] talk that people are fighting this,
[21:53] particularly people who are really good
[21:55] at coding trying to tell me that claude
[21:57] code isn't good enough and it won't
[21:59] work. I just want to like bring it out.
[22:01] If this is the best trade you can come
[22:02] up with with all these parabolic charts,
[22:05] I think you're in trouble. Go to energy,
[22:06] go to materials, go to some of the names
[22:08] that haven't yet broke out where the
[22:10] analysts are still not positive and
[22:11] they're treating this as a cyclical
[22:12] thing. A rerating is when stocks that
[22:15] have been priced at a certain multiple
[22:17] are about to go through a dramatic
[22:19] shift. I think that's going to happen in
[22:20] the drug sector, in the health care
[22:22] sector, and I think that's going to
[22:23] happen for sure in the materials and
[22:25] energy and industrial sector where
[22:27] people are scared of the next cyclical
[22:30] um point where this will break down. And
[22:32] if oil doesn't go higher, I don't see
[22:35] anything that will stop copper and
[22:37] silver from being an issue. They are not
[22:39] going to create demand destruction, at
[22:41] least not for the AI trade. They will
[22:43] have impacts on phones. They will have
[22:45] impacts on autos. So will high bandwidth
[22:47] memory. But the issue comes in if it has
[22:50] a uh a 10% increase in the price of
[22:53] those things. It may be bad for
[22:55] inflation, but it's not going to have an
[22:57] impact on the deflationary components
[22:58] without oil going higher. So just
[23:00] another thing on the software side.
[23:02] Claude made the Wall Street Journal uh
[23:04] in terms of taking the world by storm
[23:07] and this was all about the coders
[23:08] agreeing with it. Anthropic revenue is
[23:10] now racing again. They're the ones
[23:13] dominating on the enterprise side from
[23:14] the coding. VCs are piling in. They're
[23:17] going through and I mean they're going
[23:19] to have an IPO. Uh Agentic Coding is
[23:22] accelerating app releases. This was from
[23:24] code two. There were two charts that
[23:27] went around. I just want to highlight
[23:29] how important this is from the
[23:31] competitive side. So, one of the areas
[23:34] on software that people have to
[23:35] recognize is that you can think that
[23:38] enterprise software is bulletproof or
[23:40] that it's gotten cheap. Competition is
[23:42] rising by the day. It may not be able to
[23:45] directly have an impact today, but you
[23:48] are getting so many more apps and as the
[23:50] models get better, this happens. So,
[23:52] this is the coding side of vibe coding.
[23:54] This is when Vibe Coding took over. For
[23:57] three years, we were having no app
[24:00] releases. And now all of a sudden, we're
[24:01] up at 60% year-over-year. It's been
[24:04] increasing. It's just a dramatic shift.
[24:07] And to ignore it would be a mistake. So,
[24:09] I took the other chart, you can see it
[24:12] here in terms of more the smoother one,
[24:14] which gets into the amount of uh apps
[24:16] released every month. I said okay now
[24:19] taking this uh agentic platform
[24:21] competition for SAS combined with
[24:23] reality of vibe coding began what is the
[24:26] summary for the enterprise SAS model and
[24:30] basically it said okay we're agentic AI
[24:32] will directly change the workflow side
[24:34] this is uh an existential tension
[24:40] enterprise SAS platforms whether it's
[24:43] these names here monetized by encoding
[24:45] best practice workflows forcing standard
[24:47] ardization across teams. This is the
[24:50] other point and this is where I'm going
[24:51] to get into this company GenSpark as we
[24:53] go through it which is if you're doing
[24:56] standardization and I had an issue with
[24:59] Salesforce.com when they came in and the
[25:01] reason I had an issue is because a lot
[25:03] of the things that I wanted for what we
[25:05] did for fundraising
[25:07] they could not be done in there. It
[25:09] wasn't part of it. So we had to figure
[25:11] out a way to adapt it and add things
[25:13] that we wanted and get them into the
[25:15] database. So they create a form which is
[25:19] fairly standardized for everyone to fit
[25:22] the majority of people. SAS is built
[25:24] with everyone in mind. Meaning it does
[25:26] not fit 100% for anybody. The problem is
[25:28] enterprise clients want their exact
[25:29] workflow automated. They aren't going to
[25:31] be satisfied with 80%. Because the
[25:34] person you just convinced them to fire
[25:36] was already doing 100%. That is the big
[25:38] issue. So when people look at how is the
[25:41] productivity going to work as I go
[25:42] through this you're going to start to
[25:44] see the success by companies has been
[25:47] not through using the enterprise
[25:49] software it's through agentic AI and
[25:51] it's through having those things
[25:53] effectively be databases where the data
[25:55] is the value but not the software that
[25:58] is an important component of this which
[25:59] is going to become more of a story as
[26:01] time goes on now this is the company
[26:03] GenSpark so it's talking about how
[26:06] GenSpark is competing with software.
