The Rise of AI Wrappers: Are AI Providers the New Telecoms?

Artificial intelligence has experienced an explosion of growth in recent years, with companies like OpenAI, Anthropic, and Google leading the charge in providing powerful foundational models. But despite their immense computational capabilities, these AI providers are increasingly finding themselves in a familiar position—one that resembles the fate of telecom giants in the past.

In the telecom industry, infrastructure providers built the backbone of communication networks, yet they eventually found themselves competing not on innovation but on price, reliability, and scale. Meanwhile, a new wave of companies emerged, offering user-friendly interfaces and value-added services on top of these networks, becoming the real consumer-facing brands. Today, a similar pattern is emerging in AI, where a new breed of AI wrappers is rapidly capturing market attention and customer loyalty.

AI Wrappers: The New Kings of the AI Economy

AI wrappers are companies or platforms that use existing AI models but differentiate themselves by offering improved usability, domain-specific expertise, or additional automation capabilities. They act as intermediaries between the raw power of foundational models and the end user, providing a tailored, more accessible experience. These wrappers often build agentic systems that extend AI capabilities beyond simple text generation, integrating AI into real-world workflows more seamlessly.

One of the most notable examples is Cursor, an AI-powered coding assistant that integrates deeply with software development environments. While OpenAI provides the underlying models, Cursor enhances them with context awareness, code-specific optimizations, and user-friendly interactions that cater specifically to developers. Similarly, Jasper has become a dominant force in AI-powered content creation, building atop existing LLMs but fine-tuning the experience for marketing and branding purposes.

Other examples include:

  • Replit Ghostwriter: An AI-powered coding assistant built into the Replit platform, offering features customized for its user base.
  • Notion AI: A productivity tool that integrates AI to help users generate content, summarize notes, and organize information in an intuitive way.
  • Synthesia: A video generation platform using AI-powered avatars and voice synthesis, making it easier for businesses to create professional content without requiring actors or video production expertise.

These AI wrappers add convenience, improve user workflows, and tailor AI capabilities for specific needs—something foundational models alone struggle to do.

Why AI Providers Are Like Telecoms

Telecom providers built the infrastructure that powered the internet, but their value quickly became commoditized. Consumers didn’t buy “bandwidth”—they bought streaming services, social media, and communication apps that provided the real utility. The underlying networks remained essential but largely invisible.

AI is heading down the same path. Foundational model providers like OpenAI, Anthropic, and Google are in an arms race to create more powerful models, but they face two fundamental challenges:

  1. Differentiation is difficult: At the model level, once competitors catch up in quality, the main differentiator becomes price and API access rather than unique features.
  2. Customer experience is owned by wrappers: Just as customers interact with Netflix rather than their internet provider, most AI users will engage with AI wrappers rather than the underlying models.

Unless foundational AI providers can create a breakthrough that redefines the way AI is used, they will likely remain the equivalent of cloud computing providers—essential but invisible to end users.

Can AI Providers Compete?

AI model creators face a strategic dilemma. Do they continue improving their base models and risk becoming commodities, or do they move up the stack, integrating more directly with users?

There are a few potential paths they could take:

  • Vertical integration: OpenAI, for instance, is already moving in this direction with ChatGPT, aiming to make its own application as sticky as possible. Google has integrated Gemini into its search products, while Anthropic is working on making Claude a more accessible assistant.
  • Exclusive partnerships: By offering custom AI models for select partners, providers can maintain an edge. This strategy mirrors what Nvidia has done with its GPU dominance—selling to cloud providers while also enabling high-performance, exclusive partnerships.
  • Fine-tuned solutions: Rather than offering general-purpose models, AI providers could develop industry-specific solutions for enterprise applications, ensuring they remain a step ahead of generic AI wrappers.

However, even with these efforts, the fundamental shift in value remains: users prefer tailored experiences over raw power, and AI wrappers are best positioned to deliver those experiences.

The Future of AI: Who Wins?

The current trend suggests AI providers will continue building the foundation, but the real profits and brand recognition will go to those who package and distribute AI in a way that users love. AI wrappers are already dominating certain verticals, and as agentic AI systems become more advanced, we will see an even greater separation between infrastructure and experience.

If AI providers fail to adapt, they risk becoming the “AT&T” of the AI age—powerful but replaceable. Meanwhile, the new kings of AI will be the platforms that understand user needs, integrate AI seamlessly, and create interfaces that feel indispensable.

The AI landscape is rapidly shifting, and while foundational models remain crucial, they are no longer the defining factor in AI’s success. The future belongs to those who can bridge the gap between raw AI power and user-friendly experiences. As we move forward, expect more AI wrappers to emerge, reshaping industries and pushing AI providers further into the background.

In the end, AI might power the revolution—but the wrappers will own the kingdom.

AI: A Gift for Junior Developers, a Curse for Tech Leads

Artificial intelligence is revolutionizing software development, making it easier for less experienced developers to write code, generate solutions, and build applications faster than ever before. But as AI lowers the barrier to entry, it creates an unexpected challenge—an increasing burden on tech leads who must navigate a landscape filled with AI-assisted code that often lacks structure, scalability, and maintainability.

