GlobalData has identified several trends likely to settle in the year ahead, one being that small language models (SLMs) will become more commonplace.

Training and operating large language models (LLMs) involves steep costs, with expensive computing resources required to process the vast amounts of data used in AI. Specialised SLMs deliver better value and accuracy.

Why SLMs will grow

Oftentimes, companies start by experimenting with very large general-purpose models to explore different use cases, only to find out later that the compute cost doesn’t justify the scale of the transaction.

In 2024, as Generative AI penetrates the enterprise space and business cases become more conspicuous, companies will start leveraging SLMs, and tuning them with their proprietary data to get the performance they want for a specific use case, for a fraction of the cost.

2024 will see the passage of comprehensive regulatory policies to guide artificial intelligence project deployments and management. The world will look towards Europe, which is poised to approve ground-breaking legislation via the AI Act, which, among other measures, bans the real-time use of biometric analysis via sensitive characteristics in public spaces (with an exception for law enforcement).

Synthetic data will help further democratise access to data, but increasing utilization may raise questions on the openness of the data generation techniques, especially when it comes to transparency and explainability. The importance of quality control will become apparent as the use of the technology becomes more widespread.

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In 2024, organizations will embrace a technique called Retrieval Augmented Generation (RAG). With RAG, businesses can augment LLM prompts and responses with information from reliable internal or external sources, enabling them to access a wide range of data repositories. Off-the-shelf or custom LLMs can connect with additional databases without the need for fine tuning models, enabling access to the latest, most up-to-date and accurate information.

The semiconductor industry for AI workloads will experience unprecedent growth, and NVIDIA will lose some market share to rivals due to rising competition in the sector. As companies of all sizes work to reduce their dependence on Nvidia, alternative chip architectures will grow stronger, with CPUs, TPUs, ASIC chips and other designs gaining ground.

Globaldata growth forecasts

GlobalData forecasts that the overall artificial intelligence market will be worth $909bn by 2030, having grown at a compound annual rate of 35% between 2022 and 2030.

In the Generative AI space, revenues are expected to grow from $1.8bn in 2022, to $33bn in 2027, a CAGR of 80%. Generative AI will impact every industry and become a catalyst for broader AI capabilities such as machine learning and computer vision.

2024 will see the number of live AI implementations grow exponentially in the corporate space, particularly in the fields of customer experience and marketing.

The impact of Generative AI will expand across sectors and business functions as the technology becomes more accurate and is able to provide more reliable factual advice. 2024 will also see businesses begin to adopt multi-modal tools. AI workloads will become even more demanding, and there will be a greater need for AI-optimised semiconductors to run the data center infrastructure that houses these applications and enables their operation.