2024 was a defining year for generative artificial intelligence (Gen AI), with groundbreaking developments that pushed the boundaries of what AI can achieve. From the rise of multi-modal systems to the shift in focus towards Model-based reasoning, Gen AI yet again, proved its potential across industries. Here are 7 key trends that we believe shaped the trajectory of Generative AI in 2024.

1. Multi-Modal AI Models Redefined Possibilities

Multi-modal models are AI systems capable of processing and generating content across multiple data types, such as text, images, and audio. These models bridge different modalities to deliver more contextual and holistic outputs.

• A significant highlight in 2024 was OpenAI's launch of GPT-4o, an optimized flagship model capable of real-time text, audio, and image processing. Compared to its predecessor, GPT-4o offered enhanced speed, reduced costs, and higher rate limits, setting new standards in multimodal functionality.
• Other notable advancements included Google's Gemini 2.0, with it’s multimodal functionality and integration into tools like Deep Research and Project Astra positions Gemini 2.0 as a highly capable AI system for complex tasks.

2. AI Agents Stepping Into the Spotlight

AI agents, designed to autonomously execute complex tasks, gained mainstream traction in 2024. These systems integrate planning, reasoning, and execution, making them ideal for handling multi-step processes.

• January, 2024 saw OpenAI introducing the GPT Store, a platform enabling users to discover, create, and share custom versions of ChatGPT, known as GPTs. These GPTs are designed for specific tasks, such as coding education, design assistance, and academic research, and can be developed without requiring coding expertise. The store features a diverse range of GPTs created by partners and the community, categorized into areas like, writing, research, programming, education, and lifestyle.
• In December 2024, Google introduced Gemini 2.0, its most advanced AI model to date, marking a significant advancement in AI capabilities. Gemini 2.0 can ‘think multiple steps ahead', ushering in what Google refers to as the "agentic era" of AI, with its ability to anticipate and execute multi-step tasks autonomously.
• In a survey conducted by LangChain, about 51% of respondents said they were using agents in production, with mid sized companies (100-2000 employees) leading the pack at 63%.

3. AI’s Bold Move into Video Creation

Video generation models evolved dramatically in 2024, enabling the creation of high-quality videos from text, image, or video inputs. These breakthroughs have unlocked new levels of creativity and efficiency, transforming how industries approach content creation and production.

• ‍OpenAI’s Sora, released in December, 2024, showcased the potential of generative AI in video creation, producing minute-long, high-quality videos. Sora can generate realistic videos from text/image prompts, enabling users to create and edit videos up to 20 seconds long in various formats, offering features like storyboarding, image-to-video conversion, and video remixing.
• ‍Google’s Veo 2, announced in December, 2024, marked another groundbreaking advancement. Veo 2 is an advanced AI video generation model that can create realistic videos up to 4K resolution and several minutes in length, with an improved understanding of real-world physics and human movement. This development solidified its position as a leading tool in the video AI space, offering unparalleled quality and usability.

4. Rise of Open-Source AI

Open-source AI models are machine learning models whose code, architecture, and training data (in some cases) are publicly available for anyone to use, modify, and distribute. Open source models allow the AI community to build upon existing architectures, fostering innovation and collaboration. In 2024, this movement gained momentum with several key developments.

• ‍March saw Elon Musk’s xAI open-sourcing Grok-1, a 314-billion-parameter Mixture-of-Experts language model, releasing its weights and architecture under the Apache 2.0 license.
• Another significant milestone was Meta’s release of LLaMA 3.1 in July, 2024 featuring configurations ranging from 8 billion to 405 billion parameters. With Meta's latest state-of-the-art model, it extended the context length to 128K, incorporated support for eight languages, and introduced Llama 3.1 405B, marking the debut of the first frontier-level open-source AI model.
• Open source models have come a long way, offering greater control and the ability to customize for specific use cases. Models like DeepSeek V3 and LLaMA 3.1 405B have showcased their capability to rival and even surpass top-tier closed-source models such as GPT-4o and GPT-4o mini, as seen in independent evaluations.

These developments foster transparency and collaboration within the AI community, contributing to the advancement of large language model research and development.

5. Voice Agents Redefined Interaction

Conversational AI is a type of artificial intelligence (AI) that can simulate human conversation. It uses natural language processing (NLP) to understand and process human language.

• ‍Mercedes-Benz announced at the CES, 2024 that it is integrating Google’s conversational AI into its MBUX Voice Assistant System to enhance in-car virtual assistance. The conversational agent uses Gemini and runs on Google Cloud’s Vertex AI. This upgrade enables more natural, context-aware voice interactions, making tasks like navigation, communication, and infotainment control more intuitive and user-friendly for drivers and passengers. ‍
• 2024 also saw Apple unveiling an AI-enhanced Siri integrated with the new "Apple Intelligence" platform, offering improved contextual awareness and the ability to link commands for a more seamless user experience.‍
• 11ElevenLabs emphasized the importance of voice AI in making technology feel intuitive and accessible, aiding teachers, empowering people with disabilities, and enriching media content. 11ElevenLabs is an AI Audio platform, with their AI Voice Generator delivering high-quality, human-like speech in 32 different languages. Their focus on tools to verify and responsibly use AI-generated content underscored the need for safeguards in audio AI.
• Another notable development was Hume AI's integration of their flagship speech-language foundation model, Empathic Voice Interface (EVI) with Anthropic's Claude. This emotionally intelligent voice AI adapts its tone based on user context and expressions, enabling fluid and context-aware conversations. As of November, 2024, Hume AI has achieved over 2 million minutes of AI voice conversations, and 80% reduction in costs and 10% decrease in latency through prompt caching.