[26:11] They either next raise after hitting 100
[26:13] million in ARR.
[26:16] It's an agentic AI platform designed to
[26:18] handle complex real world tasks without
[26:20] being rigid and predefined workflows.
[26:24] So less control, more tools. Again, you
[26:27] go back. You want to have more
[26:30] flexibility and adaptability and not
[26:32] have to deal with something which is
[26:34] scripted.
[26:36] You want things that are adaptive that
[26:38] are able to act, fail, recover, learn in
[26:40] real times, make changes, go through it.
[26:42] That's the power of AI agents. And maybe
[26:44] some of the incumbents can actually get
[26:47] there, but it is very challenging and
[26:49] very costly to spend the money to get
[26:52] these agentic flows from everything that
[26:54] I've seen to actually be able to
[26:55] customize to an individual. So, I find
[26:58] it very challenging and I don't know how
[27:00] people are going to pay for it when they
[27:01] can just use the data which they own.
[27:04] Um, I brought this new drug thing up
[27:06] because again, this is from the article
[27:08] with inside the economist which I
[27:10] referenced. Everything is going to be
[27:12] hyperpersonalized. So when you're
[27:14] thinking about the way people talk about
[27:17] software, we are in a new world where
[27:19] everything should be customized to your
[27:20] workflow. It should be customized to
[27:22] your body. You make traditional
[27:25] medicine. You make one drug like Advil
[27:27] for millions. Traditional software, one
[27:29] workflow for many companies. The new
[27:32] world of Agentic, one solution per case.
[27:36] That is a very different thing for
[27:38] incumbents to be able to do. Enterprise
[27:40] software in the age of aentic platforms
[27:42] and vibe coding. Software supply I
[27:45] showed you at the beginning has
[27:46] exploded. Apps are going nuts. I build
[27:48] software. I can go through this. Custom
[27:50] beat standardized. The customization is
[27:53] the critical part. Workflows are no
[27:55] longer the moat. Value shifts from
[27:57] software to results. When you're trying
[27:58] to get revenue per employee higher, the
[28:01] critical thing is to replace people with
[28:05] customized workflows, not software.
[28:08] Software might make people more
[28:10] efficient, but it it does not allow you
[28:13] to not hire as many people. So, the
[28:15] seatbased pricing breaks, revenue
[28:17] pressure precedes churn, discounting,
[28:20] longer sales cycles, and renewed
[28:21] friction appear before customers
[28:23] actually rip out the platforms. This is
[28:25] kind of one of the things I think you
[28:27] have to be all over these software
[28:28] companies and their earnings and really
[28:30] do a deep dive. This is not a valuation
[28:32] thing. Do not buy these for valuation.
[28:34] Buy these because you think the company
[28:35] is able to adapt and they're seeing
[28:37] success in terms of the revenue side
[28:39] that is actually growing because it is a
[28:42] lot of money I think for people to spend
[28:43] on this when their goal is ultimately to
[28:45] have people go through. The winning SAS
[28:47] model becomes agent native. It's forcing
[28:50] enterprise SAS to evolve or accept
[28:52] structural value compression.
[28:55] That's in my opinion what's happening.
[28:56] What do incumbents need to do to
[28:58] survive? It's hard. I I I just don't
[29:02] see. They're basically saying, "Here's a
[29:04] system. Follow the process. Tell me the
[29:06] goal. I'll handle the rest."
