At the end of 2025, artificial intelligence has completed its decisive transition from an experimental technology and competitive differentiator to a critical global infrastructure. This was the year that artificial intelligence left the laboratory and became embedded in everyday life, corporate operations, public services and geopolitical strategy.
From generative artificial intelligence to agentic artificial intelligence
The most significant technical change in 2025 was the transition from passive generative systems to agent-based artificial intelligence. Large language models (LLM) have evolved from conversational assistants to autonomous systems capable of planning, executing multi-step workflows, and adapting to changing conditions with limited human supervision.
This shift has changed the way organizations use AI. Instead of asking models for answers, enterprises are increasingly delegating tasks to AI agents: research, coding, procurement, customer service and internal operations. Major companies including Microsoft, Google, OpenAI, and Anthropic have reoriented their platforms around this paradigm, embedding agent-based planning into productivity suites, operating systems, and development tools.
Over time, more and more enterprise applications will integrate AI agents tailored to specific tasks. The implications are structural: Successful organizations will redesign workflows around AI supporting routine task execution, while humans focus on supervision, creativity, and complex judgment.
Vibe coding: rapid development and hidden risks
In addition to agentic AI, 2025 has popularized a new programming culture known as vibration coding. With increasingly efficient coding models, programmers (and non-programmers) began to generate large amounts of software by describing intent rather than writing down logic. Applications were submitted through prompts, with minimal source code review.
While vibration coding has dramatically lowered barriers to entry and accelerated prototyping, it has also introduced systemic risk. Code bases have become opaque, fragile, and difficult to maintain. Security vulnerabilities and license violations multiplied as understanding gave way to trust in the model's output. By the end of 2025, several high-profile outages and breaches had been linked to unverified AI-generated code, prompting a renewed emphasis on code audits, testing, and human oversight.
As AI coding agents mature in 2026, organizations are expected to move beyond vibrational coding toward regulated agent development, where AI writes the code but humans remain responsible for architecture, security, and correctness.
The model race and the shaken AI hierarchy
In 2025, groundbreaking models were introduced that changed 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 gains from benchmarking. Both models have pushed agentic behavior deeper into consumer and enterprise ecosystems.
However, the most breakthrough moment came in January, when the Chinese company DeepSeek released its R1 model. Trained at a fraction of the cost of leading Western systems, DeepSeek R1 has quickly climbed the global performance rankings. The release of open source software forced a strategic shift across the industry. By mid-year, OpenAI and Meta were racing to release competing open models to maintain developer loyalty and cultural influence.
The episode highlighted the broader reality of 2025: AI leadership is no longer determined solely by capital scale, but by efficiency, openness, and trust in the ecosystem.
The explosive growth of synthetic video generation
2025 was a watershed year for AI video generation, evolving from short, incoherent clips to high-quality, multi-second (and sometimes minute-long) videos with realistic physics, coherent narrative, and, most importantly, native synchronized audio. Models have moved towards cinematic realism, improved motion consistency and creative control, giving creators and marketers access to professional-quality video.
Breakthrough releases from leading labs such as Sora from OpenAI, Veo from Google, Gen from Runway, and HunyuanVideo from Tencent took center stage. These advances have broken down barriers to video production, resulting in a surge in social media content, brand marketing, educational materials, and rapid prototyping across industries. Native audio integration addressed a long-standing limitation, and improved physics simulation and character consistency minimized eerie artifacts.
AI slop and the quality crisis
As the market has been flooded with artificial intelligence tools, there has been a decline in AI: low-quality, repetitive and often misleading content generated at scale. The internet, app stores, social media platforms like YouTube and TikTok, and even enterprise knowledge bases have become saturated with text, images, code, and especially videos optimized for volume rather than value, created by artificial intelligence.
Search engines had difficulty separating the signal from the noise. Disinformation generated by artificial intelligence, SEO spam and synthetic media have eroded trust and worsened the information environment.
In response, regulators, publishers and platforms have begun to prioritize quality metrics, watermarking and authentication, signaling that the next phase of AI adoption will reward validation and trustworthiness over raw data output. “Slop” was even named Merriam-Webster’s Word of the Year for 2025, reflecting widespread cultural fatigue with this flood.
