Authors: Mike, Frank, MSX Maitong
Since 2025, two men's 'calls' have been the most effective in the market.
One is Jensen Huang. Whenever he stands on the stage talking about GPUs, Blackwell, or data centers, the market reimagines the ceiling of AI. The other is Donald Trump. Beyond directly touting specific stocks, his public statements and policy pushes can influence expectations for an entire industrial chain.
Interestingly, recently, Trump legally filed a personal financial disclosure with the Office of Government Ethics, detailing his holdings of stocks, funds, transaction records, and their value ranges. While the disclosure documents cannot prove every trade was personally decided by Trump, nor should they be simply interpreted as explicit buy/sell recommendations, they at least provide an observation window:
When accounts related to a person with the most policy influence start showing significant directional adjustments, the market naturally wonders: what industrial judgments are reflected behind this?
MSX's deep dive reveals that the most noteworthy aspect of this Q1 disclosure is precisely that Trump-related accounts have begun frequent trading, with a clear directional shift towards AI infrastructure, particularly by significantly reducing some legacy platform tech and defensive assets, and increasing bets on the supply side of AI infrastructure.
Undoubtedly, as the ultimate decision-maker for U.S. policy, his portfolio structure, to some extent, reflects his judgment on future industrial directions. It also serves as a window for ordinary investors to understand what the world's most powerful "smart money" is thinking.
I. $220 Million in Trading Volume, Over 3,700 Trades
Looking at the most straightforward data first, it shows an exemplar of "diligent trading."
According to the disclosure, Trump-related accounts completed 3,711 securities transactions in Q1. Roughly converted to actual trading days, this is almost equivalent to dozens of operations per day. Cumulatively based on the lower bounds of the reported ranges, the trading scale exceeded $220 million. This is clearly not an account lying quietly still; it approaches the quarterly trading volume of a small to medium-sized hedge fund.
More interestingly, this differs significantly from the investment style during Trump's first term (2017-2021). Disclosures at that time showed he held about 100 individual stocks, covering finance, healthcare, industrials, and other sectors, overall resembling a diversified blue-chip portfolio. After entering the White House, his assets were managed by family and related institutions, with individual stock holdings noticeably shrinking and active trading much less pronounced than now.
It's worth mentioning that previous presidents like Obama invested funds into treasury bills and diversified mutual funds, while Biden has conducted no stock trades during his tenure. Past presidents generally chose to divest assets or establish blind trusts to avoid conflicts of interest. Trump's approach in his second term completely breaks from this convention.
Upon closer examination, one can find a very thematic portfolio adjustment.
First, let's see where the money exited.
In Q1, the largest sales in Trump-related accounts were concentrated in three companies: Microsoft, Amazon, and Meta. According to the disclosed ranges, these transactions all reached the highest tier of $5 million to $25 million. These three companies undoubtedly remain core assets among U.S. tech stocks, but they share a common characteristic—they represent the super-winners of the previous era of consumer internet, advertising platforms, e-commerce, and cloud services.
Microsoft has software and cloud, Amazon has e-commerce and AWS, Meta has social networks and advertising systems. They are not without AI stories; in fact, they are major AI investors. But from a portfolio perspective, these companies have already reaped very full valuation gains over the past few years. Therefore, large-scale selling doesn't necessarily equal bearishness. More accurately, it's about reducing the weight of legacy platform tech.
Particularly noteworthy is that the disclosure does not show complete liquidation of these companies; some still have small purchase records. This "large sales, small buys" structure seems more like active reduction of exposure rather than a complete exit.
Also appearing on the large-scale sell list were ETFs like the Vanguard Dividend Appreciation ETF. This indicates that the capital outflow isn't just from legacy tech giants but also includes some defensive, stability-oriented assets.
This is crucial. If only selling Microsoft, Amazon, and Meta to buy other tech stocks, that would merely be rotation within tech. But if even defensive ETFs are being reduced, it suggests the overall risk appetite of the portfolio might be rising, with capital shifting from stable, legacy platform assets towards more offensive industrial directions.
So, where did the money go?
The answer is clear—semiconductors, AI hardware, enterprise software, consumer electronics, broad-based indices, and some bonds and preferred stocks.
