Invest Like the Best with Patrick O'Shaughnessy

Investors should maintain long-term exposure to top-tier Artificial Intelligence (AI) and software leaders that are achieving massive scale and market dominance far faster than previous tech generations. Prioritize AI platforms driving broad productivity gains, focusing on long-term economic expansion rather than near-term labor disruption. Consider an investment in Xiaomi Corporation (1810.HK / XIACF) as the company successfully expands beyond consumer electronics to capture market share in the high-performance electric vehicle (EV) sector. Broaden your equity strategy to favor agile management teams with a proven track record of redeploying supply chain mastery and software expertise into large adjacent industries. Finally, for allocations in early-stage venture capital and private tech markets, direct capital toward exceptional founders who harness modern software tools for extreme operational leverage rather than traditional funds relying on disappearing information advantages.

Investors should maintain core exposure to U.S. Big Tech platforms like Alphabet Inc. (GOOGL), which are structurally insulated from physical trade barriers and positioned to drive productivity through artificial intelligence in enterprise and life sciences.

Within the defense sector, reallocate capital away from legacy heavy hardware toward software-driven defense technology, autonomous drones, and counter-drone systems built for modern, rapid-iteration warfare.

For international diversification, allocate to India as a leading long-term emerging markets play driven by demographic expansion and an accelerating domestic tech sector.

Real estate investors can capitalize on the ongoing shift to hybrid work by investing in suburban and exurban residential development and remote work infrastructure that expand traditional commuting zones.

Lastly, seek long-term exposure to frontier deep-tech leaders like SpaceX, OpenAI, and Anthropic, as computational and aerospace capabilities increasingly align with national strategic infrastructure.

Investors should build long-term positions in nuclear energy (specifically Small Modular Reactors) and natural gas infrastructure to solve data center power constraints ahead of the critical 2030 to 2032 bottleneck.

Reduce supply-chain risk by investing in domestic foundries and alternative specialized silicon accelerators to diversify away from single dependencies like Taiwan Semiconductor Manufacturing Company (TSMC).

Target computational biology and pharma AI platforms such as Chai Discovery, which are actively generating multi-million-dollar enterprise revenues by accelerating pharmaceutical drug discovery.

Prepare for near-term commercial breakthroughs in physical AI and semi-humanoid robotics by tracking innovators like Sunday Robotics ahead of consumer beta launches scheduled for late this year into early next year.

Allocate toward high-conviction vertical enterprise AI software, led by platforms like Harvey, that demonstrate strong enterprise pricing power by automating complex workflows in legal, accounting, and compliance sectors.

Investors should maintain core exposure to NVIDIA (NVDA) for premium real-time AI workloads and Taiwan Semiconductor Manufacturing Company (TSM) for indispensable advanced chip manufacturing leverage. Capitalize on critical hardware bottlenecks by investing in memory suppliers like Micron Technology (MU), SK Hynix, and Samsung, which stand to profit from persistent shortages in High Bandwidth Memory (HBM) and DRAM. Consider Advanced Micro Devices (AMD) as a compelling value play as major tech firms adopt cost-effective chip alternatives to diversify away from premium GPU pricing. Hold Intel Corporation (INTC) as a long-term strategic hedge against potential Asian semiconductor supply chain disruptions. Finally, expand into the emerging AI Inference Infrastructure and flexible power generation sectors as data center demand shifts toward low-cost, decentralized computing for background AI tasks.

Invest in Alphabet (GOOGL) and Meta Platforms (META) for immediate, high-margin returns from AI-optimized advertising, complemented by Amazon (AMZN), which lowers cloud costs through custom silicon like Trainium.

Accumulate Intel (INTC) to capture upside from its foundry turnaround as Big Tech actively diversifies away from severe Taiwan Semiconductor Manufacturing Company (TSM) supply shortages projected to last through 2028–2029.

