Over 77,999 tech jobs lost to AI already in 2025. WHY???

The reason behind the loss of over 77,999 tech jobs to AI in 2025 is primarily due to the rapid advancement of AI technologies, particularly large language models (LLMs) and agentic AI. These AI systems are automating tasks traditionally performed by human workers, significantly reducing the need for manual or repetitive work in tech roles. Here are some factors contributing to this trend:

1. AI-Powered Automation of Repetitive Tasks

• Routine tasks like coding, debugging, data entry, and system administration can now be efficiently performed by AI agents. AI can process vast amounts of data much faster than humans, handling tasks like software testing or system optimization with minimal human oversight.

2. Agentic AI

• Agentic AI (AI agents that act autonomously and make decisions) is evolving rapidly. With frameworks like OpenAI's agents and Anthropic’s MCP, AI systems are increasingly capable of handling complex tasks such as problem-solving, decision-making, and even customer interaction without human input.

3. Generative AI and LLMs

• Generative AI models like GPT-4 and Claude are being used for content generation, code writing, and natural language understanding. These models can perform tasks that were traditionally the domain of tech workers, such as creating documentation, designing systems, and even performing some software development tasks.

4. AI in DevOps & IT Operations

• With the integration of AI-driven DevOps tools, monitoring, debugging, and deploying software can be automated. AI agents can now monitor servers, detect errors, and even deploy fixes without the need for human intervention.

5. Cost and Efficiency

• For businesses, automating tech roles via AI reduces the cost of hiring and training new employees, while increasing the speed and accuracy of processes. As a result, many companies are opting for AI solutions to handle tasks that would traditionally require large technical teams.

6. AI in Product Management and Design

• AI agents are also starting to take over tasks in product management and design by generating product strategies, design mockups, and even evaluating user experiences based on real-time data analysis.

Future-Proofing Your Career

While the rise of AI poses a threat to certain job categories, it also opens up opportunities for new career paths. To future-proof your career, learning how to build and work with AI systems is crucial. Here's a resource stack to help you get started:

Here’s a resource stack to future-proof your career.

Top 10 Videos to Understand AI and Build Agents:

1. LLM Introduction: https://lnkd.in/gN8sv7Q5

2. LLMs from Scratch: https://lnkd.in/gJt-SQj2

3. Agentic AI Overview (Stanford): https://lnkd.in/gk4GKdxa

4. Building and Evaluating Agents: https://lnkd.in/ghBiVjdF

5. Building Effective Agents: https://lnkd.in/g_m78sid

6. Building Agents with MCP: https://lnkd.in/gAzBzr3W

7. Building an Agent from Scratch: https://lnkd.in/g9GR9b9F

8. Philo Agents: https://lnkd.in/gnxRq9n9

Top 4 Repositories for Building AI Agents

1. GenAI Agents: https://lnkd.in/gvfAc-9H

2. Microsoft's AI Agents for Beginners: https://lnkd.in/gK8MiVfv

3. Prompt Engineering Guide: https://lnkd.in/gJjGbxQr

4. AI Agent Papers: https://lnkd.in/gfKQ82Fs

Top 5 Guides to Master AI Agents

1. Google's Agent Whitepaper: https://lnkd.in/gFvCfbSN

2. Google's Agent Companion: https://lnkd.in/gfmCrgAH

3. Building Effective Agents by Anthropic: https://lnkd.in/gRWKANS4.

4. Claude Code Best Agentic Coding practices: https://lnkd.in/gs99zyCf

5. OpenAI's Practical Guide to Building Agents: https://lnkd.in/guRfXsFK

Top 7 Research Papers to Dive Deep into AI Agents

1. ReAct: https://lnkd.in/gRBH3ZRq

2. Generative Agents: https://lnkd.in/gsDCUsWm.

3. Toolformer: https://lnkd.in/gyzrege6

4. Chain-of-Thought Prompting: https://lnkd.in/gaK5CXzD.

5. Tree of Thoughts: https://lnkd.in/gRJdv_iU.

6. Reflexion: https://lnkd.in/gGFMgjUj

7. Retrieval-Augmented Generation Survey : https://lnkd.in/gGUqkkyR.

Top 10 Courses to Learn About AI and Build Powerful Agents

1. HuggingFace's Agent Course: https://lnkd.in/gmTftTXV

2. MCP with Anthropic: https://lnkd.in/geffcwdq

3. Building Vector Databases with Pinecone: https://lnkd.in/gCS4sd7Y

4. Vector Databases from Embeddings to Apps: https://lnkd.in/gm9HR6_2

5. Agent Memory: https://lnkd.in/gNFpC542

6. Building and Evaluating RAG apps: https://lnkd.in/g2qC9-mh

7. Building Browser Agents: https://lnkd.in/gsMmCifQ

8. LLMOps: https://lnkd.in/g7bHU37w

9. Evaluating AI Agents: https://lnkd.in/gHJtwF5s

10. Computer Use with Anthropic: https://lnkd.in/gMUWg7Fa

11. Multi-Agent Use: https://lnkd.in/gU9DY9kj

12. Improving LLM Accuracy: https://lnkd.in/gsE-4FvY

13. Agent Design Patterns: https://lnkd.in/gzKvx5A4

14. Multi Agent Systems: https://lnkd.in/gUayts9s

Top 6 Newsletters to Keep Updated on AI Trends

1. Gradient Ascent: https://lnkd.in/gZbZAeQW

2. DecodingML by Paul: https://lnkd.in/gpZPgk7J

3. Deep (Learning) Focus by Cameron: https://lnkd.in/gTUNcUVE

4. NeoSage by Shivani Virdi: https://blog.neosage.io/

5. Jam with AI by Shirin and Shantanu: https://lnkd.in/gQXJzuV8

6. Data Hustle by Sai: https://lnkd.in/gZpdTTYD

Don’t fear the agents replacing everyone else.

Learn to build them and own the advantage.