Video Title: Kevin O'Leary Reveals His Next Big Bet

Video Author: Stock Sharks

Editor's Note: As AI investment moves from model competition into the infrastructure expansion phase, the market's focus is shifting from "who can develop the most powerful model" to "who can provide the foundation for the entire AI industry to keep running." Chips, compute power, and model capabilities remain important, but as data center scale expands, power supply, energy costs, and long-term infrastructure contracts are beginning to become key variables affecting AI investment returns. As technological progress gradually becomes a market consensus, a more fundamental question begins to emerge: if it is impossible to determine in advance the ultimate winner of technological competition, how should investors share in the returns generated by the entire industry's expansion?

In an interview on Stock Sharks' podcast The Deep End, well-known Canadian investor and Shark Tank regular Kevin O'Leary (nicknamed "Mr. Wonderful") shared his latest views on AI, energy, Crypto, quantum computing, and asset allocation. O'Leary built his fortune early on by founding the software company SoftKey, and today his investment portfolio spans technology, energy, and crypto assets. Rather than chasing a single hot asset, he focuses more on the resource constraints behind technological expansion, business models, and whether growth can ultimately translate into sustainable capital returns.

In this conversation, O'Leary essentially breaks down "what should be invested in next" into a set of more fundamental structural questions: what resources does technological expansion depend on? Who ultimately captures the industry's value? How do new technologies translate into cash flow? And when the market cannot determine the ultimate winner, how should investment risk be controlled?

First, the logic of AI investment is shifting from technological leadership to resource constraints. Over the past few years, AI investment has mainly revolved around large technology companies, advanced chips, and model capabilities, with investors trying to identify companies with technological advantages. But as compute demand increases, electricity is becoming an important condition affecting data center expansion. O'Leary therefore places his investment focus for the next three years on energy, with particular attention to natural gas, power infrastructure, uranium mining, and regions such as Canada and the Nordic countries that have low-cost power resources. He adopts a classic "picks and shovels" strategy: there is no need to predict which AI company will ultimately win, but rather to find companies that can provide key resources for the entire industry. This means that investment opportunities in the AI industry may further spread from technology companies to energy and infrastructure segments, but whether resource advantages can translate into excess returns still depends on project costs, contract pricing, and execution capability.

Second, the valuation logic of Crypto is shifting from market trading to actual adoption. Previously, institutional capital mainly focused on Bitcoin and Ethereum, while other crypto assets faced issues of liquidity concentration, regulatory uncertainty, and a lack of clear commercial demand. But O'Leary began re-examining public blockchains such as Avalanche, not because of short-term coin price performance, but because enterprises in fields such as finance, logistics, sports assets, and contract management may need different types of blockchain infrastructure. The question that arises is whether future blockchain applications will concentrate on a few general-purpose networks, or form multiple specialized networks serving different industries. This shift may create opportunities for other public blockchains, but enterprise adoption of blockchain technology does not necessarily mean that the related tokens can capture corresponding economic value. What truly needs to be verified is network usage, commercial revenue, and the mechanism by which value is transmitted to token holders.

Third, quantum computing is introducing a new long-term security variable for crypto assets. In the past, Bitcoin's main market risks centered on price volatility, regulation, and capital flows. With the development of quantum computing, whether existing cryptographic systems can withstand potential attacks in the future is becoming another issue worth discussing. O'Leary suggested that even if a quantum computer truly capable of breaking encryption has not yet appeared, market expectations for this risk may still affect asset pricing in advance. However, there is still no definite timetable for the so-called Q-Day, and progress in quantum technology cannot be directly equated with the failure of blockchain security mechanisms. Rather than predicting when Bitcoin will face a threat, he is also paying attention to the layouts of companies such as IBM and Google in quantum computing and security technology. What is involved here is not just Crypto risk, but also the possible future upgrade of cryptographic infrastructure facing the financial system.