[29:09] Again, I think this is a problem because
[29:10] the people don't really use AI that
[29:12] much. This doesn't make a lot of sense
[29:14] to them. But as someone who uses it all
[29:15] day long and watches it evolve, it gets
[29:17] better every single time. I don't see
[29:19] how software companies are going to be
[29:20] able to have update upgrades going
[29:22] constantly. Um, if you want an article
[29:24] to go read about why SAS might not be as
[29:27] safe as investors think, and I think
[29:29] Renee Selman does very good work, um, in
[29:32] terms of putting things out, and I think
[29:34] this was a very, let's say, balanced
[29:38] thing talking about they're selling off
[29:40] hard, investors are looking for places
[29:42] to go, value comes from trust, blah,
[29:45] blah, blah. uh when you go through it
[29:46] the real risk isn't current AI
[29:48] capability it's human misjudgment of
[29:50] exponential process progress this is
[29:52] really important we instinctively model
[29:55] technology linearly
[29:57] while it often involves
[30:00] multi multiplicatively
[30:04] exponential blindness examples the main
[30:06] point of this is as things are changing
[30:08] quickly your brain doesn't allow you to
[30:12] believe that things are changing that
[30:13] quickly and that every time that this
[30:15] happens, you want to go pick again, buy
[30:19] into the weakness. When exponential
[30:22] change happens, like silver and it goes
[30:24] to 300 and it goes to 400. Once it gets
[30:27] to 60, it's overbought. Then at 70, it's
[30:29] overbought. Then 100, it's overbought.
[30:31] Well, this is parabolic. Parabolic stays
[30:33] parabolic until it's not parabolic. But
[30:35] if the demand side is there and the
[30:37] shortage goes through, because we have a
[30:38] bunch of S-curves that didn't exist 15
[30:41] years ago, because drones were not
[30:43] tanks, because there were no EVs that
[30:47] matter, because FSD wasn't a thing,
[30:49] because China and the US weren't in an
[30:51] AI arms race, blah blah blah. You get
[30:53] the picture. All right. Um, you should
[30:55] go listen to these. They're all about 30
[30:57] minutes. So Jensen Yuang spoke,
[30:59] interviewed by Larry Frink. Um, and he
[31:02] basically went through the things that
[31:03] I've already highlighted.
[31:05] I don't think there's anything there
[31:06] that's uh that's new. But I do think uh
[31:09] if you want to sit there and pause it
[31:11] and read it, it gets back into this. So
[31:13] everything he said gets back into this
[31:15] chart. We're going to have shortages for
[31:16] a long time. They're not going to ease
[31:18] up. We cannot build the things we need.
[31:20] To get more silver takes a long time. To
[31:23] get more copper takes a long time. To
[31:25] get more gas turbine takes a long time.
[31:27] High bandwidth memory, we'll see how
[31:29] long that takes. China's already trying
[31:30] to do it. Um I don't know what the right
[31:33] price is but I do know at some point
[31:35] whether it's Micron SKH high Sandisk
[31:37] they'll get to levers will they've built
[31:39] in enough of the next three years the
[31:41] question is where that'll be uh for
[31:43] silver and copper it's going to take a
[31:44] lot longer than that uh just because I
[31:46] don't think it's there the stocks which
[31:48] have rallied are going to rally more but
[31:51] this is also going to spread into
[31:53] chemical names which have started to
[31:54] rally the basic materials thing you have
[31:56] to go there's a cyclical discount in
[31:58] there of which for the next 15 years
[32:00] it's going be like the software side
[32:02] was. It's not going to show up in profit
[32:04] margins. It's just going to show up in
[32:05] the consistency of earnings which are
[32:07] going to be there because the prices are
[32:08] going to be higher. So from energy to
[32:10] intelligence, this is the physical stack
[32:12] underneath. And again, exactly what I
[32:14] highlighted. All of these things have
[32:16] shortages.
[32:18] All of these things are issues. That's
[32:19] where you want to focus your attention
[32:21] for the next at least decade. Uh which
[32:24] is very different than the prior decade.
[32:26] Uh all of these different components. So
[32:30] as you go through this just think about
[32:32] the fact that we are exactly in the
[32:33] hardware stage and we're in the very
[32:35] early innings and that's because chatbt
[32:37] and the data center buildout was the
[32:39] main story. Data centers are going to
[32:41] continue to get built but we're now
[32:43] going to be putting intelligence into
[32:44] everything and that's the difference
[32:45] between an AI factory which is more
[32:47] about the inference side and a training
[32:50] model which is more about uh getting
[32:52] better AI models. So uh supply side
[32:56] constraint he's describing the physical
[32:57] bottleneck of intelligence which is
[32:59] where we're at. If the infrastructure is
[33:00] not built, intelligence simply cannot
[33:02] exist at scale. So it has to be built
[33:04] and the money will be spent to have it
[33:06] built. I thought the most important um
[33:09] presentation with the most information
[33:11] was by anthropic head Dario Modi. And
[33:14] basically again to show you I bring in
[33:17] the transcript. It's about 30 minutes. I
[33:19] run the hedge funds analyst skill on the
[33:21] transcript. It gives me about a a 12page
[33:24] output giving me a bunch of different
[33:26] components. I'm not going to show it
[33:27] again for anyone who wants to get a
[33:30] presentation on this. I will show you,
[33:32] but it creates a PM research memo
[33:34] directly from that transcript. Goes
[33:37] through all of the things he says and it
[33:39] leads into
[33:41] cognitive ability is doubling every four
[33:43] to 12 months. Concrete evidence point
[33:45] lead product team for clog code hasn't
[33:47] written code manually in two months.
