Your Code Is Good, But AI's Getting Better: A Wake-up Call

From writing tests to commanding AI minions - a practical guide to staying ahead in 2025

Last Saturday, I had a thought-provoking conversation with a fellow Tech Lead. Despite being at the top of his game, he expressed genuine concern about his future career path and ability to provide for his family. His worries struck a chord with me - if someone at his level is thinking about upskilling, perhaps we all should be.

In the next 5 minutes, you'll discover:

• What modern AI coding agents can actually do today

• Where the demand for programmers is likely headed

• 3 career paths you can explore (or combine!) to stay ahead

The Reality of AI Code Agents in 2025

The landscape of AI coding assistants has evolved dramatically. Before we look at the key players, let's understand what's actually possible today:

Current Capabilities

✅ Advanced Autocompletion: Far beyond traditional suggestions - now includes whole function predictions

✅ Documentation Generation: Automated, context-aware documentation

✅ Small-Scale Problem Solving: Finding solutions within code snippets is largely solved

✅ Pull Request Management: Automated summaries and code review assistance

🟨 Whole Project Understanding: Getting there - tools like Devin and Cursor are showing promising results

🟨 Complex System Design: Still requires human expertise, but AI can assist with implementation

⚠️ Complete Automation: Despite the hype, AI isn't replacing developers - it's augmenting their capabilities (for now)

For those proficient with these tools, they significantly streamline development work. It would be almost wasteful not to leverage this technology.

Key Players in the Space

GitHub Copilot - The Pioneer

Microsoft's AI pair programmer has set the standard for code completion and suggestion. It's deeply integrated into the development workflow and continues to improve with each update.

Devin AI - The "Software Engineer" Assistant

Developed by Cognition Labs, Devin can autonomously plan, code, debug, and deploy software. Its unique feature? The ability to create detailed plans and delegate to "minion" agents for complex projects.

Cursor - Your IDE Companion

🚀 This is my go-to solution these days. I use it both for coding and as a research, writing, and thinking partner—working across multiple Markdown (.md) files and switching models based on my needs works surprisingly well.

Think Visual Studio Code but supercharged with AI. It works right in your development environment, offering real-time feedback and rapid iterations. Many developers prefer its seamless integration for day-to-day tasks.

Amazon CodeWhisperer - The Enterprise Choice

Amazon's entry into the AI coding space focuses on:

• Enterprise-grade security

• Multi-language support

• Specialized support for data analysts

• Deep AWS integration

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We're witnessing an intense race among genAI models to achieve better code generation capabilities. Recent benchmarks show significant improvements in areas like competitive programming tasks and real-world GitHub issue resolution.

Here’s a link you can use to check current leaderboards across most popular benchmarks:
https://paperswithcode.com/task/code-generation

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The Future Market for Programmers

Let's be honest - making predictions about the future is impossible. Each of us has to make our own bet and invest (or not) time into upskilling based on that decision.

Here's what I'm fairly confident about:

• Landing that first job as an "old school programmer" will become increasingly challenging

• Job transitions will get tougher - smaller market, bigger competition pool

• What once required a full team might soon be accomplished by a fraction of the workforce (Yes, I know new positions will open up, but I'm focusing specifically on current developers here)

There are two extreme views in the "AI impact on developers" debate:

1. "AI will replace coding entirely - we'll just describe apps in prompts"

2. "AI is just hype - I tried Copilot and it sucks"

For the first view - maybe you're right. But isn't it better to maximize our potential with current valuable skills than wait for the apocalypse? For the second view - I can only smile and walk away. These folks will find every excuse not to embrace new technology - ironically, while working in tech themselves.

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FYI: I don't put much stock in big tech CEOs' announcements - they often have their own agendas. Such claims (check the short below) seems a bit too optimistic, at least for now ;)

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3 Paths to Consider (Mix & Match Encouraged!)

These paths aren't mutually exclusive - think of them as guideposts for potential career development. They're ranked from most essential to most specialized, with Path 1 being practically mandatory for modern developers.

Path 1 - Fluent AI Programmer (The Must-Have)

This path is becoming essential for any programmer. Whether you're a junior developer looking to accelerate your learning or a senior engineer aiming to multiply your productivity, this skill set is becoming as fundamental as version control.

