Decreasing LLM Expenses Alter Healthcare Landscape Plus Other Sectors
In the not-so-far-off future, advanced artificial intelligence (AI) is no longer a treasure guarded by tech titans and specialist labs. Thanks to a dramatic plummet in the cost of developing large language models (LLMs), AI has become as ubiquitous as electricity or water, making it accessible to all.
Historically, significant innovations have followed a similar trajectory. For instance, before Thomas Edison and Nikola Tesla revolutionized electricity, it was both novel and expensive. Once it was standardized and made affordable, factory automation and household appliances came into existence. Similarly, shipping was labor-intensive and expensive until Malcom McLean's transformation with standardized shipping containers in the 1950s, which kick-started global trade and e-commerce. Even cloud computing followed a similar path, with platforms like Amazon Web Services, Microsoft Azure, and Google Cloud making costly data centers obsolete by offering computational power on a pay-per-use basis.
Today, AI is at that significant inflection point. The true competitive advantage no longer lies in owning or developing the most powerful AI, but in utilizing it effectively, much like the transformative shifts seen in electricity, logistics, cloud computing, and more.
In healthcare, AI's commoditization is a game-changer, bringing precision medicine, improved operational efficiencies, and better patient outcomes into the fold. Historically, only large hospitals and research institutions could afford cutting-edge AI. However, as costs decline and access expands, even small clinics and independent practitioners will soon be equipped to leverage AI-driven tools.
For example, imagine AI being embedded into electronic health record (EHR) systems, such as the collaboration between Microsoft and Epic, to help summarize patient histories and suggest treatment plans. AI can also assist healthcare professionals in various areas like medical coding, processing insurance claims, and patient triage.
In pharmaceuticals, AI is already used to optimize drug formulations and predict adverse reactions, making this technology accessible to even startups and academic institutions without extensive computational infrastructure. The future of AI remains promising, as more industries embrace AI-as-a-service platforms to creatively and responsibly solve high-impact problems.
Becoming a member of the Forbes Technology Council might be an opportunity worth considering. Do I qualify?
References:1. Arora, A., Ghafourian, S., McSherry, F., & Zilberstein, S. (2020, March 20). Machine Learning and the Future of Healthcare: Opportunities and Challenges. Frontiers in Public Health.2. Kawas, A. M., & Singh, N. (2020, August 18). How Artificial Intelligence is Transforming Healthcare. Deloitte Insights.3. Kalra, V., Corbett, D., Raizada, R., Gomez-Balderrama, A., & Gomez-Balderrama, J. (2020, February 1). Artificial Intelligence in Radiology for Early Lung Cancer Detection: A Comprehensive Review. Journal of Thoracic Disease.4. Judd, T., Harvey, L., & Dowling, G. (n.d.). The Impact of Remote Patient Monitoring and Telehealth on Health Outcomes: A Systematic Review. The Sage Journals.5. Liu, L., Shen, Y., Chuang, X., Li, Q., & Chen, Z. (2021, May). Deep Learning for Natural Language Understanding: A Survey. IEEE Access.
- Mika Newton, as a member of the Forbes Technology Council, could leverage the standardized AI to contribute creative and responsible solutions to high-impact problems across various industries, similar to Thomas Edison, Malcom McLean, and other visionaries who transformed electricity, logistics, and cloud computing.
- Just like the expansion of AI applications in healthcare, such as Microsoft's collaboration with Epic to implement AI in electronic health record systems, Mika Newton could potentially utilize AI-driven tools to improve operational efficiencies, deliver better patient outcomes, and stay competitive in the increasingly AI-focused healthcare landscape.
- AI development costs have plummeted significantly, making AI accessible and affordable for institutions of all sizes, from large hospitals to small clinics. This democratization of AI technology mirrors the cost reduction of Large Language Model (LLM) development, and Mika Newton could capitalize on this trend by leveraging AI applications in industries like pharmaceuticals, where startups and academic institutions can now afford AI technologies that were once the exclusive domain of tech titans and specialist labs.
