HOW AI IS RESHAPING SOFTWARE DEVELOPMENT

What’s happening in the U.S. market and what it signals for Brazil

A Market in Motion and Signals That Can’t Be Ignored

In recent weeks, the debate around AI’s impact on software development has moved beyond speculation and is now grounded in concrete developments across the U.S. market.

The release of Anthropic’s code modernization playbook has brought visibility to the use of AI in complex systems, particularly in legacy languages such as COBOL. At the same time, tools like Claude Code and Cursor IDE are seeing rapid adoption among developers and enterprises. In parallel, organizational moves — such as efficiency-driven adjustments at companies like Block, Inc., which significantly reduced its engineering workforce — are being interpreted as early indicators of a broader reconfiguration of software development work.

More than isolated events, these signals point to a structural shift: software development is not being replaced, it is being fundamentally transformed.

What Has Actually Changed: Five Key Areas of AI Impact

From Autocomplete to Agents: A Quiet but Profound Shift

The initial impact of AI on software development was widely popularized by tools like GitHub Copilot. According to GitHub, developers can complete tasks up to 55% faster with AI assistance. (https://github.blog/2023-02-14-github-copilot-x-the-ai-powered-developer-experience/) However, what is emerging now goes beyond the concept of “advanced autocomplete.”

Newer tools can interpret broader context, execute end-to-end tasks and iteratively refine solutions. This marks the transition to what is increasingly being referred to as agentic coding.

In practice, the role of the developer is beginning to change. Writing code is no longer the central activity. The focus shifts to guiding, reviewing, and making decisions about what is being produced.

A new operating model is taking shape: AI executes, but humans direct.

Testing and Debugging: Real Gains, but Not Absolute

Another clear area of progress is the use of AI in testing and debugging. Current models can automatically generate test cases, suggest edge scenarios and even explain complex errors in natural language.

Recent studies indicate meaningful productivity gains. A McKinsey & Company report suggests that generative AI can increase developer productivity by 20% to 45%, particularly in tasks such as coding, documentation and testing (https://www.mckinsey.com.br/capabilities/tech-and-ai/our-insights/unleashing-developer-productivity-with-generative-ai)

However, there is an important nuance that is often overlooked in more optimistic narratives. These gains are real, but not complete. AI generates tests based on patterns and observable code behavior. It does not fully understand system intent or business context. As a result, test coverage can increase without a corresponding improvement in actual software quality.

In practice, this is already revealing a new class of issues: systems that pass more tests but remain functionally incorrect. What AI can efficiently automate is code verification. Validating whether the solution actually solves the right problem still depends on human judgment.

Accelerated Prototyping and the Shift of the Bottleneck

The speed of software creation has also changed dramatically. Teams can now build functional prototypes in a matter of days, test hypotheses quickly and iterate at low cost.

This has a direct impact on innovation cycles, particularly in startups. The cost of testing ideas has dropped significantly. However, the most relevant effect is not just acceleration, it is the relocation of the bottleneck.

Building software is no longer expensive. Validation remains challenging. If validation does not evolve at the same pace, the outcome is not necessarily more innovation, but rather a higher volume of immature solutions being pushed into production.

Smaller, More Productive Teams?

One of the most sensitive topics in this debate is engineering team size. Recent developments in the U.S. market have fueled the narrative that AI will enable organizations to operate with smaller teams.

While partially true, this interpretation is incomplete. What is happening is not simply reduction, it is reconfiguration. As AI absorbs more operational tasks, the weight of work shifts toward activities that require judgment, contextual understanding and decision-making. This changes the profile of the professional who creates the most value.

Developers who primarily act as executors — those who take specifications and translate them into code — are likely to lose relevance. This is precisely the function AI can replicate most efficiently.

In contrast, the role of the software engineer, in its fullest sense, becomes more critical: someone who understands the problem, evaluates trade-offs, makes architectural decisions, guides AI systems and can identify when generated outputs are incorrect, even if they appear superficially valid.

Emerging Skill Sets

As this shift unfolds, the set of valued skills is also evolving.

Clarity in requirements definition becomes central. Knowing what to ask for becomes more important than knowing how to implement. Systems thinking — the ability to understand how different components interact — gains prominence. Code review moves beyond syntax to behavior: is the system doing what it is supposed to do?

In addition, a new competency is emerging: the ability to structure prompts with the same rigor as technical specifications.

These changes signal an important transition. Competitive advantage is no longer driven by implementation speed, but by the quality of decision-making.

The New Bottleneck: The System, Not the Code

Perhaps the most relevant and least obvious shift is this: writing code is no longer the primary constraint in development.

With AI, production capacity increases significantly, but that does not mean the system as a whole can keep up. The bottleneck shifts to other stages: validation, real-world testing, integration and operations. If these processes do not evolve at the same pace, the result is not acceleration, it is simply a shift in where constraints occur.

This is the central point: AI is not just a productivity tool. It is a force that reconfigures the entire development system. It amplifies individual output, but does not automatically increase an organization’s ability to absorb and operationalize that output.

Strategy Remains the Critical Factor

Despite technical advances, one point remains constant: the greatest challenge is not implementation, it is problem definition. Without clarity on requirements, business objectives, and architecture, AI will only accelerate the execution of flawed decisions.

What This Signals for Brazil

Although this movement is more advanced in the United States, its effects are likely to materialize quickly in Brazil. Key implications include:

• increased pressure for higher productivity across engineering teams

• reassessment of team structures

• growing adoption of AI tools

• the need to evolve processes, not just technology

Companies that anticipate these shifts will have an advantage. Those that treat AI as an isolated tool may face increased complexity, more rework and loss of control over their systems.

More Than Tools, a Question of Direction

Recent developments in the U.S. market suggest that effective AI adoption in software development is not just about tools, it is about direction.

Organizations making consistent progress are those that combine technology with well-grounded structural decisions—from architecture to governance.

This is the context in which Axoma operates. By closely tracking this transformation, both in client engagements and internally, we support companies through this transition, connecting technical decisions to business objectives and helping them integrate AI without losing control over their systems.