A look ahead at the fast-paced evolution of technology, regulation and business A look ahead at the fast-paced evolution of technology, regulation and business Moments like this don’t come around often. The coming years will see outsized investment in generative AI, LLMs and foundation models. What’s unique about this evolution is that the technology, regulation, and business adoption are all accelerating exponentially at the same time. In previous innovation curves, the technology typically outpaced both adoption and regulation. The technology stack The complex technology underpinning generative AI is expected to evolve rapidly at each layer. This has broad business implications. Consider that the amount of compute needed to train the largest AI models has grown exponentially – now doubling between every 3.4 to 10 months, according to various reports. 8 Cost and carbon emissions are therefore central considerations in adopting energy-intensive generative AI. Figure 1: Each layer of the generative AI tech stack will rapidly evolve Applications: Generative AI and LLMs will be increasingly accessible to users in the cloud via APIs and by being embedded directly into other applications. Companies will consume them as they are or will customize and fine-tune them with proprietary data. Fine-tuning: The importance of model fine-tuning will create demand for a multidisciplinary set of skills spanning software engineering, psychology, linguistics, art history, literature and library science. Foundation models: The market will rapidly mature and diversify as more pre-trained models emerge. New model designs will offer more choices for balancing size, transparency, versatility and performance. Data: Improving the maturity of the enterprise data lifecycle will become a prerequisite for success – requiring mastery of new data, new data types and immense volumes. Generative AI features within modern data platforms will emerge, enhancing adoption at scale. Infrastructure: Cloud infrastructure will be essential for deploying generative AI while managing costs and carbon emissions. Data centers will need retrofitting. New chipset architectures, hardware innovations, and efficient algorithms will also play a critical role. 9 A new era of generative AI for everyone |