Fourth, the investment approach is shifting from betting on winners to managing uncertainty. O'Leary does not deny the appeal of high-growth assets, but his early experience shorting Yahoo made him place greater emphasis on position limits. He insists that a single stock not exceed 5% of the portfolio and a single industry not exceed 20%, and he controls concentration risk by reducing positions in batches. This approach is consistent with his investment logic in energy, Crypto, and quantum computing: when the technological path is still unclear, participate in potential growth through diversified allocation, rather than letting a single prediction determine the performance of the entire portfolio. At the same time, his analysis of private companies always revolves around customer acquisition cost, retention rate, and cash flow, emphasizing that technology must ultimately be reflected in real operational improvement.

If this conversation is compressed into one judgment, it is this: in the next stage of investment competition, the goal may no longer be simply to find the companies with the strongest technological capabilities, but to identify which resources, infrastructure, and business models can steadily capture value in the process of continued technological expansion.

In this sense, what O'Leary is discussing is no longer just specific assets such as AI, Crypto, or energy, but how the market reallocates capital, measures risk, and determines asset value after the tech industry shifts from narrative-driven to commercial delivery. What truly determines long-term returns may not be who first bet on the right technology trend, but who can continuously generate cash flow throughout the industry's development and avoid paying too high a price for growth that has yet to materialize.

The following is the original content (edited for easier reading):

If he could choose only one investment theme for the next three years, Kevin O'Leary's answer is energy.

This may differ from what the market expects from a tech investor. Over the past few years, the core of AI investing has been NVIDIA, model companies, and large tech enterprises. Investors tried to determine who had the most advanced chips, the most powerful models, and the technology platform most likely to dominate the future.

But O'Leary believes that as the AI industry continues to expand, investors need to rethink where value actually comes from.

His approach is not to exit tech investing, but to reduce reliance on a single technology winner. No matter which company ultimately wins the model competition, it will need electricity, land, data centers, and other infrastructure.

That is why he is shifting his investment focus to energy.

And in the Crypto market, O'Leary is conducting a similar reassessment: can blockchain assets that previously derived valuations mainly from market trading and capital inflows prove in the future that they have sustainable economic value through real commercial applications?

The next round of AI opportunity is not in models, but in powering them

O'Leary is not pessimistic about the long-term outlook for AI.

He acknowledges that large tech companies generated considerable returns in the previous round of AI investment, and investors holding related stocks or indexes have already benefited. But for the next stage of investing, he is more focused on one question: if it is unclear which model company will ultimately win, can one still participate in the growth of the AI industry?

His answer is to invest in energy.

O'Leary calls it Picks and Shovels. In a gold rush, rather than predicting who will strike gold, it is better to invest in the companies that supply tools to the gold diggers.

The AI industry has a similar commercial structure. Model companies need chips, chips need data centers to run, and data centers need large amounts of stable electricity. Therefore, whether OpenAI, Anthropic, or other tech companies gain more market share, electricity demand may increase as computing infrastructure expands.

O'Leary said that over the next 36 months, he will focus on energy companies, as well as areas such as natural gas power generation, turbines, power infrastructure, and uranium mining. What he values is not that these assets carry an AI concept, but that they may become essential inputs for the development of the entire industry. This also explains why he is bullish on Canada and the Nordic region.

In the interview, O'Leary repeatedly mentioned the energy advantages of Finland, Norway, and Alberta, Canada. He believes these regions have relatively low-cost power resources, with some long-term power supply contracts priced below 6 cents per kilowatt-hour.

For large data centers that need to operate over the long term, power costs may directly affect project profit margins. Even if the competitive landscape among model companies changes, enterprises with land, grid access conditions, and long-term power contracts may still be able to provide services to different customers.

Bitzero, in which he invested, is a concrete embodiment of this approach. According to O'Leary's introduction, Bitzero once used Bitcoin mining to generate cash flow, but what he values more are the long-term power contracts and infrastructure resources the company obtained in Norway and Finland.

For him, mining is just one of the existing businesses, and what may truly have long-term value is the company's ability to secure energy. If cloud computing or AI companies need to build data centers locally in the future, Bitzero has an opportunity to use its existing resources to participate in related projects. The appeal of this business model is that infrastructure suppliers do not necessarily need to judge which AI model customers will ultimately use.

