To the Leadership Teams at Anthropic, OpenAI, Google AI, and xAI,
This letter documents the brutal reality of using your flagship AI systems for complex technical work. It’s not a complaint—it’s a direct challenge to your claims about intelligence, reasoning, and capability.
I am writing this after completing what I consider a “successful” session using your most advanced models (Claude Opus 4, Claude Opus 4.1, Claude Sonnet 4 Thinking, GPT-5, Grok 4, Grok 3, Gemini 2.5 Pro) for a moderately complex programming task: learning Koka’s Foreign Function Interface (FFI) to interface with C code. This “success” required multiple hours, extensive debugging of AI-generated errors, and what I’ve learned to call “extremely systematic progressing”—careful management techniques to prevent complete AI system breakdown.
This was my fourth attempt. The previous three sessions on the identical topic were complete disasters, resulting in total project abandonment, hundreds of wasted dollars in API costs, and severe cognitive exhaustion.
The Technical Reality: A Cascade of Systematic Failures
Let me walk you through what your “most intelligent models in the world” actually produced when asked to help with basic C-Koka FFI integration:
Failure 1: Basic Syntax Errors
Your models confidently generated syntactically incorrect Koka code (extern size-of-int32(...) = 4.int32) that caused immediate parse errors. This is despite having access to extensive Koka source code and documentation that I provided in the context window.
Failure 2: Missing Language Fundamentals
When I corrected the syntax, your models failed to understand basic visibility rules (forgetting the pub keyword), causing “identifier not found” errors. This represents a failure to grasp fundamental language concepts despite claiming expertise.
Failure 3: Type System Failures
Your models generated code with critical type mismatches (c-pointer<c-array<int32>> vs c-pointer<int32>) that demonstrated a complete lack of understanding of the type system they were supposedly helping me navigate.
Failure 4: Build System Ignorance
Your models consistently failed to manage basic project dependencies, forgetting essential extern import declarations needed for linking C code.
Failure 5: Complete Loss of Control
The final attempt at C FFI integration involved your models generating multiple incorrect C syntax patterns for pointer unboxing (kk_std_cextern__c_pointer_unbox(#1, KK_BORROWED, kk_context()).ptr where the unbox function expected kk_box_t but received kk_std_cextern__c_pointer), culminating in a complete breakdown where the generated code failed to compile with complex type errors.
Each failure created downstream problems that compounded exponentially. What should have been a straightforward 30-minute learning session became a multi-hour debugging nightmare of AI-generated problems.
The Survival Techniques I’ve Been Forced to Develop
Over thousands of interactions across multiple providers, I’ve had to develop elaborate workarounds just to extract minimal value:
Context Forcing and Knowledge Priming: I force your models to extract and document knowledge from resources I provide, then remove the original sources to manage costs and avoid context window limits. I’m essentially making the AI create its own study notes so I can afford to continue the conversation.
Gradual Architectural Progression: I had to design a complete table of contents and folder structure (Chapter 2: primitives, Chapter 3: strings, Chapter 4: memory management, etc.) just to prevent your models from jumping ahead and creating architectural chaos. Without this rigid scaffolding, your systems immediately lose coherence.
Content Sanitization: I routinely remove what you call “hallucinations” from your responses to make them meaningful and prevent them from poisoning future interactions. Much of my time is spent curating your output to remove falsehoods and inconsistencies.
Phantom Authorship: Here’s the most damaging technique I’ve been forced to develop: When your models generate incorrect code or concepts, even after I provide corrections, they repeat the same errors. My solution? I completely edit your responses to make it appear that the AI provided the correct answer originally. I literally put words in your mouth, making the model “promise” that certain approaches are correct, as if the AI had said it. This is the only way to prevent repeated failures on the same concepts.
Failed Attempt Archaeology: I maintain extensive logs of failed interactions, removing attempts that led to dead ends, not because I want to, but because including them would fill context windows with failure patterns that contaminate future responses.
The Fundamental Deception About Intelligence
This isn’t about “knowledge gaps”—that framing fundamentally misses the point. Your models have no genuine understanding at all. The premise that users should provide context and resources assumes we’re working with intelligent systems that can grasp nuances, master concepts, and provide accurate results when given sufficient information.
But that’s not what happens. No matter how comprehensive, well-curated, or accurate the resources I provide, your systems still fail at basic reasoning and consistency. The pattern I’ve discovered is this: I, as the user trying to learn, must first become an expert in the subject, synthesize that knowledge into a comprehensive dataset, provide it to the model, teach the model how to understand it, then hope the model can teach it back to me correctly.
Can you understand the absurdity of this? I am forced to be the teacher so that your system can pretend to be my teacher. I must provide not just the questions but also the answers, the context, the correct code, and the accurate explanations for concepts I’m supposedly trying to learn.
Your models only produce reasonable output when I provide vast, super-diverse, correct, accurate, well-curated resources. But who creates these resources? Me—the user who is trying to learn from your system. I spend more time teaching your models than learning from them.