There are two main approaches:

The Pair Programming Buddy Approach

• Use AI as your collaborative partner

• Write your code, feed detailed prompts

• Let AI help with brainstorming and boilerplate code

• Develop your own "prompt library" over time

• Focus on code review and validation

• Use AI for documentation and test generation

The Army of Minions Approach

• Orchestrate multiple AI agents for larger features

• Focus on managing, monitoring, and fine-tuning output

• More experimental now, but showing promise in startups and MVPs

• Requires strong system design knowledge

• Learn to delegate tasks between different AI Agents, tune them to your needs and experiment.

Key Activities:

• Master prompt engineering for better AI output - or in other words sharper your communication skills ;)

• Learn the strengths and limitations of different AI coding tools

• Build a workflow that combines multiple AI assistants

• Always validate and test AI-generated code

• Stay updated with new AI coding tools and features

Perfect for:

• Junior developers wanting to accelerate their learning curve

• Senior developers looking to multiply their productivity

• Anyone working on projects with tight deadlines

• Developers who want to focus more on architecture and less on boilerplate

🎁 If you don’t now who to follow on linkedin with “army of minions” apporach rUv is experimenting a lot with them: https://www.linkedin.com/in/reuvencohen/

Path 2 - AI Integrator

The LLM hype is real, but businesses are approaching it cautiously. They're thinking about:

• AI performance reliability

• Process definition

• Business impact potential

• Data privacy and security

• Integration with existing systems

Good news: Despite the LLM hype, there aren't many developers who know how to build systems around them. Even fewer have commercial implementation experience. And here's the kicker - it doesn't require heavy math or ML skills!

Pros:

• No need for deep AI knowledge

• Growing demand as open-source models improve

• Close connection to business (a pro for some!)

• Opportunity to shape AI adoption in organizations

• Work with cutting-edge technology while staying practical

Cons:

• Field changes faster than frontend development

• Some skills may become obsolete as genAI evolves

• Measuring success can be subjective and challenging

• Requires constant learning and adaptation

• Need to balance technical and business requirements

Things to learn:

• python 🐍

• prompt engineering

• frameworks / libs: LangChain, LangGraph, CrewAI, LLamaIndex, smolagents

• RAG, AI Agency

• genAI limitations 💡

Who to follow? LangChain and CrewAI both put a lot of effort to show best practices and whats currently possible.

Path 3 - LLM Ninja

This is the most specialized path, betting that the market will need specialized LLMs for specific tasks. It's different from Path 2 because you focus solely on LLMs rather than building systems around them. In practice some projects will require people from both of this world.

Perfect for:

• ML engineers wanting to specialize in LLMs

• Developers fascinated by the cutting edge of AI

• Those willing to invest time in deep technical expertise

• Engineers who enjoy optimization challenges

Key Focus Areas:

• Understanding model architectures

• Fine-tuning and optimization techniques

• Data privacy and security considerations

• Performance benchmarking and evaluation

• Keeping up with latest research and developments

Why consider it? Businesses don't want to send sensitive data to OpenAI, Google, or Anthropic. They need similar performance but with data privacy. Enter LLM ninjas who know which open-source models to use and how to optimize them.

Pros:

• High demand for specialized skills

• Close to cutting-edge AI development

• Rewarding work teaching models new tricks

Cons:

• Could become less relevant as genAI progresses

• Requires strong mathematical background to really understand what and why you’re doing

• Takes time to build expertise

• Business awareness still crucial

Things to learn:

There’s a lot to learn. I’d start with NLP course from huggingface + some additional materials for things you do not understand. Also read Philipp Schmid blog and thank me later - https://www.philschmid.de

Final Thoughts

The future of programming isn't about choosing one path - it's about finding your unique combination. Path 1 (Fluent AI Programmer) is becoming essential for everyone, while Paths 2 and 3 offer interesting pivot opportunities if you're concerned about long-term career prospects.

Remember: The goal isn't to predict the future perfectly but to stay adaptable and keep learning. Choose what resonates with you, experiment, and don't be afraid to adjust your course as the technology landscape evolves.

Stay curious, stay learning! 📚✨