AI, investing, and the rise of the young investor
Maximilian Ullrich and Aiona Gambucci (Left to right) Montage: Paperjam
A student with a laptop can now ask AI to read earnings calls, compare stocks and build a portfolio before breakfast. The tools once reserved for Wall Street are moving into everyday hands. The real question is whether they create smarter investors, or simply more confident ones.
Imagine a 20-year-old investor sitting at their desk. No Bloomberg terminal, no expensive research reports, no finance degree. Just a laptop, a brokerage app, and access to the same artificial intelligence (AI) tools used by some of the world’s largest asset managers. They ask an AI to summarise a company’s last earnings call. They run a stock screen in seconds. They get a portfolio suggestion tailored to their risk appetite. All before their morning coffee.
This is not a distant scenario. It is already happening. AI is transforming investing at every level, from hedge funds managing hundreds of billions to students investing their first €100. But does easier access to information create better investors, or simply new risks?
AI is already changing the market
To understand the shift, it helps to be precise about what AI in investing actually means. There are several distinct tools: machine learning models that detect patterns across enormous datasets, large language models (LLMs) like ChatGPT or Claude that process and summarise unstructured information, algorithmic trading systems that execute orders in milliseconds, and robo-advisors that build and rebalance portfolios automatically. When we talk about AI in investing, it is thus important to define and distinguish its different forms and ways of use. That is the first step to getting the complete picture.
For professional investors, AI has become infrastructure. Hedge funds feed millions of data points, such as satellite imagery, shipping data, and social media sentiment, into models that no human team could process at the same speed. Earnings calls are transcribed and analysed for tone shifts before analysts finish their notes. News is monitored in real time and priced into trades before most people have even read the headline. Hence, the difference is not necessarily skill. It’s speed. The critical point, however, is that AI is not replacing investors. It is augmenting them. The judgement on what matters, what to believe, when to hold, still sits with humans.
That is where the story becomes interesting for the normal retail investor, such as you and I. Twenty years ago, quality investment research required a Bloomberg terminal running at more than €2,000 per month and access to analyst notes most retail investors never even knew existed. Today, a student with a free account on an AI platform can summarise annual reports, screen thousands of companies, and compare portfolio strategies in minutes. The information gap between institutional and retail investors has narrowed more and more.
AI as an assistant
Young investors are part of the digital-native generation: they have grown up with information technology and gained a better understanding of how to use digital tools. It is thus to no surprise that this same generation wants to manage their money in their own way, by using fintech apps for example. Part of this cultural shift implies that young investors are able to use AI to explain concepts such as ETFs, diversification, and portfolio construction in a simple way. One may argue that this is increasing their skepticism to traditional ways of investing, as youngsters are less inclined to pay for a service or knowledge they believe they could replicate themselves.
Educating themselves with the help of AI and investing on a digital platform is proof that investing has lower barriers to entry nowadays. For instance, a young investor can use AI to understand a company’s financial reports, recent earnings that have been posted, or to compare different companies and sectors without requiring expensive resources or expertise that only business students might have. What once required an internship or textbook to access specific knowledge can now be answered using AI, as long as one is willing to ask the right questions.
The hidden risks
None of this means that the playing field is suddenly level. It means the risks have changed in shape.
Firstly, the most immediate danger is one that feels counterintuitive: AI sounds confident even when it is wrong. LLMs do not flag uncertainty. Rather than admitting when they are unsure, or when they have no idea, they tend to present invented information as fact. They generate plausible, well-structured responses regardless of whether the underlying information is accurate or current. An investor asking about a company’s recent performance may receive a detailed, convincing answer that is simply out of date, or in some cases factually incorrect. The output looks like expertise, even though it can be completely wrong.
Second, a more significant risk is what happens when investors stop engaging critically with the information they receive. Confirmation bias can be powerful on its own. Giving people a tool that can instantly turn their instincts into an investment thesis therefore worsens the problem. Rather than it being the fear that AI replaced investors, the alarming scenario we should think about is that investors might stop thinking by themselves when outsourcing the analysis part of their investment decision.
Finally, there is also a structural risk that often goes unnoticed. When a large number of retail investors use similar AI tools, such as ChatGPT or Claude, which are trained on similar data and prompted in similar ways, they risk arriving at similar conclusions. It is common for institutional investors to make similar investment decisions, creating what are known as crowded trades. If AI starts influencing those decisions, this behavior could happen on a much larger scale. As a result, profitable opportunities may disappear more quickly, and market prices could become more volatile and harder to predict.
This essentially means that the same tools lowering barriers to entry can, if misused, create new and less visible ones.
What’s next?
What could investing look like in ten years? One can only make assumptions on where AI in combination with the investment industry will head. The general consensus of the industry is that AI will increase operational efficiency within financial firms, instead of replacing jobs. Furthermore, the use of technology will be rewarding to those that are able to use tools and create new skills that can improve repetitive tasks or speed up processes. For an investor or advisor, idea generation will always remain human. Whether this idea can be improved or built on using AI is an add-on that the financial industry can benefit from.
Capitalising on AI-driven technology is good news for a young investor! Tools will keep improving in terms of access to data, speed, and information dissemination. However, one must be cautious regarding investment performance: those outperforming may not be the investors with the best AI tool, but those that know when not to trust AI. The new skill lies in knowing which questions to ask an AI tool, which answers to challenge, and identifying when the model is lacking something that the data was not able to capture.
AI is doing for investment research what smartphones did for brokerage accounts: making it faster, cheaper, and accessible to a generation that previously had little foothold in financial markets. The information barriers that once separated retail from institutional investing are coming down in real time. This also means that young investors that are not studying a finance-related degree or are not that financially literate yet are able to learn and act with the help of AI.
For young investors, this is genuinely exciting. The ability to learn, research, and form an investment opinion without expensive resources represents a structural shift in who gets to participate meaningfully in financial markets. But the fundamental challenge of investing has not changed. Markets reward patience, discipline, and the ability to think independently when the consensus is wrong. Algorithms can process information at a scale no human can match. They cannot replace judgment, and they cannot produce long-term thinking.
The future of investing will not belong to those who rely entirely on AI, nor to those who ignore it. It will belong to those who learn to work alongside it.
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