TLDR: AI pioneer Andrew Ng advocates for a ‘Move fast and be responsible’ approach in the era of AI programming, highlighting that AI-assisted tools have drastically reduced the cost and barrier to entry for prototyping applications. He stresses the importance of rapid experimentation and a shift in developer roles towards system design and ‘AI conduction’.
In a recent address at the first Buildathon and during his keynote at the 2025 ASU+GSV Summit, renowned AI authority Andrew Ng outlined critical survival principles for the evolving landscape of AI programming. Ng, a co-founder of Google Brain and Coursera, and managing general partner at AI Fund, emphasized the new Silicon Valley motto: ‘Move fast and be responsible.’ This philosophy marks a significant evolution from the older ‘Move fast and break things’ mantra, acknowledging the need for rapid innovation while maintaining accountability.
Ng highlighted that AI-assisted programming has dramatically lowered the barrier to entry for developing prototypes, enabling a tenfold acceleration in independent prototype creation. The reduced cost of prototyping makes it feasible for teams to systematically pursue innovations by building numerous prototypes to discover what truly works. He stated that it’s ‘actually fine if lots of proof of concepts don’t see the light of day’ because this iterative approach is an efficient mechanism for invention.
A key takeaway from Ng’s insights is the changing role of developers. He posits that ‘code is depreciating,’ and developers must transform into ‘system designers and AI conductors.’ The core skill of the future will be ‘precisely telling the computer what to do.’ This shift is driven by the rapid iteration of programming tools, from GitHub Copilot to AI-enabled IDEs and highly agentized programming assistants, which create substantial efficiency gaps.
Ng also touched upon the practicalities of rapid prototyping, suggesting that initial prototype development can prioritize speed over security and scalability. He humorously noted that for software running only on one’s laptop, ‘it’s fine if you have insecure software,’ as long as there’s no intent to self-hack. This approach allows for quick validation or falsification of ideas, with full investment in security and scalability only occurring if a prototype proves valuable.
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Furthermore, Ng observed a shift in bottlenecks within product development. With engineers becoming significantly faster due to AI tools, the bottleneck is increasingly moving towards ‘product management work, getting user feedback, deciding what features to build.’ He also stressed that while AI democratizes intelligence, humans remain essential, particularly those who can effectively command computers. The most effective developers, he noted, are experienced professionals who are also proficient in leveraging cutting-edge AI tools, combining deep understanding with AI knowledge.
