AI Generated Content Crisis: Why It
AI Generated Content Crisis: Why It's Insulting Readers
The AI generated content flood is reaching a breaking point. As I write this on October 28, 2025, we're witnessing a perfect storm that's exposing the fundamental problem with the content industry's lazy embrace of artificial intelligence. Microsoft's latest AI Call Center Stack release and ongoing controversies like the criminal complaint against Clearview AI are forcing us to confront an uncomfortable truth: we've prioritized efficiency over authenticity, and readers are paying the price.
The internet is drowning in soulless, AI-generated blog posts that insult the intelligence of anyone who dares to read them. As someone who's architected platforms supporting 1.8M+ users and led engineering teams through complex technical challenges, I'm tired of watching consultancies and tech companies destroy their credibility with lazy content strategies.
The Current State of AI Content: A Race to the Bottom
Let's be brutally honest about what's happening. Every day, thousands of "thought leadership" articles are published that read like they were written by someone who's never actually solved a real technical problem. The telltale signs are everywhere:
- Generic advice that could apply to any industry
- Buzzword-heavy introductions that say nothing
- Lists of "best practices" with no real-world context
- Technical explanations that sound impressive but lack depth
The recent discussion on whether AI can code but not build software perfectly illustrates this crisis. AI tools excel at generating syntactically correct content that passes surface-level scrutiny, but they fundamentally lack the experiential knowledge that makes content valuable.
Why AI Generated Content Is Fundamentally Insulting
It Assumes Readers Are Stupid
When a consultancy publishes an AI-generated article about "Top 10 Machine Learning Best Practices," they're essentially telling their audience: "We think you're too naive to notice that we've never actually implemented ML in production." Readers aren't idiots. They can sense when content lacks authentic experience.
I've spent years debugging production ML pipelines, dealing with data drift, and explaining to stakeholders why their "simple" AI request will take three months to implement properly. That experience creates a perspective that no language model can replicate.
It Devalues Real Expertise
Every generic AI-generated piece dilutes the value of genuine expertise. When someone who's actually scaled teams and modernized enterprise systems publishes insights, they're competing for attention against an endless stream of artificial content that costs nothing to produce but offers nothing of value.
The market is becoming saturated with content that looks professional but provides no real insight. This creates a race to the bottom where authentic expertise gets buried under algorithmic mediocrity.
It Breaks the Trust Contract
Content marketing has always been based on an implicit contract: "We'll share valuable insights in exchange for your attention and trust." AI generated content breaks this contract by taking attention while providing minimal value. Readers invest their time expecting to learn something useful, only to encounter recycled platitudes.
The Microsoft AI Call Center Example: When AI Makes Sense
Interestingly, Microsoft's new AI call center stack demonstrates the right way to think about artificial intelligence. Instead of replacing human expertise, it's augmenting specific workflows where AI genuinely adds value:
- Voice processing for customer interactions
- SMS automation for routine communications
- Memory systems for context retention
This is AI integration done thoughtfully – identifying specific problems where machine learning provides clear benefits while maintaining human oversight for complex decision-making.
The Clearview AI Controversy: A Warning About AI Ethics
The criminal complaint against Clearview AI serves as a stark reminder that AI implementation without ethical considerations can have serious consequences. This controversy highlights why authentic, experienced voices are crucial in AI discussions.
When consultancies publish AI-generated content about AI ethics, they're creating a dangerous feedback loop where artificial intelligence discusses its own implications without human insight. Real ethical considerations come from professionals who've grappled with the actual implications of deploying AI systems at scale.
How Consultancies Are Damaging Their Brands
The Template Problem
Most AI-generated technical content follows predictable templates:
- Generic problem statement
- List of obvious solutions
- Superficial technical explanation
- Call-to-action that promises expertise
These templates are immediately recognizable to anyone who's read more than a few technical blogs. They signal that the company behind them either doesn't have real expertise or doesn't respect their audience enough to share it.
The Expertise Paradox
Here's the paradox: companies that could actually provide valuable insights are often the ones most tempted by AI content generation because they're too busy solving real problems to write about them. Meanwhile, companies without deep expertise use AI to manufacture the appearance of thought leadership.
This creates a market where authentic expertise becomes increasingly rare and valuable, while artificial expertise becomes increasingly common and worthless.
Practical Strategies for Ethical AI Content Use
The 80/20 Rule for AI Integration
Based on my experience implementing AI systems across multiple organizations, I recommend the 80/20 rule for content creation:
- 80% human expertise, experience, and insight
- 20% AI assistance for editing, research, and optimization
AI should enhance human expertise, not replace it. Use AI tools to help research topics, improve clarity, or optimize for SEO – but the core insights must come from genuine experience.
The Authenticity Test
Before publishing any content that involved AI assistance, apply the authenticity test:
- Experience Check: Can you provide specific examples from your own work?
- Depth Test: Does the content go beyond surface-level observations?
- Unique Perspective: What insight are you sharing that others can't?
- Practical Value: Will readers be able to apply this information?
If your content fails any of these tests, it's probably insulting your readers' intelligence.
The Attribution Standard
When AI tools contribute to your content creation process, consider being transparent about it. Not because you need to apologize, but because it demonstrates thoughtful integration rather than lazy substitution.
The Future of Technical Content Marketing
The AI generated content crisis is forcing a market correction. Companies that continue to publish artificial content will find their audience increasingly skeptical and disengaged. Meanwhile, organizations that invest in authentic expertise will stand out dramatically.
Quality Will Win
The sheer volume of AI-generated content is creating an opportunity for genuinely valuable content to break through. Readers are becoming more sophisticated about identifying authentic expertise, and they're actively seeking it out.
Tools vs. Replacement
The most successful content strategies will use AI as a tool to amplify human expertise, not replace it. This means better research, clearer writing, and more efficient production – but always in service of sharing genuine insights.
Why This Matters for Technical Leaders
As technical leaders and consultancies, we have a responsibility to maintain the quality of industry discourse. When we publish AI-generated content, we're contributing to the degradation of knowledge sharing that our entire industry depends on.
The best technical content comes from professionals who've solved real problems and can share specific insights from that experience. No AI model can replicate the knowledge that comes from debugging a production system at 3 AM or explaining complex technical concepts to skeptical executives.
Moving Forward: A Call for Authentic Expertise
The AI generated content crisis represents both a challenge and an opportunity. Companies that recognize the value of authentic expertise and invest in sharing genuine insights will build stronger relationships with their audiences and establish themselves as true thought leaders.
At Bedda.tech, we've seen firsthand how authentic technical expertise resonates with clients. When you can share specific examples from scaling platforms, integrating AI systems, or modernizing enterprise architecture, that experience becomes a competitive advantage that no AI tool can replicate.
The solution isn't to abandon AI tools entirely – it's to use them thoughtfully in service of sharing genuine expertise rather than manufacturing artificial thought leadership.
The readers deserve better. The industry deserves better. And frankly, your brand deserves better than contributing to the crisis of AI-generated mediocrity that's insulting everyone's intelligence.
It's time to choose authenticity over efficiency, expertise over automation, and respect for readers over content volume. The companies that make this choice will find themselves with increasingly engaged audiences in a world drowning in artificial alternatives.