AI Evolution: Generative AI, Multimodal AI & the Race to AGI

Artificial Intelligence is advancing at an unprecedented pace, reshaping industries and redefining human-machine interaction. However, not all AI is the same. The three key developments driving this transformation—Generative AI, Multimodal AI, and Artificial General Intelligence (AGI)—represent distinct stages in AI's evolution, each with unique capabilities and implications.

Generative AI refers to systems capable of producing new content, whether text, images, music, or even video, by learning from vast amounts of data. Unlike traditional AI, which follows pre-programmed rules, generative AI can generate original, contextually relevant outputs based on patterns it has learned. This technology is already widely used in content creation, customer service, marketing, healthcare, and finance, automating processes and enhancing creativity. The power of generative AI lies in its scale. It relies on massive datasets, sophisticated computing power, and advanced neural networks known as transformer architectures. Reinforcement learning from human feedback further refines these models, making them more aligned with human expectations. Tools like ChatGPT, and DALL·E exemplify this approach, enabling machines to generate human-like text and visuals with remarkable accuracy.

While generative AI focuses on producing content in a single modality, Multimodal AI represents a significant leap forward by integrating and processing multiple types of data—text, images, video, and audio—simultaneously. This advancement makes AI more versatile and capable of understanding complex real-world scenarios. Google’s Gemini Ultra is one of the first major examples of this shift, demonstrating the ability to interpret both text and images together. According to Gartner, multimodal AI will account for 40% of generative AI applications by 2027, a dramatic increase from just 1% in 2023. This advancement is critical for industries requiring complex data interpretation. Medical diagnostics can combine patient history, lab results, and imaging for better analysis. Autonomous driving relies on real-time data from multiple sensors, while robotics benefits from AI that understands and interacts with physical environments more naturally.

While generative and multimodal AI are transforming industries today, Artificial General Intelligence (AGI) remains a theoretical concept. Unlike current AI models, which excel in specific, predefined tasks, AGI would possess human-like intelligence, enabling it to reason, learn, and adapt across multiple domains without explicit programming. In essence, AGI would not just process information—it would understand, think, and make independent decisions. Despite significant advances in deep learning, neuromorphic computing, and reinforcement learning, AGI is still years away. Research labs like OpenAI, DeepMind, and Anthropic continue to explore pathways toward AGI, but experts agree that current AI, no matter how powerful, still lacks true reasoning, self-awareness, and the ability to generalize knowledge across unrelated domains.

Even though AGI remains theoretical at this stage, AI as we know it is evolving at an astonishing pace. Tech giants are investing heavily in AI infrastructure, with Meta committing $65 billion and Microsoft expanding its AI capabilities with an $80 billion investment. Meta, in particular, is betting on AI-driven innovations by building a massive data center in Louisiana to support its generative AI initiatives. This move aligns with CEO Mark Zuckerberg’s vision of integrating AI into the metaverse, enhancing user interactions, and automating content generation at scale.

Generative AI is expected to grow from 4% of total tech spending in 2025 to 12% by 2032, reaching $1.3 trillion in global revenues generated. Key drivers of such growth will be spending for AI infrastructure as a service ($247 billion), digital ads ($192 billion), and AI assistant software ($89 billion). On the hardware side, revenue will come from AI servers ($132 billion), AI storage ($93 billion), computer vision products ($61 billion), and conversational AI devices ($108 billion) – Bloomberg Intelligence report.

As AI continues to integrate deeper into our lives, businesses face a crucial decision—not whether to adopt AI, but how quickly they can implement it to stay ahead. While the debate over AGI's future continues, one thing is certain: AI, whether generative, multimodal, or general, is revolutionizing industries and redefining the way we interact with technology.

---

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.