Browsers, interfaces and the end of passive computing
Another decisive trend was the reinvention of the web browser. Traditional browsing – searching, clicking, reading – has given way to native AI interfaces that can act on the user's behalf. Perplexity has launched Comet, an agent-based browser that navigates websites and completes transactions independently. OpenAI followed with Atlas, introducing a persistent storage layer that allows for multi-step research, planning, and purchasing without constant prompts.
Voice interfaces and AI-based browsers are increasingly replacing forms, menus and tabs. Computing has become more conversational, goal-oriented and invisible – an early sign of what human-machine interaction might look like in the agent-driven era.
AI is moving from laboratories to lives
In 2025, the real impact of artificial intelligence on the world has become undeniable. In healthcare, AI-designed molecules have shown measurable improvements in chemotherapy outcomes, while diagnostic systems have identified rare conditions based on ECG and imaging data. Education systems have struggled with near-universal student adoption of AI tools, prompting large-scale teacher retraining and curriculum redesign.
Weather forecasting has improved with AI-enhanced models at agencies like NOAA, improving the prediction of extreme weather. Enterprises deployed multimodal agents that could read documents, analyze images, process speech, and take actions across systems, eliminating workflows that previously required multiple teams.
At the same time, public trust faced a new test. Injection attacks, model hallucinations and AI-generated disinformation have increased dramatically. Stanford AI Index 2025 documented an increase in real-world AI-related incidents, reinforcing calls for standardized security assessments. Creative industries have also pushed back, with actors and artists forming coalitions to prevent unauthorized use of likenesses and voices.
Regulation: from paper to practice
After years of debate, regulations have moved from theory to enforcement. The EU Artificial Intelligence Law began its phased implementation in 2025, and prohibitions on AI systems posing “unacceptable risks” became legally binding in February 2025. In August 2025, obligations on providers of general-purpose AI models entered into force, including transparency requirements such as technical documentation, copyright compliance and training data summaries. These measures have influenced draft codes of conduct and similar initiatives outside Europe.
While the EU has tightened compliance requirements, the US and UK have favored a lighter, innovation-led approach. Multinational companies were forced to maintain parallel management and implementation models, increasing operational complexity but accelerating the internal management of AI risk.
Looking ahead, high-risk EU systemic obligations covering auditing, documentation and energy efficiency will come into force in mid-2026, with similar frameworks being considered elsewhere.
Synthetic data and privacy-first artificial intelligence
Amid tightening data regulations and rising privacy expectations, synthetic data has entered the mainstream. Organizations increasingly relied on synthetic datasets to train and validate models without revealing sensitive information or amplifying real-world biases. This approach has proven particularly useful in healthcare, defense and humanitarian contexts, where access to high-quality data is both critical and limited.
Synthetic data has become a key enabler of regulatory-compliant, scalable AI development, reducing regulatory risk while increasing innovation potential.
Infrastructure, energy and the development of green artificial intelligence
As models became larger and inference requirements increased, the physical reality of AI became impossible to ignore. Energy consumption in the data center has proven to be a strategic constraint. In response, major tech companies have announced unprecedented investments in energy infrastructure, including refurbishing nuclear power plants and developing small modular reactors to support AI workloads.
“Green AI” has become a key performance metric. Startups focused on small language models (SLMs) – efficient systems that can run on laptops and mobile devices – have gained popularity as a cost-effective, privacy-preserving alternative to massive cloud-based models. Sustainability has moved from a marketing slogan to a board-level concern.
Outlook for 2026
As 2026 approaches, artificial intelligence is at a turning point. Adoption is already widespread – research shows that more than half of organizations use AI in some form, but expectations are shifting from experimentation to measurable return on investment. Rising application costs, energy demands and regulatory pressures may fuel consolidation, mega acquisitions and selective market adjustments.
Experts broadly agree that 2026 will be the “year of the agent,” with autonomous systems becoming standard workplace collaborators. Physical AI will also advance: robotics, service robots and warehouse automation are expected to scale rapidly, raising new questions about safety, liability and labor displacement.
The main challenge for the future is adaptation. Artificial intelligence is no longer rare; is ubiquitous. Autonomous agents are increasingly influencing financial systems, infrastructure and information flows. Ensuring these systems operate in a transparent, sustainable and consistent manner with human values will define the next phase of the AI era.
