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 laboratory and fundamentally changed that 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 hugely amplifies their ability to discover new things.
What does AI do in the lab?
Peter Lee of Microsoft Research best describes the new reality: "AI will generate hypotheses, control experiments and collaborate with AI colleagues." This is not a vision of the distant future – it is a description of what is already happening in 2026.
Specifically, AI is used in research for four main types of tasks:
- Hypothesis generation: the model analyses thousands of published studies and proposes connections or research directions that a human would have missed or would have taken years to identify.
- Experiment design: 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 synthesises them and presents what the scientific world knows about a subject, updated in real time.
An example that shows the 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 astonishing: AI compressed entire weeks of laboratory work into a few hours, a 79-fold efficiency gain over the classical method.
This is not an isolated case. MIT has developed AI models for understanding how proteins – the molecules that make life possible – fold into three-dimensional structures. The University of Hawaii created an AI system that builds the 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 chart below shows the level of AI use in the main fields of scientific research:
Active use
Growing
A surprising case: video games as training for robotics
NVIDIA has developed a system called NitroGen that trained AI on at least 1,000 video games. The goal was not entertainment, but learning movement and coordination skills that can then be transferred to physical robots. It is an example of how unexpected the directions can be from which scientific progress comes when AI is involved.
What does this mean beyond the laboratories?
Accelerating scientific research is not just good news for scientists. It means drugs discovered faster, stronger and lighter materials for industry, better solutions for climate change and more accessible medical technologies. In time, the benefits reach each of us.
What comes next?
Estimates show 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 – that is, 10 times faster than today. This is not an exaggeration: it is a logical consequence of the fact that AI can process and connect information at a speed no human can match.
If you work in research, education or a field where scientific innovation matters, the Altanet Craiova team follows these developments and can help you understand which AI tools are already available to you. Visit our contact page and let's talk.
This article is part of the Altanet series on AI trends in 2026. Next article: Quantum AI: When the Quantum Computer Surpasses Classical Limits for the First Time. See also the complete guide to the series.