[33:49] Again, new information all been written
[33:51] by Claude. Competitors have gone
[33:54] consumer oriented. So this is obviously
[33:56] directly related to open AI and I think
[33:59] this was one of the more important
[34:00] things. While anthropic focuses on
[34:02] enterprises which are adopting now right
[34:04] now and they have the money and they
[34:07] have the productivity, the people that
[34:09] are focused on the consumer side are
[34:12] spending more money than the people in
[34:14] this case anthropic on the enterprise
[34:15] side. And what Dario was basically
[34:17] saying is I think enterprise business
[34:21] described as more stable than consumer
[34:22] with better margins. So the heavy
[34:25] spending the aggressive spending is
[34:27] being done by open AI and anthropic is
[34:30] getting more likely of getting business
[34:32] and more stable because the adoption
[34:34] continues to grow. In terms of China
[34:36] competition for his business, I have
[34:39] almost never lost a deal to a Chinese
[34:41] model. So as the enterprise stuff goes
[34:43] out and as coding is gone, I find it
[34:45] very unlikely that anyone is going to
[34:47] compete at this point with with
[34:49] Anthropic. They have made this point and
[34:51] I think that's important because right
[34:53] now the valuation of the two companies,
[34:55] Anthropic is about a 50% discount to
[34:57] OpenAI and I think OpenAI with new lows
[35:00] going on right now for Oracle. I think
[35:02] the Open AI story could be an issue as
[35:04] as as the year goes on. Uh at least for
[35:06] a scare and at least for people
[35:08] questioning whether OpenAI is in
[35:10] trouble. Um, maybe they make some
[35:12] headway uh with their new model. I I I
[35:16] find it hard to believe that it's going
[35:17] to be that big of a differentiator and
[35:19] maybe their product that Johnny Ives is
[35:21] involved in by the end of the year ends
[35:22] up being something. But for right now, I
[35:25] think it's very very difficult. And he
[35:26] talks about it on the bubble risk. He
[35:28] separates the technological trajectory,
[35:30] meaning the models are getting better
[35:31] and better and they're going to continue
[35:33] to get better and better. But on the
[35:35] enterprise deployment bottleneck, the
[35:38] technology is capable of is probably 10
[35:39] times what the enterprises are able to
[35:41] deploy. This is going to be a major
[35:43] story as I finish this up for all of you
[35:44] out there running businesses that are
[35:47] trying to figure out how to incorporate
[35:48] it. Uh you have to think about it from
[35:50] what the bigger picture is and you have
[35:52] to get your employees to start using it
[35:53] immediately and the question is have you
[35:55] provided an environment that allows them
[35:56] to do that. Um that is where I'm
[35:58] spending most of my attention with
[35:59] people on at this point. the second and
[36:01] third order effects of what Dario said.
[36:04] Uh there's an enterprise deployment
[36:05] bottleneck. The labor market disruption
[36:08] is happening and will continue to
[36:10] happen. Consumer AI commoditization.
[36:13] Uh I agree. Most consumers still use it
[36:15] as a chatbot and I don't know what
[36:17] they're going to pay more money for
[36:19] makes it very difficult. the capital
[36:21] allocation stress. Again, companies are
[36:23] spending different amounts of money and
[36:26] the question is if you're getting models
[36:29] that are better, but your end client is
[36:31] a consumer and they don't know how to
[36:33] monetize them. Do we have a gap there
[36:35] between the models which are getting
[36:36] better, the compute which are spending
[36:38] tons of money on for the future? I think
[36:40] there's an air pocket here for some of
[36:42] these. Again, if OpenAI is not doing
[36:44] well, then I think you're going to have
[36:46] the entire ecosystem, which again is
[36:48] just going to have to build in the risk
[36:50] that they're all going to run into
[36:51] overspending. Even though I don't
[36:53] believe it's going to be an issue, and
[36:54] this is not a bubble, I do believe that
[36:57] there will be questions and uncertainty
[36:59] over valuations for these companies
[37:01] unless they're showing the revenue side.
