Editor's note: This is AI Impact, Newsweek's weekly newsletter where each week, we will explore how business leaders are unlocking real value through artificial intelligence. Tap or click here to get this newsletter delivered to your inbox. Good morning and, as always, thanks for joining me.
Before we dive in today, I want to spend a little time on a project I worked on with my colleagues here at Newsweek: the AI Policy Scorecard. It looks at the public records of all 537 sitting members of Congress across regulation and governance, data centers and energy, jobs and the economy, safety and privacy and national security and China. The premise behind it is fairly simple: AI has become a major political issue before either party has settled on what supporting or opposing it actually means.
In the story I co-wrote about the project, we get into how that produced a map that often cuts across familiar party lines, with lawmakers who can look like boosters on one part of AI policy and skeptics on another. With the midterms approaching, that makes this an important moment to establish a baseline. Congress will change, but the questions underneath the scorecard—who writes the rules, how AI affects workers and communities, how much influence the industry should have and how the U.S. competes globally—will remain well beyond Election Day in November.
One of those questions, what AI means for work, also runs through this week’s Signal Capture. Signals from the frontlines of AI adoption Twilio Product Chief: AI Could Erode the Path to Engineering Expertise Artificial intelligence can increase how much code engineers ship while reducing some of the trial and error that has traditionally helped them develop technical instincts, according to Inbal Shani, chief product and technology officer, and head of R&D, at cloud communications company Twilio. Historically, building complex software required engineers to think through system architecture, edge cases and how connected components interacted.
The training problem is particularly acute early in a career, when repetitive work, failed attempts and debugging have traditionally helped engineers learn how software behaves. Her answer is to put more emphasis on systems engineering and design during onboarding. Shani pointed to debugging as a particularly important part of how engineers build technical understanding.
AI also changes where expertise has to sit inside an engineering organization. Shani contrasted that with previous waves of automation, which tended to concentrate specialized knowledge: compilers reduced the need for most programmers to work directly in assembly language, while cloud computing moved more infrastructure expertise into specialized teams. The expertise required to check AI-generated work cannot sit only in a specialist group.
Extract — continue reading at the source.
