Python OSS in Danger: How AI Is Eating Our Libraries One Prompt at a Time

Hey everyone! I'm Archana, an AI developer for the last 8 years and an open-source contributor to several libraries. Today, I want to talk about something that keeps me up at night: AI is slowly making many Python libraries unnecessary, and we need to talk about it.

The Problem Is Real

Last month, I watched a junior developer solve in 30 seconds what had taken me a couple of days to build back in 2020. They didn't write a single line of code. They just asked an AI to "parse this CSV, clean the dates, and visualize the monthly trends."

That's when it hit me: the Python library I had used for years—a simple but useful date parsing tool with over 50,000 monthly downloads—might not exist in a year or two.

I'm not here to be dramatic. I'm here because after 5 years as an engineer and now working in community management, I see a crisis nobody seems to be talking about: AI is making entire categories of Python open source libraries obsolete. I watched the developer use an OpenAI API call to perform simple tasks that otherwise we would write code for:

• Process and format text (instead of using regex or deepparse)
• Formatting dates (instead of using datetime or arrow)
• Simple web scraping (instead of using BeautifulSoup)

When I asked why they didn't use these libraries, they said: "Why add dependencies when AI can do what I need with a simple API call in seconds?"

This hit me hard. Many Python libraries might disappear if developers stop using them because AI can do those tasks, one-shot and on demand.

What Libraries Are At Risk?

Not all Python libraries face the same danger. Here's what I'm seeing:

Libraries Most At Risk:

• String utilities: Libraries like inflect, humanize, and python-string-utils​​. These libraries perform basic string manipulation that AI can perform, zero-shot, with just a few lines of prompt. For simple formatting operations with limited scope, AI can easily generate equivalent code that requires no external dependency.
• Simple validators: Many validation libraries for dates, emails, URLs, currencies and amounts, etc. These are particularly vulnerable as AI can generate validation regex patterns or simple checking functions.
• Format converters: Simple JSON, YAML, CSV processing libraries. For small files, they can be handled by prompting an AI to clean and return data in a structured format.
• Basic web scrapers: Simple HTML parsing can be replaced by sending the entire web-page to an AI. For instance, extracting headlines from news articles or data from tables can be done by AI using a one sentence prompt.

Libraries That Will Survive:

• Scientific Computing: Libraries like numpy and pytorch. Performance-optimized computation is difficult to replace. Its highly optimized C implementations for numerical operations are orders of magnitude faster than any equivalent prompt or Python code that AI could generate. AI libraries have optimized tensor operations, automatic differentiation algorithms, computational graphs and GPU optimization that must be implemented with extreme precision.
• API and Web Development: Libraries like flask, fastapi, and django. Complex frameworks with many integrated components. For instance, Django has an integrated ecosystem with authentication, ORM, admin interfaces, and security protections that work together seamlessly.
• Security and Cryptography:  Libraries like cryptography and pyOpenSSL. Security-critical code needs deterministic behavior. These packages have complex primitives that must be exactly correct to provide security guarantees. Even minor variations or "creative implementations" by AI could introduce  many vulnerabilities.
• Hardware interfaces:  Libraries like PySerial. Libraries connecting to physical systems.

Libraries performing simple data parsing, extraction, or transformations face significant displacement risk, while those handling performance-critical operations, hardware interfaces, or security-sensitive functions will remain essential (for now).

Why This Is Happening: AI Changes Everything

The traditional software development flow used to be:

1. Find a problem
2. Look for a library
3. Install and learn it
4. Use the library to solve your problem

With AI, the flow becomes:

1. Find a problem
2. Ask AI to solve it
3. Replace code with an LLM API call
4. Maybe use specific AI generated code

This is exactly what's changing our ecosystem. The AI-first architecture looks completely different:

The Failure Points Are Everywhere

Let's look at what's actually happening to software architecture. For decades, we've followed a pretty standard process:

In traditional architecture:

1. We gather requirements from users and business stakeholders
2. Architects design high-level systems and choose technologies
3. Engineers create detailed designs for components and write code following those designs
4. QA tests everything thoroughly
5. Ops deploys to production
6. Everyone maintains the system over time
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When companies move to AI-first architectures, I've seen these critical problems appear:

The diagram shows where things go wrong in AI development:

1. Requirements Co-creation - AI helps generate requirements, but often hallucinates non-existent needs
2. Architectural Exploration - AI suggests architecture patterns, frequently misapplying them
3. Implementation Collaboration - AI generates code with subtle logical flaws
4. Continuous Verification - AI testing misses critical edge cases
5. Adaptive Deployment - AI makes inefficient resource decisions
6. Proactive Evolution - AI suggests changes that break existing functionality

Each of these failure points has made projects fail. But the bigger crisis is what happens to our Python ecosystem when nobody needs small utility libraries anymore.

