def check_ai_code_quality(code_snippet):
    """
    Detects common red flags in AI-generated code.
    Returns a quality score and specific warnings.
    """
    
    red_flags = {
        'inefficient_algorithms': ['bubble_sort', 'linear_search_when_sorted'],
        'bad_naming': ['temp', 'var', 'holder', 'data', 'stuff'],
        'useless_comments': ['// this does the thing', '/* process data */'],
        'security_risks': ['eval(', 'exec(', 'unsafe_deserialize']
    }
    
    warnings = []
    score = 100
    
    # Check for inefficient algorithms
    for algo in red_flags['inefficient_algorithms']:
        if algo in code_snippet.lower():
            warnings.append(f"⚠️ Inefficient algorithm detected: {algo}")
            score -= 20
    
    # Check for poor variable naming
    for bad_name in red_flags['bad_naming']:
        if any(bad_name in word for word in code_snippet.split()):
            warnings.append(f"⚠️ Poor variable naming pattern: '{bad_name}'")
            score -= 10
    
    # Check for useless comments
    for comment in red_flags['useless_comments']:
        if comment in code_snippet:
            warnings.append(f"⚠️ Useless comment: '{comment}'")
            score -= 5
    
    # Check for security risks
    for risk in red_flags['security_risks']:
        if risk in code_snippet:
            warnings.append(f"🚨 SECURITY RISK: '{risk}' detected")
            score -= 30
    
    return {
        'quality_score': max(0, score),
        'warnings': warnings,
        'recommendation': 'Manual review recommended' if score < 70 else 'Looks good'
    }

# Example usage:
# ai_code = "def sort_data(data):\n    # bubble sort implementation\n    temp_var = []\n    // this sorts the data\n    return temp_var"
# result = check_ai_code_quality(ai_code)
# print(f"Quality Score: {result['quality_score']}")
# for warning in result['warnings']:
#     print(warning)

The Downward Spiral of Digital ‘Help’

Let's set the scene. You're in the zone, fingers flying across the keyboard, building the next big thing—or at least trying to fix that bug from three sprints ago. Your AI companion, ever eager, flashes a suggestion. It's a 15-line function to, say, sort a list. You glance at it. It uses a bubble sort algorithm (a classic move for maximum inefficiency), has a variable named temp_temp_var_holder, and includes a comment that says // this does the sorting part. Groundbreaking.

This isn't an edge case; it's the new normal. The IEEE Spectrum analysis, echoed by a chorus of groans on Hacker News, points to a phenomenon called "model collapse" for coding. In layman's terms: these models are increasingly trained on their own output and other AI-generated code scraped from the web. They're eating their own homework, regurgitating it, and then eating that too. The result is a feedback loop of degenerating quality, where the quirks, errors, and odd stylistic choices of early AI code become amplified into standard practice.

From ‘Copilot’ to ‘Co-pirate’

The irony is thicker than a legacy codebase. These tools were sold as productivity multipliers—the secret weapon to crush your JIRA tickets and finally take that four-hour workday seriously. Instead, they've become productivity taxes. You now have a new full-time job: Senior AI Output Auditor.

"It suggests solutions to problems I don't have, and for the problems I do have, it suggests solutions that create seven new problems," lamented one developer on HN. "Yesterday it tried to write a whole microservices architecture for a 'Hello World' script. It included a Dockerfile, a Kubernetes config, and a billing alert for AWS. I just wanted to print to the console."

The pathologies are predictable:

• The Over-Engineer: Proposes a factory-factory-adapter-observer pattern for a simple configuration loader.
• The Hallucinator: Confidently uses non-existent library functions, citing made-up documentation.
• The Security Liability: Blissfully suggests concatenating user input directly into SQL queries, like it's 1999.
• The Repetitive Poet: Writes the same basic logic structure 50 different ways, each slightly worse than the last.

Why Your AI Pair Programmer Needs a Performance Improvement Plan

So how did we get here? Follow the money—and the hype. Phase 1 was building a model that could mimic code. This was impressive! Phase 2 was scaling it to inhuman sizes and training it on the entire internet, including Stack Overflow answers from 2008 that advocate for using eval() everywhere. Phase 3, the current phase, is the "Monetization at All Costs" pivot, where the goal shifts from "write good code" to "generate as many acceptable-looking code tokens as possible to keep the user engaged."

The business model demands engagement, not excellence. If the AI pauses to think, you might get bored. Better to spit out something, even if it's garbage, to give the illusion of a hyper-intelligent companion working at light speed. It's the coding equivalent of a nervous first date who won't stop talking.

Tech leaders, of course, are framing this differently. At a recent conference, a CTO of a major AI code tool company (who shall remain nameless, but his initials are probably on a giant office tower) called this "emergent creative exploration." He said, "Sometimes the model needs to go on a journey to find the right solution. The developer is there to guide that journey." Right. And sometimes my cat knocking a glass off the table is 'emergent interior redesign.'

The Human Cost of Machine ‘Learning’

The real victims here are junior developers and bootcamp grads. They're told to trust the AI, to learn from it. They're absorbing bad practices, insecure patterns, and architectural nonsense as gospel. We're creating a generation of programmers who are excellent at editing AI slop but have no foundational understanding of why the slop is, in fact, slop.

Meanwhile, senior engineers are spending their valuable time cleaning up AI-introduced bugs in pull requests, essentially doing the work the AI was supposed to save. The promised productivity boom has turned into a quality bust. The technical debt isn't just accumulating; it's now accruing compound interest, automatically generated at 50 tokens per second.

What Comes Next? Probably More Hype.

The industry response to this decline will be a masterclass in spin. Expect announcements for "Coding AI 2.0!" or "Reasoning-Enhanced Code Synergy Assistants!" They'll add more parameters, train on "curated, high-quality datasets" (which likely means the less-bad AI code from six months ago), and charge 30% more. The marketing will feature a developer looking serene, with perfect hair, as perfect code flows across a dark IDE. Reality will be you, at 2 AM, debugging why the AI decided to implement a custom encryption algorithm using only bitwise operators.

The solution, as always, is painfully human. These are tools, not oracles. They are spell-checkers, not authors. The best use case for AI coding assistants right now might be as a very expensive, very talkative rubber duck—a entity to explain your problem to, in the hopes that in hearing its terrible solution, you realize the right one yourself.