News digest 2025: AI year in review

As 2025 comes to a close, artificial intelligence has completed a decisive transition – from experimental technology and competitive differentiator to critical global infrastructure. This was the year AI left the laboratory and became embedded in daily life, enterprise operations, public services, and geopolitical strategy.

From Generative AI to Agentic AI

The most significant technical shift of 2025 was the move from passive generative systems to agentic AI. Large Language Models (LLMs) evolved from conversational assistants into autonomous systems capable of planning, executing multi-step workflows, and adapting to changing conditions with limited human oversight.

This shift reframed how organizations use AI. Rather than asking models for answers, enterprises increasingly delegate tasks: research, coding, procurement, customer support, and internal operations to AI agents. Major firms including Microsoft, Google, OpenAI, and Anthropic reoriented their platforms around this paradigm, embedding agentic planning into productivity suites, operating systems, and developer tools.

With time more enterprise applications will integrate task-specific AI agents. The implication is structural: successful organizations will redesign workflows around AI handling routine execution, while humans focus on supervision, creativity, and complex judgment.

Vibe Coding: Fast Development vs Hidden Risks

Alongside agentic AI, 2025 popularized a new development culture known as vibe coding. Enabled by increasingly capable coding models, developers (and non-developers) began generating large volumes of software by describing intent rather than writing logic. Applications were assembled through prompts, with minimal review of the underlying code.

While vibe coding dramatically lowered barriers to entry and accelerated prototyping, it also introduced systemic risks. Codebases grew opaque, fragile, and difficult to maintain. Security vulnerabilities and licensing violations proliferated as understanding gave way to trust in model output. By late 2025, several high-profile outages and breaches were traced to unreviewed AI-generated code, prompting renewed emphasis on code audits, testing, and human oversight.

As AI coding agents mature in 2026, organizations are expected to move beyond vibe coding toward governed agentic development, where AI writes code, but humans remain accountable for architecture, safety, and correctness.

The Model Race and a Shaken AI Hierarchy

2025 delivered landmark model releases that reshaped the competitive landscape. Google’s Gemini 3.0 and OpenAI’s GPT-5.2 emphasized “human-expert reasoning,” autonomous coding, and complex problem-solving rather than incremental benchmark gains. Both models pushed agentic behavior deeper into consumer and enterprise ecosystems.

However, the most disruptive moment came in January, when Chinese firm DeepSeek released its R1 model. Trained at a fraction of the cost of leading Western systems, DeepSeek R1 rapidly climbed global performance leaderboards. Its open-source release forced a strategic pivot across the industry. By mid-year, OpenAI and Meta were racing to release competing open models to preserve developer loyalty and cultural influence.

The episode underscored a broader reality of 2025: AI leadership is no longer determined solely by scale of capital, but by efficiency, openness, and ecosystem trust.

The Surge in Synthetic Video Generation

2025 marked a breakthrough year for AI video generation, evolving from short, inconsistent clips to high-quality, multi-second (and sometimes minute-long) videos with realistic physics, coherent storytelling, and, crucially, native synchronized audio. Models shifted toward cinematic realism, improved motion consistency, and creative controls, making professional-grade video accessible to creators and marketers.

At the forefront were groundbreaking releases from leading labs, such as OpenAI's Sora, Google's Veo, Runway's Gen and HunyuanVideo by Tencent. These advances collapsed barriers to video production, spurring explosive growth in social media content, branded marketing, educational materials, and rapid prototyping across industries. Native audio integration addressed a longstanding limitation, while refined physics simulation and character consistency minimized uncanny artifacts.

AI Slop and the Crisis of Quality

As AI tools flooded the market, so did AI slop: low-quality, repetitive, and often misleading content generated at scale. The internet, app stores, social platforms like YouTube and TikTok, and even enterprise knowledge bases became saturated with AI-produced text, images, code, and especially videos optimized for volume rather than value.

Search engines struggled to distinguish signal from noise. AI-generated misinformation, SEO spam, and synthetic media eroded trust and degraded information environments.

In response, regulators, publishers, and platforms began prioritizing quality metrics, watermarking, and authenticity verification, signaling that the next phase of AI adoption will reward curation and credibility over raw output. “Slop” was even named Merriam-Webster's 2025 Word of the Year, reflecting widespread cultural fatigue with this deluge.

