Core Insights - The Dow Jones Industrial Average (DJIA) is up nearly 13% year to date, with leading stocks not being pure AI plays but rather companies like Caterpillar, Goldman Sachs, and IBM [1][2][3] Caterpillar (CAT) - Caterpillar has seen a significant stock increase of approximately 66.3% year to date, driven by strong global demand for construction and mining equipment [4] - Despite a decline in adjusted profits to $4.95 per share on $17.6 billion in sales, sales rose by 10% due to higher end-user equipment volumes [5] - The U.S. infrastructure push and lower interest rates have contributed to a substantial backlog, indicating sustained revenue visibility [6] - Caterpillar is also pivoting towards AI-enabling infrastructure, with partnerships that position it as a beneficiary of the tech boom [7] - Analysts view Caterpillar as a hybrid play, justifying a forward earnings multiple of 27 times [8] Goldman Sachs (GS) - Goldman Sachs ranks second among Dow leaders with a stock increase of over 49% year to date, attributed to a rebound in dealmaking and economic stability [9] - The third-quarter earnings were $12.25 per share on $15.2 billion in revenue, surpassing estimates [9][10] - The firm has advised on over $1 trillion in announced M&A volume this year, benefiting from sustained trading revenues and a healthy growth outlook [10] - Goldman Sachs' institutional focus allows it to capture upside from corporate optimism, driving shares to an all-time high of $856 [11] IBM (IBM) - IBM has achieved a 40% year-to-date gain, with shares nearing record closing highs of approximately $308 [12] - The turnaround is driven by software and consulting segments, with third-quarter revenue up 7% to $16.3 billion at constant currency [12] - The watsonx AI platform and Red Hat integration have boosted bookings and hybrid cloud adoption, with a consulting backlog of $31 billion [13] - IBM is also making strides in quantum computing and has raised full-year guidance to 5% growth and $14 billion in free cash flow [14]

Core Insights - The AI industry is facing challenges as new models require significant computational resources, but DeepSeek's recent success with lower resource usage raises questions about future trends [1] - OpenAI's GPT 4.5 model is costly and offers limited real-world applications, indicating that more computing power may not be the ultimate solution [2] - Recent AI models are producing incorrect information more frequently, which could impact their reliability and adoption [3] IBM's Strategy - IBM is focusing on developing small, efficient AI models rather than competing in the race for the most powerful models, positioning itself to thrive in a potentially challenging economic environment [4][5] - The Granite family of AI models is designed for enterprise customers seeking cost-effective solutions that meet safety benchmarks, outperforming competitors in producing harmful content [7] - The Granite 3.3 model requires significant GPU memory, ranging from 28 GB to 84 GB, making it reliant on expensive data center GPUs [8] Granite 4.0 Developments - The upcoming Granite 4.0 models aim to run on inexpensive consumer-grade hardware, with the Granite 4.0 Tiny model requiring 72% less memory than its predecessor, operating on as little as 12 GB of GPU memory [9] - This shift to a new hybrid architecture allows for better performance on lower-cost hardware, making it suitable for enterprises looking to reduce costs [10] Economic Considerations - In a recession, enterprises are likely to prioritize projects that save money or enhance productivity, which aligns with IBM's focus on efficient AI solutions [10] - The unpredictability of U.S. tariff policies may impact the economy, but demand for projects with clear returns on investment is expected to remain strong [11] - IBM's emphasis on efficiency in its AI models could yield positive results if economic conditions worsen [12]