Investing in Intelligence: Navigating the Shifting Landscape of AI

21 oktober 2025

It’s hard not to notice how artificial intelligence (AI) has permeated our daily lives, as more and more machines try to anticipate our next move. AI technologies continue to evolve rapidly, with several ground-breaking developments from both industry leaders and new entrants. While ChatGPT has maintained its lead as the most popular consumer AI product, it is not yet a profitable business, and competition remains wide open for viable business models.

By Damien Zhang, CFA, Senior Vice President, Head of Research, MDT Advisers, a Federated Advisory Company

Until recently, AI development has found unprecedented success through training ever-larger models using ever-larger datasets. This ‘training-time scaling’ has propelled hundreds of billions of dollars in capital expenditures to achieve the model sizes necessary to compete at the cutting edge. This approach has been termed the ‘bitter lesson’ by researcher Richard Sutton because it seemed to imply that human knowledge and cleverness were inferior to simply throwing more data and compute at the problem.

Recent developments have challenged the notion that human ingenuity is no longer necessary. Training-time scaling of AI foundation models has appeared to hit a wall due to increased demand for, and scarcity of, new chips, energy and data. Researchers have refocused their efforts toward cleverly applying and refining the model they already have.

Competition remains fierce, and even the most well-resourced firms are not guaranteed success.

In 2024, OpenAI released Chat-GPT ‘o1’, a reasoning model that thinks before responding. Its inner workings involve refining its existing language model to have a conversation with itself. This ‘chain of thought’ mechanism gives o1 the ability to think before answering, as opposed to the prior design of blurting out the first response that came to mind. This innovation produced more accurate results to some challenging math and reasoning problems and opened a pathway to a new way for scaling to improve accuracy – ‘inference-time scaling.’ A reasoning model tends to be more accurate if it spends more time thinking about its answer, so it is now possible to spend more time computing and producing a better answer after the model is already trained.

Another development along these lines was Chinese lab DeepSeek’s release of its R1 reasoning model, a competitor to o1, early in 2025. The release challenged US firms’ dominance in AI in what many commentators called a ‘Sputnik’ moment.

The R1 model learned to reason by training itself using reinforcement learning, which refines a pre-trained foundation model through simulated interactions that reward accurate answers. The success of this approach has created a third scaling pathway called ‘post-training scaling,’ where models are refined to become more capable but not retrained from scratch. DeepSeek also upped the high-stakes AI ante by releasing the model to the public under an open-source license.

These developments suggested that winners will not be determined solely by the size of capital expenditures but by fierce competition among incumbents and new entrants, where durable leads can be challenging to maintain. Even with significant infrastructure in place, companies will have to allocate resources correctly along three different dimensions of scaling. And even as capital floods into AI, sustainable business models remain an unsolved problem.

Some of AI’s strongest proponents claim that this technology will radically reshape the economy and have a far-reaching impact on society. Although it is far from certain whether these promises can be fully realised, today, this technology is already being felt in industries as varied as search, hardware and education. Competition remains fierce, and even the most well-resourced firms are not guaranteed success.

Investing in AI

At MDT, the quantitative equity arm of Federated Hermes, we’ve long believed in the power of artificial intelligence and machine learning to enhance investment decision-making. Our models, including decision trees, are designed to generate alpha forecasts with a high degree of transparency and accountability—qualities we believe are essential in any investment process.

While generative AI and large language models have captured widespread attention, their off-the-shelf versions often fall short in investing contexts. They can lack reliability, contextual depth, and explainability, making it difficult to trust or trace their recommendations. That’s why we view AI not as a single solution, but as a toolbox. We selectively adopt the most relevant innovations, integrating them into our process in ways that preserve clarity and control.

The pace of AI innovation is remarkable, but not every company or industry will be impacted in the same way. That’s why we avoid taking directional bets on individual firms. Instead, we construct risk-managed portfolios that are diversified across AI-related opportunities, limiting exposure to any single winner, loser, or correlated group. This helps us navigate the uncertainty and volatility inherent in such a fast-moving space.

As AI continues to evolve, we remain committed to evaluating new developments and incorporating the most effective ideas into our investment framework. This dynamic approach allows us to stay ahead of the curve and uncover new sources of alpha, while managing risk in a thoughtful and disciplined way.

Federated Hermes MDT US Equity

Federated Hermes MDT Advisers apply an active, systematic investment approach which seeks to generate excess returns by leveraging sophisticated modelling to identify forecasting techniques tailored to different company types. The recently launched MDT US Equity Fund in UCITS format builds on MDT’s 30-year track record of successfully managing the same quantitative US equity strategy through various market cycles. Targeting long-term capital appreciation, the fund invests across all market caps and styles within the Russell 3000, using MDT’s proprietary model to enable disciplined stock selection, enhance returns, and manage risk.

Visit www.federatedhermes.com/mdt

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The value of investments and income from them may go down as well as up, and you may not get back the original amount invested.

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