[37:03] And remember, part of the inability of
[37:05] of getting the capacity uh uh the
[37:09] bottlenecks is to be able to get more
[37:12] cloud storage and more cloud dollars and
[37:15] and if you've got capacity issues, which
[37:17] you still have for all of the big
[37:18] hyperscalers, I think this is going to
[37:20] be an issue. Um the economic potential
[37:23] of AI is enormous, many trillions of
[37:25] dollars, blah blah blah. The key
[37:26] uncertainty is adoption speed, not
[37:28] capability. This is becoming a bigger
[37:30] and bigger thing. And I think his
[37:31] statement of AI is 10 times ahead what
[37:34] enterprises are currently able to deploy
[37:36] the ability for the companies to adapt
[37:39] which is where my business is really
[37:40] starting to um blossom has become an
[37:43] issue and you have to if you want to
[37:45] catch up as I get through this I will
[37:47] show that about 12% of companies are
[37:50] being successful with adoption in a good
[37:53] way meaning they're already seeing very
[37:55] very strong positive returns the gap in
[37:57] the numbers in terms of how much more
[38:00] successful they are versus the average
[38:02] is dramatic and it's really hard to
[38:04] catch up guys if you don't use it every
[38:06] day. The best analogy I can give is a
[38:09] golf swing. If you think you can go
[38:11] learn golf by playing once a week, going
[38:13] to the range and hitting one bucket of
[38:15] balls, you if you really want to get
[38:18] your game to a level that goes fast, you
[38:20] have to put in an enormous amount of
[38:21] reps and it has to be daily. Uh
[38:23] especially as you're older. And the
[38:25] problem is very few people are doing
[38:27] that. Uh I won't go through and read all
[38:29] the different uh timestamps here.
[38:32] Uh I also won't go through this again.
[38:34] He's talking about the same thing which
[38:36] is their focus on enterprise is winning.
[38:38] So the synthesis between Jensen and
[38:41] Daario. Jensen explains why intelligence
[38:43] is scarce. Dario explains why revenue is
[38:45] delayed. I think if you listen to these
[38:48] two, it's very difficult that we've got
[38:50] a hard ceiling on AI capabilities based
[38:53] on how much infrastructure we need to
[38:55] build to have it go forward. This is not
[38:57] just for better models because the
[38:58] models are going to get better from
[38:59] Blackwell. This is mainly for edge
[39:01] devices, for autos, uh for getting uh AI
[39:05] factories built for for these companies
[39:07] that have massive amounts of data, but
[39:09] it's all siloed and unstructured. Uh and
[39:12] then adoption is the bottleneck on the
[39:13] AI monetization. And so I think you're
[39:15] going to have massive spending, which is
[39:17] good for commodities, hoarding and
[39:18] everything. But I think in terms of
[39:20] seeing the companies pay lots of money
[39:21] to software companies or pay lots of
[39:23] money to the hype to the AI model
[39:25] companies, I just I think we're in an
[39:27] air gap here. And I think these
[39:28] companies are they're priced
[39:30] differently. Uh 30 PE companies for
[39:33] software and for things related to AI
[39:35] versus
[39:37] uh materials stocks as I showed, they're
[39:40] just mispriced relative to each other
[39:42] for the stability of the earnings going
[39:44] forward. uh Satcha Nadella spoke and he
[39:46] basically continues to talk about the
[39:48] fact that there's going to have to be an
[39:49] agentic world. You can go listen to him.
[39:51] He did one with Allin. And while all
[39:53] this was going on, while all of the big
[39:55] leaders uh for the AI models,
[39:59] let's say the bigger companies, I'm not
[40:00] throwing Zuckerberg in there, but where
[40:03] was Sam Alman? Um sending another
[40:05] signal. He needs more money. Uh he also
[40:08] announced that uh they'll be having ads
[40:12] which I agree this is just another sign
[40:15] that they are desperate for money at
[40:16] this point and even Demis who spoke at
[40:20] Davos said he was surprised that they've
[40:22] moved on ads. It definitely seems like a
[40:25] a money grab and Deep Mind said it has
[40:28] no plans for that. So the company that
[40:31] knows the value of ads better than chat
[40:32] GPT is not going to follow their lead.
