Famous investor Kevin O'Leary: The next opportunity in AI is in energy, and Crypto is looking for new value

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

Video Author: Stock Sharks

Editor’s Note: As AI investment transitions from model competition to infrastructure expansion, the market's focus is shifting from "who can develop the strongest model" to "who can provide the ongoing infrastructure for the entire AI industry." Chips, computing power, and model capabilities remain important, but as data centers scale up, power supply, energy costs, and long-term infrastructure contracts are becoming key variables affecting AI investment returns. As technological advancements gradually become a market consensus, a more fundamental question arises: if it is impossible to predict the ultimate winner of the technological competition in advance, how should investors share in the benefits brought by the expansion of the entire industry?

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

In this discussion, O'Leary essentially breaks down "what to invest in next" into a set of more fundamental structural questions: What resources does technological expansion rely on? Who ultimately captures the industry value? How does new technology translate into cash flow? How should investment risks be managed when the market cannot determine the final winner?

First, the logic of AI investment shifts from technological leadership to resource constraints. In recent years, AI investment has primarily revolved around large tech companies, advanced chips, and model capabilities, with investors trying to identify companies with technological advantages. However, as demand for computing power increases, electricity is becoming an important condition affecting data center expansion. Therefore, O'Leary is focusing his investment in the next three years on energy, particularly natural gas, power infrastructure, uranium mines, and regions like Canada and Northern Europe that have low-cost electricity resources. He adopts a typical "picks and shovels strategy": instead of predicting which AI company will ultimately win, he seeks companies that can provide critical resources for the entire industry. This means that investment opportunities in the AI industry may further spread from tech companies to energy and infrastructure sectors, but whether resource advantages can translate into excess returns still depends on project costs, contract pricing, and execution capabilities.

Second, the valuation logic of crypto shifts from market trading to actual adoption. Previously, institutional funds mainly focused on Bitcoin and Ethereum, while other crypto assets faced issues of liquidity concentration, regulatory uncertainty, and lack of clear commercial demand. However, O'Leary has begun to re-examine public chains like Avalanche, not due to short-term price performance, but because companies in finance, logistics, sports assets, and contract management may need different types of blockchain infrastructure. The resulting question is whether future blockchain applications will concentrate on a few general networks or form multiple specialized networks serving different industries. This change may create opportunities for other public chains, but corporate adoption of blockchain technology does not necessarily mean that related tokens can capture corresponding economic value. What truly needs to be validated is the network usage, commercial revenue, and the mechanism by which value is transmitted to token holders.

Third, quantum computing is introducing new long-term security variables for crypto assets. In the past, the main market risks for Bitcoin were concentrated on price volatility, regulation, and capital flow. With the development of quantum computing, whether the existing cryptographic systems can withstand potential attacks in the future is becoming another topic worth discussing. O'Leary suggests that even if truly capable quantum computers have not yet emerged, market expectations of this risk may preemptively influence asset pricing. However, the so-called Q-Day still lacks a definitive timetable, and advancements in quantum technology cannot be directly equated with the failure of blockchain security mechanisms. Rather than predicting when Bitcoin will face threats, he is also paying attention to companies like IBM and Google in the fields of quantum computing and security technology. This involves not only crypto risks but also the potential upgrades to the cryptographic infrastructure that the financial system may face in the future.

Fourth, investment methods shift from betting on winners to managing uncertainty. O'Leary does not deny the appeal of high-growth assets, but his early experience shorting Yahoo has made him place greater emphasis on position limits. He insists that no single stock should exceed 5% of the portfolio, and no single industry should exceed 20%, controlling concentration risk through gradual reduction of positions. This approach aligns 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 allowing a single prediction to determine the performance of the entire portfolio. Meanwhile, his analysis of private companies always revolves around customer acquisition costs, retention rates, and cash flow, emphasizing that technology must ultimately manifest as real operational improvements.

If this discussion can be condensed into one judgment, it is that the next phase of investment competition may no longer be just about finding the companies with the strongest technological capabilities, but rather identifying which resources, infrastructure, and business models can steadily capture value during the ongoing technological expansion.

