Testing all the AIs on the market to "find out which one is the best"?
It's tempting… and often ineffective.
🎯 The real question isn't "which AI is the best?"“
The question is: which AI is best for MY use, with MY constraints (time, budget, level of requirements, risks)?
1) Choose your “business objective” before testing
There are two very different strategies:
ROI strategy (pragmatic): quickly find a tool that covers the essentials and saves you time.
Benchmark strategy (expertise/purchase): compare several models in detail to select “the best” based on a set of specifications.
👉 If your goal is ROI, the 80/20 rule is more than enough: you are looking for AI that meets 80% of needs with 20% of learning effort.
2) The trap: AIs are constantly changing
Comparing AI “in absolute terms” is almost impossible, because the versions are constantly evolving.
Without a robust method, it's easy to get confused:
- Real improvement
- “Novelty” effect”
- Random variation
- And… subjectivity.
🧪 So, a simple rule: no metric = no conclusion.
3) My usage framework: 3 axes, 3 levels of requirement
I work on AI for:
✍️ Text optimization: useful, but difficult to measure properly without a protocol (style, consistency, repetitions, errors).
🖼️ Image generation: here, I have a reproducible test. Example: given a profile with a shoulder bag, request the other profile. Very often, the AI produces a mirror image (the bag changes shoulders). This isn't a "detail": it's an indicator of limitations in spatial consistency and fine-grained control.
📚 Self-publishing / strategy: here, the gain is massive. Starting from scratch on certain subjects, AI plays the role of a versatile “staff” (organization, production, proofreading, marketing).
✅ Conclusion: For results-oriented use, a single AI can suffice. In my case, I'm closer to 90% of satisfaction for 10% of energy, as long as I remain clear on my objective: to produce better, not to constantly benchmark.
➡️ And you: do you use AI to save time… or to compare performance?
