AI Development Paradigm Shift: Insights from Programming Thoughts on 6 Major Evolutions of LLM Technology by 2025

Standing at the end of 2025, looking back on this year’s AI development, the most remarkable aspect is not the performance breakthrough of a single model, but the systemic transformation of the entire tech stack and even the development mindset. Observers of programming musings will find that AI in 2025 is not only iterating in capability but also revolutionizing in paradigm. From new directions in reinforcement learning to explorations of graphical interfaces, every change is redefining our interaction with intelligent systems.

Verifiable Reward Reinforcement Learning Becomes Mainstream: From Passive Feedback to Active Exploration

In recent years, the training stack of large language models has been relatively stable: pretraining → supervised fine-tuning → reinforcement learning from human feedback (RLHF). Since this combination was established in 2022, it has seen little change. But by 2025, a new technology has become standard in all AI labs—Verifiable Reward Reinforcement Learning (RLVR).

This shift is far more profound than it sounds. Traditional RLHF relies on human judgment, whereas RLVR allows models to self-train in “objectively verifiable” environments such as math problems and programming tasks. Models begin to learn to decompose problems, perform recursive reasoning, and attempt multiple solution paths—appearing to exhibit a form of “thinking.” The papers on DeepSeek-R1 detail this phenomenon, and OpenAI’s o1 (late 2024) and o3 (early 2025) have made the industry realize that this is not just academic progress but a leap in productivity.

From a programming perspective, this means AI is no longer merely a “condition reflex mechanism,” but has gained a systematic problem-solving ability. Computation shifts from model size to “thinking time”—more reasoning trajectories become a new dimension for expansion, opening a whole new space for model development.

A New Form of Intelligence: Ghosts Rather Than Animals

2025 prompts the entire industry to view AI through a completely new lens. We are not cultivating some kind of “digital animal,” but summoning a “ghost”—a form of existence entirely different from biological intelligence.

The human brain evolved in jungle environments, optimized for tribal survival; whereas large language models are optimized through internet text, mathematical rewards, and human likes. Their objective functions are fundamentally different, leading to distinct forms of intelligence. This new understanding yields an interesting prediction: AI performance will not develop uniformly but will exhibit a “sawtooth” pattern—performing exceptionally in verifiable domains (math, programming) but struggling in areas requiring real-world common sense.

This also explains why benchmarks became less reliable in 2025. When all tests are in “verifiable environments,” RLVR’s characteristics cause models to “over-optimize” near the test set, creating false illusions of performance. “Training on the test set” has become the new reality.

The New Generation of LLM Application Layer: Cursor and Claude Code

If foundational models represent “generalists,” then emerging application layers represent “specialists.” Cursor, as an AI assistant for code editing, exemplifies this—it’s not about users directly calling OpenAI or Claude APIs, but about integrating, orchestrating, and optimizing these LLM calls, along with contextual engineering, cost control, and user interfaces. This combination has made Cursor a new benchmark for application layers in the LLM era and has inspired industry thinking about what “Cursor in the XX field” might look like.

Programming musings also hint at this: the division of labor between foundational models and applications is reshaping. Foundation models are increasingly like “generalist college graduates”—broad knowledge but not deeply specialized; while applications are responsible for assembling these “graduates” into “professional teams,” equipped with private data, specialized toolchains, and user feedback loops.

The emergence of Claude Code breaks through another dimension—local deployment. Unlike OpenAI, which places intelligent agents in cloud containers, Claude Code chooses to “reside” on users’ local computers, tightly integrated with developers’ work environments. This choice reflects a reality: during the transition period of uneven capabilities, local collaboration is more practical than cloud orchestration. It redefines human-AI interaction—no longer just accessing a website, but becoming part of the work environment.

Achieving Programming Democratization: Development Directions from Vibe Coding

“Vibe Coding” may be the most disruptive concept of 2025. It describes a phenomenon: users can specify needs in natural language, and AI completes the code implementation, without requiring deep understanding of underlying technical details.

From the perspective of programming musings, this paradigm has proven its value. Developers can write a BPE tokenizer in Rust without mastering all Rust intricacies, or quickly create one-time tools to debug issues because the code becomes “free, ephemeral, and malleable.” This empowers ordinary people and also greatly boosts the productivity of professional developers—many software prototypes that previously seemed impossible can now be rapidly validated.

The deeper significance of this shift lies in the change of the cost function of programming. Tasks that once required days or weeks to develop small tools now might only take hours. This will reshape the economics of the entire software ecosystem.

The Next Step in Multimodal: Nano Banana and the Return of Graphical Interfaces

Google’s Gemini Nano Banana represents a deeper paradigm shift. If large language models are the next-generation computing paradigm following the personal computing revolution of the 70s and 80s, then the evolution of human-computer interaction should follow a similar historical path.

The transition from command line to graphical interfaces was fundamentally about “adapting to human perceptual preferences”—humans naturally dislike reading text and prefer visual and spatial information transfer. The same logic applies in the AI era. Pure text dialogue, while effective, is not humans’ first choice. Nano Banana’s breakthrough is not only in its image generation capabilities but also in the integration of text, images, and world knowledge into a unified model weight—this is the next step in multimodality and a signal of the dawn of graphical interfaces.

From a programming musings perspective, this suggests we may be witnessing a second major revolution in UI/UX. The first was from CLI to GUI; the second could be from text-based dialogue to multimodal interaction.

Summary: Chain Reaction of Programming Paradigms

These shifts in 2025 are not isolated. RLVR introduces new capabilities, prompting application layers to seek specialization, which in turn makes Vibe Coding more feasible. Meanwhile, the maturation of multimodality opens the door to more natural human-AI interactions.

From the perspective of programming enthusiasts, we are experiencing a rare paradigm shift—not only a change in what AI can do but also in how humans and AI collaborate and how the cost structure of collaboration evolves. How this will develop next depends on whether these new paradigms can truly merge into a deeper productivity breakthrough.