Hold Apple (AAPL) as a low-risk AI distribution play that captures high-margin software integration on consumer hardware without incurring heavy data center buildout costs.

Exercise caution with NVIDIA (NVDA), as long-term gross margins face compression from custom hyperscaler chips and deferred balance-sheet liabilities tied to compute buybacks through 2030.

Monitor Microsoft (MSFT) closely, as high computing costs and friction from usage-based pricing on enterprise tiers (such as the $100 per user per month E7 tier) may compress traditional software margins.

Capitalize on the artificial intelligence boom by investing in market-leading hardware provider NVIDIA ($NVDA**), which powers the essential compute infrastructure for AI models. Look beyond pure AI software and invest in the dominant cloud oligopoly of Amazon Web Services ($AMZN), Microsoft Azure ($MSFT), and Google Cloud Platform ($GOOGL), as enterprise adoption accelerates. Take advantage of severe multiple compression in the Software-as-a-Service ($SaaS)** sector by selectively buying established enterprise software companies successfully pivoting to AI. Play the massive energy bottlenecks caused by surging data center demand by investing in power generation, electrical grid modernization, and nuclear energy infrastructure. Avoid zero-sum traps in emerging technologies and instead build a balanced portfolio across these interconnected semiconductor, cloud, software, and energy themes over the next 3 to 5 years.

NVIDIA (NVDA) currently trades at its cheapest forward P/E multiple in a decade, presenting a prime long-term entry point for investors despite recent AI sector volatility. Underlying fundamentals for NVIDIA (NVDA) remain exceptionally strong as token growth and GPU rental pricing continue to accelerate rapidly. Investors should capitalize on short-term market panics to buy top-tier hardware and infrastructure providers like Microsoft (MSFT), Meta (META), and Amazon (AMZN), whose hyperscaler cash flows are surging. As older contracted compute bases expire, upcoming price re-adjustments will significantly drive revenue acceleration across the semiconductor sector.

To capitalize on the booming artificial intelligence sector, focus your investments on foundational infrastructure providers rather than speculative application startups. Microsoft (MSFT) and **Oracle (ORCL) **remain top choices for capturing surging enterprise cloud demand and hosting large-scale AI operations. Meanwhile, NVIDIA (NVDA) sits at a critical supply chain bottleneck, providing the essential advanced hardware and GPUs required for all modern AI workloads. Investors should also target specialized chip designers and energy providers that drive crucial efficiency gains as data center power demands skyrocket. Always monitor potential risks related to permitting challenges and public pushback regarding the massive physical footprint of these new facilities.

Investors should prepare for a structural Natural Gas supply deficit starting in 2028, which could drive prices from the current $3.50 range to over $8.00 per MCF. Expand Energy (EXE) is a high-conviction play currently trading at a low 4x EBITDA, offering significant upside due to its control of critical Haynesville shale inventory. For exposure to the Appalachian region, EQT Corporation (EQT) and Range Resources (RRC) are top-tier producers positioned to benefit as gas becomes the primary fuel for U.S. power generation. To hedge against rising electricity costs, look toward utility-scale solar providers like NextEra Energy Partners (NEP) and Clearway Energy (CWEN), which can reprice power contracts at much higher margins. In the nuclear sector, Cameco (CCJ) and BWX Technologies (BWXT) are the strongest long-term bets to benefit from the necessary expansion of large-scale reactor infrastructure.

Focus on high-growth infrastructure providers like WorkOS, Ramp, and Vanta, which are becoming essential for enterprise AI adoption and automated compliance. Monitor the Defense Tech sector closely, but prioritize firms with a long-term track record over those recently pivoting to the trend to avoid "diluted differentiation." When evaluating new ventures like Lila Sciences, apply the Persuasion Equation by ensuring the team has a clear plan to mitigate risk while driving high desire through "scientific superintelligence." Look for investment opportunities that exhibit true Scarcity, similar to the Benchmark model, as these assets move capital the fastest and maintain the best terms. Prioritize companies with a simple, defensible value proposition, as high Complexity in a business story often signals a lack of reliability and slows down institutional support.

