My takes and predictions for Generative AI in 2025¶
As we enter 2025, the AI landscape is shifting from raw model scaling to practical implementation and efficiency. Three key trends are reshaping how we build and deploy AI systems: the emergence of dialogue engineering as a new paradigm for human-AI collaboration, the mainstream adoption of RAG, and a growing focus on model efficiency over size. Chinese AI research continues to push boundaries despite hardware constraints, while environmental concerns are driving innovation in model optimization. This analysis explores these developments and their implications for developers, businesses, and the broader tech ecosystem.
Meanwhile, the rapid evolution of AI agents and synthetic data generation is creating new opportunities and challenges - particularly around API development and authentication. Together, these trends point to a 2025 where AI becomes more practical, efficient, and deeply integrated into development workflows.
Evolving Approaches to AI Development¶
Dialogue Engineering: The Next Evolution in Human-AI Collaboration¶
Dialogue engineering is a more practical approach, solving problems in small coherent steps rather than asking LLMs to solve more complex problems in one attempt. This allows more validation and refinement of the generated solution.
Breaking down complex tasks into smaller, manageable chunks allows for better validation and error checking at each step. For instance, when writing complex software, having the LLM help design individual components or functions separately rather than attempting to generate an entire system at once. This approach also makes it easier to identify and correct any mistakes or misunderstandings.
At the moment we ask AI to generate vast numbers of lines of code at once. With Dialogue engineering the human and LLM work together in much smaller steps. The human might write two or three lines of code, then have the LLM suggests the next action such as reviewing or improving the code or suggesting approaches for the next task. This feedback loop results in more maintainable better code. Potentially better than what human or LLM could create alone themselves.
Model Architecture and Training Advances¶
RAG Goes Mainstream: Local Models Transform Enterprise AI¶
The adoption of Retrieval Augmented Generation (RAG) is enabling organizations to leverage their private data more effectively. This includes companies running local instances of models that can access internal documentation, knowledge bases, and historical data, while maintaining data privacy and reducing dependency on cloud services.
RAG is evolving into a more sophisticated technology, moving beyond simple question answering to become a tool for decision-making and report generation. Effective Hybrid search approaches are likely to become more common. Fine-tuning of embedding models and re-rankers will become more mainstream to improve retrevial accuracy. We will see more techniques to reduce hallucination and to optimize context windows.
Fine-tuning Persists: Reports of Its Death Were Premature¶
In 2024 there were so many statements similar to "Fine-tuning is dead", even in a course about fine tuning LLMs there were presenters stating that. Fine-tuning for LLMs will still have its place, especially to get LLMs solutions out proof of concept and into production for some use cases. Fine-tuning of other Generative AI models in Image and Video will continue.
The Rise of Efficient LLMs: Bringing AI to Edge Devices¶
The trend towards LLM efficiency is driving innovations in quantization techniques and architecture optimization. We are starting to see more models that can run effectively on mobile devices and edge computing platforms while maintaining reasonable performance. This includes developments in techniques like 4-bit and even 2-bit quantization.
Chinese AI Innovation: Will they be the new Leaders in LLMs and Vision Models¶
Chinese AI research is showing impressive results, particularly in efficient training and model optimization. Their approach to utilizing synthetic data for training and validation is proving especially effective, potentially due to different regulatory environments and data access policies. Hunyuan and Hailou's MiniMax to name just two for video. DeepSeek-V3 and the Qwen family of LLMs from Alibaba Cloud to name a few for LLMs.
At the moment the Chinese researchers appear to make better use of limited resources, with the export restricions on many high end AI chips and GPUs.
Infrastructure and Optimization Trends¶
The Slowdown of Scaling: Computing Limits Drive Innovation¶
The exponential growth in model size and computing requirements is becoming unsustainable. Companies will need to focus on making existing models more efficient rather than just making them bigger. This includes developing better training methodologies and more efficient architectures.
The Energy Crisis in AI: Datacenter Challenges Drive Green Innovation¶
Energy used by datacentres will become more of an issue.
The environmental impact of AI training and inference is becoming a critical concern. While Small Modular Reactors (SMRs) could potentially provide clean energy solutions, their widespread deployment is still years away. This is driving interest in more energy-efficient training methods and model architectures together with chip architectures specifically designed for AI energy efficiency.
Optimization Over Innovation: Maximizing 2024's AI Breakthroughs¶
A focus on better using the model releases and concepts from 2024, rather than the pace of new releases continuing.
Instead of continuous new model releases, there will be more emphasis on optimizing and better utilizing existing architectures. This includes developing better prompting techniques, fine-tuning strategies, and integration methods to maximize the value from current models.
Emerging Applications and Integration¶
Synthetic Data Revolution¶
Its now possible to fully train models on synthetic data without model collapse. We'll see synthetic data used more effectively in 2025, more tools for synthetic data generation and more breakthroughs in data quality validation.
Video Generation Breakthrough: Open Source Models and LoRA Adaptations¶
An increase in open source Generative video models and video adaptors (LoRAs) allowing consistent actors and styles.
The advance in generative AI video technology is enabling new creative possibilities. LoRA adaptations for video are starting to emerge for models like Hunyuan making it possible to maintain consistent character appearances and artistic styles across generated content, while keeping computational requirements manageable.
We will also see advances in real-time video generation and editing capabilities.
AI Agents Get Real: From Demos to Practical Solutions¶
We're seeing AI agents move beyond simple demos to handling practical tasks like scheduling, data analysis, and basic decision making. For example, agents that can autonomously research topics, synthesize information, and present findings, or systems that can manage and optimize resource allocation in real-world scenarios.
We will see more standards for agent-to-agent communication emerging and more progress in agent autonomy and decision-making capabilities. These will help tackle some of the real world challenges AI Agents need to overcome.
The API Evolution: Fine-Grained Control for AI Integration¶
As a consequence of the progression of AI Agents I think we will see more websites offering APIs and more APIs starting to offer fine-grained authentication tokens.
The growth in AI agent capabilities is driving demand for more sophisticated API access controls. This includes the development of more granular permission systems, rate limiting, and usage tracking to enable safer and more controlled interaction between AI systems and web services.
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