Trump's Q1 Holdings Reveal a Shift Toward AI Infrastructure

Written by: Mike, Frank, MSX Maotong

Since 2025, two men's trade signals have been the most effective in the market.

One is Jensen Huang; whenever he takes the stage at a product launch to speak about GPUs, Blackwell, or data centers, the market reimagines the ceiling of AI. The other is Trump; beyond directly naming specific stocks, his public statements and policy initiatives significantly influence expectations across entire industries.

Interestingly, recently Trump also filed his personal financial disclosures with the Office of Government Ethics, including holdings in stocks and funds, transaction records, and value ranges. Although these disclosures do not prove that each transaction was personally decided by Trump, nor can they be simply interpreted as clear buy or sell recommendations, they do provide at least a window for observation:

When a person with significant policy influence begins to make clear directional adjustments to their related accounts, the market naturally wonders what industry insights these moves may reflect.

However, after a thorough analysis, MSX found that the most noteworthy aspect of this Q1 disclosure is the increased trading activity in Trump-related accounts, with a clear shift toward AI infrastructure—particularly through significant reductions in older technology and defensive assets, and increased investment in AI infrastructure supply.

Undoubtedly, as the ultimate decision-maker on U.S. policy, his portfolio composition reflects, to some extent, his judgment on future industry directions and serves as a window for ordinary investors to understand what the world’s most powerful “smart money” is thinking.

If you start with the most straightforward data, you'll find it's a prime example of diligent trading.

According to disclosed filings, Trump-related accounts executed 3,711 securities trades in Q1, equating to roughly dozens of trades per trading day; based on the lower end of the reported range, the total trading volume exceeded $220 million—clearly indicating an active account, approaching the quarterly trading volume of a mid-sized hedge fund.

More interestingly, this differs significantly from his investment style during his first term (2017–2021), when disclosures showed he held approximately 100 individual stocks across sectors such as finance, healthcare, and industrials, resembling a more diversified blue-chip portfolio. After entering the White House, he transferred his assets to family members and related institutions; his individual stock holdings notably decreased, and his active trading activity was far less pronounced than it is now.

Notably, previously Obama invested in Treasury bills and diversified mutual funds, while Biden did not trade stocks at all during his tenure. Past presidents generally chose to divest assets or establish blind trusts to avoid conflicts of interest, but Trump’s approach in his second term completely broke this tradition.

Looking more closely, you can identify a highly thematic set of adjustments.

First, look at where the funds are leaving from.

In the first quarter, the largest sales from Trump-related accounts were concentrated in Microsoft, Amazon, and Meta. According to the disclosed ranges, these transactions all fell within the highest bracket of $5 million to $25 million. These three companies are undoubtedly still core assets in the U.S. tech stock sector, but they also share a common trait—they represent the dominant winners of the previous era of consumer internet, advertising platforms, e-commerce, and cloud services.

Microsoft has software and cloud services, Amazon has e-commerce and AWS, Meta has social networks and advertising systems—they are not without AI stories, and in fact, they are all major players in AI investment. However, from a portfolio perspective, these companies have already reaped substantial valuation gains over the past few years, so significant share reductions do not necessarily indicate bearish sentiment; rather, they more accurately reflect a strategic reduction in exposure to legacy technology platforms.

Particularly note that the disclosure documents do not completely liquidate these companies; some holdings still show small purchase records. This “large sell, small buy” structure appears more like an active reduction of exposure rather than a complete exit.

Also appearing on the large sell-order list are dividend-focused ETFs such as the Vanguard Dividend Appreciation ETF. This indicates that capital outflows are not limited to traditional tech giants but also include some defensive, stable assets.

This is crucial. Simply selling Microsoft, Amazon, and Meta to buy another set of tech stocks would only represent an internal rotation within the tech sector. But if defensive ETFs are also being reduced, it suggests the portfolio’s overall risk appetite may be increasing, with capital shifting from stable, established assets toward more aggressive industry sectors.

So, where did the money go?

The answer is also clear—semiconductors, AI hardware, enterprise software, consumer electronics, broad-market indices, and certain bonds and preferred stocks.

If you only buy NVIDIA, you're merely betting on the leader in AI computing power—but what's more noteworthy in this disclosure is that Trump-related accounts didn't buy a single asset, but an entire AI infrastructure chain.