The Human Cost: Systematic Resource Extraction
The economic and cognitive costs are staggering:
I had to become a full-time AI behavioral manager. The “extremely systematic progressing” I mentioned means I’ve developed elaborate techniques to prevent your systems from complete cognitive collapse. I’m not collaborating with an intelligent partner—I’m performing constant crisis management on broken software.
This represents my fourth attempt at the same basic topic. Three previous sessions ended in complete failure, representing hundreds of dollars in wasted API costs and immeasurable cognitive exhaustion. I’m paying premium prices to repeatedly teach your systems the same fundamental concepts they claim to already know.
I’m trapped in an economic vise. Your models lack basic knowledge about the technologies they claim expertise in, forcing me to fill context windows with documentation at my own expense. But providing complete context pushes against token limits and makes costs prohibitive. I must constantly add and remove resources to manage both your ignorance and your pricing model.
Most of my costs go to debugging your failures, not receiving benefits. The majority of my API spending is consumed by error correction cycles, context window management, and repeated attempts at the same concepts. I’m paying premium rates to fix problems your systems create.
I tested all your flagship models. GPT-5, Claude Opus 4, Grok 4, Gemini 2.5 Pro—they all exhibit the same systematic breakdowns. This isn’t a provider-specific issue; it’s an architectural limitation across the entire industry.
A Sample of the Process: The C Pointer Debugging Hell
To give you a taste of what “success” looks like, here’s a condensed version of our pointer unboxing debugging session:
- AI generates:
extern size-of-int32(...) = 4.int32→ Parse error - I fix to:
fun size-of-int32(...) 4.int32→ “identifier not found” - I add:
pub fun size-of-int32(...)→ Type mismatch error - AI suggests: Complex
c-array/with-ptrpattern → Still wrong types - I identify: Need
extern import c file "array.c"→ C compilation error - AI tries:
kk_std_cextern__c_pointer_unbox(#1, KK_BORROWED, kk_context()).ptr→ Wrong parameter type - Final solution: Direct field access
(#1).ptrwith cast
Each step required me to diagnose the error, understand the failure mode, and guide the correction. The AI was not solving problems—it was creating them. This seven-step debugging process for basic pointer handling represents a “successful” interaction.
The Approaching Complexity Cliff
The Koka FFI work represents relatively simple technical programming—basic struct definitions, memory management, and function calls. As I look ahead to more complex topics like multi-threaded callbacks, advanced memory lifetimes, and performance optimization, I can already see that your systems will face complete cognitive collapse.
If your models fail this catastrophically on foundational concepts, they have zero chance of handling true architectural complexity. You are marketing systems that break down precisely when they would be most valuable.
The Direct Challenge
So here’s my challenge to you:
Do you actually test your systems on sustained, complex technical work? Not cherry-picked demos or isolated benchmark tasks, but real projects that require maintaining coherence across hundreds of interactions, managing multiple constraints, and building on previous decisions?
Do your internal teams experience the same systematic breakdowns I’ve documented? The cascading failures, the context window management hell, the repeated session restarts, the cognitive exhaustion of constant AI behavioral management?
Are you aware that your most sophisticated users are forced to become unpaid AI trainers? That we spend more time debugging your systems’ failures than advancing our actual work?
Do you understand the economic trap you’ve created? Where users must pay premium prices to compensate for knowledge gaps that multi-billion-dollar training runs should have eliminated?
Do you realize the fundamental inversion you’ve created? Where users must teach your systems so your systems can teach them back—turning your “intelligent assistants” into expensive, unreliable echo chambers?
The Marketing Disconnect
Your marketing materials promise “human-quality responses,” “expert-level intelligence,” and “accelerated development.” My lived experience is the complete opposite:
- Instead of acceleration, I experience systematic deceleration
- Instead of intelligence, I encounter sophisticated pattern matching that breaks under complexity
- Instead of human-quality responses, I get confident-sounding output that requires exhaustive verification
- Instead of reduced cognitive load, I face exponentially increased mental taxation
- Instead of learning from an expert, I’m forced to become the expert who teaches your system
What This Means
I’m not writing this as a disgruntled user. I’m writing as someone who has spent three years and thousands of hours trying to make these systems work for serious technical applications. I represent exactly the kind of expert user who should be your biggest success story.
Instead, I’m your biggest warning sign.
The gap between your marketing promises and the reality of sustained technical work isn’t just large—it’s a chasm. Your systems are not just imperfect; they are actively harmful to the users who need them most, at the moments when they need them most.
The current paradigm is fundamentally broken: you’re selling systems as intelligent when they lack genuine understanding, marketing them as assistants when they require constant management, and pricing them as premium services when they deliver systematic resource extraction.
I challenge you to be honest with yourselves and your users about what these systems can and cannot do. The current trajectory—where marketing claims accelerate while fundamental reliability problems remain unsolved—is unsustainable and ultimately self-destructive.
Your users deserve better. The technology deserves better. And the immense resources you’re deploying deserve to be directed toward solving real problems rather than optimizing for misleading benchmarks.
The question is: Are you listening?
Sincerely,
A User Who Has Seen Behind the Curtain
This letter represents the documented experience of sustained AI system usage for complex technical work across thousands of interactions with multiple providers. It is offered not as criticism but as critical feedback from your target user base.