Overwhelmed and overloaded

[glennamire]

Over the last several months, I have extensively used ChatGPT 4o and 4.5 to build complex, interlocking projects including marketing campaigns, webcast development, educational platforms, and personal storytelling initiatives. My typical operational load pushed near maximum sustainable AI conversation depth (average 12–15 active threads per session) and ran multi-hour sessions often exceeding 150–200 individual task exchanges per work cycle. While GPT-4o excels at creative ideation and short-term task bursts, it consistently struggled under sustained high-integrity production demands. Issues included memory drift, delayed task execution, failure to honor internal task commitments, and degradation of continuity after approximately 90–120 cumulative active tasks. Despite model improvements in creativity and tone, current architectures lack persistent task memory, real-time prioritization enforcement, and error recovery mechanisms necessary for heavy operational use. This experience is not a criticism, but a recognition: today’s AI systems are extraordinary partners for insight and creativity, but not yet reliable substitutes for disciplined, deadline-driven operational execution at human enterprise scales. I offer this experience in the hope it contributes to improving memory persistence, integrity under load, and operational fidelity in future versions.