The first layer consists of semiconductors, with companies such as NVIDIA, Broadcom, Texas Instruments, Intel, AMD, Micron, and Marvell appearing on the buy or increase holdings list. This includes GPUs, CPUs, analog chips, memory, and interconnect technologies—encompassing both the most commercially dominant AI computing leaders and U.S.-based manufacturers with strong policy relevance, providing comprehensive coverage across the entire supply chain.

NVIDIA and Broadcom need no further explanation: the former is a core player in AI computing power, while the latter benefits from trends in custom chips, networking chips, and hyperscalers’ in-house chip development. AMD represents the narrative of GPU and data center computing alternatives, Micron corresponds to storage demand, and Marvell is tied to interconnects, custom chips, and high-speed data transmission.

More interestingly, Synopsys and Cadence are also on the buy list; these companies produce EDA tools—software used for chip design—that average investors might not immediately think of. However, within the semiconductor supply chain, they occupy a highly upstream position, akin to “selling shovels.” Almost every complex chip, from design to fabrication, relies on these tools, further demonstrating that this portfolio adjustment is not merely chasing the hottest AI leaders but rather extending upstream along the semiconductor supply chain toward foundational tools.

The second layer consists of AI hardware and servers, with Dell being one of the most sensitive and widely discussed targets. Disclosure documents show that Trump-related accounts established a position in DELL worth between $1 million and $5 million on February 10. Several months later, Trump publicly endorsed Dell’s hardware products, after which Dell secured substantial government contracts and its stock price rose significantly.

This timeline is sensitive precisely because it follows the sequence: account purchase first, followed by public endorsement, then government procurement, and finally a rise in stock price. From a rigorous standpoint, disclosure documents alone cannot prove a causal relationship between the trade, public statements, and subsequent contracts. However, from a market observation perspective, such transactions naturally attract attention, as they align with three highly sensitive points: AI hardware, government procurement, and public statements by the president.

Intel represents a different kind of sensitivity; unlike Dell, Intel’s core significance lies not just in business logic but in policy logic. The U.S. government has already decided to make a major equity investment in Intel, and Intel has long been a central focus in U.S. initiatives for domestic semiconductor manufacturing, supply chain security, and industrial policy. Against this backdrop, Trump-related accounts repeatedly bought INTC in the first quarter, a move naturally amplified by the market.

NVIDIA represents the commercial winner in AI computing power, while Intel represents the domestic manufacturing foundation that the U.S. government aims to support. Their motivations differ, but both point in the same direction: AI infrastructure is no longer just a market theme—it is increasingly being driven by both industrial policy and fiscal resources.

The third layer consists of enterprise software companies such as Oracle, ServiceNow, Adobe, and Workday, all of which also appear on the buy list. Unlike NVIDIA, Dell, and Intel, which provide computing power and hardware, these companies directly integrate AI into enterprise workflows. Oracle corresponds to databases and cloud infrastructure, ServiceNow to enterprise process automation, Adobe to creative and marketing productivity, and Workday to human resources and financial management systems.

The logic behind this trend is clear: AI must eventually move beyond just models and chatbots—it needs to integrate into real corporate budgets and into everyday workflows such as operations, customer service, marketing, finance, HR, development, and data analysis. Ultimately, the greatest advantage of enterprise software companies is that they are already embedded in their customers’ workflows. Once AI capabilities become a default feature of these software platforms, the impact won’t just be a new narrative—it could transform retention rates, pricing power, module upgrades, and customer loyalty.

So, what’s truly noteworthy in this disclosure isn’t just which AI hardware companies were purchased, but also that the AI transformation of enterprise software is emerging as another important clue.

The fourth layer is consumer electronics—for example, Apple has received significant increased holdings with multiple follow-up purchases. Compared to pure AI chips and enterprise software, Apple more closely represents the gateway to AI devices. Whether it can truly capitalize on the AI device cycle remains a topic of market debate, but within a portfolio covering both AI infrastructure and end-user applications, Apple is an undeniable super-gateway.

