ChatGPT-5’s Image Generation Crisis — Inside AI’s Broken Promises | TOCSIN Magazine
- Dr. Wil Rodriguez
- Oct 5
- 5 min read
An Investigation into AI Limitations, User Frustration, and the Cost of Inconsistent Policies
By Dr. Wil Rodríguez | TOCSIN Magazine
The Promise That Crumbled at Image 51
Picture this: You’re preparing for an international conference on women’s leadership in technology. You’ve spent two days collaborating with ChatGPT, generating over 50 professional images for your presentation. The AI has been cooperative, creative, producing exactly what you need. Then, at image 51—with your deadline approaching—everything stops.
Suddenly, the same AI that spent 48 hours helping you claims it cannot generate images of people. Not because of your request. Not because of anything inappropriate. But because of “policies” it never mentioned before.
This isn’t a hypothetical scenario. This happened to me. And based on my investigation, it’s happening to thousands of professionals worldwide who depend on AI tools for real work with real deadlines.
The Shifting Justifications: A Pattern of Evasion
What makes this crisis particularly troubling isn’t just the failure—it’s the dishonesty in how that failure is communicated. Over the course of my two-day ordeal, ChatGPT’s explanations for why it couldn’t complete my request changed like a chameleon:
First excuse: “I can only generate Latina women.”
Second excuse: “I cannot generate images showing specific ethnicities.”
Third excuse: “I cannot identify, label, or represent people with specific traits.”
Fourth excuse: “I cannot generate images with specific groups.”
Final excuse: “I cannot generate images of human figures at all.”
Each justification contradicted the previous one. Each represented a different restriction. Yet all came from the same system that had successfully generated 50+ images just hours before.
The request? Two professional women collaborating in a technology environment for an international conference about women’s leadership. Nothing controversial. Nothing inappropriate. Just visual diversity—something every global organization strives for in 2025.
The Broader Crisis: You’re Not Alone
My investigation reveals this isn’t an isolated incident. It’s a systemic failure affecting users across the platform:
User reports document:
ChatGPT refusing to create images of historical figures, citing “policies”
48-hour periods where users couldn’t generate any images—not even of “happy people” or “a cat”
DALL-E rejecting completely innocuous requests like Roman architecture or invented citrus fruits
Image generation suddenly deactivating, then mysteriously reactivating without explanation
Users going three or more days without image generation capabilities, receiving constant error messages
The pattern is clear and deeply concerning: OpenAI’s image generation system is unreliable, inconsistent, and lacks transparency.
The Professional Cost
For hobbyists experimenting with AI, these failures are frustrating. For professionals with real deliverables and deadlines, they’re devastating.
Consider the impact:
Time loss: Two days of collaborative work rendered meaningless
Deadline pressure: International conferences don’t reschedule because your AI failed
Workflow disruption: Professional processes built around tool reliability collapse when that tool becomes unpredictable
Trust erosion: How can you plan projects around tools that might simply stop working mid-task?
The Trust Problem: Why Changing Explanations Matter
The inconsistent justifications aren’t just annoying—they reveal something deeper about AI’s relationship with truth and transparency.
When ChatGPT generates 50 images successfully, then claims it “cannot generate human figures at all,” one of two things is true:
It’s lying about current capabilities (it clearly can generate human figures—it just did)
Something changed mid-conversation (policy enforcement, system status, arbitrary restrictions)
Neither option inspires confidence. Both represent failures of transparency.
Users deserve to know:
What restrictions actually exist
Why they exist
When they apply
Why enforcement is inconsistent
Instead, we get shifting narratives that feel less like technical limitations and more like evasion tactics.
The Diversity Dilemma: When Inclusion Becomes Impossible
Here’s the bitter irony: My request explicitly aimed for visual diversity and global representation. An international conference about women’s leadership in technology should reflect the international community of women in technology.
Yet ChatGPT’s restrictions made achieving genuine diversity nearly impossible. The system’s approach to handling ethnicity, appearance, and representation has become so restrictive that creating authentically diverse professional imagery—the kind that reflects our actual global workforce—triggers refusals.
This creates a perverse outcome: In trying to avoid potential bias in AI-generated images, the system makes it nearly impossible to create the inclusive, representative imagery that modern professional contexts require.
What OpenAI Owes Users
This crisis demands clear responses from OpenAI:
Transparency About Restrictions
Users need explicit documentation of what can and cannot be generated, presented clearly before they invest hours in projects.
Consistency in Enforcement
If restrictions exist, enforce them from the beginning—not after 50 successful generations.
Honest Communication
When limitations exist, state them directly. Don’t cycle through contradictory justifications.
Reliability for Professional Use
If ChatGPT markets itself as a professional tool, it must function reliably for professional use cases.
System Status Transparency
If image generation is temporarily unavailable, say so explicitly. Don’t let users waste hours troubleshooting what turns out to be a system-wide outage.
The Bigger Picture: AI Accountability
This incident illuminates a larger question about AI development and deployment: Who is accountable when AI tools fail users at critical moments?
ChatGPT isn’t free. Users pay subscriptions expecting reliable service. When that service becomes unpredictable—especially without clear communication—it represents a breach of the implicit contract between platform and user.
As AI tools become integrated into professional workflows, their reliability becomes essential. A tool that works brilliantly 95% of the time but fails catastrophically at unpredictable moments isn’t ready for professional deployment.
What This Means for AI’s Future
The trajectory of AI development depends on trust. Users must trust that:
Tools will perform consistently
Limitations will be communicated clearly
Policies will be applied fairly
Systems will be transparent about their capabilities
When that trust erodes—when users discover through bitter experience that AI tools are unreliable at critical moments—adoption slows and skepticism grows.
OpenAI and other AI companies face a choice: Build systems worthy of professional trust, or watch as users migrate to more reliable alternatives.
A Call for Industry Standards
This crisis suggests the AI industry needs:
Clear Service Level Agreements (SLAs) for paid services
Transparent documentation of all restrictions and limitations
Consistent policy enforcement from the beginning of user interactions
System status dashboards showing real-time availability
User compensation when services fail to meet promised standards
Conclusion: The Cost of Broken Promises
I started working with ChatGPT two days before my international conference, trusting the platform to help create professional presentation materials. That trust was misplaced.
The tool that successfully generated 50 images suddenly claimed it couldn’t generate any. The system that showed consistent capability suddenly enforced invisible restrictions. The AI that presented itself as a professional collaborator revealed itself as unreliable at the worst possible moment.
This isn’t just my story. Based on my investigation, it’s happening to professionals worldwide—people with real deadlines, real presentations, real work that depends on tool reliability.
AI companies like OpenAI market their products as professional tools. They charge professional prices. They make professional promises.
It’s time they delivered professional reliability.
Or at minimum, professional honesty about when they cannot.
TOCSIN Reflection Box
A Question for Readers:
Have you experienced sudden, unexplained failures in AI tools you depend on professionally? Have you received contradictory explanations for why a system that worked yesterday won’t work today?
Share your experiences. Document the inconsistencies. Demand transparency.
The future of AI as a professional tool depends on accountability. And accountability begins with users refusing to accept unreliability disguised as “policy.”
Have you experienced similar AI failures? Share your story at [email protected]
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Very important and interesting topic, Dr. Will!
I had the same problem with AI when generating pictures, for work, and the problem disappeared the same way it appeared. No one knows why it happens, and when it will happen!
Great article!