Why OSS Developers Should Worry

When I shared these concerns at GDG Dublin, several developers asked, "Isn't this just progress? Technologies always change."

Yes, but the speed and comprehensive nature of this change are unprecedented. Consider:

1. Traditional evolution was gradual - Developers had time to adapt libraries to new paradigms
2. Functions evolved into libraries - Code reuse improved over time
3. Libraries served as entry points - New OSS contributors often started with small libraries

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With AI, we're seeing:

1. Instant functional generation - AI generates utility code faster than you can import a library
2. Libraries becoming invisible - AI uses libraries internally but hides them from developers
3. Entry-level contributions disappearing - The ladder into OSS is missing its bottom rungs

Impact of Using AI for Utility Tasks

Asking an AI to generate code seems like a productivity silver bullet. However what initially appears as cost savings quickly becomes technical debt.

Accumulating API Costs and Latency

On the surface, it's a simple equation: developer time costs $50-200 per hour, while an AI API call costs pennies. However, a mid-sized development team making 200-300 AI coding requests daily can rack up surprisingly large monthly bills. Those API calls also add significant latency, something your customers may not be willing to deal with.

Reliability Issues

AI responses remain fundamentally probabilistic rather than deterministic, creating several reliability issues. The same prompt can generate different outputs each time, making reproducibility nearly impossible. AI outputs might also have subtle non-deterministic flaws that can go undetected for a long time. Moreover, if the AI API is down, then your application stops working, even if it isn’t an AI app.

Knowledge Atrophy

Developers who rely heavily on AI-generated outputs and code often don't develop deep understanding of the underlying systems. When complex problems arise, they lack the knowledge foundation to solve them. When AI generates code that reimplements library functionality, the "hidden dependencies" on that approach aren't tracked in requirements.txt or package.json files. This creates invisible breaking changes when AI models are updated.

Security Blind Spots

Libraries undergo security audits and have CVE tracking that provides clear vulnerability management. AI-generated code requires manual security review with each generation, and vulnerabilities aren't systematically tracked or patched.

Standardization

One of open source's biggest benefits is standardization—thousands of developers using the same battle-tested code. AI-generated code creates thousands of slightly different implementations of the same functionality, fragmenting the ecosystem.

What Can We Do?

After talking with Python developers, I see three paths forward:

Evolve Libraries into Frameworks

Don't just provide utilities—provide opinions and constraints. AI can generate code, but it can't decide architectural boundaries. Libraries like LangChain are showing how to thrive alongside AI by guiding it rather than competing with it.

Focus on Developer Experience

AI-generated code often works but isn't well-documented or maintainable. Make libraries that help developers understand, test, and maintain utility code.

80/20 Principle: Libraries for Common Tasks, AI for Edge Cases

I've come to believe that the healthiest approach is what I call the "80/20 model": use battle-tested libraries for the common 80% of functionality, and reserve AI for the challenging or unique 20% where libraries may fall short.

Consider an example: date parsing. A common data sanitizing task is to handle user-submitted date strings in various formats. The temptation to ask an AI to sanitize each data point is strong, but consider this alternative approach: The python-dateutil library handles many date formats out of the box, with guaranteed correct behavior, thorough documentation, and extensive test coverage. Instead of asking AI to parse your dates, use python-dateutil which should handle 80% (probably even more) of your inputs. Only use AI for truly unusual formats that it cannot handle and call your LLM API.

This 80/20 approach makes sure that core functionality relies on versioned, documented libraries. These libraries almost always outperform an LLM API and by using AI for edge cases, you reduce expensive API calls while maximizing accuracy.

Our Community Needs to Talk About This

I'm not saying the sky is falling. I love Python and our community. But we need to talk openly about how AI is changing what we do.

• If you maintain a library, how are your download numbers?
• If you use AI coding tools, what libraries do you use less now?
• What changes are you making to your libraries to stay relevant?

The Python ecosystem represents decades of community knowledge, optimization, and refinement. Rather than bypassing this valuable resource, we should view AI as a complementary tool that helps us leverage it more effectively. The most successful teams aren't choosing between libraries and AI—they're strategically combining both.