Generating AI is entering a more mature stage in 2025. Models are refined for accuracy and efficiency, and companies incorporate them into their everyday workflows.
The focus is shifting from what these systems can do to how they can be applied reliably and at scale. What is emerging is a clear picture of what is needed to build a reliable generative AI that is not merely powerful.
New Generation of LLM
Large-scale language models abandon their reputation as resource-hungry giants. The cost of generating responses from models has dropped by 1,000 times over the past two years, in line with the costs of basic web searches. This shift makes real-time AI much more feasible for everyday business tasks.
This year’s priorities are also under control. The main models (Claude Sonnet 4, Gemini Flash 2.5, Grok 4, Deepseek V3) are still bigger, but are built to respond faster, more clearly and more efficiently. Size alone is no longer a differentiator. What’s important is whether the model can still handle complex inputs, support integration, and provide reliable outputs, even when complexity increases.
Last year, we saw a lot of criticism about the trends in hallucination in AI. In one well-known case, a New York state lawyer faced sanctions for citing a legal case invented by ChatGpt. Similar failures across the delicate sector have pushed this issue into the spotlight.
This is what LLM companies are fighting for this year. Higher Generation (RAG) of search, combining search and generation with grounded output of actual data, has become a common approach. It helps reduce hallucinations, but does not eliminate them. The model may continue to conflict with the retrieved content. New benchmarks such as RGB and Ragtruth have been used to track and quantify these obstacles, marking a shift towards treating hallucinations as measurable engineering problems rather than acceptable defects.
Navigate rapid innovation
One of the decisive trends for 2025 is the speed of change. Model releases are accelerated, features shift monthly, and what counts as cutting edge is always redefine. For enterprise leaders, this creates knowledge gaps that can quickly turn into something competitive.
To move on means to maintain information. Events like AI and Big Data Expo Europe provide an unusual opportunity to see where technology is moving forward through real-world demonstrations, direct conversations and insights from those building and deploying these systems at scale.
Enterprise Adoption
In 2025, the shift is moving towards autonomy. Many companies already use generated AI across their core systems, but now the focus is on agent AI. These are models designed to not only generate content, but also perform actions.
According to a recent survey, 78% of executives agree that they need to build a digital ecosystem for AI agents, just like people for the next three to five years. The expectation is to shape how the platform is designed and deployed. Here, AI is integrated as an operator. It can trigger workflows, interact with software, and process tasks with minimal human input.
Break the data wall
One of the biggest barriers to advances in generating AI is data. Training large-scale models traditionally relied on cutting down a huge amount of real-world text from the Internet. But in 2025, it’s dry wells. High quality, diverse, ethically usable data is difficult to find, and processing is more expensive.
This is why synthetic data is becoming a strategic asset. Rather than pulling it from the web, synthetic data is generated by the model and simulates realistic patterns. Until recently, it was not clear whether synthetic data could support large-scale training, but research from Microsoft’s Synthllm project has confirmed that it is possible (if used correctly).
Their findings show that synthetic datasets can be tuned for predictable performance. Importantly, they also found that larger models needed less data to effectively learn. It allows teams to optimize their training approach, rather than throwing resources into issues.
Make it work
Generation AI for 2025 is growing. Smarter LLM, coordinated AI agents, and scalable data strategies have become central to real-world adoption. For leaders navigating this shift, AI & Big Data Expo Europe has a clear view of how these technologies are applied and what they need to do to make them work.
reference: Tencent releases a versatile open source Hunyuan AI model
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