Additionally, broad-market indices such as the S&P 500 ETF, Russell 1000 ETF, and QQQ also appear on the large-buy list, indicating that this portfolio is not entirely detached from the broader market or solely betting on a single thematic line. Instead, it maintains overall exposure to the U.S. equity market while actively increasing positions in AI infrastructure and critical supply chains.

The disclosure documents also include numerous bond transactions, such as municipal bonds, corporate bonds, high-yield bond ETFs, and bank preferred shares. The municipal bonds span multiple states, while the corporate bonds include those issued by Netflix, Occidental, and CoreWeave.

Therefore, from a portfolio perspective, we can create a clear investment self-portrait: maintaining a foundation and liquidity with broad-market indexes, bonds, and preferred stocks on one side, while enhancing growth potential with semiconductor, server, enterprise software, and AI infrastructure assets on the other.

Seeing this disclosure, many people’s first reaction might be whether they should buy following it.

But simply copying homework doesn't really make sense, and the reason is simple:

• First, OGE disclosures have a time lag—by the time retail investors see the filings, the trades have already occurred;
• Second, the disclosed amounts are ranges, not exact figures—for example, $1 million to $5 million, or $5 million to $25 million—with significant variation between the upper and lower bounds, making it difficult to determine the actual position weights based on this information;
• Third, the relevant accounts may be independently managed by third-party institutions, and it is not publicly known whether each transaction is the result of active judgment, portfolio rebalancing, or model-based allocation;

Therefore, this disclosure should not be used as a signal for short-term trading.

Where it truly holds value is in revealing a larger directional shift: the most astute "smart money" is moving away from legacy platform technologies and certain defensive assets toward AI infrastructure supply, specifically shifting from the previous generation of internet core assets—such as advertising, e-commerce, and traditional cloud services—to chips, servers, storage, interconnects, local manufacturing, and the AI transformation of enterprise software.

This direction also overlaps to some extent with current U.S. policy priorities.

After all, domestic semiconductor manufacturing, supply chain security, AI infrastructure, government procurement, and corporate digitalization are not merely market narratives—they are directions driven collectively by policy, fiscal measures, industry initiatives, and capital. For companies like Intel, their significance extends beyond earnings elasticity; they represent America’s desire to regain control over advanced manufacturing and semiconductor supply chains.

This is also the most noteworthy aspect of Trump-related accounts increasing their holdings in Intel: it doesn’t necessarily mean Intel is the best chip stock, but it indicates that, within the AI infrastructure theme, the market currently favors companies positioned at the center of policy and resource allocation. Similarly, Dell’s case illustrates that AI infrastructure is not limited to the GPU layer—servers, hardware, government procurement, and enterprise deployments are all part of how AI capital expenditures translate into real-world applications.

Therefore, what ordinary investors should truly take away from this disclosure is not any single stock, but three structural insights.

• AI trading is evolving from models and applications toward infrastructure: Previously, the market bought into AI through speculation on large model potential and computing power expectations; now, capital is increasingly focusing on who can provide chips, servers, storage, networks, packaging, design tools, and enterprise software.
• Semiconductors are no longer just about NVIDIA: NVIDIA remains the core focus, but this disclosure shows that capital is also flowing into key players across the supply chain, including Broadcom, AMD, Micron, Marvell, Intel, Synopsys, and Cadence. As AI infrastructure deepens, it’s no longer just a story about a single leader—it’s about the repricing of the entire supply chain.
• The AI integration of enterprise software may be the most underestimated aspect: hardware builds the computing power, while enterprise software puts AI into practice. The value of companies like Oracle, ServiceNow, Adobe, and Workday lies not in their ability to tell a brand-new AI story, but in their capacity to embed AI into existing workflows and convert customer retention and product upgrades into revenue.

Regarding the significant sell-offs by Microsoft, Amazon, and Meta, it’s not accurate to simply interpret this as “these companies are going to decline.” More precisely, this signals a reallocation of capital—after all, when established platform giants have already risen substantially, capital naturally begins seeking assets closer to the next wave of capital spending, policy support, and infrastructure development.

Regardless, the era's benefits of consumer internet have not disappeared, but AI infrastructure, semiconductor localization, and the AI integration of enterprise software are indeed accelerating as the next phase of focus for capital.

This is also the most noteworthy aspect of the Q1 portfolio rebalancing disclosure from the world's most powerful individuals.