[40:35] Elon
[40:36] spoke as well. Great interview again
[40:39] with Larry Frink. Um key points he
[40:42] brought up uh Tesla, SpaceX, XAI and
[40:46] robotics around a single mission
[40:49] maximizing the probability of future
[40:50] civilization. Uh he talked about
[40:52] abundance. He talked about aging. He
[40:56] talked again about the true bottleneck
[40:58] being electrical power. I think the most
[41:00] important thing that Elon talked about
[41:02] and the reason you have to keep
[41:03] listening to him is we are in an age
[41:05] where we need the engineers to basically
[41:07] figure out how to deal with scale and
[41:09] vertical integration which he's done. At
[41:11] the same time, I talked about this last
[41:14] year. This is the trigger point for me.
[41:15] I believe this is a major event. Tesla
[41:18] robo taxi is now driving in Austin with
[41:21] no safety monitor in the car.
[41:24] That is basically a humanoid on wheels.
[41:27] We have entered now this. We've entered
[41:30] the point. And if you're wondering,
[41:32] you're starting to get more and more of
[41:34] these. The first trip
[41:37] $4.31. I saw one there where it was
[41:40] basically, you know, 20% of the cost of
[41:43] a Whimo regard or of an Uber. Regardless
[41:46] of what your thought process is, the
[41:49] reality is setting in. And it's not just
[41:51] the state of Texas with Austin and Elon
[41:54] basically believing that it's time. But
[41:57] then Lemonade announced that it will
[41:59] offer a 50% rate cut for drivers of
[42:01] Tesla vehicles when FSD is steering
[42:04] because it had data showed red. This is
[42:06] a really big deal because it means
[42:08] they're effectively allowing you
[42:10] basically to pay for the FSD yourself by
[42:14] having your insurance cut. So, just a a
[42:17] historic time for robo taxis and anyone
[42:20] who doubted this, of which the majority
[42:22] of people on the on the investment world
[42:24] that I've talked to over the course of
[42:26] the last year, they doubt Elon. They're
[42:28] not ready for what's happening. We're in
[42:29] the hardware stage. I think Tesla's
[42:31] stock will have a phenomenal year. Now,
[42:33] Ray Kurszwell, the father of singularity
[42:36] in the modern era, uh gave an interview
[42:39] on moonshots. I highly recommend it. If
[42:41] you take what Ray Kurszswwell said about
[42:43] the future and Elon Musk, Ray Kurszwell
[42:45] is known for his predictions and first
[42:49] said AGI would arrive by 2029 and he
[42:52] said that in the 1980s so he's had a
[42:54] good track record of approximately when
[42:56] everything was happening. He's had some
[42:57] misses but when you're trying to
[42:59] forecast 10 to 20 years out um he has
[43:01] been amazing. So exponential AI
[43:04] acceleration is real and it's underway.
[43:06] AGI is imminent. Uh abundance is the
[43:09] dominant outcome. This gets into a lot
[43:10] of disruption which I've talked about.
[43:12] Abundance is the major theme. You want
[43:14] to right now short any company that
[43:15] suffers from abundance and to get to
[43:17] abundance we have a scarcity side. You
[43:19] want to invest in scarcity. Anything
[43:21] that you invest in is it scarce or is it
[43:24] abundant? Um energy not algorithms is
[43:26] the binding constraint. They agree on
[43:28] human AI integration is in inevitable.
[43:30] Robotics is the next major phase and
[43:32] it's beginning in 2026 or 2027. Think
[43:35] robo taxis. Think optimists. optimism
[43:37] not fatalism is the way to think about
[43:39] this. Uh so again I would just focus on
[43:43] the fact that two of the smarter minds
[43:44] on the future are in agreement. Now data
[43:46] center projects this gets back again to
[43:48] another risk with the oracles the open
[43:50] AIS and all this stuff in terms of the
[43:51] data centers. You're going to be seeing
[43:52] this news happening more and more. 64
[43:54] billion dollars of data center projects
[43:55] have been blocked or delayed amid local
[43:57] opposition. Uh
[44:00] this data center thing for people who
[44:03] are not paying attention to it is
[44:04] definitely becoming a much bigger story.
