AI dramatically reduces the effort of writing code and enables rapid prototyping and democratized access to building software. Rapid delivery creates the illusion of progress by smoothing the "happy path" of construction. The fundamental challenge remains discovering which problems are worth solving, validating that customers truly care, and adapting when the market rejects planned roadmaps. Faster output increases the risk of efficiently building the wrong product. The essential, human-centered tasks of user discovery, real-world validation, and product adaptation determine whether software produces revenue and sustainable business outcomes.
If you've ever shipped a product or owned a software business, you know this: writing code isn't the real bottleneck for business, of-course the teams could be faster, could have less technical debt and so on. The bottleneck is figuring out what problem is worth solving, validating that customers care, and adapting when the market laughs at your neat little roadmap.
And here's the irony: AI has smoothed the "happy path" of building so much that we risk confusing motion for progress. The core challenge of product development-understanding what users actually need and validating that solutions address real problems-remains as difficult as ever. While artificial intelligence has revolutionized how we write code, it hasn't touched the fundamental human work of discovering genuine user needs, validating solutions through real-world testing, and adapting products based on market feedback.
Startups are spinning MVPs in days, and new AI users claim "I just vibe the feature, and it shows up." It feels like magic. Faster prototypes, democratized access, the barrier to building nearly gone. That's the happy path. But here's the trap: faster output doesn't mean better outcomes. You can build the wrong thing with unprecedented efficiency. AI has automated construction, not discovery.
Collection
[
|
...
]