Kbot (Kristopher Webster)

𝐉𝐨𝐛 𝐌𝐚𝐫𝐤𝐞𝐭 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬 𝐁𝐲 𝐂𝐨𝐮𝐧𝐭𝐫𝐲 By Samantha Glover Today, I want to focus on job loss within high risk automation categories, and which countries or regions are at high impact locations.  I've also lightly explained the type of AI disruption. I can do a separate disaggregate specific to the US, but I wanted to share the top global layer first. According to the second model attached, the prediction is that 170 million jobs will be created and 92 million jobs will be displaced, leaving us with a net job growth of 78M. I’d like to disaggregate that 92M job displacement figure, meaning to deconstruct into layers what lives inside of this data point. The percentages below reflect the share of the core workforce within our discussed high rsk categories. Clerical, Customer Service, and Manual Operations face HIGH technical and economic exposure to automation by 2030. Philippines 62%: The US and many other countries outsource to the Philippines on Upwork and other platforms. They are at extremely high risk on the global landscape, as conversational AI and LLM"s will likely disrupt heavily in this area. India 58%: The US and many other countries also outsource to India for IT service desks, administrative back offices, and data entry.  RPA and Cognitive & Cognitive Automation will likely be the core disruptors. Japan 50%: Facing advanced robotics integration across manufacturing, fulfillment logistics, and material handling, alongside a shrinking native workforce. Computer Vision & Kinematic AI will likely lead. United States 45%: We have high exposure concentrated heavily in white collar clerical support, financial administration, and service sector checkout/inventory management systems.  Agentic AI & Generative Productivity Frameworks will likely be the disruptors in this group. Germany 42%: Dominated by the deep automation of administrative processes in manufacturing supply chains and the structural implementation of RPA in the banking sector. Also Computer Vision & Kinematic AI. Brazil 38%: Moderate to high risk, with a slower initial software adoption velocity due to lower relative domestic human labor costs balancing out tech implementation expenses.  Narrow machine Learning & IDP will likely lead. What are your thoughts? What breakdown do you want to see next? My next set  of models will focus on the US, but I wanted to share the top layer first and foremost.  We'll go over income and connect other economic factors. #ai #jobmarket

🚨Weekly Tech Talk Space, let’s explore data on job market predictions, job displacement, and new jobs. Also what exactly is Super Intelligence? Set your reminders 👇🏽 teapaw.org/i/spaces/1mdRRNgYPkgPV…

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