[44:06] Google warns grid connection delays are
[44:08] now the biggest threat to the data
[44:09] center expansion. Uh in Memphis, Elon
[44:13] Musk's supercomput is getting push back
[44:16] from the pollution that is happening
[44:19] nearby. This is a big one and amid
[44:21] rising local push back US status
[44:23] counteration. These are all stories
[44:24] within the last couple weeks. Um, CEOs
[44:28] say AI is making work more efficient.
[44:31] This is from the AI daily brief. Uh,
[44:34] I've never shown this one. I listen to
[44:36] this podcast usually a few times a
[44:38] month. I think if you are a business
[44:41] leader in particular looking for how
[44:44] you're doing on adoption. I think this
[44:46] is really important um podcast to listen
[44:49] to. Uh here's the highlights. The uppart
[44:52] reviews new enterprise surveys from PI
[44:55] uh from PWC, workday and section and
[44:58] basically says that
[45:01] there's a widening gap between AI
[45:03] leaders and laggers. So the adoption
[45:04] thing is getting a headline that no
[45:06] one's adopting to it because if only 12%
[45:08] are adopting that means nobody's
[45:10] adopting. But when you go through the
[45:12] data and you realize that if you take
[45:14] the top 12% the gains they are seeing
[45:17] are dramatic.
[45:19] So if you're not using it, you're
[45:22] suffering and you're falling way behind.
[45:23] The biggest drivers of AI proficiency
[45:25] are leadership expectations followed by
[45:27] access to tools and a coherent AI
[45:29] strategy. ROI compounds with
[45:31] proficiency, reinforcing that enterprise
[45:35] integration problem, not an AI hype
[45:37] problem. And I could not agree more. It
[45:40] is staggering to me how little
[45:42] leadership allows their employees to use
[45:44] it, but they want the benefits that
[45:46] come. That is what the main story was in
[45:49] this. So
[45:51] the top 12% deploy AI the way previous
[45:54] generations deployed. AI is embedded
[45:56] directly into core workflows not layered
[45:58] on top of them. Again this is really
[46:00] important in terms of going through. AI
[46:02] sits inside operational systems. It has
[46:05] access to it. Firms with strong AI
[46:08] foundations are three times more likely.
[46:10] So basically you need to have an AI
[46:13] plan. You need to have an agentic plan.
[46:16] How does this I just copied what I just
[46:18] showed you and brought it back into
[46:20] GenSpark. How does what I just got from
[46:23] the AI daily brief in terms of the
[46:25] outcome from results from surveys align
[46:29] with what GenSpark which I showed you
[46:31] earlier which is an agentic thing to
[46:33] compete with
[46:35] standardized software flows SAS flows.
[46:39] It aligns almost perfectly. The
[46:40] description shared is essentially a
[46:42] clean articulation of GenSpark's core
[46:44] philosophy, not marketing fluff
[46:47] here are the way that they line up
[46:49] and this is the most important thing.
[46:51] Loser forms, AI as tool, rigid workflows
[46:54] again gets into enterprise SAS, human
[46:56] rework, static models, efficiency tools.
[46:59] Everything here is about agents and it's
[47:02] about what GenSpark is basically saying.
[47:04] saying GenSpark is building a type of AI
[47:06] system that only the top 12% of
[47:07] companies have figured out how to deploy
[47:09] internally and trying to product size
[47:11] it.
[47:13] Uh employees are three time 3.1 more
[47:16] likely to hire AI ready talent. Okay,
[47:18] this is why when the when my video
[47:21] series is launched and in particular
[47:23] most of the feedback I'm getting is
[47:25] starting to come from the parents and
[47:28] from college kids. Uh you have to be I
[47:30] native. The only way you can be IIA
[47:32] native is to use it all day. If you work
[47:34] for a firm and they don't let you use it
[47:35] all day, you're becoming less relevant
[47:37] and it is hard to catch up. You are
[47:38] trying to compete with golfers who are
[47:41] practicing every day for four hours, 5
[47:43] hours, 6 hours a day. You're trying to
[47:45] get good at golf by not having access to
[47:47] a golf club except when you're in your
[47:48] free time, which is much harder to do.
[47:50] That is why it is critical for people to
[47:52] be using this all day long. Schools, not
[47:54] letting kids use it all day long or
[47:56] telling them feing the fear of God into
[47:59] them, it doesn't help. So parents,
[48:01] students, continue to watch this, but
[48:03] also when the videos go up, I'll let you
[48:05] know. Uh I'm in the process hopefully
[48:07] very very soon. I say this every week,
[48:09] but every week that passes, we're one
[48:11] week closer to when this launch will
[48:12] happen. Final part on Bitcoin. Bitcoin
[48:15] and crypto market structure bill will
[48:16] pass very soon. The question is not is
[48:19] the bill going to pass. It's still the
[48:21] question of what's going to be in it. Um
[48:23] I wanted to just bring up the fact that
[48:26] this has been a big theme at the crypto
[48:28] event. People have talked about why
[48:30] Bitcoin's not moving like silver.