Of course, controlling power resources does not mean already having stable profits. Projects still need to solve issues such as grid access, equipment procurement, customer contracts, and capital expenditure, and the price advantage of long-term power supply contracts must also be able to cover comprehensive construction and operating costs. But O'Leary believes that, compared with seeking the sole winner in model competition, energy infrastructure offers another way to participate in AI growth.

The growth in AI demand may not only be reflected in the revenue of technology companies, but may also be reflected in the economic value of upstream energy, equipment, and infrastructure.

From power further extending to uranium mining

O'Leary's energy investments have not stopped at existing power supply. He is also studying the energy structure for the next seven to eight years, and is especially bullish on the development potential of Small Modular Reactors (SMRs).

SMR is a nuclear energy technology route that uses smaller reactor modules, and some designs aim to improve the flexibility of nuclear power projects through modular manufacturing and deployment. In O'Leary's view, if the power demand from AI data centers grows over the long term, nuclear energy has the opportunity to become an important option for stable power supply, while uranium mining is an upstream resource in the related industrial chain.

As a result, he began to increase his focus on uranium mining investments. This judgment still depends on the commercialization progress of nuclear power technology. Whether SMRs can achieve competitive power generation costs within the expected timeframe still depends on engineering construction, regulatory approvals, financing, and fuel supply.

But O'Leary's investment logic is consistent: rather than rushing to bet on whether a certain technology will ultimately become the market winner, he studies in advance the foundational resources that technology expansion depends on.

Canada is an important part of this approach. He believes Canada possesses natural gas, uranium, potash, aluminum, and other strategic resources, while being adjacent to the United States, a major energy consumption market. If North America continues to increase investment in computing power and power infrastructure in the future, Canada could benefit.

O'Leary revealed that his team built approximately a 10% allocation exposure to the Canadian market through the large-cap Canadian ETF XIU. The trade initially did not receive unanimous support from his team, as US-Canada trade friction at the time heightened market concerns about the Canadian economy.

But O'Leary viewed this pessimism as a contrarian investment opportunity. He believes that as Canada's policy environment changes and resource development projects advance, the market may reassess the value of local assets.

He said in the interview that this investment at one point outperformed the S&P 500 Index by 158 basis points, or 1.58 percentage points. However, the interview did not disclose the full comparison period or calculation methodology, so this figure can only serve as a description of his personal investment performance.

Whether Canada can outperform over the long term still depends on energy project execution, commodity prices, trade relations, and policy implementation efficiency. For O'Leary, betting on Canada is not simply a judgment on economic recovery, but a bet that global technology expansion may re-elevate the strategic value of energy and natural resources.

Crypto Can No Longer Rely Solely on Capital Flows — The Real Question Is Whether Public Blockchains Can Generate Revenue

If AI investment is shifting from models to energy, then O'Leary's reassessment of Crypto is shifting from financial market trading to actual commercial demand.

In the past Crypto investment framework, an important assumption was that institutional capital entering the market would drive broader asset allocation demand across the entire industry. But O'Leary found that institutional capital does not necessarily flow evenly to all crypto assets.

He recalled that at an industry conference with a large number of institutional investors participating, some analysts believed that through Bitcoin and Ethereum alone, one could gain exposure to approximately 97% of the price volatility in the crypto market they were studying.

This figure is a research judgment he relayed, not the market capitalization share of BTC and ETH, but the allocation logic behind it is worth noting. For large institutions, if a few leading assets can already meet their primary Crypto allocation needs, then the necessity of purchasing a large number of small and mid-cap tokens may decline. This could lead to more pronounced capital divergence in the Crypto market.

O'Leary stated that his portfolio once held 27 Crypto-related positions, and he later sold some assets, while some of the remaining investments also suffered significant losses. The main assets he ultimately retained include Bitcoin and Ethereum.

However, he did not conclude from this that other public chains have completely lost their investment value. On the contrary, he began searching for a new standard of judgment: if the entire Crypto market no longer rises, how can other public chains prove their own economic value?

He believes the answer may come from actual enterprise adoption of blockchain technology.

