AI education pioneer and deeplearning.ai founder Andrew Ng published a long article in The Batch newsletter on April 13, discussing the future of software engineering after AI agents accelerate program development. His core viewpoint is: once building becomes easy, “deciding what to build” will become the real bottleneck—what he calls the “Product Management Bottleneck.”
Five trends that are already clearly visible
Ng lays out five trends that are already clearly apparent in AI’s impact on software engineering:
Once AI makes coding easier, more people will participate in software development
Handwritten code—even reading AI-generated code—becomes less important, because you can directly ask the LLM questions about the code and operate at a higher level of abstraction
Making tailored software for smaller, niche audiences becomes economically viable, and custom applications will increase significantly
Deciding “what to build” becomes a bigger bottleneck than “actually building it”
The cost of paying down technical debt is decreasing (AI can help you refactor)
Pushing back against the “AI job apocalypse” narrative
Ng explicitly pushes back against the “AI will lead to large-scale unemployment” narrative that is popular in today’s tech and policy circles. He calls this view “AI jobpocalypse,” arguing that the real impact “won’t be as bad as those commentators trying to show how powerful their own AI is have predicted.”
He cites Citadel Research’s latest report, saying that software engineering job openings are growing rapidly. If the biggest impact of AI is on software engineering, yet software engineering employment is expanding instead, that’s an encouraging signal for other industries.
Ng also acknowledges that graduating students do face job search difficulties, and that some CEOs have attributed layoffs to AI—but he points out that a large part of this is “AI washing,” where companies choose to blame layoffs on AI even though AI has not actually changed their internal operations.
Open questions about the future of software engineering
Ng raises a series of questions still being explored: What are the key skills for future senior software engineers? How should computer science courses change? If everyone can build features, what will be the competitive advantage for individuals and companies? How should software teams be structured? How will AI agents change machine learning engineers’ workflows?
These questions are directly related to the trends at Harness Engineering and Vibe Coding. As building costs approach zero, taste, judgment, and the ability to choose problems—rather than pure technical capability—will become an irreplaceable human advantage.
This article, Andrew Ng: AI makes coding easier, but “deciding what to do” is becoming the new bottleneck, first appeared on 鏈新聞 ABMedia.
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