Third Quarter Equity Outlook

by John Osterweis, Gregory Hermanski, Nael Fakhry of Osterweis Capital Management, 7/24/25

What Market Selloff? Thank you, AI (and the Tariff Retreat)

As the market marches to new highs, just two months after a violent 20% selloff in equities, we ask ourselves how the narrative has shifted so quickly. Our simple answer is that economic fundamentals remain strong, with corporate profits rising, unemployment low, and inflation still under control for the moment. While the Trump administration threatened punishing tariffs that we believe risked an almost certain recession driven in part by the ensuing rampant inflation, the White House quickly retreated — leading to a snapback in equity prices. However, this still begs the question as to why the economy continues to grow and hence the stock market continues to move higher.

We believe the ever-expanding technology sector driven by the AI boom is playing a critical role in driving continued economic growth. We examine the role of AI in this outlook and conclude that direct participants and the broader economy are beginning to see a payoff, justifying the significant AI investments observed across sectors.

The AI Rollercoaster: Increasing Payoff Despite the Volatility

There have been several key milestones in the AI revolution over the past few years, most notably the public launch of ChatGPT in late 2022. But arguably the most critical recent milestone was the January 2025 release of DeepSeek’s Reasoning Large Language Model (LLM) R1. At the time, markets were roiled by the revolutionary Chinese LLM, which promised nearly the same performance of incumbent models at a small fraction of the cost. The question on everyone’s mind was whether DeepSeek would advance or impair the ability of technology companies to monetize AI and whether their enormous investments would ever see a payoff. Our analysis leads us to conclude that the pivotal DeepSeek moment helped unleash and accelerate the next wave of AI innovation while actually improving return on investment (ROI) on the enormous budgets being spent on LLMs.

Evolution of AI Scaling Laws

DeepSeek’s release demonstrated that highly capable AI models can be developed at a fraction of the previously estimated cost. With a reported spend of only $5.6 million for its final training run (roughly 95% cheaper than OpenAI’s o1 model), DeepSeek challenged the long-held belief that only tech giants with immense capital budgets could compete at the forefront of AI innovation. This revelation initially sparked considerable market volatility, particularly affecting semiconductor vendors and hyperscalers racing to build the next state-of-the-art model, as investors questioned the ROI and long-term sustainability of the massive capital expenditures directed towards building AI data centers in the U.S.

However, the industry swiftly reacted and adapted to the DeepSeek release, with the focus shifting towards a more sophisticated understanding of AI development. While pre-training of LLMs remains crucial, DeepSeek’s success highlighted the increasing importance of post-training and test-time scaling (see graphic below), which involves a series of steps deployed after feeding models with raw data to improve relevancy and specificity. Models are now able to break down complex tasks into smaller steps, explain each step, check for possible answers, and review their own rationales before coming up with a response. This has led to significant performance improvements and a focus on optimizing models for efficient and powerful reasoning capabilities that resemble how humans “think.”

Phases of AI revolution

In fact, the vast majority of state-of-the-art LLMs are now reasoning models that can think and reason like humans when tackling complex multi-step problems and tasks. Western AI labs have quickly caught up and even surpassed some of DeepSeek's initial benchmarks. Alphabet, for instance, has released two state-of-the-art reasoning models this year. OpenAI followed suit, releasing its newest mini models in mid-April 2025, simultaneously slashing costs by over 80% and further democratizing access to advanced AI capabilities.