II. From Chips to Servers, to Enterprise Software: The AI Infrastructure Chain Systematically Covered
If only NVIDIA were bought, that would merely be betting on the AI compute leader. But what's more notable in this disclosure is that Trump-related accounts didn't buy a single target; they bought an entire AI infrastructure chain.
The first layer is semiconductors. NVIDIA, Broadcom, Texas Instruments, Intel, AMD, Micron, and Marvell all appear on the buy or increase list. This includes GPUs, CPUs, analog chips, memory, and interconnect. It covers both the commercially strongest AI compute leader and policy-sensitive representatives of U.S. domestic manufacturing—truly a full-chain coverage.
NVIDIA and Broadcom require little explanation. The former is the core AI compute play, while the latter benefits from trends in custom chips, networking chips, and large cloud vendors' in-house chip development. AMD corresponds to the GPU and data center compute alternative narrative, Micron to memory demand, and Marvell to interconnect, custom chips, and high-speed data transmission.
More interestingly, Synopsys and Cadence also appear on the buy list. These companies make EDA tools—chip design software. Ordinary investors might not immediately think of them, but in the semiconductor industry chain, they belong to the very upstream "shovel-selling" segment. Almost every complex chip from design to tape-out relies on such tools. This further indicates that this reallocation isn't just chasing the hottest AI leaders but extends upstream and into the foundational tools of the semiconductor chain.
The second layer is AI hardware and servers, with Dell being the most sensitive and discussed target. Disclosure documents show Trump-related accounts established a position in DELL in the $1 million to $5 million range on February 10th. Months later, Trump publicly endorsed Dell hardware products, after which Dell secured a large government-related contract, and its stock price rose significantly.
The sensitivity of this timeline lies precisely in the sequence: first the account purchase, then public endorsement, followed by government procurement and stock price increase. Strictly speaking, the disclosure alone cannot prove a causal relationship between the trade, public statements, and subsequent contracts. But from a market observation perspective, such trades naturally attract attention because they hit several highly sensitive nodes: AI hardware, government procurement, and presidential public statements.
Intel represents a different kind of sensitivity. Unlike Dell, Intel's core is not just commercial logic but also policy logic. The U.S. government previously decided on a major equity investment in Intel, and Intel has always been a core target in U.S. semiconductor domestic manufacturing, supply chain security, and industrial policy. Against this backdrop, Trump-related accounts' multiple purchases of INTC in Q1 are naturally amplified in market interpretation.
NVIDIA represents the commercial winner in AI compute, while Intel represents the domestic manufacturing base the U.S. government wants to build up. Their logics differ, but both point to the same direction: AI infrastructure is no longer just a market theme; it's becoming a direction jointly propelled by industrial policy and fiscal resources.
The third layer is enterprise software. Companies like Oracle, ServiceNow, Adobe, and Workday also appear on the buy list. They differ from NVIDIA, Dell, and Intel by not providing compute and hardware but by directly embedding 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 of this line is also clear: AI cannot ultimately remain just in models and chatbots; it must enter real enterprise budgets, daily office work, customer service, marketing, finance, HR, development, and data analysis processes. Ultimately, the biggest advantage of enterprise software companies is that they are already embedded in customer workflows. Once AI functionality becomes a default capability of this software, it brings not just a new story but potential changes in renewal rates, pricing power, module upgrades, and customer stickiness.
Therefore, what's truly noteworthy in this disclosure isn't just which AI hardware companies were bought, but also that the AI-ification of enterprise software is becoming another important thread.
The fourth layer is consumer electronics, with Apple receiving a substantial increase and multiple additional purchase records. Compared to pure AI chips and enterprise software, Apple is more like a representative of the AI terminal gateway. Whether it can truly usher in an AI device cycle remains debated by the market. But in a portfolio covering AI infrastructure and applications, Apple is undoubtedly an unavoidable super-gateway.
Additionally, the fifth layer includes broad-based indices like S&P 500 ETF, Russell 1000 ETF, and QQQ, which also appear on the large purchase list. This indicates this set of accounts is not completely detached from the broader market or betting one-sidedly on a single theme. Instead, it's actively increasing bets on AI infrastructure and key industry chains while maintaining overall exposure to the U.S. equity market.