The Rise of AI-Assisted Development

Tools like GitHub Copilot, ChatGPT, and other AI-powered coding assistants have significantly boosted developer productivity. Junior and mid-level developers can now produce complex code snippets, automate repetitive tasks, and solve problems they previously struggled with. While this sounds like a win for the industry, the reality is more nuanced.

AI-generated code is often syntactically correct but semantically flawed. It may work in isolation but lack architectural integrity when integrated into a broader system. AI lacks the human intuition necessary to understand business logic, future scalability, and team-specific best practices. This means that while development velocity increases, so does the risk of accumulating technical debt.

The Burden on Tech Leads

Tech leads, already responsible for guiding teams, making architectural decisions, and ensuring high code quality, now face an additional challenge: reviewing and correcting AI-generated code. Here’s why AI can be a curse for tech leads:

1. Increased Review Workload

AI accelerates code production, but not always in the right direction. More code means more pull requests, more code reviews, and more debugging. Tech leads spend significant time analyzing whether AI-assisted code is functionally correct and adheres to best practices.

For example, a junior developer might use AI to generate an API endpoint for handling user authentication. The AI might produce working code but fail to implement essential security measures such as rate limiting, input validation, or proper session management. The tech lead must then review, refactor, and educate the team on why these aspects are critical.

2. Poor Architectural Decisions

Junior and even mid-level developers often rely on AI to solve immediate problems without considering the long-term architectural impact. AI doesn’t inherently enforce good design patterns, leading to monolithic structures, poorly optimized queries, or brittle integrations that tech leads must later refactor.

Imagine a situation where AI generates multiple SQL queries inside a loop instead of optimizing them into a single batch query. While the code functions correctly, it causes unnecessary database load, which can lead to performance bottlenecks in production. A tech lead must identify and correct such inefficiencies before they escalate.

3. False Confidence in AI Solutions

Developers using AI often assume the generated code is correct because it “works.” However, AI does not understand the nuances of business requirements, security concerns, or industry-specific regulations. This creates hidden bugs and vulnerabilities that tech leads must catch before they become production issues.

For instance, an AI-generated function might handle user passwords but store them in plaintext instead of hashing them properly. If a less-experienced developer deploys this code, it could lead to a serious security breach. A tech lead must not only fix the issue but also implement processes to prevent similar mistakes in the future.

4. AI-Driven Technical Debt

Without proper oversight, AI can rapidly generate unmaintainable code that accumulates as technical debt. Tech leads must dedicate additional time to reworking solutions, refactoring poor implementations, and ensuring code consistency across the codebase.

Consider a scenario where an AI tool generates dozens of functions across different files with inconsistent naming conventions and redundant logic. While the code technically works, it becomes difficult to navigate, update, or debug. A tech lead must step in to unify the code, remove duplication, and enforce better organizational practices.

The Future: More Tech Leads, Not Fewer

Paradoxically, as AI tools become more advanced, the need for strong technical leadership increases. More tech leads will be required to:

  • Establish clear coding guidelines and best practices for AI-assisted development.
  • Educate teams on AI’s limitations and how to critically assess its output.
  • Enforce architectural principles to prevent the system from collapsing under its own complexity.
  • Prioritize code quality and long-term maintainability over short-term gains.
  • Introduce automated testing and linting rules to catch common AI-generated mistakes before they reach production.

How Tech Leads Can Adapt

To thrive in this AI-assisted environment, tech leads need to adjust their approach to leadership and mentorship. Some strategies include:

1. Establish AI Review Processes

Tech leads should implement specific review processes for AI-generated code, ensuring that every piece of code passes through a structured evaluation. Code review checklists should include:

  • Security best practices (e.g., input validation, authentication measures).
  • Performance optimizations (e.g., avoiding redundant queries or inefficient loops).
  • Maintainability and readability (e.g., clear variable names, well-structured functions).

2. Promote AI Literacy Among Developers

Instead of discouraging AI use, tech leads should train their teams to use AI responsibly. This means educating developers on when to trust AI-generated code, how to refine prompts to get better results, and how to critically evaluate AI suggestions.

3. Use AI to Help, Not Replace, Thoughtful Engineering

Tech leads can leverage AI themselves—not just to generate code, but to automate tedious tasks like refactoring, documentation generation, and code linting. By integrating AI into CI/CD pipelines, tech leads can ensure AI-generated code is continuously checked for quality issues before deployment.

AI is undoubtedly a powerful tool for accelerating development, but it is not a substitute for experience, intuition, and thoughtful engineering. While junior developers benefit from AI assistance, tech leads bear the responsibility of ensuring that AI-generated code aligns with best practices and long-term business objectives. As AI continues to shape the industry, the role of tech leads will become even more critical—not just in fixing what has been done, but in guiding teams to use AI responsibly and effectively.

In the end, AI doesn’t replace the need for great engineering—it amplifies the need for strong technical leadership. The companies that recognize this will be the ones that successfully integrate AI into their development processes without sacrificing code quality, security, or scalability.