Investors should target "Old" Private Companies (6–7 years old) that have recently inflected due to AI catalysts, as their slow starts often lead to significant undervaluation compared to newer startups. Avoid traditional SaaS companies with per-seat pricing models and instead prioritize AI-native software firms that utilize usage-based pricing to offset higher compute costs. For private market exposure, prioritize Emerging Managers over large growth funds, as smaller managers are more psychologically and financially aligned with generating high returns rather than collecting fees. Retail investors should exploit their lack of "career risk" by holding high-conviction, "weird" assets like Bitcoin or "Long Elon" plays (Tesla, SpaceX secondary markets) that institutional mandates often restrict. Monitor X (formerly Twitter) as a leading indicator for market sentiment, as algorithmic narratives now drive high-variance pricing in the Mag 7 and broader tech sectors.

The shift from AI training to inference (running models) is creating a massive opportunity for specialized hardware that outperforms general-purpose GPUs. Investors should look toward TSMC (TSM), which remains the essential manufacturing partner for next-generation AI chips like those from Etched and big-tech hyperscalers. While NVIDIA (NVDA) remains the industry benchmark, its high margins are under threat from application-specific chips (ASICs) designed to run "Transformer" architectures more efficiently. Consider exposure to the "Token Economy" by focusing on companies that maximize "tokens per watt," as energy availability is now the primary bottleneck for AI scaling. By 2027, the market is expected to pivot from "per-seat" software pricing to "per-inference" costs, benefiting high-growth AI agent platforms like Perplexity and Anthropic.

Investors should prioritize US-based assets over international markets, leveraging the country's energy abundance and "permissionless innovation" as a structural advantage. Focus on Asset-Backed Finance and Direct Lending as high-conviction alternatives to traditional bond portfolios to capture yield in a shifting credit environment. Consider reducing long-term exposure to legacy software giants like Salesforce (CRM) and Oracle (ORCL), as AI-native competitors threaten their 30-year terminal value. Allocate capital toward Data Center Infrastructure and Energy projects, which are essential to supporting the massive physical requirements of the AI revolution. Maintain a "just-in-case" investment posture by favoring private equity and infrastructure over public markets to avoid short-term volatility and quarterly earnings pressure.

Investors should prioritize usage-based SaaS platforms like Clay that replace traditional per-seat pricing with value-driven models, aligning perfectly with AI-driven productivity gains. Focus on "picks and shovels" infrastructure providers like Vanta and WorkOS, which capture value by enabling AI startups to meet essential enterprise security and compliance standards. In the financial sector, look for unified platforms like Ridgeline and AI-agent tools like Rogo that consolidate fragmented data and automate complex investment banking workflows. High-conviction opportunities lie in tools that democratize "computation," allowing non-technical teams in marketing and operations to build custom automated systems without engineers. When evaluating founders, favor those practicing "long-term greed" by prioritizing high integrity and reputation over short-term gains, as these leaders are best positioned to navigate genuine market risk.

Investors should prioritize NVIDIA (NVDA) as the essential infrastructure play, as its specialized chips remain the primary beneficiary of a "tornado of demand" that continues to exceed market earnings models. For foundational AI exposure, Google (GOOGL) is a high-conviction pick due to its technical superiority in document processing and its guaranteed spot in the emerging three-player model oligopoly. Look for "de-commoditized" hardware winners like Celestica (CLS) for its 60% share in AI Ethernet switches and Corning (GLW) for the massive fiber optic demand required to connect data centers. While Anthropic is the top private conviction for its $500 billion coding market opportunity, public investors should pivot away from traditional SaaS stocks like Salesforce (CRM), which face severe budget cannibalization from AI. We are currently at only 0.1% penetration of AI adoption, suggesting significant upside remains for companies where average selling prices are rising due to increased manufacturing complexity.