Simultaneously, the disclosure documents also show many bond trades, including municipal bonds, corporate bonds, high-yield bond ETFs, and bank preferred stocks. Municipal bonds cover multiple states, while corporate bonds include Netflix, Occidental, CoreWeave, etc.
Thus, from a portfolio perspective, we can derive a clear investment self-portrait—using broad-based indices, bonds, and preferred stocks to maintain a core position and liquidity on one side, while using semiconductors, servers, enterprise software, and AI infrastructure targets to enhance offensive positioning on the other.
III. Can We Copy the Homework?
Seeing such disclosures, many people's first reaction might be: can we follow and buy?
But directly copying the homework isn't very meaningful, for simple reasons:
- First, OGE disclosures have a time lag. By the time ordinary investors see the document, the trades have long occurred.
- Second, the disclosed amounts are ranges, not precise figures—e.g., $1M-$5M, $5M-$25M—with huge gaps in between, making it difficult to judge the true position weight.
- Third, the related accounts might be managed independently by third-party institutions. Outsiders don't know if each trade is an active judgment, portfolio rebalancing, or model-driven allocation.
Therefore, this disclosure isn't suitable as a short-term trading signal.
Its real value lies in allowing us to see a larger directional shift: namely, that the most keenly sensitive "smart money" is moving from legacy platform tech and some defensive assets towards the supply side of AI infrastructure. Specifically, from advertising, e-commerce, traditional cloud services—core assets of the previous internet era—towards chips, servers, storage, interconnect, domestic manufacturing, and the AI-ification of enterprise software.
This direction also overlaps somewhat with current U.S. policy priorities.
After all, domestic semiconductor manufacturing, supply chain security, AI infrastructure, government procurement, and enterprise digitalization are not merely market stories. They are directions jointly propelled by policy, fiscal resources, industry, and capital. Especially for targets like Intel, their significance is no longer just about earnings elasticity but about the U.S. desire to regain the initiative in advanced manufacturing and chip supply chains.
This is also the most noteworthy aspect of Trump-related accounts increasing their Intel holdings. It doesn't necessarily mean Intel is the best chip stock, but it indicates that within the AI infrastructure theme, the market currently prefers to see who stands in the position most concentrated with policy resources. Similarly, the Dell case shows that AI infrastructure isn't just happening at the GPU level; servers, hardware, government procurement, and enterprise deployment will all become part of AI capital expenditures landing in the real world.
Therefore, for ordinary investors, what's truly worth learning from this disclosure isn't any single stock but three structural clues.
- AI trading is shifting from models and applications towards infrastructure: In the past, buying AI meant more about buying big model imagination and compute expectations. Now, capital is starting to further focus on who provides chips, servers, storage, networking, packaging, design tools, and enterprise software.
- Semiconductors are no longer just about NVIDIA: NVIDIA remains the core target, but this disclosure shows capital is also covering industry chain nodes like Broadcom, AMD, Micron, Marvell, Intel, Synopsys, and Cadence. The further AI infrastructure development goes, the less it's a story about a single leader and more about the repricing of the entire supply chain.
- The AI-ification of enterprise software might be the more underestimated part: Hardware is responsible for building the compute power; enterprise software is responsible for putting AI to use. The value of companies like Oracle, ServiceNow, Adobe, and Workday lies not in whether they can tell a brand-new AI story, but in whether they can embed AI into existing workflows and turn it into revenue through customer stickiness and product upgrades.
As for the large-scale selling of Microsoft, Amazon, and Meta, there's no need to simplistically interpret it as "these companies will fall." More accurately, it's a signal of capital reallocation. After all, when legacy platform giants have risen significantly, capital naturally begins searching for assets closer to the next round of capital expenditures, policy support, and infrastructure construction.
Regardless, the era红利 of consumer internet hasn't disappeared, but AI infrastructure, semiconductor localization, and the AI-ification of enterprise software are indeed accelerating to become the main themes capital is more willing to chase in the next phase.
This is also the most noteworthy aspect of this Q1 portfolio adjustment disclosure from the world's most powerful person.