In past market narratives, Ethereum was often seen as one of the networks most likely to become general-purpose blockchain infrastructure. But O'Leary began to doubt whether all industries in the future would really adopt the same public chain. Industries such as finance, logistics, real estate, contract management, and sports assets do not have exactly the same needs for blockchain.

Financial institutions may focus on stablecoin payments and asset transfers; logistics companies place more emphasis on supply chain management and contract execution; sports clubs may want to use blockchain to manage collectibles and digital assets.

Different business needs may give rise to different technical architectures. This is also why O'Leary re-examined Avalanche. He specifically mentioned Avalanche's ability to support enterprises in deploying customized blockchains, believing that this model may be suitable for sports assets, collectibles, and other scenarios requiring independent business networks.

In his view, enterprises may no longer need to place all business on the same public network, but instead choose different blockchain infrastructures according to industry needs. This means that future blockchain competition may form two paths: one in which a few general-purpose networks dominate, and another in which multiple networks separately serve different industries and achieve interconnection through technical interfaces.

At present, it is still impossible to determine which model will ultimately win. More importantly, there is no inevitable connection between commercial adoption rates and token investment returns. Even if an enterprise adopts Avalanche technology, it does not mean that demand for the AVAX token will necessarily grow in tandem. Investors also need to study how enterprises pay network fees, how commercial revenue is generated, and whether this economic value can be transmitted to the token.

Therefore, O'Leary did not indicate that he intended to re-enter altcoins on a large scale, but rather considered building smaller-scale public blockchain positions after studying specific applications. Unlike past crypto investments driven by market liquidity, he has begun to focus on whether enterprises truly need these blockchains and whether demand can generate sustained commercial revenue.

From this perspective, the next phase of competition in crypto may no longer be just about competing for investors' trading capital, but about competing for real enterprises' business demand.

Quantum computing is changing the long-term risk assessment for crypto

As O'Leary began re-examining the commercial value of public blockchains, he also raised another risk that could affect the entire crypto industry: quantum computing.

In the past, when investors assessed Bitcoin's risks, they mainly focused on market prices, liquidity, policy regulation, and the macroeconomic environment. However, with the development of quantum computing technology, whether existing cryptographic mechanisms will need to be upgraded in the future has also become a question worth studying.

O'Leary refers to the point in the future when quantum computing may break through some existing cryptographic algorithms as Q-Day.

His judgment is not that Bitcoin has already suffered a quantum attack, but that market expectations of a potential threat may affect prices before a technological breakthrough occurs. If institutional investors begin to worry that future quantum computers could attack existing digital signature systems, then even if a real attack has not yet occurred, it could still reduce their willingness to allocate assets.

He personally speculates that some institutional investors may refrain from allocating to Bitcoin long-term due to quantum risk. However, this proportion has not been verified by public statistics and remains his personal judgment.

At the technical level, a distinction also needs to be made: quantum computing poses a potential threat to some public-key cryptographic algorithms, but that does not mean all of Bitcoin's security mechanisms will fail simultaneously at some point. The relevant networks may also adopt post-quantum cryptography through protocol upgrades.

For O'Leary, this type of risk leads to another investment opportunity. If financial institutions need to upgrade their security systems in the future, then companies capable of providing quantum computing technology and quantum-resistant solutions may gain new business demand. He therefore began to pay attention to the quantum computing businesses of companies such as IBM and Google.

However, differences still exist between quantum computing R&D capabilities, commercialization revenue, and post-quantum security products, and investors cannot simply equate technological progress with corporate profit growth.

From energy to quantum computing, O'Leary is essentially examining the same category of problems. When a technology achieves widespread adoption, it often not only creates new products but also places higher demands on existing infrastructure. AI requires more energy, enterprise blockchain needs networks capable of meeting real-world business demands, and the development of quantum computing may drive upgrades in cryptography and information security systems.

The investment opportunities brought about by industrial shifts do not necessarily exist only in the new technologies themselves, but may also exist in the infrastructure that must be upgraded to accommodate those new technologies.

From Betting on Technology Winners to Managing Investment Risk, It Ultimately Comes Back to Cash Flow

Although O'Leary has made many positive assessments about energy, blockchain, and quantum computing, he does not believe investors should concentrate large amounts of capital betting on any single technology trend. Instead, he regards diversification as one of the most important investment disciplines.