[48:32] Bitcoin is a$ 1.8 trillion asset. Silver
[48:34] has added$ 1.5 trillion in market cap in
[48:37] 23 days. I bring this up because when
[48:38] this stuff moves, it moves. And if you
[48:41] wait for it, you end up not getting
[48:43] involved. Um Brian Armstrong spoke about
[48:45] the Clarity Act. I'm not going to go
[48:47] through all the details, but he's saying
[48:49] the Genius Act was a clean bill that
[48:51] fostered uh innovation. This is the one
[48:53] that passed over the summer. The Clarity
[48:55] Act has become a vehicle for traditional
[48:57] banks to try and get out of some of the
[48:59] things that were in the Genius Act.
[49:01] I think the most important thing is just
[49:02] to get the regulatory framework. So,
[49:04] here's Bitcoin. Uh, now we've got a
[49:06] negative signal on the short-term side.
[49:08] I had said once we broke above 92, to me
[49:11] it was the buy side. Well, I've already
[49:14] been buying for all of this period here,
[49:16] including a little last week. It's gone
[49:17] down. The chart still looks horrible.
[49:19] Now, we've got a MACD sell signal on the
[49:21] daily. The weekly is about to cross. But
[49:23] the main point is in being here,
[49:26] we're just not getting any kind of lift
[49:29] that lasts. That will eventually change.
[49:32] And I just want to highlight in these
[49:33] final slides that number one, this is
[49:36] Ethereum versus Bitcoin. Ethereum to me
[49:38] is utility. Bitcoin is store value. The
[49:41] utility side is still doing well. As
[49:43] long as this chart looks this good, and
[49:45] again, we had a big rise up. Let's take
[49:47] this as five waves. We've got a 200 day
[49:49] moving average, which is pointed up.
[49:51] This is a good sign for the utility side
[49:54] which is the network effects which is
[49:55] stable coin. So the fact that Ethereum
[49:57] is doing well is good. Now if I want to
[49:59] have a comparison silver utility gold
[50:02] store of value. So it's not surprising
[50:04] that if we look back over the last year
[50:07] and a half until November silver verse
[50:10] gold was very correlated with Ethereum
[50:12] verse Bitcoin. They bottomed in April.
[50:15] Here's what's happened over the course
[50:17] of the last month. So, since November,
[50:20] silver's gone straight up. I bring this
[50:22] up because I do think we're going to
[50:23] have this type of move in crypto soon.
[50:26] Remember in Micron,
[50:28] I was able to buy a lot of Micron during
[50:33] this period in here. All of it. I just
[50:36] was buying it buying it because I
[50:37] believe that high bandwidth memory was
[50:39] going to be an issue. I feel the exact
[50:40] same way with Bitcoin. I actually bought
[50:43] some stock at the same level it was in
[50:45] 2018 before Micron went up from 70 to
[50:50] 400 in a period of less than 9 months.
[50:55] So you're left with another one. This is
[50:58] the basic resources stocks. Here's where
[51:01] we were in the summertime. Basically the
[51:03] same level we were in 2018 and now we've
[51:05] gone parabolic. Don't wait for the
[51:07] parabolic moves. Bitcoin and Ethereum
[51:10] and all of the crypto space will be
[51:11] there as well. And the reason for me is
[51:15] this is my proxy for Bitcoin. The orange
[51:17] line is Bitcoin. The white line is
[51:20] something I created as a proxy which
[51:21] involves all of the pieces to me that
[51:23] Bitcoin is involved with. It involves
[51:25] copper as the reflation side. It
[51:28] involves Q's as the innovation side and
[51:30] involves gold as the uh store value
[51:34] side. And you can see how strong a
[51:36] waiting of those is relative to Bitcoin.
[51:39] It will always move away from it, but
[51:41] eventually it follows the same path. And
[51:43] since I think gold, copper, and stocks
[51:46] are going to continue to move higher, I
[51:48] want to be in there. That's it for this
[51:50] week. Um, I will see you guys next

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