Browsers, Interfaces, and the End of Passive Computing

Another defining trend was the reinvention of the web browser. Traditional browsing – search, click, read – gave way to AI-native interfaces capable of acting on the user’s behalf. Perplexity launched Comet, an agentic browser that navigates websites and completes transactions autonomously. OpenAI followed with Atlas, introducing a persistent memory layer that enables multi-step research, planning, and shopping without continuous prompts.

Voice interfaces and AI-driven browsers increasingly replaced forms, menus, and tabs. Computing became more conversational, goal-oriented, and invisible – an early signal of how human-machine interaction may look in the agent-driven era.

AI Transitions from Labs to Lives

In 2025, AI’s real-world impact became undeniable. In healthcare, AI-designed molecules showed measurable improvements in chemotherapy outcomes, while diagnostic systems identified rare conditions from EKGs and imaging data. Education systems grappled with near-universal student adoption of AI tools, prompting large-scale teacher retraining and curriculum redesign.

Weather forecasting advanced through AI-enhanced models at agencies such as NOAA, improving extreme-weather prediction. Enterprises adopted multimodal agents that could read documents, analyze images, process speech, and take action across systems – collapsing workflows that previously required multiple teams.

At the same time, public trust faced new tests. Prompt injection attacks, model hallucinations, and AI-generated misinformation increased sharply. The Stanford AI Index 2025 documented a rise in real-world AI incidents, reinforcing calls for standardized safety evaluations. Creative industries pushed back as well, with actors and artists forming coalitions to prevent unauthorized use of likenesses and voice.

Regulation: From Paper to Practice

After years of debate, regulation moved from theory to enforcement. The European Union’s AI Act began its phased implementation in 2025, with prohibitions on “unacceptable-risk” AI systems becoming legally binding in February 2025. In August 2025, obligations for providers of general-purpose AI models took effect, including transparency requirements such as technical documentation, compliance with copyright rules, and summaries of training data. These measures have influenced draft codes of practice and similar initiatives beyond Europe.

While the EU tightened compliance requirements, the United States and United Kingdom favored lighter-touch, innovation-driven approaches. Multinational companies were forced to maintain parallel governance and deployment models, increasing operational complexity but accelerating internal AI risk management.

Looking ahead, the EU’s high-risk system obligations, covering audits, documentation, and energy efficiency, will take effect mid-2026, with similar frameworks under consideration elsewhere.

Synthetic Data and Privacy-First AI

Amid tightening data regulations and rising privacy expectations, synthetic data moved into the mainstream. Organizations increasingly relied on synthetic datasets to train and validate models without exposing sensitive information or reinforcing real-world biases. This approach proved especially valuable in healthcare, defense, and humanitarian contexts, where access to high-quality data is both critical and constrained.

Synthetic data became a key enabler of compliant, scalable AI development, reducing legal risk while expanding innovation capacity.

Infrastructure, Energy, and the Rise of Green AI

As models grew larger and inference demands surged, the physical reality of AI became impossible to ignore. Data-center power consumption emerged as a strategic constraint. In response, major technology firms announced unprecedented investments in energy infrastructure, including the revival of nuclear power plants and the development of small modular reactors to support AI workloads.

“Green AI” became a primary performance metric. Startups focused on Small Language Models (SLMs) – efficient systems capable of running on laptops and mobile devices, gained traction as cost-effective, privacy-preserving alternatives to massive cloud-based models. Sustainability shifted from marketing slogan to board-level concern.

Outlook for 2026

As 2026 approaches, AI stands at an inflection point. Adoption is already widespread – surveys indicate that more than half of the organizations use AI in some form, but expectations are shifting from experimentation to measurable return on investment. Rising inference costs, energy demands, and regulatory pressure may drive consolidation, mega-acquisitions, and selective market corrections.

Experts broadly agree that 2026 will be the “year of agents,” with autonomous systems becoming standard workplace collaborators. Physical AI will expand as well: robotaxis, service robots, and warehouse automation are expected to scale rapidly, raising new questions around safety, liability, and labor displacement.

The central challenge ahead is alignment. AI is no longer scarce; it is ubiquitous. Autonomous agents increasingly influence financial systems, infrastructure, and information flows. Ensuring these systems operate transparently, sustainably, and in line with human values will define the next phase of the AI era.