5 key insights about LLMs that emerged in 2024

LLM development in 2024 saw significant technical advances, efficiency gains, and evolving business models that reshaped the AI landscape.

Major technical breakthroughs: The AI industry witnessed substantial improvements in model performance and accessibility throughout 2024.

• Multiple organizations successfully developed models that surpassed GPT-4’s capabilities, effectively breaking what was known as the “GPT-4 barrier”
• Significant efficiency improvements enabled GPT-4 class models to run on consumer laptops
• Multimodal capabilities became standard features, with models now able to process text, images, audio, and video simultaneously
• Voice interfaces and live camera integration enabled more natural human-AI interactions

Market dynamics and accessibility: The competitive landscape drove significant changes in how LLMs are deployed and monetized.

• Intense competition led to a dramatic decrease in LLM pricing
• The initial wave of free access to top-tier models was replaced by paid subscription tiers
• Prompt-driven application generation became a commoditized feature across various platforms
• The promise of fully autonomous AI agents remained largely unrealized

Technical infrastructure: New developments in model evaluation and training methodologies emerged as critical focus areas.

• Rigorous evaluation and testing protocols became essential for model development
• Novel “reasoning” models introduced the capability to scale compute resources during inference
• Synthetic training data proved to be an effective method for model development
• Environmental impact per prompt improved, though overall infrastructure expansion increased total energy consumption

User experience challenges: The evolution of LLM technology introduced new complexities for users.

• The term “slop” emerged to describe unwanted AI-generated content
• Increasing model complexity made systems more challenging for average users to navigate effectively
• Knowledge about LLM capabilities and developments remained unevenly distributed among users
• The gap between technical possibilities and practical implementation widened

Future implications: While technical capabilities have expanded dramatically, the industry faces important challenges in balancing advancement with accessibility and practical implementation. The uneven distribution of knowledge about LLM developments suggests a need for improved education and user interfaces to make these powerful tools more accessible to mainstream users.