I had the opportunity to observe a fascinating shift in tech leadership dynamics last week at the Silicon Valley AI Summit, where Klarna CEO Sebastian Siemiatkowski revealed something surprising: despite leading a $6.7 billion fintech company, he’s getting his hands dirty with code again—with the help of AI.
“For the first time in 15 years, I’m writing code again,” Siemiatkowski told the packed audience. “Tools like Cursor AI have fundamentally changed what’s possible for someone in my position.”
This revelation signals a significant trend emerging among tech executives. The rise of AI coding assistants is blurring traditional roles, enabling leaders like Siemiatkowski to participate directly in product development in ways previously impossible due to time constraints and evolving technical skills.
The fintech CEO explained that he now regularly creates product prototypes and tests iterations himself rather than merely describing his vision to engineering teams. This direct involvement has apparently accelerated Klarna’s development cycles by eliminating communication barriers between executive vision and technical implementation.
According to Anthropic’s recent industry report, AI coding tools have reduced development time by approximately 23% across organizations that have implemented them effectively. Klarna appears to be at the forefront of this transformation.
What makes Siemiatkowski’s approach noteworthy is that he’s not simply delegating AI tool usage to his engineering teams. He’s personally embracing these technologies to reconnect with the company’s technical foundation.
“There’s a profound difference between explaining what you want built and actually building a rough version yourself,” he noted. “My teams get a clearer direction when I can show rather than tell.”
The tool primarily responsible for this shift is Cursor AI, a code-focused editor that helps write, understand, and debug code using large language models. Unlike more general AI assistants, Cursor is specifically designed for software development, offering contextual code completion and explanation features that make programming more accessible to those who might be rusty or learning.
Industry observers have noted this represents a potential sea change in how companies operate. Morgan Stanley’s technology analyst report released last month suggested that AI coding assistants could flatten organizational hierarchies by allowing more team members to contribute to technical discussions and development.
Not everyone is convinced this approach is universally beneficial. Critics point out that executive-level coding might create confusion about roles and responsibilities. Sarah Chen, CTO at fintech rival PayPulse, cautioned at the same conference that “there’s a difference between prototyping and production-ready code.”
Nevertheless, the trend appears to be gaining momentum. A recent Developer Nation survey indicated that approximately 42% of executives at technology companies now use some form of AI coding assistance to stay connected to their products’ technical aspects.
For Klarna, the results seem promising. The company has launched three new features in the past quarter that Siemiatkowski initially prototyped himself. The payment giant’s latest earnings report showed a 17% increase in user engagement with these new features compared to previous releases.
“When leaders understand the technical challenges firsthand, they make better strategic decisions,” explained Dr. Eliza Montgomery, professor of organizational behavior at Stanford. “AI tools are essentially democratizing technical contribution across organizational levels.”
The implications extend beyond individual companies like Klarna. This shift suggests a future where technical fluency, augmented by AI, becomes increasingly valuable for leaders across industries. As coding becomes more accessible through AI assistance, the traditional divide between technical and non-technical roles may continue to erode.
For developers concerned about job security, Siemiatkowski offered reassurance: “This isn’t about replacing engineers. It’s about improving collaboration between vision and execution. My prototypes still need significant refinement from our professional developers.”
The fintech leader’s experience highlights how AI coding tools are reshaping productivity across organizational structures, potentially creating more technically informed leadership and more efficient product development cycles.
As these tools evolve, we might see more executives following Siemiatkowski’s lead, rolling up their sleeves and getting involved in the technical aspects of their products—something that would have been impractical just a few years ago.