6. AI's Breakthrough in GPUs, TPUs, and the Future of Chip Architecture

The implementation of generative AI in chip architecture redefined hardware capabilities in 2024.

• The semiconductor industry witnessed a surge in generative AI, which has been playing an increasingly significant role in chip development. Gen AI chips are specialized packages that integrate advanced GPUs, CPUs, high-bandwidth memory (HBM3), and cutting-edge 2.5D packaging, along with other chips for connectivity in data centers. According to Deloitte's 2024 global semiconductor industry outlook, gen AI chip sales were projected to reach $50 billion by the end 2024, accounting for about 8.5% of total semiconductor sales.
• March also saw NVIDIA introducing their Blackwell platform. Blackwell is a groundbreaking GPU architecture designed to power real-time generative AI on trillion-parameter models. With the introduction of Blackwell, NVIDIA aims to cut AI inference costs and energy consumption by up to 25 times compared to its predecessor. Major cloud providers and AI companies, including AWS, Google, Meta, Microsoft, and OpenAI are on track to adopt Blackwell as well.  
• Simultaneously, Gen AI played a pivotal role in advancing Tensor Processing Units (TPUs), which are essential for efficient AI training and deployment. In May 2024, at the Google I/O, they introduced Trillium, the sixth generation of its Cloud TPUs. Trillium TPUs achieved a 4.7x increase in compute performance per chip compared to TPU v5e, supported by larger matrix multiplication units, increased clock speed, and doubled High Bandwidth Memory (HBM) capacity. They also featured third-generation SparseCore accelerators for handling ultra-large embeddings. These innovations positioned Trillium TPUs as a cornerstone of AI workloads, catering to the growing demands of generative AI applications while maintaining a focus on energy efficiency and scalability.
• Additionally, 2024 saw Groq emerging as a strong competitor to challenge NVIDIA's reign over the booming market for artificial intelligence chips, raising $640 million in funding from investors like BlackRock, Cisco, and Samsung Catalyst Fund, bringing its valuation to $2.8 billion. Groq’s innovation centers around its Language Processing Unit (LPU), designed specifically for AI inference—the process of using trained models to respond to queries. The LPU claims to deliver faster and more power-efficient inference compared to NVIDIA’s chips, emphasizing deployment over training. With plans to deploy over 108,000 LPUs by March 2025, Groq is poised to make a significant impact in AI chip deployment while challenging NVIDIA’s dominance.

7. Leap Toward Advanced Reasoning and Multi-Agent Systems

Model-Based Reasoning

• In 2024, the AI community witnessed a significant shift towards enhancing reasoning capabilities within models. A prime example of this shift is OpenAI's release of the o1 model series in September 2024. Unlike its predecessors, o1 was engineered to allocate additional processing time to deliberate on responses, enabling it to generate detailed chains of thought before arriving at a conclusion.
• On 20th December, 2024, Open AI closed off its "12 Days Shipmas" event with a huge announcement, introducing the o3 and o3 mini, part of the o3 model family— successor to the o1 model family. These models push the boundaries of reasoning capabilities by excelling in multiple technical benchmarks, showcasing superior problem-solving skills. A high compute version (172x) of OpenAI's o3 system, trained on the ARC-AGI-1 Public Training set (a benchmark proposed to measure intelligence), achieved a breakthrough score of 87.5%. For perspective, ARC-AGI-1 progress has been considerably slow, advancing from 0% with GPT-3 in 2020 to just 5% by 2024 with GPT-4o.

Multi-Agent Architecture/Framework

The evolution of multi-agent architectures marked a defining moment in 2024, enabling collaborative problem-solving and dynamic task allocation.

• In the LangChain v0.1 announcement, LangChain introduced LangGraph, a library built on top of LangChain, offering seamless integration with its ecosystem. It enables the creation of cyclical graphs, a powerful feature for building complex agent runtimes.
• ‍CrewAI, another key player, focuses on enabling anyone to create agents easily, making multi-agent systems accessible to broader audiences. Their innovations in 2024 led to a significant leap in code generation accuracy for clients, improving from 10% to over 70%, highlighting their contributions to enhanced productivity and intelligent task allocation.
• Additionally, Microsoft’s AutoGen set new benchmarks in dynamic task orchestration in 2024 by introducing a redesigned architecture built on the actor model of computing. AutoGen enables developers to create distributed, scalable, and event-driven agentic systems. The framework’s modular design facilitates integration with third-party tools and frameworks, enhancing flexibility and composability for building complex agentic patterns.
• Finally, SyncIQ, an up-and-coming multi agent orchestration platform showcased its robust system, combining AI expertise with domain-specific knowledge. SyncIQ’s robust system showcases how multi-agent architectures could streamline operations in sectors like healthcare, supply chain, and finance, highlighting the potential of agent collaboration in tackling complex, multi-dimensional tasks.

Conclusion

These advancements collectively highlight the remarkable progress of Gen AI in 2024. We’ve witnessed breakthroughs in video generation, open-source collaboration, advanced reasoning, and multi-agent orchestration—all fueling innovation across industries. Throughout 2024, AI has evolved as both a creative tool and an operational powerhouse and hinted at even greater possibilities in the future. As we move into the next phase of AI’s evolution, the synergy between cutting-edge research and responsible deployment will continue to define what’s possible. The future of AI holds even more promise and remains limitless.

What do you think was the most innovative Generative AI development of 2024? Reach out and let us know!