As 2025 draws to a close, it's clear that this was a pivotal year for large language models (LLMs). Far beyond incremental metric improvements on benchmarks, the field witnessed fundamental shifts in training paradigms, our understanding of AI "intelligence," and how we build applications on top of these systems.
Drawing from insights shared by industry leaders like Slava Vasipenok, CEO of QUASA, alongside broader developments, here are the standout themes that defined the year.
1. RLVR: The New Pillar of LLM Training
The traditional LLM training stack - pretraining on vast data, followed by supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) - got a powerful upgrade in 2025 with Reinforcement Learning from Verifiable Rewards (RLVR).
RLVR trains models not on subjective human preferences but on objective, automatically verifiable outcomes in domains like mathematics, coding, and logic puzzles. Models learn to generate reasoning traces, break down problems, test hypotheses, and self-correct - all driven by clear "correct/incorrect" signals.
This approach proved remarkably efficient. Labs shifted significant compute from endless pretraining to extended RLVR runs, yielding dramatic gains in reasoning capability per dollar spent. Pioneered in models like DeepSeek R1 and scaled in OpenAI's o3 series (released April 2025), RLVR enabled breakthroughs on tough benchmarks like AIME math competitions and SWE-Bench coding tasks.
A key side effect: test-time compute emerged as a new performance lever. Models like o3 demonstrated that allocating more inference-time "thinking" (longer chain-of-thought reasoning) intuitively boosts accuracy, turning compute into a tunable dial for real-world use.
2. Ghosts, Not Animals: Embracing Jagged Intelligence
2025 forced the industry to confront a hard truth: LLMs don't exhibit the smooth, general competence we associate with human (or animal) intelligence. Instead, their capabilities are jagged - brilliant in narrow, verifiable domains but surprisingly naive elsewhere.
Optimized for text imitation and reward maximization on formal tasks, LLMs spike dramatically in areas amenable to RLVR (e.g., advanced math or code) while remaining vulnerable to simple tricks, jailbreaks, or out-of-distribution scenarios.
This "ghostly" intelligence - summoned through optimization rather than evolved for survival - explains why benchmarks lost much of their credibility.
Most are verifiable by design, making them easy to "overfit" via synthetic data or targeted RLVR, without broader generalization.
As Vasipenok noted, beating benchmarks no longer signals proximity to AGI; it often just highlights optimized jaggies.
3. Cursor and the Rise of Specialized LLM Layers
Tools like Cursor redefined LLM applications in 2025, creating a new category: "Cursor for X" platforms that transform general models into domain experts.
These aren't mere chat interfaces. They handle advanced context engineering, orchestrate multi-call workflows (as directed acyclic graphs), balance cost/quality tradeoffs, and provide tailored UIs with "autonomy sliders."
The vision: Frontier labs build versatile base models ("universal students"), while applications layer on domain data, tools, sensors, and feedback loops to create specialists.
Cursor's explosive growth - acquiring code-review startup Graphite and adding visual design tools - underscored this shift toward composable, agentic ecosystems.
4. Local Agents: Claude Code and the Personal AI Companion
Anthropic's Claude Code marked a breakthrough: the first compelling local AI agent that lives on your machine, accessing files, terminals, and context directly.
Running in CLI or IDEs (via partnerships like JetBrains), it proved far more practical than cloud-based agent swarms for everyday tasks. Developers gained a constant "companion" for coding, debugging, and research - blurring the line between tool and teammate.
This form factor shift - from web chats to ambient, always-on assistants - highlighted how local deployment addresses privacy, latency, and integration needs in a jagged-capability world.
5. Vibe Coding: Programming Without the Code
Coined by Andrej Karpathy, vibe coding exploded in 2025, democratizing software creation. Users describe intentions in natural language ("vibes"), and LLMs generate full applications - no manual syntax required.
Enabled by tools like Lovable and Opal (integrated into Gemini), it let non-coders build apps, while pros iterated faster and bolder. Code became cheap, experimental, and disposable: spin up a program for one-off tasks, then discard it.
This paradigm reshaped software economics and professions, making complex development accessible and accelerating innovation.
6. Toward True Multimodal Interfaces: Gemini's Nano Banana
Chats proved limiting - text is machine-friendly but not human-optimal. 2025 saw early steps toward richer LLM GUIs, with Google's Gemini Nano Banana (image generation/editing models) as a standout.
Blending text, images, diagrams, and animations seamlessly, it hinted at interfaces where knowledge flows visually and interactively. Nano Banana Pro excelled at precise visuals with legible text, paving the way for intuitive, multimodal AI experiences.
Looking Ahead
2025 revealed LLMs as a distinctly new form of intelligence: sharper in structured reasoning than anticipated, yet fundamentally uneven. We've tapped perhaps 10% of their potential, with vast untapped applications in agents, interfaces, and hybrid human-AI workflows.
Progress will accelerate, but challenges remain - regulatory scrutiny, data limits, and ethical alignment chief among them. As Vasipenok observed, the field is wide open: a sea of work and ideas awaits. The next breakthroughs won't just scale models; they'll redefine how we think, create, and collaborate with AI.
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Author: Slava Vasipenok
Founder and CEO of QUASA (quasa.io) - Daily insights on Web3, AI, Crypto, and Freelance. Stay updated on finance, technology trends, and creator tools - with sources and real value.
Innovative entrepreneur with over 20 years of experience in IT, fintech, and blockchain. Specializes in decentralized solutions for freelancing, helping to overcome the barriers of traditional finance, especially in developing regions.
