Software Engineering Daily

The rapid rise of AI has set off a flurry of legal and regulatory activity. There is no single AI law to follow. Instead, there are overlapping circles of regulation, where privacy, cybersecurity, consumer protection, employment, and IP laws all continue to apply alongside newer AI-specific rules. Europe has its EU AI Act, but the U.S. has no federal equivalent yet. In its pl...

Over the past couple of years, companies raced to get their employees using AI, often encouraging them to lean on it as much as possible. However, as AI coding tools spread through engineering teams, many companies watched their AI bills explode. That kind of runaway cost growth can threaten a business, so the new challenge is capturing the productivity gains from AI without ...

Codebases in enterprise organizations often span hundreds of thousands, or even millions, of lines of code. In many cases, changes must follow established patterns precisely, or risk introducing costly security or operational issues. A common challenge with AI coding tools is that they struggle at this scale: LLM context windows cover only a fraction of a large codebase, and ...

In recent years, the main impact of AI has been on the digital world, and most notably software engineering. However, its longer-term effect on the physical world may prove even more profound, reshaping how cars drive themselves, how factories run, and how robots move through homes and warehouses.

World models are at the center of the movement to bring AI into the p...

Software testing is widely understood as essential to building reliable systems, and it’s one of the hardest things to get right. A fundamental issue is that tests are good at confirming the behavior a team expects, but the most damaging failures tend to hide in the edge cases no one thought to check during development. Those blind spots are only growing as AI produces more c...