AI and the skipping Junior Level
LinkedIn Post
Content preparation
AI is progressing very fast, just to illustrate with areas:
- Responding to basic customer support inquiries
- Doing cold outreach & trying to book sales meetings
- Writing basic code, fixing small issues and testing software
An interesting question raised: What jobs will young people do with novice knowledge levels, and how will we get senior people in the future?
Two important climatic patterns to strongly consider:
- Everything evolves through supply and demand competition - so I’m very sure that jobs will fade, that are mainly based on administrative tasks that can be handled by AI. With what speed, let’s see. My assumption - very fast!
- Success breads inertia. And it increases the more successful the past model was — I can sense that in my age group, I’m 47, that investing in AI and massive upskilling and learning every day is not (yet) a major topic.
Too much stress causes us to oversimplify, become paralyzed, default to old habits or prior success routines! There is just too much to protect and too little urgency yet for the current “seniors”. I think this inertia is a huge advantage for young people, being much more hungry and in demand to earn their living.
The defining feature of a complex adaptive system is its ability to learn. So we reach higher adaptability if we improve the way we learn. I think the path from junior to senior can be heavily accelerated, thanks to AI, if:
- we implement an AI first approach. When I start a topic I immediately ask myself, how can AI support me with that.
- we treat AI like our 24/7 mentor - and learn super fast and effective
- work alongside AI - providing thinking and let AI do the implementation
Just yesterday I had another eye opening moment with AI. For my post about LinkedIn statistics I was using Julius.ai for the data analysis and creating charts. I provided my dataset and asked questions about my data. And I provided some expert input for the analysis to be done. I watch Julius.ai generating Python code that analyzed the data and that created charts. And I remember just some years ago and my learning journey to understand R and Python to work better with data. It was difficult to find the right functions to call and I invested a lot of time debugging, scrolling endless documentation, reading Goggle insights. The change now: With watching Julius.ai generating such code on the fly, I could use that for learning how to implement that for myself too. I watch some example code unfold and was just impressed how much better I got the context and how these examples would have helped me to learn the data analysis much faster and easier.
This showed me the power of AI amplified learning. And I can highly recommend to adjust your learning approaches to heavily embed AI in the learning journey. I can learn:
- in my context and explore based on my examples
- can switch between explanation and experimentation very fast
- create the learning context that is accessible at a later point in time to build on top of it
Let me know if you want support on getting to a higher level of efficient and effective learning. Build on my expertise of combining Second Brain for knowledge management with AI for learning and for AI first task implementation.
An interesting Reddit discussion: https://www.reddit.com/r/cscareerquestions/comments/1b8yqym/addressing_the_whole_ai_will_replace_us_concern/
Learn Wardley Mapping: https://learnwardleymapping.com/climate/
Posted on LINKEDIN on 2024-03-24_Sun
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