In this sense, O'Leary is discussing not just specific assets like AI, crypto, or energy, but how the market reallocates capital, measures risk, and determines asset value after the technology industry transitions from narrative-driven to commercial realization. What truly determines long-term returns may not be who first bets on the technological trend, but who can continuously generate cash flow during the industry's development and avoid paying too high a price for unfulfilled growth.

The following is the original content (for ease of reading and understanding, the original content has been reorganized):

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

This may differ from market expectations of a tech investor. In recent years, the core of AI investment has been NVIDIA, model companies, and large tech firms. Investors have tried to determine who has the most advanced chips, the most powerful models, and the most likely to dominate future technology platforms.

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

His thinking is not about exiting tech investments but rather reducing reliance on a single technological winner. Regardless of which company ultimately wins the model competition, they will need electricity, land, data centers, and other infrastructure.

This is also why he is shifting his investment focus to energy.

In the crypto market, O'Leary is conducting a similar reassessment: can blockchain assets, which previously relied on market trading and capital inflows for valuation, prove their sustainable economic value through actual commercial applications in the future?

The Next Opportunity in AI is Not in Models, But in Powering the Models

O'Leary is not pessimistic about the long-term prospects of AI.

He acknowledges that large tech companies have generated substantial returns in the last round of AI investment, and investors holding related stocks or indices have benefited. However, for the next phase of investment, he is more concerned with one question: if it is uncertain 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 refers to this as the Picks and Shovels strategy. During the gold rush, rather than predicting who could find gold, it was better to invest in companies that provide tools to the prospectors.

The AI industry has a similar business structure. Model companies need chips, chips need data centers to operate, and data centers require large and stable electricity. Therefore, whether it is OpenAI, Anthropic, or other tech companies gaining more market share, the demand for electricity may increase as computing infrastructure expands.

O'Leary states that in the next 36 months, he will focus on energy companies, as well as natural gas power generation, turbines, power infrastructure, and uranium mines. What he values is not that these assets have AI concepts, but that they may become necessary inputs for the entire industry's development. This also explains why he is optimistic about Canada and Northern Europe.

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

For large data centers that require long-term operation, electricity costs can directly impact project profitability. Even if the competitive landscape among model companies changes, companies that have land, grid access conditions, and long-term power contracts may still provide services to different clients.

His investment in Bitzero is a concrete manifestation of this idea. According to O'Leary, Bitzero once generated cash flow through Bitcoin mining, but he values more the long-term power contracts and infrastructure resources the company has secured in Norway and Finland.

For him, mining is just one of the existing businesses; the real long-term value may lie in the company's ability to acquire energy. If cloud computing or AI companies need to build data centers locally in the future, Bitzero has the opportunity to participate in related projects using existing resources. The attractiveness of this business model lies in the fact that infrastructure providers do not necessarily need to determine which AI model the customer will ultimately use.

Of course, controlling electricity resources does not equate to having stable profits. Projects still need to address issues such as grid access, equipment procurement, customer contracts, and capital expenditures, and the price advantage of long-term power contracts must be able to cover comprehensive construction and operational costs. However, O'Leary believes that compared to searching for a single winner in the model competition, energy infrastructure provides another way to participate in AI growth.

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

Extending from electricity to uranium mines

O'Leary's investment in energy does not stop at existing electricity supply. He is also researching the energy structure for the next seven to eight years, particularly optimistic about the development potential of Small Modular Reactors (SMRs).

SMRs are a nuclear energy technology route that uses smaller reactor modules, with some designs aiming to improve the flexibility of nuclear power projects through modular manufacturing and deployment. In O'Leary's view, if the electricity demand of AI data centers continues to grow in the long term, nuclear energy has the opportunity to become an important choice for stable power supply, while uranium mines are the upstream resources in the related industrial chain.

Therefore, he has begun to increase his focus on uranium mining investments. This judgment still relies 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 remains consistent: rather than rushing to bet on whether a certain technology will ultimately become a market winner, he prefers to study the fundamental resources that technological expansion relies on in advance.

Canada is an important part of this idea. He believes that Canada has natural gas, uranium, potash, aluminum, and other strategic resources, while being close to the large energy-consuming market of the United States. If North America continues to increase investments in computing power and electricity infrastructure in the future, Canada may benefit from it.

O'Leary revealed that his team has established about a 10% exposure to the Canadian market through the Canadian large-cap ETF XIU. This trade initially did not receive unanimous support from the team, as the increasing trade friction between the U.S. and Canada raised concerns about the Canadian economy.

