It’s 2025, and the AI “Revolution” is in full swing. However, whether this technological shift represents a true revolution or just an evolution remains to be seen. Regardless, at the heart of this global, multi-industry movement are AI chatbots, pushing the boundaries of artificial reasoning and increasingly assisting both organizations and everyday users in decision-making.

As most people know, OpenAI ignited the recent AI boom with ChatGPT’s public release in 2022. Classified as a Large Language Model (LLM), ChatGPT was trained on an incomprehensibly vast dataset, allowing it to generate coherent responses by predicting the most probable word in a given context. In short, it excels at emulating natural language. But now, a new contender has entered the AI space—the Reasoning Model.

ChatGPT remains the most heavily invested-in AI chatbot, likely due to its prestigious status as the original industry leader. It can respond to prompts across virtually every discipline with acceptable accuracy. However, its reliability falters in deeper research topics, often making factual errors and struggling to correct itself even after user feedback.

More concerning than its accuracy issues, however, is the growing disconnect between investment and improvement. OpenAI recently released GPT-4.5 to premium subscribers, but to their disappointment, it failed to significantly address many of its predecessor’s shortcomings. As AI companies continue receiving massive funding, the opportunity cost of AI development—in terms of environmental impact—has become a serious issue. AI models require extensive water and energy resources to run, and as OpenAI continues upgrading its servers and supercomputers, the strain on global power grids will only intensify.

Fortunately, not all hope is lost. In an unexpected turn of events, a small Chinese company stole the spotlight in January with Deepseek, a groundbreaking AI model. Unlike ChatGPT, which is trained on massive datasets, Deepseek was developed using only a fraction of that data.

While still technically an LLM, Deepseek takes a different approach—responding to prompts using a Chain-of-Thought reasoning style. Instead of producing a single answer outright, it breaks problems down into smaller logical steps, visibly displaying its thought process. This method results in slower but generally more accurate responses, particularly in fields like mathematics and deep research, where critical thinking is essential.

Deepseek’s high efficiency and low development cost quickly made waves, inspiring similar reasoning-based chatbots like X’s Grok and, ironically, new models from OpenAI itself. However, despite its impressive performance, Deepseek faces privacy concerns due to its ties to the Chinese Communist Party (CCP) and China’s reputation for data collection. Nevertheless, from a technological standpoint, Deepseek represents a real innovation in AI, proving that massive datasets aren’t always necessary to achieve high-quality results. It seems unlikely that LLMs and Reasoning Models will compete to the point of replacing one another. Instead, they will likely coexist, complementing each other’s strengths.

Of course, these aren’t the only notable chatbots in existence. Other models continue to emerge, each tailored to specific use cases. Claude Haiku excels in complex coding tasks, while Microsoft Co-Pilot offers integrated AI assistance for PC users. Ultimately, the future of AI chatbots depends on striking a balance between efficiency, accuracy, and sustainability. Here’s to hoping for a less power-hungry and more competent AI-driven future!