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Tech Investor's 139-Percent Surge Since April: How AI-Driven Trades Are Dominating Her Portfolio

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  Published in Stocks and Investing on Sunday, November 16th 2025 at 23:36 GMT by Insider

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A Tech Investor’s 139‑Percent Surge Since April: How AI‑Driven Trades Are Dominating Her Portfolio

The latest story in MSN Money’s “Saving & Investing” column tells the tale of a young, independent tech‑investor whose portfolio has surged 139 % since the start of April—a gain that’s far outpacing the broader market and, by all accounts, setting a new personal record. The piece is more than a headline‑grabber; it’s a deep dive into the mechanics of a trade that’s now becoming a “signature move” for anyone looking to ride the AI wave. Below is a comprehensive recap of what the article covers, its underlying logic, and the investor’s own top‑stock pick that could offer a blueprint for the rest of us.

Who Is the Investor and Why the Attention?

The investor in question is Katherine “Katie” Li, a former data‑science analyst at a Silicon Valley hedge fund who left to pursue a career as a self‑directed trader. Li is a frequent contributor to The Wall Street Journal’s “Tech Investing” column, and she’s known for using proprietary machine‑learning models to sift through the noise and find companies with the highest AI‑related upside. Her performance is not an anomaly; the article notes that Li’s portfolio is anchored in the same high‑growth, AI‑heavy constituents that are driving the NASDAQ‑100 and the S&P 500’s recent rally.

The article begins by establishing Li’s track record: “Since the beginning of April, her portfolio has increased by 139 % – a figure that eclipses the 14 % gain seen in the S&P 500 over the same period.” A quick link in the text takes readers to a spreadsheet that maps Li’s positions against the index, highlighting the out‑performance relative to both tech‑heavy peers and broader market benchmarks.

The Trade That’s Driving the Dominance

Li’s “AI trade” is a multi‑legged strategy that blends long positions in high‑growth AI‑enabled companies with short positions in industries that are either disrupted by AI or lagging in their AI adoption. The article lays out the logic in three distinct parts:

1. Long AI Infrastructure
   Li has piled into companies that provide the physical and software infrastructure for AI. The piece cites NVIDIA, Advanced Micro Devices (AMD), Microsoft (Azure), and Google Cloud as the core holdings. A link in the article directs readers to the latest earnings releases of NVIDIA and AMD, which detail how GPU demand has surged as “generative AI” workloads expand. Li’s own analysis—shared in an interview she gave to Bloomberg—shows that the compound annual growth rate (CAGR) of GPU revenue for the past five years is now >40 %.

2. Long AI‑Application Companies
   Beyond infrastructure, Li’s portfolio includes companies that are building AI applications. Palantir, Snowflake, and Databricks are highlighted. The article references a CNBC feature that discussed how Palantir’s “Foundry” platform is being adopted by government and private sectors alike, providing a data pipeline that is “AI‑ready” from day one. Li’s own data‑driven model flags companies that can “scale AI” rapidly as high‑risk, high‑reward bets.

3. Short Disruptive Industries
   Li has also opened short positions in traditional retail, conventional manufacturing, and manual‑based logistics companies that are either slow to adopt AI or are being outpaced by AI‑driven substitutes. A link in the article leads to a research note that explains how AI‑powered warehouses have cut labor costs by up to 35 % for major e‑commerce players, thereby eroding margins for legacy distributors. Li’s short bets are meant to capture the “displacement risk” associated with this shift.

The combination of these three legs has produced a “high‑beta, high‑return” portfolio that has outperformed the market. Li’s performance is supported by the article’s citation of a Financial Times analysis that attributes the “AI bubble” to a combination of institutional money flowing into the sector and a genuine technological leap.

What Is Li’s Top Stock Pick?

Despite the diversified long positions, Li singles out Microsoft’s Azure AI services as her “top stock pick.” The article quotes her: “Azure is not just a cloud platform; it’s the AI super‑hub that enables every other player to deploy models at scale.” She explains that the company’s recent partnership with OpenAI to embed ChatGPT into its suite of productivity tools is a “game‑changer” that will further accelerate AI adoption across enterprise software.

Li’s conviction is backed by data: the article points to an MSN Money sidebar that lists Azure’s revenue growth of 18 % in Q2 2024, a 23 % increase in AI‑specific services, and a projected CAGR of 12 % over the next five years. A link leads to Microsoft’s investor presentation, where the company outlines its strategy to invest $30 billion in AI and machine‑learning infrastructure.

How Li Builds and Manages Her Portfolio

Beyond the specific positions, the article gives readers insight into Li’s investment process. She uses a proprietary machine‑learning model that ingests 50 data points—including ESG metrics, patent portfolios, and macro‑economic indicators—to generate a “confidence score” for each potential investment. Li’s model also incorporates sentiment analysis from social media and analyst reports, allowing her to capture market “hype” before it turns into overvaluation.

Li explains that risk is managed through a combination of dynamic hedging and portfolio rebalancing. The article includes a link to a Harvard Business Review case study on option‑based hedging for high‑beta tech stocks, which Li applies to her short positions in the manufacturing sector.

Broader Market Context and Take‑Away Lessons

The piece frames Li’s success within a larger narrative: the AI surge has not only re‑shaped tech valuations but also created a “technology‑displacement” story that can be leveraged for strategic positioning. Li’s performance demonstrates that the key to capitalizing on AI lies in:

1. Investing early in AI infrastructure
2. Positioning for AI application proliferation
3. Taking advantage of displacement in lagging sectors

The article ends by urging readers to consider their own exposure to AI. It includes links to several research tools—such as Morningstar’s AI index and Yahoo Finance’s AI ETF—to help investors gauge where they stand.

In Summary

The MSN Money article showcases how a single investor, Katie Li, turned a deep‑dive AI analysis into a 139 % return over a matter of months. By combining long positions in AI infrastructure and application companies with shorts on traditional, AI‑resistant sectors, Li has built a portfolio that outperforms the market by a wide margin. Her top pick, Microsoft’s Azure AI services, is presented as a cornerstone of her strategy, supported by solid revenue growth and a clear roadmap for AI integration.

For anyone looking to emulate Li’s success, the article provides a wealth of resources—from earnings releases and research reports to model‑building tools and risk‑management frameworks. It’s a case study that underscores the transformative power of AI and the importance of a disciplined, data‑driven approach to investing in this rapidly evolving space.