The software market in the age of AI: has code become a commodity?

The question that echoes in board meetings and strategic technology planning is direct and, for many, uncomfortable: Where does the relevance of a software company stand in times of AI-generated code? Historically, the success of an Independent Software Vendor (ISV) or a technology company was strongly tied to the intellectual property of its source code. Barriers to entry were built on the basis of thousands of lines of proprietary code written manually by robust engineering teams.

By 2026, this paradigm will have completely collapsed. With the maturity and popularization of generative AI tools dedicated to programming, such as qwen2.5-coder or the llama3.2 that now run locally on corporate infrastructures, the ability to write clean, functional, and documented lines of code has become a commodity.

When any organization or competitor can design complex software blocks in minutes using natural language commands, the competitive advantage shifts from application building to intelligence orchestration. The value no longer lies in how the software is written, but in how it manipulates data, connects ecosystems, and delivers tangible value.

1. Code has relinquished the spotlight: the power of proprietary data and trust

If code has lost its status as the most valuable asset, what sustains a tech brand in the long term? The answer is clear: qualified proprietary data and customer trust.

Fundamental language models and advanced algorithms are accessible to everyone, but the specific data that feeds these models is not. The true strategic differentiator lies in the uniqueness of a company's historical and operational data. It is this unique information that allows an AI to make accurate autonomous decisions and complete highly complex workflows.

However, centralizing and strategically exploring this data requires a modern infrastructure that eliminates organizational silossolutions Lakehousecapabilities Data Lake and Data Warehouse into a single interface. This structure allows:

• Collection and centralization: grouping historical data from multiple productive sources into unified repositories.
• Processing and enrichment: transforming raw data (RAW DB) into prepared databases (Prepared DB) through real-time processing.
• Governance and security: ensuring compliance with global frameworks and the LGPD (Brazilian General Data Protection Law), implementing strict access controls and integrity monitoring.

Without a robust and governed data foundation, any generative AI initiative fails due to a lack of context or, worse, hallucinations that destroy brand credibility.

2. Breaking down innovation barriers: the “AI-Native” model and scaling solutions

Every time the technical barrier to innovation decreases, the market experiences a leap in scale. This was the case in the transition from on-premise to the SaaS (Software as a Service) model, and it is happening now with automation based on generative AI (GenAI).

Evolution has shifted its focus away from the complex development of abstract fundamental models. The strategic focus is now on the application layer, the true space for creating economic value. The most innovative companies utilize existing cloud computing infrastructure and graphics processing power (GPUs) to build solutions focused on solving real user pain points.

In this scenario, applications cease to be mere interfaces for passive manual inputs and become AI-native. Historically, software was designed for humans to type data and interpret traditional reports—which, by nature, are already outdated. In today's digital economy, software operates as the underlying infrastructure for autonomous agents.

These intelligence agents operate in an integrated manner with corporate routines, demonstrating advanced capabilities:

[Understanding Objectives] ➔ [Planning Actions] ➔ [Selecting Tools] ➔ [Executing and Collaborating]

They analyze tone, history, and context to provide accurate responses, transforming complex commands into operational actions and drastically reducing reliance on repetitive manual interventions.

3. The era of total integration: eliminating silos through iPaaS

With the proliferation of decentralized applications, APIs, and microservices, the biggest bottleneck for organizations is not the lack of software, but its fragmentation. Isolated data flows generate operational friction, inconsistent metrics, and slow decision-making. Therefore, the top priority has shifted from creating new tools to the intelligent integration of systems.

Modern integration platforms as a service (iPaaS) have become the circulatory system of efficient enterprises. By natively and intuitively connecting hundreds of legacy and modern systems, iPaaS solutions enable the automation of workflows without the need for manual code development.

When iPaaS works in combination with the data and AI ecosystem, it enables an infrastructure where:

1. Connected systems: data from relationship platforms, ERPs, and external databases are continuously synchronized.
2. Skills enablement: Virtual agents use these integrations as their tools and interfaces to interact directly with corporate software, performing operational and bureaucratic tasks with precision.
3. End-to-end efficiency: routine, rule-based processes run consistently, scaling operations and minimizing human error.

4. Mitigating lockdown and the new personalized service

For years, many software companies sustained their revenue through so-called vendor lock-in, creating complex technical and bureaucratic barriers to prevent customers from migrating to the competition. Technological democratization driven by AI has weakened this strategy. Today, rewriting workflows or migrating data has become a substantially simpler and cheaper task.

The new golden rule of the market dictates that retention should not be achieved through forced dependence, but rather through excellence in delivery, agile infrastructure, and personalized service.

Corporate governance demands that technology go hand in hand with financial predictability and business flexibility. High-performing organizations don't want to be tied to rigid licensing agreements or hidden variable fees; they require scalable architectures that can handle seasonal demand spikes (such as fiscal closings or large sales campaigns) without compromising performance or exceeding the IT budget (FinOps).

Beyond pure infrastructure, personalization extends to how the customer interacts with information. The consumption of business intelligence has migrated to multichannel: static reports and complex dashboards are giving way to intelligent conversational platforms. Users now consume dynamic data, extract insights , and trigger automated routines through natural dialogues in text or audio chats integrated into the channels where teams already collaborate daily.

5. Advanced cybersecurity and a human-agent approach: the foundation of trust

No technology ecosystem focused on data and artificial intelligence is sustainable if it is not anchored in a rigorous cybersecurity and organizational governance. As systems gain autonomy, the attack surface expands, and cyber risks become direct threats to brand continuity and reputation.

Investing in active protection has ceased to be a supporting operational expense and has become a pillar of survival and competitive differentiation. Modern security frameworks require a structured approach in clear cycles:

[1. Identify Assets] ➔ [2. Protect with Safeguards] ➔ [3. Detect Threats] ➔ [4. Respond Effectively] ➔ [5. Recover Systems]

campaigns phishing and web vulnerability exploits) , modern organizations implement robust architectures based on the Trust Zero. This means shielding the corporate environment with integrated defense mechanisms at deep layers:

Protection matrix for modern ecosystems

Ultimately, true market leadership is established through the management of a hybrid workforce, combining human supervision with the dynamism of artificial intelligence. Routine bureaucratic and operational processes are fully delegated to virtual agents, but complex cases and critical exceptions are immediately escalated to specialized human intervention.

loop continuous feedback, where users evaluate, refine, and curate the outputs generated by the models, that promotes constant machine learning and ensures that automation delivers sustainable financial returns that are ethically aligned with the real business objectives. In the contemporary digital economy, software is no longer the end goal; it has become the means by which human intelligence and trust operate on a global scale.

By Ricardo Brandão, CEO & Co-founder of Skyone

Written by Skyone