How Artificial Intelligence Will Shape the Future

The Future of AI: How Artificial Intelligence Will Change the World

The future of AI refers to the evolving role of artificial intelligence in shaping how people live, work and interact with their daily lives — driven by advances in generative AI models, AI automation and ethical AI innovation.

Innovations in the field of artificial intelligence continue to shape the future of humanity across nearly every industry. AI is already the main driver of emerging technologies like big data, robotics and IoT, and generative AI has further expanded the possibilities and popularity of AI.

How Will AI Impact the World?

Artificial intelligence is a powerful tool capable of automating work and accelerating scientific discovery. As it advances and becomes more useful, AI will have a wide variety of impacts on the world, both good and bad, ranging from environmental degradation as well as more efficient hybrid workforces.

As of 2024, about 42 percent of enterprise-scale companies have actively deployed AI in their business. Plus, 92 percent of companies plan to increase their investments in AI technology from 2025 to 2028.

With so many changes coming at such a rapid pace, here’s what shifts in AI could mean for various industries and society at large.

The future of artificial intelligence
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.

How AI continues to develop in the next 10 years

Between now and 2034, AI will become a fixture in many aspects of our personal and business lives. Generative AI models such as GPT-4 have shown immense promise in the short time they've been available for public consumption, but their limitations have also become well known. As a result, the future of AI is being defined by a shift toward both open source large-scale models for experimentation and the development of smaller, more efficient models to spur ease of use and facilitate a lower cost.

Initiatives such as Llama 3.1, an open source AI model with 400 billion parameters and Mistral Large 2, released for research purposes, illustrate the trend of fostering community collaboration in AI projects while maintaining commercial rights. The growing interest in smaller models has led to the creation of models such as the 11 billion parameter mini GPT 4o-mini, which is fast and cost-effective. It won't be long before there's a model suitable for embedding in devices such as smartphones, especially as the cost continues to decrease.

This movement reflects a transition from exclusively large, closed models to more accessible and versatile AI solutions. While smaller models offer affordability and efficiency, there remains a public demand for more powerful AI systems, indicating there will likely be a balanced approach in AI development to attempt to prioritize both scalability and accessibility. These new models deliver greater precision with fewer resources, making them ideal for enterprises needing bespoke content creation or complex problem-solving capabilities.