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

In an interview, he mentioned that this investment once outperformed the S&P 500 index by 158 basis points, or 1.58 percentage points. However, the interview did not disclose the complete comparison period and valuation criteria, so this figure can only be seen as a description of his personal investment performance.

Whether Canada can outperform in the long term still depends on the implementation of energy projects, commodity prices, trade relations, and policy execution efficiency. For O'Leary, betting on Canada is not merely a judgment on economic recovery, but a bet on the potential for global technological expansion to re-elevate the strategic value of energy and natural resources.

Crypto Can No Longer Rely Solely on Capital; The Real Question is Whether Public Chains 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 business demand.

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

He recalled that at a previous industry conference with many institutional investors, some analysts believed that they could gain exposure to about 97% of the price volatility in the crypto market through Bitcoin and Ethereum.

This figure is a research judgment he relayed, not the market cap share of BTC and ETH, but the underlying allocation logic is worth noting. For large institutions, if a few leading assets can already meet the main Crypto allocation needs, then the necessity to purchase a large number of mid- and small-cap tokens may decrease. This could lead to a more pronounced capital differentiation in the Crypto market.

O'Leary stated that his portfolio previously held 27 crypto-related positions, but he later sold some assets, and the remaining investments also experienced significant losses, with the main assets retained being Bitcoin and Ethereum.

However, he did not conclude that other public chains have completely lost investment value. On the contrary, he began to look for a new criterion: how can other public chains prove their economic value if they no longer rely on the overall Crypto market rising?

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

In past market narratives, Ethereum has often been seen as one of the networks most likely to become a universal blockchain infrastructure. But O'Leary began to doubt whether all industries would really adopt the same public chain in the future. The demand for blockchain varies across industries such as finance, logistics, real estate, contract management, and sports assets.

Financial institutions may focus on stablecoin payments and asset transfers; logistics companies may prioritize supply chain management and contract execution; sports clubs may wish 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 is re-examining Avalanche. He specifically mentioned Avalanche's ability to support enterprises in deploying customized blockchains, believing 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 their operations on the same public network but instead choose different blockchain infrastructures based on industry needs. This suggests that future blockchain competition may take two paths: one where a few universal networks dominate, and another where multiple networks serve different industries and interconnect through technical interfaces.

It is still uncertain which model will ultimately prevail. More importantly, there is no necessary correlation between commercial adoption rates and token investment returns. Even if a company adopts Avalanche technology, it does not mean that the demand for the AVAX token will necessarily increase in tandem. Investors still need to study how companies pay network fees, how commercial revenue is generated, and whether this economic value can be transmitted to tokens.

Therefore, O'Leary did not indicate a desire to massively reinvest in altcoins but is considering establishing smaller positions in public chains after researching specific applications. Unlike past Crypto investments driven by market liquidity, he is now focusing on whether enterprises genuinely need these blockchains and whether that demand can generate sustainable commercial revenue.

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

Quantum Computing is Changing the Long-Term Risk Assessment of Crypto

As O'Leary began to re-examine the commercial value of public chains, he also raised another potential risk that could impact the entire Crypto industry: quantum computing.

In the past, investors assessing the risks of Bitcoin primarily focused on market prices, liquidity, regulatory policies, 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 time when quantum computing may break through some existing cryptographic algorithms as Q-Day.

His judgment is not that Bitcoin is currently under quantum attack, but that market expectations of potential threats may influence prices even before a technological breakthrough occurs. If institutional investors begin to worry that future quantum computers could attack the existing digital signature system, then even if a real attack has not yet occurred, it may lower their willingness to allocate assets.

He personally speculates that some institutional investors may refrain from allocating Bitcoin in the long term due to quantum risks. However, this proportion has not been publicly statistically verified and remains his personal judgment.

On a technical level, it is also necessary to differentiate: the potential threat of quantum computing to some public key cryptographic algorithms does not mean that all security mechanisms of Bitcoin will fail simultaneously at some point. Related networks may also adopt post-quantum cryptographic technology through protocol upgrades.

For O'Leary, this type of risk introduces another investment opportunity. If financial institutions need to upgrade their security systems in the future, companies that can provide quantum computing technology and solutions against quantum attacks may gain new business demand. He has therefore begun to pay attention to the quantum computing businesses of companies like IBM and Google.

