AI and its impact on employment in Software Industry — Sanjay Hejib

AI and its impact on employment in Software Industry

AI is definitely going to help humans to become more and more powerful and smarter. Productivity
will be boosted exponentially. Decision making is going to become much faster. Products will get
rolled out at much faster rate than before. Products will also become creative and cheaper.
So, is AI good or bad is not a question at all!

But after hearing a lot of news about ongoing layoffs, especially in the software development
companies across globe, people ask, what about unemployment, what about widening gap between
AI enabled and not enabled workforce?

Most of the leaders answer this with a confident and smiling face that up gradation of the skill set is a
good solution, in fact the only solution, to this. Everyone must learn the new and new AI tools and
techniques. No option. If there is a metro available, why walk?

But really will all the laid off workers get jobs even after they getting trained in AI? Will the freshers
awaiting job offers get jobs even after getting degrees comprising AI and related subjects? Will there
be so many positions for humans in the world of AI?

A quick question asked to chatGPT gives following answer to what kind of new roles being created
due to AI:

 Technical and Engineering Roles

o AI/ML Engineer
o Data Scientist
o Prompt Engineer
o AI Research Scientist
o MLOps Engineer
o AI Infrastructure Engineer
o Generative AI Developer
o Synthetic Data Engineer
o AI Quality Assurance Engineer
 Ethics, Governance, and Compliance
o AI Ethicist / AI Ethics Officer
o AI Policy Analyst / Advisor
o Responsible AI Officer
o AI Compliance Manager
o Algorithmic Auditor
 Business and Strategy Roles
o AI Product Manager
o AI Strategy Consultant
o AI Evangelist
o AI Project Manager
o AI Procurement Specialist
o GenAI Application Specialist
 Creative and Content Roles (Driven by Generative AI)
o AI Content Creator
o AI Video Editor / Animator
o AI Music Composer
o Virtual Experience Designer
o AI UX Designer
 Education, Training, and Human-AI Interaction
o AI Trainer / Annotator
o Human-in-the-Loop Specialist
o AI Literacy Coach / Educator
o AI Chatbot Designer
 Domain-Specific AI Roles
o Healthcare AI Specialist
o Finance AI Analyst
o Legal Tech AI Consultant
o Retail AI Analyst
o Manufacturing AI Engineer
 Emerging Leadership Roles
o Chief AI Officer (CAIO)
o Chief Data Officer (CDO)
o Chief Digital Officer (CDiO)
o Chief Innovation Officer (CIO)
o VP/Head of Machine Learning
o Head of Generative AI / VP of GenAI
o AI Governance Lead / Director of Responsible AI
o AI Transformation Lead

Wow! So many new roles, isn’t it?

But what about numbers? Will they accommodate all the software engineers  , test engineers ,
managers being laid off and those students being not going to get hired after graduation?
No one talks on this!

In the past, when it used to take a team of 10 to do a job, now that is getting done by 4-5 members.
Of course, understanding  the code generated by AI and debugging it  is  a big challenge, expectations in
terms of speed and cost from clients are increasing. And due to these pressure, more and more
employers are deploying more and more AI and reducing workforce to cope up with the competition
and EBITDA.

That is the reason, especially major software services companies and even software product
companies are laying of thousands of people every week. What will these guys do? What will the
aspiring freshers or students studying in colleges do? Will they all get jobs even after having AI, ML,
block chain and all in their syllabus?

I recently gone through a 4 year degree syllabus for a typical Computer Science & Engineering (AIML) course. It covers following AI related topics:
o Foundations of Artificial Intelligence & Machine Learning
o Statistics & Probability for Data Science
o Mathematical Foundations for Machine Learning
o Data and Visual Analytics
o Machine Learning Techniques & Optimization
o Deep Learning & Neural Networks
o Natural Language Processing (NLP)
o Big Data Analytics & Cloud Computing
o Pattern Recognition, Computer Vision
Fantastic syllabus! Must for any student opting for the computer field.

But again, what about numbers? Will all these students get jobs even after studying these subjects?
And what about those who have not done these kind programs?

I don’t want to sound pessimistic, but we need to think on this.

At least can we say that the “golden days”  for  software engineers are over and they should choose this
field wisely along with some domain knowledge.

Sanjay Hejib