One thought I had on AI recently was sparked by simple machines.
Basically the "force multiplier" effect you may have already seen or hear, AI is like a lever where it multiplies your effort to lift a load. You can 5x your capabilities with AI especially if you are skilled in your domain of expertise.
However, if you are not skilled yet, AI can only enhance that limited capability. You still need to develop the domain expertise. That will help you create better prompts, understand the generated content, and verify that the AI-produced content is accurate.
Using a simple math model, if you're a experienced knowledge worker and your skill level is a 8/10 and AI will multiply your skill and productivity 5-fold, imagine the effect to someone with a skill level 1/10 with a 5x effect-- they are still less skilled that experienced person.
This is why there's data that suggests that senior developers are thriving with AI and the beginners are spinning their wheels with AI in their workflows. The seniors are optimizing and refining their battle-tested approaches while the junior devs are seeing hieroglyphics. That can create a productivity gap with the same tools and same AI models.
There is no silver bullet to close this gap. Skill-building and upskilling will be part of that conversation to ensure AI will be the same force multiplier for everyone.
If you haven't already, deepen you domain knowledge while getting up to speed on AI tools.
Top comments (1)
I used to measure AI's value in 'time saved' and was always disappointed. Then I reframed it as 'attention saved' and everything clicked. The 30 minutes AI saved on boilerplate wasn't valuable because of the time — it was valuable because I wasn't mentally depleted when I got to the hard problem at 3pm.