The Three-Speed AI Software Economy

Software is no longer written in one way, nor is it produced at one speed. Instead, the industry has fractured into a three-speed economy, where generational cohorts define not just how fast code gets shipped but also what “production” even means. The students, the bridges, and the evaluators each embody a distinct tempo—and together, they explain why vibe coding has already crossed from experiment to production reality.

Speed 1: The Students (18-24)

For those entering the workforce now, code is not something you type line by line. Code is conversation. They build apps by learning how to direct AI, not by learning syntax.

25% of this group is active on Replit, a platform where “projects” are often conversational scaffolds rather than engineered artifacts.75% may never write a line of code in their careers. Instead, they will orchestrate systems by prompting.To them, elegance is irrelevant. What matters is fluency in directing AI toward outcomes.

This is a radical break. Previous generations climbed through a ladder of craft, architecture, and shipping. This generation skips those ladders entirely. They begin where others struggled to arrive: treating AI as the primary interface of software creation.

Speed 2: The Bridges (25-44)

The largest and most influential group today are those aged 25 to 44. They are not simply “users of AI”—they are the ones redefining its place in production.

41% of them are active on Cursor, a platform designed for coding with AI at the core.They can code, but increasingly they choose not to.They consistently pick speed over elegance, normalizing AI outputs in production environments.

This is why they are called “the bridges.” They connect two worlds: the engineering traditions of the older cohort and the conversational mindset of the younger one. Their contribution is not only technical but cultural. They are changing what companies consider “acceptable production code.”

To them, vibe coding is not an experiment. It is the fastest path to market—and therefore the dominant paradigm.

Speed 3: The Evaluators (45+)

Older cohorts, especially those in senior roles, operate at a third speed. They are the evaluators: the architects, tech leads, and enterprise decision-makers.

Their focus is strategic tool adoption rather than hands-on shipping.They are often higher on platforms like Base44, which emphasize governance and control.Their worldview is rooted in code as engineering or craft.

This group is slower, more methodical, and more concerned with long-term risk. Yet they are the gatekeepers. AI in production will not scale without their approval, because they control budgets, enterprise standards, and procurement.

The paradox is that while they emphasize stability, their decisions are increasingly shaped by the speed and pressure of the younger cohorts below them.

The Quality Paradox

One might expect this three-speed system to balance itself out. In reality, it produces a new form of fragility: the quality paradox.

Developers using AI are 19% slower in reality—but they believe they are 20% faster.Refactoring has dropped dramatically, from 25% of code changes to less than 10%.Copy/paste coding is on the rise, moving from 8.3% to 12.3%.On large-scale systems, debugging time has increased by 41%.And most striking: 1 in 5 AI-generated suggestions contain errors.

Yet despite these warning signs, adoption accelerates. The paradox is simple: quality is deteriorating, but velocity is addictive. The market rewards shipping, not elegance. The bridges cohort knows this—and they are reshaping norms accordingly.

The Market Has Spoken

If quality metrics suggest caution, market signals suggest inevitability.

41% of all code is now AI-generated.In Y Combinator’s Winter 2025 batch, 95% of startups integrate AI into their codebase.Lovable, a new AI-native dev platform, reached $100M ARR in just 8 months.Cursor’s valuation hit $9.9B—a number that would have been unimaginable for a developer tool just three years ago.

Markets don’t lie. Adoption curves like this don’t reverse. AI coding has crossed the chasm from experiment to infrastructure.

The Uncomfortable Truth

The most striking shift is not about adoption rates or valuations—it is about semantics. The industry is not waiting for AI to reach “good enough.” Instead, it is redefining what good enough means.

As the analysis puts it:

“Vibe coding isn’t becoming production coding—production coding is becoming vibe coding.”

That distinction matters. If we frame AI tools as “not ready yet,” we assume the bar for production remains fixed. But the 25-34 bridges have quietly moved the bar. They define production as whatever gets shipped, tested, and iterated fast enough to satisfy customers. AI doesn’t need to reach perfection because the meaning of production has already been rewritten.

Why the Bridges Matter

Just as in the demographic destiny of code, the 25-34 layer is decisive. They are young enough to abandon the engineering dogmas of their predecessors, but old enough to be trusted with real systems.

This unique positioning gives them disproportionate power:

They normalize AI use in production.They rewrite organizational standards.They shape the perception of “quality” for the next decade.

Without them, the students would remain experimental, and the evaluators would remain cautious. It is the bridges who accelerate adoption by proving that speed beats purity.

Conclusion: Redefining Production

The three-speed software economy reveals a deeper truth: software is not only a technological system but a generational one. Each cohort moves at its own pace, with its own values.

Students see conversation as the new programming.Bridges see speed as the new moat.Evaluators see engineering as the anchor of credibility.

The collision of these speeds produces both fragility and momentum. Fragility, because quality paradoxes remain unresolved. Momentum, because market incentives overpower technical objections.

The bridges have already decided the outcome. Production coding is not waiting for AI to mature—it is adapting itself to AI’s quirks, biases, and accelerations. In this reality, vibe coding is not a phase. It is the foundation of the next software economy.

The post The Three-Speed AI Software Economy appeared first on FourWeekMBA.