TLDR: Harsh Varshney, a machine learning software engineer at Google, has revealed how generative AI has revolutionized the software engineering landscape, drastically reducing task completion times from days to hours. He emphasizes the shift towards building intelligent systems and the critical need for strong computer science fundamentals, AI expertise, and a focus on real-world impact in the evolving tech industry.
Harsh Varshney, a machine learning software engineer at Google, has shed light on the profound impact of artificial intelligence, particularly generative AI, on the role and responsibilities of software engineers. In an interview with Business Insider, as reported by Hindustan Times on September 7, 2025, Varshney stated that AI tools have accelerated workflows, enabling tasks that previously required days to be completed in mere hours.
Varshney, who holds a bachelor’s degree in electrical engineering from BITS Pilani and a master’s in computer science from Carnegie Mellon University, joined Google two years ago after gaining experience at companies like Flipkart, Amazon Web Services, and Splunk. He noted that the ‘world of Big Tech has become much more dynamic and results-oriented’ since he began his career. This shift has streamlined traditional software development, moving away from lengthy planning phases and multi-week sprints towards more agile and faster execution.
The Google engineer highlighted that the rise of generative AI represents the most significant transformation. He recalled that in 2017, when he started his tech journey, AI was a specialized, niche field. Today, however, it has become a fundamental skill integrated into nearly every aspect of software development. Varshney emphasized, ‘You’re no longer just building an application; you’re expected to build the intelligent systems that power it.’ This has led to a substantial increase in demand for experts in machine learning and emerging technologies such as agentic AI.
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Varshney elaborated on the practical benefits of AI in his daily work, stating, ‘AI tools now shape how I debug, experiment, and optimize, turning workflows that once took days into hours.’ He underscored the evolving expectations within the industry, where simply creating functional software is no longer sufficient. The new mandate is clear: ‘It’s not enough to build something that works — it has to be smart.’ He concluded by asserting that ‘The rules of data have fundamentally changed,’ indicating a paradigm shift in how engineers approach problem-solving and system design.