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

From energy to quantum computing, O'Leary is essentially studying the same type of issue. A technology's large-scale adoption often not only creates new products but also places higher demands on existing infrastructure. AI requires more energy, enterprise blockchains need networks that can meet actual business demands, and the development of quantum computing may drive upgrades in cryptography and information security systems.

Investment opportunities arising from industrial changes may not only exist in the new technology itself but also in the infrastructure that must be upgraded to adapt to the new technology.

From Betting on Technological Winners to Managing Investment Risks, Ultimately Returning to Cash Flow

Although O'Leary has made many positive judgments about energy, blockchain, and quantum computing, he does not believe that investors should concentrate large amounts of capital on a single technological trend. On the contrary, he views diversification as one of the most important investment disciplines.

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

This rule is related to his early investment experiences. During the internet bubble, O'Leary once shorted Yahoo. As the stock price continued to rise, he faced ongoing margin call pressures. According to his recollection, this trade caused his net worth to experience a significant drawdown of about 40% to 50%. Although Yahoo later experienced a stock price decline, the long-term experience of bearing unrealized losses and margin calls led him to decide not to engage in short-selling again. This also changed the way he manages his investment portfolio.

Today, he tends to establish foundational positions through index funds and then appropriately increase allocations to companies he is optimistic about, while always controlling the weight of any single stock.

Tesla is an example. O'Leary recalled that he initially did not recognize Tesla's high valuation, but a point made by his son changed his judgment: Tesla is not just a car manufacturer but also a technology company that accumulates vast amounts of data through its vehicles. He ultimately built a position and sold off portions multiple times as the stock price rose to avoid an excessive weight of Tesla in his portfolio.

This approach may have caused him to miss part of the subsequent rise, but it also reduced the impact of any single stock on the overall asset performance. He mentioned in the interview that for some individual stock trades, he typically sets about a 17% return as the minimum target threshold and considers selling more than half of the position once the target is reached.

These are his personal trading rules and do not imply that fixed profit-taking can guarantee higher long-term returns. What is truly important is that he does not want the performance of his portfolio to depend entirely on the judgment of a single company.

In private equity investments, O'Leary similarly emphasizes validating investment logic with operational data. He stated that when evaluating companies, he places the most 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 assess whether existing customers can continue to be retained.

If a company must continuously increase marketing expenditures to maintain revenue growth while customers are continuously churning, then the apparent rapid growth may not have good economics.

O'Leary also studies a company's cash flow, debt levels, and operational data from multiple past quarters. This is also one of the reasons he is optimistic about AI enterprise applications. In the private companies he invests in, AI tools have already been used for financial analysis, customer acquisition, and daily operations. He believes that one of the true commercial values of AI is helping companies reduce operating costs and increase profit margins.

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

However, regardless of which supplier is chosen, the ultimate standard for measuring the value of AI tools remains operational improvement. Whether the technology is advanced enough and whether the enterprise can profit from it are two different questions. This also explains why O'Leary, on one hand, is optimistic about the long-term development of AI, while on the other hand, he focuses a lot of attention on energy, infrastructure, and corporate cash flow.

What he is concerned about is no longer just how much new market demand technology can create, but how that demand can ultimately be transformed into profit through commercial mechanisms.

For energy companies, it is necessary to observe whether low-cost power resources can form profitable long-term contracts; for public chains, it is essential to verify whether the adoption by enterprises can be converted into real network revenue; for quantum computing, it is important to distinguish between research progress and actual commercial products; for AI applications, the focus should be on whether companies can continuously reduce costs and improve profit margins.

If these conditions are not met, even if the industry grows in the long term, related assets may not be able to achieve returns that align with market expectations.

Therefore, O'Leary's entire investment philosophy can be summarized into 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 necessary resources, generate commercial revenue, and convert growth into cash flow during the entire industry expansion process.

From AI power infrastructure to enterprise blockchain and quantum security, these investment themes seemingly belong to different industries, but they all revolve around the issue of how economic value is distributed after technology becomes widespread.

For investors, the next step that truly needs to be verified is not just whether technology continues to advance, but whether the market's pricing of future growth can be supported by actual operational results.