"AI Tripled Productivity—So Why Are Salaries Stuck?"

Despite this surge in productivity, salaries have failed to keep pace. Observations of IT freelancer rates on Wishket even show an overall decline. The "rich-get-richer, poor-get-poorer" phenomenon in developer compensation remains as prevalent as ever. If productivity has jumped three to fourfold, shouldn't salaries have at least doubled?

This is a prime example of the stark contrast between the transformative shock AI has delivered to individuals versus the minimal changes seen within organizations. Beyond that, many organizations likely feel that "there hasn’t actually been any shocking change in our company." Compared to the transformation of individuals, the gap is massive. Why does this paradox exist?

"AI does not change companies.
Only companies that implement AI as "software" are transformed."

This article begins at that very point. We will dive into the two core reasons behind this massive disparity between individuals and corporations, and provide two clear, practical directions for companies to move forward.

Part 1: AI as "Crude Oil" vs. Software as the "Refinery"

Let’s consider the relationship between LLMs and ChatGPT. We utilize LLMs through the software interface known as ChatGPT. Ultimately, generative AI—including LLMs—is merely a "Model." ChatGPT is a prime example of a case where extensive logic is implemented as "Software" to provide an agent-like conversational experience while leveraging OpenAI's latest models.

In other words, AI is like "Crude Oil." Crude oil itself possesses immense potential, but it cannot generate value on its own. The role of "Software" is to transform this raw crude into refined products—such as LPG, gasoline, or naphtha—that fundamentally change customer experiences or operational processes. This is where true Value is realized.

Now, the paradox mentioned earlier becomes clear. The fact that developer productivity has increased three to fourfold simply means that the volume of "Crude Oil" being secured has increased. However, the amount that successfully undergoes the "Refining" process to become a final product that creates actual value—namely, great software—remains at a similar or only slightly higher level.

Why is this? Because quickly generating code is a completely different challenge from converting that code into customer value. The difficulty of this "Refining" process—defining requirements, designing architecture, optimizing user experience, and implementing business logic—remains high. Compensation and professional treatment are measured by "Results," not "Input." Therefore, if the final output does not increase significantly, salaries will not rise significantly either. In fact, as more developers utilize AI, competition intensifies, leading to a downward trend in unit prices.

To summarize the core insight:

AI itself does not transform your company. Software built through AI transforms your company.

As NVIDIA CEO Jensen Huang famously said: "AI will not replace you. A person using AI will replace you."

Part 2: Why SaaS Alone Isn't Enough — The Necessity of Proprietary Software

Let’s examine the second reason. Many companies are already utilizing various SaaS (Software as a Service) solutions such as Notion, Slack, Douzone, and SAP. Yet, why do they not feel a significant transformation?

For companies with annual revenue under 5 billion KRW (approx. $3.6M) and fewer than 30 employees, simply making good use of SaaS solutions can provide sufficient AI transformation effects. As AI causes SaaS offerings to diversify and their quality to improve rapidly, smaller firms have plenty of options to directly experience the benefits of the AI era. However, for companies exceeding this scale, the situation changes completely. Mid-to-large-sized organizations have secured their market positions by maintaining unique services and processes within a fiercely competitive environment.

The problem is that SaaS solutions offer only standardized features and limited customization. While this is helpful for boosting productivity in standardized areas—such as HR, accounting, or general management support—it cannot create the innovative shifts required to transform a company’s unique competitive edge. Why? Because true competitive advantage stems from a company’s proprietary way of working—the optimization of its specific services and processes—not from standardized functions shared by everyone.

In summary, the reason why larger companies do not feel the transformation is clear: they are relying solely on standardized SaaS instead of optimizing their unique competitive advantage with proprietary software. Ultimately, for a company beyond a certain scale to properly utilize AI and innovate its competitive edge, there is no choice but to build its own software—Proprietary Software. To convert individual productivity gains into collective organizational performance, you need software that integrates AI with your company's unique operational methods.

AI Transformation (AX) cannot succeed without building proprietary software. Staying idle will only result in being overwhelmed by competitors who successfully build their own software and master the transition to AI.

"The Harsh Reality of Software Development: A 50% Failure Rate"

You might think, "Can't we just wait and try later once the technology is fully proven?" However, projects involving AI transformation and building proprietary software are not the kind where success—at least in terms of Return on Investment (ROI)—is guaranteed simply by paying the costs. At the very least, you must understand the methodology of how not to fail.

These projects face a failure rate of at least 50% from an ROI perspective. This is driven not only by technical complexity but also by various business and organizational hurdles, such as internal resistance to change, vague requirement definitions, and failures in data integration.

So, what is the solution?

"The Start of AX: How to Build Software That Doesn't Fail"

First, we must face the cold reality: building proprietary software is no easy feat. There is a clear, objective reason why over 50% of these projects end in failure.

Yet, giving up is not an option. We are currently at a crossroads of crisis and opportunity. With AI development productivity surging three to fourfold, you can now build proprietary software at speeds previously thought impossible. What once took three months can now be realized in just one. We cannot afford to stand still while competitors seize this window to pull ahead.

Fortunately, there is a way forward. We present two core strategies for building software that ensures success and eliminates the risk of failure.