His rule is: no single stock should account for more than 5% of a portfolio, and no single sector should exceed 20%.

This rule is tied to his early investment experience. During the dot-com bubble era, O'Leary once shorted Yahoo. As the stock price continued to rise, he repeatedly faced margin call pressure. According to his recollection, this trade once caused a massive drawdown of approximately 40% to 50% in his net worth. Although Yahoo later experienced a stock price decline, the experience of enduring prolonged unrealized losses and margin calls led him to decide never to engage in short selling stocks again. This also changed the way he manages his investment portfolio.

Today, he tends to build foundational positions through index funds, then appropriately increase allocations to companies he favors, but always controls the weight of any single stock.

Tesla is one example. O'Leary recalls that he initially did not endorse Tesla's high valuation, but a point raised by his son changed his judgment: Tesla is not just an automaker, but also a technology company that accumulates massive amounts of data through its vehicles. He eventually established a position and trimmed it multiple times as the stock price rose, avoiding Tesla becoming too large a proportion of his portfolio.

This approach may cause him to miss part of subsequent gains, but it also reduces the impact of any single stock on overall asset performance. He mentioned in the interview that for some individual stock trades, he typically uses a return of about 17% as the minimum target threshold, and after reaching the target considers selling more than half of the position.

These are his personal trading rules, and they do not mean that fixed profit-taking can guarantee higher long-term returns. What truly matters is that he does not want his portfolio's performance to depend entirely on a judgment about any one company.

In private enterprise investing, O'Leary similarly emphasizes using operating data to validate investment logic. He stated that when evaluating a business, he places the greatest importance on Customer Acquisition Cost (CAC) and Churn Rate. The former reflects how much capital a company needs to invest to acquire customers, while the latter is used to determine whether existing customers can be retained over time.

If a company must continuously increase marketing spending just to sustain revenue growth, while customers are simultaneously churning, then the apparent rapid growth may not have sound economics.

O'Leary also studies a company's cash flow, debt levels, and operating data from multiple past quarters. This is one of the reasons he is bullish on enterprise AI applications. Among the private companies he has invested in, AI tools have already been used for financial analysis, customer acquisition, and daily operations. He believes that one of AI's true commercial values is helping enterprises lower operating costs and improve profit margins.

He also mentioned that his company's team chose Anthropic's products as internal tools. This made him realize that different models do not perform identically in actual enterprise use, and enterprise procurement does not depend solely on public model rankings.

But regardless of which vendor is chosen, the ultimate standard for measuring the value of AI tools remains operational improvement. Whether the technology is advanced enough and whether a company can profit from it are two different questions. This also explains why O'Leary, on the one hand, is bullish on AI's long-term development, while on the other hand devoting substantial attention to energy, infrastructure, and enterprise cash flow.

What he focuses on is no longer just how much new market demand technology can create, but through what commercial mechanisms that demand will ultimately be converted into profit.

For energy companies, it is necessary to observe whether low-cost electricity resources can form profitable long-term contracts; for public blockchains, it is necessary to verify whether enterprise adoption can translate into real network revenue; for quantum computing, it is necessary to distinguish research progress from actual commercial products; and for AI applications, it is necessary to focus on whether enterprises can continuously reduce costs and improve profit margins.

If these conditions are not fulfilled, then even if the industry grows over the long term, the related assets may not necessarily deliver returns in line with market expectations.

Therefore, O'Leary's entire investment approach can be summed up in one judgment: rather than trying to predict the ultimate winner of each round of technological competition, it is better to study which companies can continuously provide essential resources, generate commercial revenue, and convert growth into cash flow throughout the entire process of industrial expansion.

From AI power infrastructure to enterprise blockchain and quantum security, these investment themes appear to belong to different industries on the surface, but behind them all lies the question of how economic value is distributed once technology becomes widespread.

For investors, what truly needs to be verified next is not just whether technology will continue to advance, but whether the market's pricing of future growth can be supported by actual operating results.

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