The New Software Economics: Cheap Code, Expensive Attention

For as long as the software industry has existed, the primary bottleneck of launching a product was technical execution.

If you had an idea, your first and highest hurdle was translating that concept into working code. You either needed deep technical expertise, a significant budget to hire an engineering team, or the patience to spend years mastering the craft yourself. The code itself was the moat.

Today, that barrier has effectively collapsed.

With the emergence of sophisticated AI tools, the timeline from “I have an idea” to “I have a working prototype” has shrunk from months to hours. AI can generate functional code blocks, draft interface designs, and stub out entire applications based on plain-language prompts.

Yet, as the barrier to building has approached zero, a different, far more difficult challenge has emerged. While the number of people shipping applications has exploded, the pool of human attention remains entirely fixed.

But the real scarcity isn’t attention by itself. Anyone can grab attention temporarily with a loud marketing campaign or a novel gimmick.

The real scarcity is attention earned through solving meaningful problems.

1. The Law of Shifting Scarcity

To understand why this is happening, we have to look at a fundamental rule of economics: every dramatic reduction in the cost of production changes what becomes scarce.

This is not a new phenomenon; it is a historical pattern.

• When manufacturing became cheaper through industrialization, production ceased to be the bottleneck. The scarce, highly valuable resource shifted to distribution and supply chain management.
• When publishing became free through the internet, the physical printing of words ceased to be the bottleneck. The scarce resource shifted to discoverability and curation.

AI is now doing the exact same thing to software.

As the cost of generating an application approaches zero, the scarce resource is no longer engineering effort. It is earning enough trust for someone to try your product—and providing enough value that they continue using it.

Markets reward scarcity. Twenty years ago, technical execution was scarce, and developers who could simply make a database talk to a user interface commanded massive premiums. Today, functional code is becoming abundant.

As abundance increases, value migrates elsewhere.

It migrates toward insight. Toward trust. Toward customer understanding and clear product strategy. These human inputs are not replaced by AI; rather, their relative value simply increases.

2. Defining “Product Judgment”

When it is cheap to build features, the temptation is to build all of them. Because an LLM can generate a new module in seconds, founders frequently mistake feature velocity for product progress. They assume that a product with twenty features is twice as valuable as one with ten.

In reality, the opposite is usually true. A product with twenty features is often just twice as confusing.

This is why the most valuable skill in modern software development is no longer coding capability. It is product judgment.

People throw this phrase around constantly, but it is rarely defined. Product judgment is the ability to consistently make decisions that improve a product rather than simply add to it.

It means knowing:

• Which features not to build, despite how easy they are to generate.
• Which customer feedback to ignore because it steers the product away from its core purpose.
• Which complexity to remove to make the user experience feel effortless.
• Which structural trade-offs are worth making for long-term sustainability.

AI can suggest possibilities. It can generate ten different variations of a checkout flow or list twenty potential features for an app. But it cannot determine which of those possibilities creates the greatest long-term value for a specific user base. That requires strategic taste—a human quality developed through observation, empathy, and real-world experience.

3. Building in an Age of Abundance

If code is no longer the constraint, the order of operations for founders must change.

Too many founders still begin with a technical question:

• “What can we build?”

Instead, they should begin with a behavioral one:

• “What painful problem keeps occurring?”

Before writing a single line of code, spend time understanding the people you are building for. Watch how they currently solve their problems. Identify where they become frustrated, where their workarounds break, and where they lose time or money. Test whether removing that frustration is valuable enough that someone would willingly change their behavior to adopt your solution.

Only then should technology enter the conversation.

AI has dramatically reduced the cost of building the wrong thing. It has done nothing to reduce the cost of misunderstanding your customer.

4. Engineering for Survival

Once you have identified the right problem to solve, the execution still matters deeply.

There is a massive difference between software that works and software that continues working.

Reality is where software gets tested.

A prototype generated quickly by an AI often assumes a happy path: a fast network, a single user, valid data entry, and perfectly reliable third-party integrations.

But reality is unpredictable. It is where fifty people click “Submit” at the exact same millisecond, where network connections drop in the middle of a payment process, and where unexpected data inputs can crash a database.

The engineering layer around the code is what protects a business from these failures. It is the invisible work of designing architectures that handle errors gracefully, implementing testing pipelines, and ensuring the system is maintainable. AI can help write the lines of code within these systems, but a human engineer must still design the boundaries that keep the system secure and stable under stress.

We covered the specific engineering layer AI does not yet replace in Working Code Isn’t Production-Ready Code, and the upstream translation problem in AI Can Write the Code. It Still Can’t Tell You What to Build.. The scarcity shift described here is the economic frame those pieces sit inside.

5. What Hasn’t Changed

At Pocket Dev, we use AI tools every day. We are not interested in writing boilerplate code by hand when a machine can do it in seconds. We use these tools to bypass the friction of development so we can focus our energy on the decisions that actually matter.

AI has changed what is easy. It has not changed what matters.

The difficult part of building software was never actually writing the code. The difficult part has always been understanding people well enough to solve a problem they genuinely care about.

That is where successful software has always come from. And as code becomes cheaper, that truth only becomes more important.