Strategy 1. Leverage the 5x+ Leap in AI Software Development Productivity

The first core strategy is to leverage the 5x increase in AI-driven software development productivity.

Anyone in the tech industry will agree: since the emergence of ChatGPT in late 2022, development productivity has surged by at least 3 to 4 times. A project that once required three months in late 2022 can now be realized in just one. More importantly, the high barrier of "exorbitant software development costs" has virtually collapsed.

Coding agents like Cursor and Copilot are increasingly automating source code development. With a solid foundation in architecture design, UI/UX module implementation, and CI/CD, high-quality results can be generated almost instantaneously.

We have entered an era where you can—and should—verify a "custom software" prototype even before signing a contract or officially kicking off a project. The days of betting everything on a "proposal" while conducting lengthy Process Innovation (PI) or Business Process Reengineering (BPR) projects are over.

In essence, leveraging AI productivity means shortening the timeline for a Minimum Viable Product (MVP) from months to mere days or weeks. The paradigm has shifted to a "build fast, test, and iterate" model. It is far more effective to verify a working piece of software than to wait for a consulting report with uncertain feasibility.

The Critical Caveat: Deployment is Everything

However, does the mere fact that "prototyping has become extremely fast" guarantee the success of proprietary software? Absolutely not. No matter how fast you build it, if it isn't utilized in actual operations—if it isn't deployed to the field—it remains nothing more than a polished showpiece.

Strategy 2. Beware of "Garbage In - Garbage Out"

The second strategy is to be vigilant against "Garbage In - Garbage Out." The traditional principle—"Garbage in, garbage out"—has become even more critical in the AI era.

Just because AI development productivity has increased doesn't mean the cost of realizing software has plummeted. This is precisely because of GIGO. Coding agents like Cursor and Copilot write code incredibly fast. However, without setting clear expected results and establishing proper direction and phases, you will inevitably end up with software that looks impressive on the surface but is utterly useless. No matter how much developer time or LLM tokens you pour in, the result will be a mess: bloated with unused features, a UI/UX that isn't necessarily "bad" but remains unintelligible, and core functionality that fails to meet business expectations.

The Illusion of Productivity vs. The Reality of Rework

Lately, the surge in development productivity is palpable in almost every software project. However, it is now common to see cases where the entire output—not just the prototype—is overhauled 2 to 3 times from scratch because the client provides feedback that the direction was wrong.

But is this a healthy phenomenon? Productivity went up and the initial cost burden seemed lower, but the total time spent remains the same. From the client's perspective, communication becomes even more frustrating. This applies whether you hire an internal developer or outsource to a firm.

Now is the ideal timing to realize "proprietary software" with optimal ROI. If achieved now, the compounding effect will be significant. Yet, many leaders are still repeating the GIGO cycle in their hiring or outsourcing processes.

How can you avoid this? The key lies in choosing the right partner. Here are two critical criteria.

How to Prevent GIGO During AI Adoption

(1) Choose a Partner with End-to-End Planning Capabilities

Whether you are hiring or outsourcing, ensure you work with a partner who possesses End-to-End (E2E) planning capabilities.

Most developers or firms (over 90%) simply implement software exactly as it was planned or designed. Since they are "builders" rather than "architects," this is to be expected. Consequently, they are often unfamiliar with defining vague requirements, proposing optimal strategic solutions, structuring project schedules, or setting meaningful milestones. It’s not that they are incompetent; they are simply executing the project the same way they always have.

As mentioned earlier, without clear expected results and strategic guidance, high-quality software cannot be realized. Many companies fail in AI transformation because they treat it as a simple outsourcing task rather than an E2E strategic partnership.

How do you judge if a partner has E2E planning capabilities? I advise you to evaluate them as if you were hiring an Operations Team Lead. You need a partner who satisfies the following:

• Do they deeply understand our business, and is communication seamless?

• Do they solve our underlying problems rather than just following orders?

• Can they grow alongside the company rather than maintaining a one-off transactional relationship?

(2) Choose a Partner with Problem-Solving Capabilities

To achieve true AI Transformation (AX), you must build proprietary software centered around the company's Core Value Chain—such as Product Development/Marketing, Sales/Logistics, Procurement/Inventory, or Manufacturing/Business Intelligence—rather than non-core, standardized functions like HR or Accounting.

Especially for companies with over 5 billion KRW in revenue or more than 30 employees, unique service and business process characteristics are what define their competitive advantage. These are the foundations for survival and profit generation.

Partners with experience only in general SaaS or B2C software find it difficult to translate this unique competitive edge into proprietary software. This realization process is a "massive problem" in itself; having the experience and capability to solve these specific, complex problems significantly increases the probability of AX success. (Remember, from an ROI perspective, the success rate is currently less than 50%.)

How can you judge if a partner has these problem-solving capabilities? Hire a partner or firm that has solved unique, specific, and difficult corporate problems—not someone who has only handled common tasks like E-commerce setups, simple admin panels, or basic website creation. Without applying these high standards, the transformation of your company's core services and business processes is highly likely to fail.

Source: https://yozm.wishket.com/magazine/detail/3468/