Every breakthrough technology is supposed to make us do more with less. And yet somehow, every time one of these arrives, we end up working more. In this episode, Igor unpacks a paradox he has been living inside for months. The AI tools that were supposed to save him time are costing him more of it. Algorithms that work perfectly for twenty runs and then fall apart on the twenty-first. The honest, unfiltered reality of working with AI when you are a maximizer who refuses to accept anything less than perfect.
[00:52] The Productivity Paradox Nobody Is Talking About:
- Every major technology breakthrough in history, from the printing press to the railway to the internet to AI, was supposed to help us do more with less. Instead, almost every time, we end up working more.
- Before AI, Igor would pull stock images, keep expectations low, and get things done. With AI, that is no longer acceptable. Now every image has to be exactly right, which means spending 30 minutes perfecting a slide that will appear on screen for ten seconds.
- 100 webinar slides took three and a half days to produce with AI. The same task would have taken half a day or a full day before AI existed.
- This is not laziness or poor planning. It is the maximizer effect. When you know the perfect version is achievable, settling for anything less becomes psychologically impossible.
- The paradox: the better the tool, the higher the standard you hold yourself to, which means more time spent, not less.
[03:41] The Three and a Half Hour Promotion:
- Igor built a custom AI tool specifically for preparing his weekly promotions. The process used to take about an hour.
- With the AI tool, the same task now takes three and a half hours. Not because the tool is broken, but because he is integrating it into a future-proof workflow while also correcting its mistakes in real time.
- This is done with the future in mind. AI is already part of the ecosystem, and the investment now is preparation for when it actually delivers on its promise.
- But justifying that investment in the moment, when the tool is taking three times as long and delivering subpar results, is genuinely difficult.
- Screaming at the computer has become a regular part of the workflow.
[04:53] The AI Staleness Problem:
- AI skills, which are detailed instruction files describing how Igor likes to write emails, craft sequences, develop promotion angles, and more, go stale over time.
- If a skill has not been used for about a month, it starts hallucinating. It ignores parts of the instructions, misinterprets others, and starts producing results that bear little resemblance to what it was trained to do.
- On the twenty-first run of a specific algorithm Igor had used twenty times successfully, he found himself making four different edits across eight different sections of the file. Something that had never happened in any of the previous twenty runs.
- The unpredictability is the most frustrating part. Consistently poor would be manageable. Consistently good would be great. Poor today, excellent tomorrow, average the next day, poor again after that, then excellent again creates expectations that are impossible to calibrate.
- Quality control is non-negotiable. You can train an AI to do a job well. You cannot trust it to execute at the same level every time.
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