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]