I recently came across a YouTube video where Andrew Ng explains how to build products faster with AI. Sharing below some key takeaways.
The Specificity Paradox
Andrew emphasises that concrete ideas drive faster execution while vague concepts, though often praised, tend to remain theoretical and unimplementable. The difference lies in how vague ideas gain broad consensus because they’re harder to disprove, while concrete ideas provoke immediate reactions and can be rapidly validated or rejected.
Each concrete attempt generates binary feedback: either an idea works or it doesn’t. These rapid yes/ no feedback loops are value adding as teams discover quickly what fails and why an idea fails and ultimately gaining increased knowledge about an idea, its feasibility and its market.
Teams that specify concretely, fail quickly, and pivot quickly based on the received feedback. Speed emerges not from avoiding failure but by failing quickly and learning from these failures.
The Prototype Revolution
AI has catalysed a fundamental shift in how we code, but the transformation goes beyond simply having AI generate code. Rather, AI has redistributed the value across the entire development lifecycle.
While programming skills have by no means become disposable, knowing what to build, how to structure experiments, and when to pivot based on empirical data has become exponentially more valuable.
When rebuilding code takes hours instead of weeks, the economics flip completely. What once made rewriting prohibitively expensive and slow has reversed, now maintaining outdated code often costs more than starting fresh with current insights.
The Great Bottleneck Inversion
The acceleration of software engineering through AI has created an unprecedented dynamic: product management, not engineering teams, are now the bottleneck. The ability to code increasingly means steering AI to produce the desired outcome rather than writing it yourself; a skill that will remain crucial for the foreseeable future. This has ultimately changed the ration between PMs and engineers. Teams that once operated with 1 product manager to 6-7 engineers are now shifting to 2 PMs per engineer.
Engineers can now build features faster than product managers can determine what to build or gather user feedback. This creates a new competitive advantage for hybrid professionals: PMs who can code or engineers with product instincts who can move between defining requirements and implementing solutions will have a competitive edge over those who don’t possess these cross-domain skills. While it remains uncertain whether this 2:1 PM-to-engineer ratio is optimal or will become the norm across diverse industries, it signals a profound restructuring of how software teams may operate when we shift from implementing to decision-making.
The Democratisation of Coding Through AI
The “don’t learn to code because AI will replace programmers” advice represents a fundamental misunderstanding of technological history. Each breakthrough that simplified programming, grew rather than shrunk the programmer population. The COBOL-era prediction of programmer obsolescence failed precisely because easier tools created new workflows faster than they eliminated old ones.
Non-technical staff who code don’t become software engineers, they become more effective at their primary roles. Recruiters automating candidate screening, financial analysts building custom models, front desk staff creating workflow tools are all becoming faster and better at what they are doing but they are not replacing developers.
The resistance to universal coding literacy stems from people confusing “learning to code” with “becoming a professional programmer.” The future isn’t everyone writing production systems; it’s everyone wielding code as naturally as they use spreadsheets. Writing code will become a tool for thought and automation rather than a specialised craft. AI doesn’t obsolete programming; it makes it as fundamental as literacy itself.
Takeaways:
- Concrete beats vague: Specific ideas generate rapid binary feedback (works/doesn’t work), enabling teams to fail fast, learn quickly, and execute faster than with vague concepts that gain consensus but remain unimplementable
- AI challenges engineering economics: Code rebuilding now takes hours instead of weeks, making it cheaper to start fresh. Thus, shifting the bottleneck from engineering to product management with rations between engineers and PMs shifting
- Coding becomes universal literacy: AI doesn’t replace programmers but democratises coding, enabling non-technical staff to enhance their primary roles through automation while making programming skills as fundamental as using spreadsheets