AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.
By Yu Dong
The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science .
By Uri Peled
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code?
The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
By Gursimar Singh
The article examines the performance of AI models on a 2,000-question test, finding that the top two models were indistinguishable at the conventional 5% significance level. When the test was reduced to 200 questions, a different model would have claimed first place roughly three times out of ten trials. The piece questions the reliability of declaring a single model as the best based on limited testing.
By Utkarsh Mangal
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
By Haden Pelletier
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From?
By Slava Polonski, PhD