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Turing's predictions about thinking machines in the 1950s laid the philosophical groundwork for later developments in artificial intelligence (AI). Neural network pioneers such as Hinton and LeCun in the 80s and 2000s paved the way for generative models. In turn, the deep learning boom of the 2010s fueled major advances in natural language processing (NLP), image and text generation and medical diagnostics through image segmentation, expanding AI capabilities. These advancements are culminating in multimodal AI, which can seemingly do it all—but just as previous advancements have led to multimodal, what might multimodal AI lead to?

Since its inception, generative AI (gen AI) has been evolving. Already, we have seen developers such as OpenAI and Meta move away from large models to include smaller and less expensive ones, improving AI models to do the same or more using less. Prompt engineering is changing as models such as ChatGPT get more intelligent and better able to understand the nuances of human language. As LLMs are trained on more specific information, they can provide deep expertise for specialized industries, becoming always-on agents ready to help complete tasks.

AI is not a flash-in-the-pan technology. It's not a phase. Over 60 countries have developed national AI strategies to harness AI's benefits while mitigating risks. This means substantial investments in research and development, reviewing and adapting relevant policy standards and regulatory frameworks and ensuring the technology doesn’t decimate the fair labor market and international cooperation.

It is becoming easier for humans and machines to communicate, enabling AI users to accomplish more with greater proficiency. AI is projected to add USD 4.4 trillion to the global economy through continued exploration and optimization.