Investors should consider Uber (UBER) as a primary play in the "physical AI" space, as the company leverages over $10 billion in free cash flow to dominate the autonomous vehicle (AV) market. Monitor Uber One membership growth, currently at 50 million members, as this recurring revenue stream is expected to drive high-margin, long-term profitability. Look for Uber to act as the essential go-to-market partner for AV firms like Waymo, Lucid, and Aurora, benefiting from a 30% higher utilization rate than standalone networks. In the broader robotics sector, expect delivery drones to reach commercial scale within the next 2 to 5 years, significantly reducing delivery times for food and groceries. For enterprise exposure, focus on infrastructure providers like WorkOS and Vanta that enable AI leaders like OpenAI and Anthropic to scale security and compliance.

NVIDIA (NVDA) remains a high-conviction "catch-up trade" with attractive valuation multiples of 15x 2027 earnings, making it a core play for the multi-year AI expansion. Investors should look to build positions in Danaher (DHR) during market sell-offs, capitalizing on its superior operational system as COVID-related inventory distortions fade. In the credit markets, X debt offers a high-conviction 12% yield for those comfortable with the underlying enterprise value of the platform. Sony (SONY) presents a significant "governance alpha" opportunity as Japanese regulatory shifts and activist pressure force the company to spin off non-core assets and unlock shareholder value. To capture the full AI Stack, diversify across Semiconductors, Semi-cap equipment, and Hyperscalers, while monitoring Anthropic's growth as a primary indicator of ecosystem health.

Investors should pivot toward "Neoprimes" that bridge commercial technology with military applications, specifically focusing on drones, signals intelligence, and automated targeting software. Monitor the KMT party in Taiwan, as their political success could signal a peaceful regional power shift that reduces the immediate risk of a kinetic conflict with China. High-conviction opportunities exist in AI firms like OpenAI, Anthropic, and Perplexity, but priority should be given to companies that can verify data provenance to prevent "data poisoning" by adversaries. For enterprise infrastructure, look to specialized service providers like WorkOS, Vanta, and Ridgeline that facilitate security and compliance for high-growth tech sectors. Be cautious of small defense-tech firms during Congressional "Continuing Resolutions," as these budget delays pose significant liquidity risks compared to legacy defense giants.

Investors should maintain core exposure to NVIDIA (NVDA), which remains historically undervalued relative to its growth as the primary "seller of shortage" in the AI supply chain. Taiwan Semiconductor (TSM) acts as the critical gatekeeper for the industry; as long as they maintain high utilization without overbuilding capacity, the structural floor for AI valuations remains intact. High Bandwidth Memory (HBM) providers like Micron (MU) and SK Hynix offer a compelling valuation play as memory shifts from a commodity to a high-value AI component with extended hardware lifecycles. For software exposure, prioritize companies in the "token path" like Databricks or Snowflake, and favor Anthropic over competitors due to its superior capital efficiency and massive revenue scaling. Finally, monitor the U.S. energy sector and Natural Gas (NG1), as domestic power advantages provide a significant competitive moat for American AI data centers through 2027.

Investors should prioritize Amazon (AMZN) and Google (GOOGL) as they secure long-term revenue moats through multi-billion dollar compute commitments from Anthropic. While NVIDIA (NVDA) remains a core holding, Anthropic’s successful use of Trainium and TPU chips suggests a growing trend of hardware diversification that reduces single-vendor dependency. For exposure to the "Enterprise-Ready" bottleneck, look toward private infrastructure leaders like WorkOS and Vanta which enable AI startups to meet the security standards of the Fortune 500. Specialized "Vertical AI" agents like Rogo are high-conviction plays for disrupting specific industries like financial services by automating complex Excel and PowerPoint workflows. The most significant growth opportunity lies in the $40 trillion global knowledge work market as AI shifts from simple chat interfaces to autonomous "Virtual Collaborators" with internal company memory.