AI in science: The lab assistant who doesn't sleep and doesn't make mistakes

Scientific research has always been a slow process. A new hypothesis, experiments repeated dozens of times, data analysis, publication, verification by other researchers – a cycle that could take years. In 2026, AI entered the lab and fundamentally changed this equation. At Altanet Craiova We believe this is one of the most valuable contributions of artificial intelligence – not because it replaces scientists, but because it enormously amplifies their ability to discover new things.

What does AI do in the lab?

Peter Lee from Microsoft Research best describes the new reality: „"AI will generate hypotheses, control experiments, and collaborate with AI peers."” It's not a vision of the distant future – it's a description of what's already happening in 2026.

Specifically, AI is used in research for four main types of tasks:

• Generating hypotheses: The model analyzes thousands of published studies and proposes connections or research directions that a human would have missed or would have taken years to identify.
• Design of experiments: AI suggests the most efficient protocol – that is, the work plan – to test a hypothesis, reducing waste of time and resources.
• Data analysis: Huge amounts of experimental data are processed in minutes, not weeks.
• Literature review: Instead of reading hundreds of articles, AI synthesizes them and presents you with what the scientific world knows about a topic, updated in real time.

An example that shows real power: 79 times faster

GPT-5 was tested on molecular cloning protocols – a complex laboratory process with dozens of interdependent steps, where a mistake in one step affects everything that follows. The result was amazing: the AI compressed weeks of laboratory work into a few hours, with a 79-fold efficiency improvement over the classic method.

This is not an isolated case. MYTH developed AI models to understand how proteins – the molecules that make life possible – fold into three-dimensional structures. University of Hawaii created an AI system that incorporates laws of physics directly into the learning process, achieving more accurate results in simulating natural phenomena.

In which scientific fields is AI already present?

The graph below shows the level of use of AI in the main areas of scientific research:

A surprising case: video games as training for robotics

NVIDIA developed a system called NitroGen that trained AI on at least 1,000 video games. The goal wasn't entertainment, but learning movement and coordination skills that could then be transferred to physical robots. It's an example of how unexpected directions scientific progress can take when AI is involved.

What does this mean beyond the labs?

Accelerating scientific research isn't just good news for scientists. It means faster drug discovery, stronger and lighter materials for industry, better solutions to climate change, and more accessible medical technologies. The benefits eventually reach each of us.

What's next?

Estimates suggest that by 2028, every major laboratory in the world will have a permanent AI assistant. The pace of scientific discovery will accelerate by an order of magnitude—10 times faster than today. This is no exaggeration: it is a logical consequence of the fact that AI can process and connect information at a speed that no human can match.

If you work in research, education or a field where scientific innovation matters, the team Altanet Craiova follow these developments and can help you understand what AI tools are already available to you. Visit our page contact and let's discuss.

This article is part of Altanet's series on AI trends in 2026. Next article: Quantum AI: When the quantum computer first surpasses classical limits. See also the complete guide to the series.