AI Speeds Up at Microsoft, Reshapes Software Testing, and Faces Policy Challenges—Are We Keeping Up?

It’s time for some big ideas. I noted an analysis at the Verge of Microsoft’s speed of release for DeepSeek.   The key insight – directions from top leadership to test and deploy R1 on Azure AI Foundary and GitHub quickly.   Here is your key quote: “Microsoft isn’t trying to produce all of the coal itself, but it wants to sell the shovels needed by AI application developers. Consumers and businesses have already shown that they’re not willing to pay extra for AI yet, so Microsoft is increasingly trying to find ways — like R1 — to drive the costs down and consumption up.”Some insights into AI on software testing.   In the New Stack, the transformative impact of generative artificial intelligence on software testing emphasizes that while AI can generate tests, it is insufficient for the complex demands of modern software development. The piece highlights that current AI-assisted testing often focuses on superficial improvements, with many tools generating a high volume of tests but requiring human oversight to interpret and maintain them. The article advocates for AI systems that generate tests and adapt and evolve them alongside code changes. It argues that as artificial intelligence improves its decision-making capabilities, it will transition from a supportive role to managing complex testing tasks independently. This evolution is crucial for driving innovation and ensuring high-quality software, ultimately allowing developers to focus on strategic goals without the burden of routine testing tasks. Integrating generative AI into testing is a pivotal step in revolutionizing software development.

I also wanted to highlight a bit of perspective of something I wrestle with – how to cover tech policy.   In a recent analysis published by The Washington Post, journalist Cristiano Lima-Strong highlighted key blind spots in tech policy coverage, emphasizing the growing importance of state-level legislation and court challenges. With the rapid evolution of technology touching various aspects of daily life, Lima-Strong notes that while many legislative efforts occur at the state level, they often go unnoticed nationally. He urges readers to pay attention to how these local laws and ongoing legal battles shape the tech landscape. Lima-Strong also stresses the need to consider consumer impacts, as significant policy changes could directly affect everyday people. He points out that despite a perception of political polarization, bipartisan cooperation is evident in tech policy, with joint efforts on issues like antitrust and online safety. As he concludes his tenure at The Washington Post, Lima-Strong encourages a broader perspective that includes international influences and diverse viewpoints in tech policy discussions.

Why do we care?

Here are your questions to consider.

How do you lean into customer demand for business outcomes around AI and machine learning data analysis without exposure to models and AI infrastructure commoditization?

Can we learn from the Software Testing progress around speeding up repetitive tasks without introducing new failure points in software that does not understand complexity?

And I’ll offer two parts.   First, as a listener, I would love to hear your take on balancing following the larger trends (say, what’s happening at Washington) with the local needs, knowing I can’t be all things to all people.  And second, how can you balance tracking tracking these fragmented policies as compliance burdens increase.